poverty literature review

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• Published: 13 June 2024

From poverty to prosperity: assessing of sustainable poverty reduction effect of “welfare-to-work” in Chinese counties

• Feng Lan 1 , 2 ,
• Weichao Xu 1 ,
• Weizeng Sun 3 &
• Xiaonan Zhao 1  

Humanities and Social Sciences Communications volume  11 , Article number:  758 ( 2024 ) Cite this article

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• Development studies
• Social policy

The “welfare-to-work” program is a comprehensive supportive policy in the 14th five-year plan period in China. In this paper, a systematical assessment of the long-run effectiveness of the welfare-to-work policy on poverty reduction is of great significance to stimulate the internal impetus of people who are lifted out of poverty to achieve income growth and prosperity and promote regional economic development. Based on the data at the county (city) level in China from 2000 to 2019 and the sustainable development theory, in this paper, a county-relative poverty evaluation system was constructed. Besides, the double difference method was employed to evaluate the effect of the welfare-to-work policy on poverty reduction and test its action mechanism. The findings are as follows: (1) the welfare-to-work policy has a significant poverty reduction effect and presents an inverted “U” shape. In addition, significant achievements have been made in “maintaining employment stability, ensuring income, strengthening skills, and supporting the economy” ; (2) the welfare-to-work policy boosts poverty reduction in counties through infrastructure construction, fiscal intervention, and financial tools; however, the financial tools play a positive role in poverty reduction in the northwest region and suppressed role in the southwest region, and has an insignificant effect in the central region; and (3) there are differences in the effect of poverty alleviation policies of the counties with different sustainable development levels, and the regions with higher development level have a stronger driving effect.

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Introduction.

In 2021, China has scored a competitive victory in the fight against poverty. The final 98.99 million impoverished rural residents living under the current poverty line have all been lifted out of poverty. All 832 impoverished counties across the country and 128,000 impoverished villages have been removed from the poverty list Footnote 1 . Regional overall poverty has been resolved, which marks that China’s poverty governance has entered a new stage from absolute poverty to relative poverty, and from income poverty to multidimensional poverty. Relative poverty and multidimensional poverty have become new forms of poverty in China (Xu et al. 2021 ).

China’s anti-poverty focus has shifted from targeted poverty alleviation in absolute poor areas to comprehensive measures to promote high-quality development in relatively poor areas. The limited ability of the relatively poor population to obtain various livelihood capital also reduces the range of livelihood activities, and aggravates the livelihood risk and vulnerability. These factors will affect the economic development of relatively poor areas and the ability of rural populations to increase their income, and the long-term and complexity of this external environment also determine the long-term sustainability of consolidating and expanding the achievements of poverty alleviation.

Since ancient times, China has had the practice of “providing employment as a form of relief”. Since 1984, welfare-to-work, as a social and economic policy, has played an important role in poverty alleviation and development. Nowadays, with the transformation of China’s anti-poverty focus from the governance of absolute poverty to the high-quality development of relatively poor areas. The welfare-to-work policy not only has the attribute of poverty alleviation, but also has the overlapping role of social welfare and economic development, maximizing the release of the effects of the policy and activating the endogenous power of county economic development. On the one hand, it can be used to resist the impact of unfavorable factors and emergencies such as the COVID-19 pandemic, earthquakes, and tsunamis. Welfare-to-work policy can effectively play the role of “relief” in the process of flood control and post-disaster recovery. Compared with direct subsidies, welfare-to-work further mobilizes the enthusiasm of the broad masses of peasants to participate in the construction of rural public infrastructure and give full play to the investment of government for supporting agriculture. On the other hand, aiming at the employment needs of low-income groups, it provided timely payment of labor remuneration, and employment skills training. With compulsory and welfare means, it enhanced the capacity and subjective motivation of poverty-stricken people and low-income groups, thus encouraging them to participate in the competition.

Through data analysis, it was found that since the implementation of the “Welfare-to-work” policy in different regions, the effects have not been the same. For instance, during the implementation process, the phenomenon of emphasizing construction while neglecting relief has come into existence. Besides, influenced by the factors in regional development level, geographical restrictions, and population flow, the current labor force has lower quality and insufficient skills and thus achieves an unsatisfactory effect on employment and income growth. Although these areas have all been lifted out of poverty under the country’s targeted poverty alleviation policy, once impacted by external uncertainties, the obtained achievements in poverty alleviation will be irrevocably lost.

Therefore, what kind of impact does the sustainability of poverty reduction performance of the welfare-to-work policy have? Is there heterogeneity in the poverty reduction effect of the welfare-to-work policy in different districts and counties? What is the role of a welfare-to-work policy in poverty reduction?

In this paper, a multi-dimensional relative poverty evaluation index system was constructed and the poverty reduction effect of the “welfare-to-work” policy was evaluated by the method of double difference (DID). In addition, the panel regression model was constructed to explore the mechanism of poverty reduction, and the Shapley method was used to quantify the contribution of mechanism variables to the poverty reduction effect. The study has several important findings. First of all, the cash-for-work policy effectively promotes poverty reduction in counties, and the higher the level of local development, the more significant the poverty reduction effect. The results are still valid after robustness testing. Second, policies promote poverty reduction at the county level through infrastructure construction, fiscal intervention, and financial instruments, but the contribution of different variables in different regions is different.

The remainder of this study is arranged as follows. Section 2 “Literature review and research hyptothesis” provides a literature review and research hypothesis. Section 3 “Research design” describes our empirical design. Section 4 “Analysis of empirical results” reports our empirical results and robustness tests. Section 5 “Test of action mechanism” reports the quantitative results of the mechanism of action. Finally, we provide a summary.

Literature review and research hypothesis

Evolution of welfare-to-work policy.

(1) From 1949 to 1983: the stage with a focus on disaster relief. At the First and Second National Civil Affairs work conference, it was emphasized that in disaster relief work, the principles of overcoming adversity through greater production, saving grain to tide over a lean year, holding on to mutual assistance, work relief, and supplementary necessary government relief should be encouraged. However, in accordance with the statement of the National Civil Affairs Conference in 1953, as the following continuous various projects in economic construction in China would inevitably attract the participation of victims, so the Welfare-to-work policy was removed from the disaster relief policy. At this stage, the planned economy system not only strengthened the government’s ability to organize disaster victims to carry out self-rescue action but also endowed the welfare-to-work policy and other policies with distinctive epochal features (Zhu, 2021 ). Specifically, the Welfare-to-work policy mainly focuses on the unemployed population and disaster relief. In addition, with efforts of supply optimization manpower allocation, and material and financial resources supply, the government organized victims in the form of mutual assistance and cooperation and built some engineering facilities in flood control, drainage, irrigation, and power generation, which have promoted the recovery of production and reconstruction of homes while providing disaster relief.

(2) From 1984 to 2000: the early stage of poverty alleviation and development. With the economic reform, poverty has been included as an important part of social and economic development. In 1984, China began to regard welfare-to-work as a means of alleviating poverty, and successively implemented six large-scale welfare-to-work programs, namely “grain and cotton cloth related welfare-to-work program”, “medium-and low-grade industrial products related welfare-to-work program”, “industrial products related welfare-to-work program”, “grain related welfare-to-work program”, “river control related welfare-to-work program” and “state-owned poor farms related welfare-to-work program”. In other words, since 1984, China has organized the impoverished people to carry out infrastructure construction in the fields, roads, rivers, houses, and toilets and has paid for grain, cotton, cotton cloth and industrial products to workers as labor remuneration. This measure not only provides employment opportunities and achieves income growth for poor workers but also improves infrastructure and public services in poor areas. In 1990, the State Council issued the Opinions on the Arrangement of Industrial Products for Welfare to Work from 1990 to 1992. According to the Opinions, 1.5 billion yuan of industrial products were used for the labor remuneration for workers in the construction of the welfare-to-work program; during the eighth five-year plan period, the Chinese government invested 1 billion kilograms of grain or equivalent industrial products per year in the Welfare-to-work program in poor areas; and in 1996, the welfare-to-work program was supported by financial funds instead of the form of in-kind cash conversion. In 1998, in the context of the financial crisis, the National Development and Reform Commission (NDRC) issued measures to support the welfare-to-work program with treasury bonds, which solved the problem of expanding domestic demand. As an important part of the central poverty alleviation fund, the proportion of welfare-to-work funds increased from 15.38% to 37.04% in the ten years from 1986 to 1996, during which the acceleration of infrastructure construction in poor areas became the main operation mode of the welfare-to-work policy. At this time, the Welfare-to-work system not only had the function of disaster relief but also promoted the construction of local infrastructure.

(3) From 2000 to 2020: the special-purpose poverty alleviation policy stage. In the 21st century, the welfare-to-work program gradually has developed with “fine, small and deep” characteristics, with a focus on the implementation in a certain region or in-depth research on the analysis of welfare-to-work on the system (Shen, 2018 ). In December 2005, the NDRC issued the National Administrative Measures for welfare-to-work Policy, which highlighted the funds' management, project implementation, and organizational management of the welfare-to-work program, thereby improving the implementation performance of the policy. In 2011, to improve the quality of cultivated land, strengthen infrastructure construction, enhance the ability to withstand natural disasters, and consolidate the foundation of economic development in poor areas, the Chinese government issued the Outline for Poverty Alleviation and Development in China’s Rural Areas (2011–2020), in which the welfare-to-work program was included as a special-purpose poverty alleviation policy measure. In December 2014, to meet the requirements of rural poverty alleviation under the new situation, the NDRC launched the revision of the administrative measures for welfare-to-work policy. In 2016, the NDRC issued the National “13th five-year plan” for the welfare-to-work program, taking small and medium-sized infrastructure in rural areas the focus of the welfare-to-work program. According to the work plan, the welfare-to-work program should be transformed from “adopting a deluge of strong stimulus policies” to “precision regulation policies”, and a new model of poverty alleviation with asset income featuring “changing assets into equity, poor households into shareholders” should be innovated. In June 2019, the NDRC issued guiding opinions on further leveraging the role of welfare-to-work policies to help win the battle against poverty, which called for the continuous focus on supporting deeply impoverished areas such as the “three regions and three prefectures” to win the battle against poverty as scheduled and consolidate poverty alleviation achievements in poor areas. In July 2019, the NDRC issued Opinions on further adhering to the original purpose of “Relief” and giving full play to the function of the welfare-to-work policy, which underscored the importance of comprehensively expanding a variety of relief modes, relying on welfare-to-work projects to extensively carry out employment skills training and public welfare position development, and exploring the implementation of quantitative dividends through asset share conversion. In November 2020, the NDRC and nine other departments issued opinions on actively promoting welfare-to-work policy in the field of agricultural and rural infrastructure construction, requiring that the welfare-to-work policy should be actively promoted in the fields of rural production and life, transportation, water conservancy infrastructure, cultural tourism infrastructure, forestry, and grassland infrastructure construction, so as to improve rural production and living conditions, which has played an important role in consolidating the achievements of poverty alleviation.

(4) From 2021 to present: multifunctional comprehensive stage. In July 2021, the NDRC issued the National “14th Five-Year Plan” for the welfare-to-work program, which comprehensively expands the implementation areas, implementation functions, beneficiary groups, construction areas, and relief modes of the policy in the next five years. In July 2022, the NDRC issued a work plan on vigorously implementing welfare-to-work policy in key engineering projects to promote employment and income growth of local people. In the work plan, the NDRC emphasized that vigorously implementing the Welfare-to-work policy in key engineering projects is an important measure to promote effective investment, ensure employment stability and people’s well-being, stimulate county consumption, and stabilize the economic market, which will drive the people to share in the fruits of reform and development. In January 2023, in the context of a new journey and new requirements in the new era, the NDRC revised the National Administrative Measures for welfare-to-work policy. The NDRC stressed that on the one hand, we should stick to the original purpose of “Relief”, insist on helping people who have talent and poverty problems, and adhere to the principles of earning more from more work and getting rich through hard work to enhance the internal impetus of income growth and prosperity. On the other hand, we should strengthen the employment assistance for the people in difficulty, and promote common prosperity.

In summary, different from the “blood transfusion” poverty alleviation policy, “the welfare-to-work policy”, as an effective tool of the national “hematopoietic poverty reduction” policy, has a comprehensive poverty alleviation function and profound poverty alleviation connotation in solving the “two no worries, three guarantees”(no worries for basic food and clothing, and guarantees for compulsory education, basic medical services, and safe housing and drinking water), optimizing redundant resources, and improving the supply of public services and infrastructure. As the focus of China’s poverty reduction strategy has shifted from “extensive poverty reduction” to “targeted poverty reduction” and then to “consolidating the achievements of poverty alleviation”, the welfare-to-work policy has transformed from “adopting a deluge of strong stimulus policies” to “precision regulation policies”. However, the policy target always focuses on boosting employment and income growth, enhancing self-development and internal impetus to become rich, and promoting regional development; the scope of implementation has changed from the national key impoverished counties to the less developed areas with poverty alleviation as the focus; the construction field has expanded from the small and medium-sized rural infrastructure field to “one key area” and “seven major areas”; the relief mode has been expanded from the single mode of participating in the construction of welfare-to-work projects to obtain labor remuneration to the various relief modes such as conducting the quantitative dividends through asset share conversion, and carrying out employment skills training and public welfare position development; and the policy function has changed from the single relief type to the comprehensive type integrating the functions of promoting employment, ensuring people’s well being, carrying out emergency disaster relief, and boosting regional development of infrastructure (Table 1 ).

Literature review on the welfare-to-work policy on poverty reduction

The relationship between welfare-to-work and sustainable poverty alleviation of relative poverty.

In the early studies on poverty, poverty alleviation policy is an important influencing factor, and related theories involve human capital theory, poverty cycle theory and sustainable livelihood theory, etc. This paper will combine these theories to analyze the relationship between welfare-to-work policy and relative poverty and put forward a research hypothesis.

Long-term governance of relative poverty is the basic goal of poverty governance in China’s post-poverty alleviation era. The exploration of the transformation of government poverty alleviation ability should not only take this as starting point of the research, but also take solid theoretical achievements as theoretical support (Wang and Wang, 2021 ). Therefore, the governance of relative poverty should start from the perspective of sustainable development. On the one hand, it promotes the sustainability of the livelihood of local low-income people, including the continuous improvement of living standards and the continuous improvement of their livelihood self-improvement ability, that is, they are endowed with development power, create development opportunities, strengthen development ability and share development achievements at the individual level (Luo et al. 2021 ), and then realize the transformation from “blood transfusion” governance to “hematopoiesis” governance. On the other hand, relatively poor areas are an important type of area to promote the formulation of sustainable development policies in underdeveloped areas in China (Zhou et al. 2020 ). In 2020, Fan’s research team introduced the concept of relative poverty into the research of regional sustainable development for the first time, and expanded the micro-sustainable livelihood model into a macro analysis framework of sustainable development in underdeveloped areas–relatively poor areas (Fan et al. 2020 ). From the regional poverty-causing factors, the poverty types were divided and the classified poverty alleviation strategies were discussed. Through 20 consecutive years of follow-up research, this paper reveals the changing process of poverty-relative poverty and explores the poverty alleviation effect and its impact on the natural ecological environment and social progress. According to scholars Li and He ( 2022 ), endogenous motivation is an important foundation for sustainable poverty alleviation, and incentive policies such as “replacing compensation with awards” and “welfare-to-work” should be adopted to promote the transformation of poverty alleviation mode from blood transfusion to hematopoiesis.

According to the cycle of poverty, the development of poverty-stricken areas and the growth of residents’ living standards are subject to the lack of capital, which is the fundamental cause of the vicious circle of poverty (Nurkse, 1957 ). However, a basic feature of underdeveloped areas is that the infrastructure is relatively backward and the production conditions are relatively poor. The welfare-to-work policy is that the state injects construction capital from the outside, provides local infrastructure construction, improves farming conditions, and provides job opportunities for local low-income groups (Xiao and Yan, 2023 ). Foreign scholars can define the welfare-to-work program as a public intervention measure to provide employment opportunities for poor families and individuals with relatively low wages. However, according to sustainable poverty alleviation, the welfare-to-work policy studied in this paper refers to a poverty alleviation policy in which the government invests in the construction of public infrastructure policy, and the recipients participate in the policy construction to obtain labor remuneration instead of direct relief. For low-income people, the welfare-to-work policy has dual value: one is to reduce poverty and vulnerability (Gehrke and Hartwig, 2018 ), and the other is to create public goods through the work done by participants. It can also contribute to a third value if the participants acquire new skills. In addition, the implementation of the welfare-to-work policy strengthens the poor groups’ resistance to external risks by promoting social cohesion and strengthening economic ties between groups (Beierl and Dodlova, 2022 ). On the other hand, based on the local resource endowment, the implementation of the welfare-to-work policy is suitable for the local environment and promotes the sustainable development of underdeveloped areas by improving infrastructure and promoting industrial development (Si, 2011 ). Therefore, this paper puts forward the following assumption:

H1: welfare-to-work policy actively promotes sustainable poverty alleviation.

The mediating effect of infrastructures

According to the analysis of the welfare-to-work policy above, it can be seen that the main methods to implement the welfare-to-work policy are to carry out infrastructure construction in the fields of transportation, water conservancy, energy, agriculture and rural areas, urban construction, ecological environment, and post-disaster recovery and reconstruction. Many scholars have paid attention to the economic growth effect of infrastructure. But to govern poverty, the government should emphasize infrastructure investment, especially agricultural infrastructure investment (Xie et al. 2018 ). Firstly, the construction and improvement of rural infrastructure have created employment opportunities for low-income groups. The more opportunities local farmers have to obtain secondary and tertiary industries, the stronger the employment income-increasing effect of local farmers will be (Gibson and Olivia, 2010 ). In addition, rural infrastructure directly and significantly improves agricultural production efficiency, increases agricultural production efficiency, and reduces agricultural production costs. Secondly, infrastructure interconnection catalyzes the “market scale effect” and improves the market potential of poor areas. The accessibility of infrastructure guarantees the expectation of investment income, leading to expanded investment in poverty-stricken areas and increased innovation support received by producers in poverty-stricken areas (Zhang et al. 2018 ). Thus, his paper puts forward the following assumptions:

H2a: welfare-to-work policy achieves sustainable poverty alleviation effect through infrastructure.

There are two opposite views on the effect of financial poverty alleviation in academic circles. Some scholars such as Skoufias and Di Maro ( 2008 ), Imai ( 2011 ), and Xie ( 2018 ) believe that special financial transfer payment is an effective fiscal policy tool to reduce income inequality and poverty. According to Ma et al. ( 2016 ) and Huang ( 2018 ), financial transfer payment stimulates economic growth in poverty-stricken areas, reduces the incidence of poverty, and promotes the equalization of regional basic public services and financial resources. In addition, it has good policy effects in improving human capital, preventing future poverty, and improving income distribution according to Liu and Qi ( 2019 ). However, other scholars believe that financial transfer payments is not effective in reducing poverty. Presbitero ( 2016 ) pointed out that the large-scale increase of public expenditure in low-income developing countries did not significantly promote economic growth, and Chen et al. ( 2018 ) used the data from China Family Panel Studies to think that financial transfer payment did not play a good role in reducing extreme multidimensional poverty. According to Musgrave’s ( 1959 ) definition of the financial function, public finance should play three basic functions: resource allocation, economic stability, and income distribution. Welfare-to-work policy mainly means that the government provides capital supply and sufficient human capital guarantee for economic development in backward areas by issuing special poverty alleviation funds from the central government. It creates development opportunities for low-income people, consolidates the foundation of individual income growth, and then enhances the ability to resist risk shocks. In addition, increasing the income of such income groups or reducing their expenditure obligations can improve the production and living conditions of poor people and strengthen their economic self-reliance. Therefore, this paper puts forward the following hypothesis:

H2b: welfare-to-work policy achieves sustainable poverty alleviation effect through fiscal intervention.

The analysis method of combining welfare economics with poverty lays a foundation for multidimensional relative poverty theory and empirical research. Amartya Sen’s sustainable livelihoods theory ( 2005 ), attributes individual endowment and welfare sources to the substantive freedom of acquisition. It emphasizes the superposition of external risk impact and internal risk resistance, which easily causes rural families out of poverty to suffer the impact of poverty again and the emergence of poverty vulnerability. All of these hinder poverty alleviation, sustainability, and common prosperity to a certain extent. Finance is an important external influencing factor for the comprehensive development of counties, and financial poverty alleviation mainly provides poor households with “two exemptions one subsidy” micro-credit funds for productive poverty alleviation, which can better meet the capital needs of poor households for industrial development. At the same time, poor households are usually required to have a supporting production and operation policy when obtaining poverty alleviation microfinance, which can ensure that credit funds are effectively used for production and operation to a certain extent. In the long run, it is conducive to improving the self-development ability of poor households (Weng et al. 2023 ). In addition, there is an obvious synergy between rural financial poverty alleviation and financial poverty alleviation measures. Rural finance can often play a greater role in poverty alleviation on the basis of financial poverty alleviation measures (Xia, 2023 ), which can drive economic and industrial development, and then have a significant effect on poverty alleviation (Liu and Liu, 2020 ). Therefore, this paper defines financial instruments as financial measures to accelerate production by improving the ability of beneficiary individuals to resist risks and promoting the flow of local production factors. In the practice of the welfare-to-work policy, the implementation area is weak in resisting risk shocks, while the local people’s savings level can effectively improve the level of resisting risk shocks. On the other hand, because of welfare-to-work projects’ characteristics of long construction periods and poor capital demand, the transmission mechanism of monetary policy is used to better match the scale and structure of bank deposits and loans. By doing so, the circulation of production factors such as manpower, capital, and materials in underdeveloped areas can be accelerated, thus realizing the comprehensive effects of expanding effective investment, driving employment, and promoting consumption. In addition, financial institutions are guided to increase financing support, attract private capital to participate, and form a more physical workload. Therefore, the research hypothesis is as follows:

H2c: welfare-to-work policy achieves sustainable poverty alleviation through financial instruments.

Currently, there are abundant studies on sustainable poverty reduction and policy assessment in academic circles, which provide good theoretical support and research perspective for this paper. But there are still several limitations: first, the current types of policy in poverty reduction mainly focus on the combination type or emphasis on the impact of poverty reduction policies implemented in a certain district, but there is room for research in the performance evaluation of a single welfare-to-work poverty reduction policy. Second, prior studies on quantifying their poverty reduction performance and action mechanism of the welfare-to-work policy by combining the policy with regional sustainable poverty reduction effects are relatively limited. Therefore, the theoretical analysis of this article is based on the theory of “welfare-to-work” and relative poverty. It summarizes the causes of relative poverty in underdeveloped areas, and on this basis, combines the characteristics of welfare-to-work to construct the theoretical analysis of poverty alleviation with welfare-to-work policy. As shown in Fig. 1 , firstly, according to the poverty cycle theory (Nurkse, 1957 ), the lack of capital formation and the contradiction between supply and demand in underdeveloped areas lead to low income and persistent poverty in underdeveloped areas. Secondly, according to the human capital theory (Schultz, 1949 ), human capital is the main factor of national economic growth. The loss of human capital caused by the difficulty of regional development has seriously affected the growth of the local national economy. In order to break this vicious circle, according to the capability approach (Amartya Sen, 2005 ), if a region wants to get rid of poverty and become rich, it must have the natural, economic, and social conditions to support its sustainable development. The welfare-to-work policy is the government’s financial investment in infrastructure to attract local people to work nearby. It is implemented in the form of government public investment to provide more employment opportunities for local people, and thus increases the opportunity cost of labor. On the other hand, stimulated by the market economy, the local labor force can have the opportunity to transfer from agricultural production to infrastructure construction conditionally, which improves the marginal income of the labor force. Firstly, they obtain skills training and increased stock of human capital; Secondly, they obtain broader horizons and more external economic information. In addition, according to development economics, the main constraint force to poverty alleviation comes from capital accumulation in which finance plays an important role. Through the intermediary function, finance can integrate social idle funds to realize the transformation from savings to investment, and promote poverty alleviation and economic growth at a higher level of equilibrium by improving the rate of capital accumulation.

Logical analysis of the impact of Welfare-to-work policy on poverty reduction.

There are several potential contributions in this paper: firstly, the difference with other articles lies in the different research perspectives. Relative poverty will exist for a long time in a certain historical period, in specific areas, and in specific conditions. These characteristics of relative poverty are the key factors that constrain the objectives, contents, and methods of poverty governance. Therefore, sustainable development is introduced into the welfare-to-work policy to alleviate relative poverty and strengthen the sustainable poverty alleviation effect of the policy. On the other hand, it lies in the comprehensiveness of the research subject. According to the 2030 Agenda for Sustainable Development promulgated by the United Nations, the sustainable development of poverty-stricken areas should also require people’s equal rights to access economic resources and inclusive and sustainable economic growth of the region. However, the relevant research on sustainable poverty alleviation directly or indirectly revolves around endogenous motivation stimulation and external resource assistance, and rarely analyzes and integrates these two themes within a unified framework (Ma et al. 2023 ). Therefore, this article takes individual and regional sustainable development as the main body, and takes the concept of sustainable development and the welfare-to-work policy as the donor, promoting the realization of sustainable governance of welfare-to-work policy in relatively poor areas. The third is to quantify the mechanism of action, because the policy will be affected by the local objective environment and show different effects in the implementation process as the regional scope of China’s welfare-to-work policy covering the central and western parts of China. This article adopts Shapley value to quantify the contribution of the action mechanism to poverty alleviation in different regions, and then provides policy reference for local governments to consolidate their poverty alleviation achievements and solve relative poverty problems.

Research design

Model construction.

The National Administrative Measures for welfare-to-work policy (2005) was first formulated by the Chinese government with the aims of standardizing and strengthening the management of the welfare-to-work policy and improving the use efficiency of welfare-to-work funds so as to improve the production and living conditions and development environment of impoverished rural areas, and to help poor residents get rid of poverty and become rich Footnote 2 . Then, it was implemented in specific regions in 2006, and subsequently amended twice and respectively, named the National Administrative Measures for welfare-to-work policy (2014) and the National Administrative Measures for welfare-to-work Policy (2023), but the starting points are to give full play to the role of welfare-to-work policy in poverty reduction.

Therefore, the author regards the welfare-to-work program as a quasi-natural experiment and takes 2006 as the processing point of welfare-to-work policy evaluation. The year before 2006 refers to before the policy implementation, which was denoted as time = 0; and the year after 2006 refers to after the policy implementation, which was denoted as time = 1. In addition, considering that the implementation areas of three revisions of the welfare-to-work policy are mainly less developed areas and ensure continuity of policy implementation, the regions that have been implementing the welfare-to-work policy since 2006 were selected as the treatment group whose dummy variable was set as treat = 1; and to ensure the net effect of the implementation of the welfare-to-work policy every year, the samples added to the implementation after 2006 were deleted, and the remaining regions without conducting the implementation of the welfare-to-work policy were defined as the control group whose dummy variable was set as treat = 0.

Drawing on the modeling practice of Yao et al. ( 2023 ), the author conducted tests on the net effect of the welfare-to-work policy on poverty reduction by studying the increment over time and non-time-varying heterogeneity among individuals through DID elimination. The model settings are as follows:

Where the explained variable is the county multidimensional development index (CMDI), the core explanatory variable is the welfare-to-work policy interaction term (did = treat × time), \({\beta }_{1}\) represents the net effect of welfare-to-work policy on poverty reduction; \({{\rm{\nu }}}_{{\rm{i}}}\) indicates individual fixed effects, which control the individual factors that affect CMDI but do not change with time; \({{\rm{\tau }}}_{{\rm{t}}}\) denotes the period effect, which controls the time factors that affect all individuals over time; and \({\varepsilon }_{{it}}\) indicates the error term.

As the selection of the implementation area of the welfare-to-work policy is subject to national regulation and coupled with imperceptible factors, to avoid selectivity bias and endogenous problems and ensure the robustness of results, the PSM-DID method was employed to conduct a robustness test, and the following model was constructed. The variables are defined as Eq. ( 2 ):

Dynamic effect model

Besides, to test the dynamic effect of the welfare-to-work policy on poverty reduction, on the basis of Eq. ( 1 ), a dynamic effect model was constructed as follows:

In the model, \({{\rm{treat}}}_{i,t}\,\times\,{{\rm{time}}}_{i,t}\) represents the dummy variable of the dynamic poverty reduction effect of the welfare-to-work policy. When the welfare-to-work policy is set at period t, its value is 1.

Test model of action mechanism

To further investigate the impact of the welfare-to-work policy on poverty reduction, the author took the modeling ideas of scholars Li and Zhang ( 2021 ) as a reference to interact with the independent variable did, so as to test the differences in sustainable poverty reduction in counties with different levels. The specific model settings are as follows:

Where M is the variable of the action mechanism, which is considered from infrastructure construction, government finance and financial poverty reduction. \({Y}_{{it}}\) is the explained variable, representing the relative poverty parameter of the county (CMDI) and a series of outcome variables about the multidimensional development of the county; \({\beta }_{1}\) represents the influence of the welfare-to-work policy on the dependent variable \({Y}_{{it}}\) through the action mechanism variables, while \({\beta }_{1}\) is significantly positive, the incentive variable \({Y}_{{it}}\) through the action mechanism variable M , M is the mechanism variable of the welfare-to-work policy; other parameters have the same meaning and benchmark model parameters.

Variable selection

Explained variable: cmdi.

The academic research on regional multi-dimensional relative poverty index systems mostly draws on the theory of man-land relationships and sustainable livelihood theory. British international development institutions based on a multidimensional perspective established a sustainable livelihood model, from the economic, natural, human, material, and social five aspects of comprehensive indicators, and the Chinese Social Science Research Institute of sustainable development strategy group combined with domestic actual situation, established to involve “government regulation, social development, scientific and technological innovation, human resources, survival, safety and environmental protection” and so on six big ability as the core of sustainable ability analysis framework.

Based on the research of the sustainable livelihood model by scholars Zhou et al. ( 2020 ) and Xu et al. ( 2021 ), this article builds a bridge between micro-main economic activities and regional high-quality development, draws lessons from the poverty alleviation requirements and tasks specified in the relevant policy documents of Welfare-to-work, and follows the principles of data availability, dynamics, and relevance.

As shown in Fig. 2 , “opinions on actively promoting welfare-to-work in the field of rural infrastructure construction” emphasizes that giving full play to the multiple functions of welfare-to-work policy, such as employment, disaster relief, investment, and income, it is combined with making up the shortcomings of infrastructure in the fields of agriculture, rural areas, and farmers to consolidate the construction of agricultural production capacity (Lan, 2021 ). Drawing lessons from the research of Liu and Zhao ( 2015 ) and Yang et al. ( 2023 ), the output efficiency of agricultural land is used to measure the agricultural production efficiency, that is, the output of crops per unit area is used. The ratio of rural employment number to the total population under the county is used to measure rural employment opportunities. The logarithmic value of Real GDP per capita (ln PGDP) is used to measure the level of county economic development the logarithmic value of rural per capita disposable income is used to measure the county living standard, and the ratio of fixed assets investment to GDP is used to to measure the level of fixed investment.

Framework of indicators for CMDI.

The identification of multidimensional relative poverty depends on CMDI. This article mainly draws lessons from the calculation ideas of Xu et al. ( 2021 ), and makes improvements on this basis. Specific calculation methods are as follows: firstly, the calculation idea is as follows: the entropy weight method is used to calculate the index weights of each dimension, and the scores of each dimension are calculated on this basis. Then, the polygon area method is used to calculate the CMDI. The reason why the area method is chosen instead of the simple weighting method is that the pentagon constitutes a stable structure and develops in a balanced way, and the five livelihood capitals influence each other, which can not only characterize the multi-dimensional relative poverty degree of the county, but also characterize its sustainability and anti-risk degree. The calculation formula is as follows:

Assuming that the scores of county i in five dimensions are a , b , c , d , and e respectively, and the angle between any two dimensions is α (α = 360°/5).

In addition, in order to avoid the difference of area caused by different sorting methods of five dimensions, the final algorithm is to calculate the average of various possible results. The larger the development index, the higher the comprehensive development potential of the county, the stronger its sustainability and anti-risk ability, and the lower the multidimensional relative poverty level. On the contrary, the higher the degree.

Control variables

In addition to the impact of the welfare-to-work policy on the sustainable development of the county, there are other influencing factors. Therefore, to eliminate interference, these exogenous factors need to be controlled. Drawing on the research ideas of Zhang et al. ( 2019 ), this paper selected the following control variables: the county-level population density was employed to control the impact of economic agglomeration on its economic development; the logarithm of the total output value of large-scale industry was employed to reflect the industrial scale level, and the social consumption level was measured by the total retail sales/total population of household registration; the ratio of the number of rural households to the total number of households in the county was selected to measure the urban-rural structure; and local telephone users were chosen to evaluate the level of regional information, and the ratio of the number of students in primary and secondary schools to the total population was used for the measurement of the educational level of the county.

Data description

The data used in this paper were sourced from the county-level Statistical Yearbook and China Regional Database. The data from 2000 to 2019 at the county (city) level in China were collected, and the counties with incomplete main variables were screened and processed, including 1687 county-level units, among which 456 counties with the implementation of welfare-to-work policy were set as the treatment group. In addition, due to the gap between the development of the eastern and western parts of China, the economically developed areas of the eastern coastal area were excluded from the control group, and the 1231 counties failing to implement the welfare-to-work policy were set as the control group. Other data come from the Statistical Yearbooks and statistical announcements of counties (districts) and cities across the country, and the data that cannot be obtained by each county (district) and city were supplemented by searching for the government data of the corresponding distract. To reduce the impact of heteroscedasticity on the results, all variables were processed by CPI index (with 2020 as the base period) and conducted logarithmic processing. The descriptive statistics of variables are shown in Table 2 .

Analysis of empirical results

Benchmark regression analysis.

The benchmark results are shown in Table 3 . The control variables were not included in column (1), resulting in a significantly positive estimated coefficient of poverty reduction through welfare-to-work policy; and the control variables were introduced in column (2) with a result of a still significantly positive coefficient, indicating that welfare-to-work policy significantly promoted the comprehensive development and alleviated the relative poverty level of counties. Different from the previous two columns, both the individual fixed effect and the year fixed effect were controlled in column (3), as a result, the poverty reduction effect of the welfare-to-work policy was still significant.

On this basis, the effectiveness of the welfare-to-work policy on poverty reduction will be further studied to analyze poverty reduction performance in various aspects in detail. The results are shown in Table 4 .

The results from columns (1–5) show that: with the implementation of the welfare-to-work policy, the GDP per capita of counties has significantly increased by 11.1%, the disposable income of rural residents by 1.3%, the level of fixed asset investment by 14.0%, the quality of cultivated land by 73.7%, and the rural employment opportunities by 0.7%. These growth data indicate that the welfare-to-work policy not only can significantly promote development and effectively alleviate the relative poverty of the counties, but also can achieve remarkable results in “stabilizing employment and ensuring income to boost the economy”, especially in the quality of cultivated land. However, although the policy plays a significant role in providing rural employment opportunities, the coefficient is the smallest. According to the relevant literature on China’s welfare-to-work and foreign welfare work policies, the author finds that welfare-to-work emphasizes the employment promotion mechanism in poverty alleviation, which is essentially consistent with the work-for-welfare concept of “work” for “welfare”, but welfare-to-work emphasizes disaster relief and regional economic development, combines government investment with public demand, and the government focuses on agricultural production development and rural infrastructure investment and construction. The implementation area is mainly concentrated in underdeveloped areas to improve the local development environment, improve the living standards of local residents, and increase the output of land by increasing investment to improve production conditions (Xiao and Yan, 2023 ). In addition, the implementation of the Welfare-to-work policy is mainly supported by the government’s financial and monetary support, while the relevant relief policies affect economic development through various transmission mechanisms, and then affect the employment problem. The policy transmission chain is too long, and employment is at the end of policy transmission, which is only a by-product of the strategy of promoting growth, and the efficiency is bound to be deficient (Tcherneva, 2014 ). Therefore, the Welfare-to-work policy has the weakest impact on employment and a greater impact on the quality of cultivated lands.

Robustness test

Parallel trend test and dynamic effect test.

By conducting parallel trend tests, the author can accurately evaluate the net effect of the welfare-to-work policy on poverty reduction by using the DID method. The condition for passing the test was that before the implementation of the welfare-to-work policy, the coefficients of the treatment group and the control group showed a parallel trend on the whole.

As shown in Fig. 3 , there is a same change trend between the treatment group and the control group before the implementation of the welfare-to-work policy, but there was a difference after the policy implementation. Especially after the policy implementation, the annual differentiation increased significantly, indicating that the economic status of the treatment group is significantly better than that of the control group, which provides evidence for the effectiveness of the policy.

Parallel trend of poverty reduction between the treatment group and the control group.

Equation ( 3 ) was employed to test the dynamic effect of welfare-to-work policy on poverty reduction, and the results are shown in Table 5 .

When other variables are controlled, the coefficient of interaction term in 2006 is 0.0063, which shows a significantly positive trend, and there is no hysteresis effect. When the policy time point moves backward year by year, the coefficient of the interaction term is significantly positive and constantly increasing, indicating that the welfare-to-work policy has a sustainable poverty reduction effect.

Placebo test

To ensure the robustness of the regression results, the author refers to the ideas of scholars Qian and Ma ( 2022 ) and adopts a “counterfactual” method. Two hundred fifty counties in all regions were randomly selected as policy implementation areas, and other regions were regarded as control groups. To avoid the influence of interaction terms on the explained variable CMDI, random sampling was set to 200 and 500 times, respectively, and the estimated coefficients of 200 and 500 interaction terms DID could be obtained, respectively.

As shown in Figs. 4 and 5 , the results obtained from the two random sampling show that most of the coefficients and values t are concentrated around 0 and follow a normal distribution. The mean value is far from the true value, and most of the estimated coefficients are not significant, indicating that other unobserved factors have no impact on the poverty reduction effect of the welfare-to-work policy, which is in line with the expectation of the placebo test.

The placebo test-sampling 200 times.

The placebo test-sampling 500 times.

Replacement of evaluation method (PSM-DID method)

First, the propensity score PS values of all samples were estimated, and then the samples with similar PS values to the treatment group were selected. That is, under the constraints of characteristic variables such as urban–rural structure, educational, medical level, and government fiscal intervention degree, the Logit model was utilized to estimate the predicted probability P(Xi) identified as implementing the welfare-to-work policy. Then the nearest neighbor matching, radius matching, and kernel matching methods were used to match the samples of the treatment group with the control group, respectively, and the control group samples with the most similar comprehensive characteristics were employed as the control group. The results are shown in Table 6 . After matching, the mean value of each covariate is not significantly different from 0 in the control group and the treatment group, which satisfies the equilibrium hypothesis test.

Then, the net effect of the welfare-to-work policy on poverty reduction was tested based on the DID method. The regression results are shown in Table 6 . Compared with the benchmark results, the three matching methods are basically consistent in terms of the estimated coefficient, symbol, and significance level, which confirms the robustness of the conclusions in this paper.

Replacement of the measuring method of the explained variable

In this paper, the CMDI which was constructed on the basis of a sustainable livelihood model was used to measure the county multidimensional poverty degree, while the academic community often adopts the A–F double critical value method and FGT method to measure the regional multidimensional poverty degree. Therefore, to ensure the robustness of the results, the author replaced the current measurement method with the A–F double critical value method and FGT method, respectively to test the robustness of the explained variable—relative poverty degree in counties.

The results are shown in Table 7 . The estimated coefficients obtained from the A–F double critical value method and FGT method are 0.01 and 0.009, respectively, with significance at the 1% level. Compared with the result of benchmark regression, the coefficients are slightly lower but still play a positive incentive role, which confirms the robustness of the benchmark regression result.

Heterogeneity analysis

County-level heterogeneity.

To explore the heterogeneity in the effect of poverty alleviation policies in the counties with different development levels, the quantile diff-in-diff method was adopted for analysis. Compared with the OLS method, the overall picture showing the conditional distribution of explained variables was more comprehensive and the outliers were more robust.

The results are shown in Table 8 . The DID coefficients of the interaction term of CMDI at 10%, 30%, 50%, 70%, and 90% quantiles were all significantly positive at the 1% level, with the highest coefficient at 90% quantile and the lowest coefficient at 10% quantile. The results indicate that there is heterogeneity in the effect of poverty alleviation policies in the regions with different sustainable development levels, and the higher the development level, the stronger the driving effect. Therefore, enormous efforts should be made in the implementation of the welfare-to-work policy. The government should take proactive moves to accelerate the construction of infrastructure in areas with low development potential, and make full use of financial tools to drive the flow of production factors. Besides, the government should encourage the cultivation of industries with local features according to local conditions to improve the regional sustainable development level and boost the county economic development, so as to prevent the large-scale poverty-return phenomenon and effectively consolidate the achievements of regional poverty alleviation.

Regional heterogeneity

With the economic development, there are obvious differences in China’s regional development due to geographical location, infrastructure, public services, economic foundation, and other factors. The absolute majority of the people who are lifted out of poverty are located in rural areas in the central and western inland provinces. It is necessary to conduct a regional heterogeneity analysis on the basic regression results. Therefore, the sample size was divided into the central, northwest, and southwest regions, and Eq. ( 1 ) was employed to carry out studies on the effect of the poverty alleviation policies in each region.

The results are shown in Table 9 . The estimated parameter of the poverty reduction effect of the welfare-to-work policy in the central region and northwest region is 0.172 and 0.157, respectively, with significance at the 1% level, while the estimated parameter of the southwest region is 0.035 with no obvious significance. The results indicate that the welfare-to-work policy has a significant effect on poverty reduction only in the central region and the northwest region, but not in the southwest region, with a higher effect in the central region followed by the northwest region. According to the relevant poverty alleviation policies implemented in China since 2005, the paper finds that a large amount of poverty alleviation resources have been invested in the central and western regions, while the central region has a higher level of development in its enterprises, a significant improvement in income level and a better driving effect of social participation compared with the western region. In addition, since the reform and opening up, although the poverty-stricken areas in the west have also developed to some extent, the gap between the eastern and central regions is widening, and the distribution of poor people is further concentrated in the western region according to Liu and Ye ( 2013 ). Moreover, the counties in the southwest region are mostly hilly counties and ethnic counties, which will significantly weaken the positive impact of the national poverty-stricken county policy on county poverty and the income gap between urban and rural (Guan et al. 2023 ). In addition, this is consistent with the conclusion of county heterogeneity mentioned above, and the sustainable poverty reduction effect of cash-for-work policy in areas with weak development degrees is weaker than that with higher development station levels. Therefore, the effect of the welfare-to-work policy in the southwest region is weaker than in other regions.

Test of action mechanism

Test of action mechanism of welfare-to-work policy on poverty reduction.

According to existing literature research, infrastructure is the main impact mechanism of policy poverty reduction, and government finance is the material guarantee for the smooth implementation of the welfare-to-work policy, which has an important impact on the poverty reduction effect of the welfare-to-work policy. In addition, financial poverty alleviation cultivates the mechanism of hematopoietic and promotes financial marketization in rural areas (Wang et al. 2021 ). With reference to the specific methods of employment assistance, the welfare-to-work program provides preferential policies such as social insurance subsidies, tax incentives, and small guaranteed loans to enterprises that recruit poor laborers to participate in project construction or provide basic jobs, so as to reduce the production cost of enterprises, thus promoting a virtuous cycle of social and economic development (Qin, 2022 ). Therefore, the action mechanism variables of infrastructure construction level, government fiscal intervention, and financial tools were added to the model to conduct a test via Eq. ( 4 ).

First, infrastructure construction is the main way to implement the welfare-to-work policy. Through the opinions on actively promoting welfare-to-work in the field of agricultural and rural infrastructure construction, the NDRC and other nine ministries and commissions emphasize that the main areas for the implementation of the welfare-to-work policy should be strengthened in the field of agricultural and rural infrastructure. Through infrastructure construction, the development environment of agriculture and rural areas will be improved, on the other hand, the effect of increasing employment income will be promoted. Referring to Qiu et al. ( 2021 ), this paper selects the logarithmic output value of capital construction to express the level of infrastructure construction. Second, the measures for the management of welfare-to-work emphasize that government investment in infrastructure construction, that is, government finance, is the material guarantee for the implementation of the welfare-to-work policy, which has an important impact on the poverty alleviation effect. More investment benefits will be achieved when government investment is closely combined with public demand, the role of beneficiaries is brought into full play in the construction of investment policy, and the active participation of beneficiaries is actively promoted (Ma, 2023 ). Referring to Zhang et al. ( 2019 ), this paper uses the logarithmic value of general expenditure of the public budget to express the degree of government financial intervention. Third, savings and credit are effective ways to significantly increase risk resistance and reduce household vulnerability to poverty (Urrea and Maldonado, 2011 ). With reference to the specific methods of employment assistance, the welfare-to-work policy gives preferential policies such as social insurance subsidies, tax incentives, and small secured loans to enterprises that recruit poor laborers to participate in engineering construction or provide basic jobs, so as to reduce the production costs of enterprises and promote a virtuous circle of social and economic development (Qin, 2022 ). Referring to Zhang et al. ( 2019 ), this paper uses the logarithm of the loan balance of financial institutions at the end of the year to represent the loan level as an indicator that directly reflects the use level of financial instruments; The balance of urban and rural residents’ savings deposits/total resident population is selected to indicate the local savings level and reflect the local indicators to resist the impact of external risks.

The results are shown in Table 10a–d : (1) the coefficient of the interaction term of infrastructure construction level is significantly positive, indicating that the county-level poverty reduction has gotten a greater degree with the improved infrastructure investment level. With the construction of infrastructure, the welfare-to-work program focuses on infrastructure fields such as “mountains, forests, paddy fields, roads, grass, and sand”, as well as, on infrastructure projects related to rural life and production. The welfare-to-work policy stimulated the development of a non-agricultural economy in impoverished areas and promoted the transformation and upgrading of employment structure to a diversified and high-value level, thus optimizing the spatial layout of infrastructure and improving the efficiency of resource allocation and giving play to the poverty reduction effect of infrastructure (Lin and Lin, 2022 ). On this basis, the author separately conducted an analysis of the target indicators of the welfare-to-work policy. The results show that except for insignificant improvement in labor efficiency, the level of infrastructure construction has played a significantly positive role in the rest of the aspects. The welfare-to-work policy has a significant impact on “boosting economic growth”, “ensuring income” and “maintaining employment stability” through the level of infrastructure construction. (2) Government financial intervention plays a significant role in the poverty reduction effect of the welfare-to-work policy. In other words, the government can effectively improve the degree of poverty reduction in the local region by increasing government financial expenditure on the welfare-to-work program. However, specifically, the government fiscal expenditure can effectively increase the per capital disposable income and fixed asset investment level of local rural people, but inhibit the development of the local economy. The possible reason lies in that the government fiscal expenditure will directly affect the increase of the local GDP. (3) The utilization of financial tools can effectively promote the poverty reduction degree of welfare-to-work policy, especially can significantly boost the local economic growth and income growth of rural residents.

Regional heterogeneity test of action mechanism

To further verify the impact of the welfare-to-work policy on poverty reduction through mechanism variables in different regions, according to the particularity of policy implementation and sample limitations, the samples were divided into three sub-samples in northwest, southwest, and central regions for regional heterogeneity test.

The results are shown in Table 11 . In the northwest and southwest regions, the three action mechanisms of infrastructure construction, government fiscal intervention, and financial tools play a significant role in the impact of the county poverty reduction degree, but there is heterogeneity among regions. Specifically, the financial tools play a positive role in poverty reduction in the northwest region but caused a significantly inhibited effect in the southwest, while the test was invalid in the central region. With regard to the heterogeneity of financial instruments among regions, the author researches the relevant theories and literature, and finds that the mitigation effect of finance on rural poverty is unbalanced among regions. However, due to the imbalance of resource endowments and economic development in central and western China, there is a gap in the level of financial development in different regions, leading to different effects of poverty alleviation (Zeng and Hu, 2023 ). Thus, the effect of financial poverty alleviation is also affected by the regional relative poverty level. When the income gap between urban and rural areas is large, the relative poverty level is high, and the downward trend is gentle, the poverty alleviation effect is more significant. The relative poverty level in the western region is higher than that in the central region, so the poverty alleviation effect in the central region is weaker than that in the western region. In addition, economically backward areas have less financial support for finance, imperfect financial infrastructure construction, and unbalanced allocation of financial resources, which restrict the availability of financial services for economic entities in economically backward areas. The shortage of financial service supply often forms a “crowding out effect” on rural low-income groups, while the development level of Southwest China is lower than that of Northwest China (Gong and Chen, 2018 ). The availability of financial services in southwest China is weak, which forms service barriers and inhibits the integration of funds.

Analysis of the contribution of the action mechanism variables

The above tests proved that the welfare-to-work policy has an impact on county-level poverty reduction performance through the infrastructure level, government fiscal intervention, and financial tools. However, there is a heterogeneity in the poverty reduction effect between regions. In order to better combine their own advantages in local counties, timely regulate the infrastructure construction, policy and financial intervention, and financial tools, so as to achieve the sustainability of poverty reduction in counties. With reference to the research of Fu and Tang ( 2022 ), the author adopted the Shapley value decomposition method to figure out the contribution degree of action mechanism variables to the county-level poverty reduction performance in different regions. The principle is to average the marginal effect of a factor by calculating the possible results of all combinations and all other factors, and then obtain the marginal contribution of the factor.

In accordance with Table 12 , the estimation results provide a good explanation of the impact of each variable on county poverty reduction. On the whole, among the three major action mechanism variables, infrastructure construction contributes the most with ratios of 58.31%, 51.96%, and 57.33% to the county economic development, the income and the employment opportunities of rural residents, respectively, and it occupies the second place in fixed asset investment, reaching 44.22%, but makes a most minor contribution to the cultivated land quality, only reaching 2.58%. The government financial intervention contributed the most to the fixed asset investment and cultivated land quality with ratios of 45.29% and 66.23%, respectively, the second to the employment opportunities reaching 23.57%, while the least to the county economic development and the rural residents’ income with ratios of 1.71% and 1.05%, respectively. Financial tools take the second place in contribution with ratios of 39.98%, 47.00%, and 2.19% to the county economic development, the rural residents’ income, and the cultivated land quality, but make the least contribution to fixed asset investment and employment opportunities, only reaching 10.49% and 19.10%.

Specifically, in southwest China, infrastructure construction makes the most significant contribution to the promotion of the county economic development, the rural residents’ income and fixed asset investment, fiscal intervention, and financial tools play a decisive role in improving the cultivated land quality with the contribution rate reaching over 40%, and financial tools have a contribution ratio of 68.13% to employment opportunities. In northwest China, infrastructure construction makes the highest contribution to the promotion of county economic development and rural residents’ income, both reaching over 60%; infrastructure construction and fiscal intervention contributed the most in terms of fixed asset investment with ratios of 37.06% and 48.75%, respectively, accounting for more than 85% of the contribution; financial tools make the highest contribution to improving the cultivated land quality, reaching 67.58%, and both the government fiscal intervention and financial tools make the contribution of over 40% to employment opportunities. In central China, infrastructure construction contributes the most to the promotion of the county economic development, rural residents’ income and fixed asset investment, financial tools play the highest role in improving the cultivated land quality, and the government fiscal intervention and financial tools make the contribution of 36.97% and 43.99% to employment opportunities, respectively.

Conclusions and policy suggestions

Conclusions.

The purpose of this study is to assess the effectiveness of cash-for-work policies in reducing poverty and to provide insights on how to improve these policies in the future. On the basis of combining the evolution of the cash-for-work policy, the impact of the welfare-to-work policy on poverty reduction is empirically analyzed by using the DID method. The basic regression results show that the sustainable poverty alleviation effect of the welfare-to-work policy reaches 16.1%, which shows that the welfare-to-work policy significantly promotes the sustainable poverty alleviation effect in county areas. which was mainly reflected in the sustainable poverty reduction effect of regional economic development and the endogenous driving force of people’s livelihood income increase and prosperity, which showed that the poverty reduction effect of the welfare-to-work policy was significant, promoted the accumulation of social capital, effectively increased the development opportunities of the local people, and improved the endogenous motivation of the local people. Furthermore, the heterogeneity of the sustainable poverty reduction effect of the cash-for-work policy verifies the heterogeneity in different regions and different levels of development, and confirms that the strength of the development level in the three regions is consistent in China. From the perspective of mechanism, the cash-for-work policy has promoted the sustainable development capacity of counties and alleviated the relative poverty level of counties through a series of measures such as infrastructure construction, fiscal intervention, and financial instruments. However, due to the differences in resource endowment and development levels among different regions, the effect of different mechanisms on the sustainable poverty reduction of cash-for-work policies is inconsistent in different regions. Among them, the impact of infrastructure construction on county-level sustainable poverty alleviation is the largest, and the impact of financial instruments on county-level sustainable poverty reduction is promoted in the northwest region, but significantly inhibited in the southwest region, while the poverty reduction effect is not significant in the central region.

The main finding of this study is that t the welfare-to-work policy for poverty reduction has achieved great results in regional economic development and people’s livelihood income increase. However, a number of challenges and constraints still need to be addressed to ensure the sustainability of poverty reduction efforts.

Policy suggestions

Based on the above analysis, this paper puts forward the following suggestions for improving the sustainable poverty reduction of cash-for-work policies:

First, sustainable poverty reduction should be achieved through the use of active and differentiated cash-for-work policies. Cash-for-work policy support cannot be simply implemented in all regions, but should be based on its own unique geographical environment and resource advantages, increase the construction of infrastructure suitable for itself, adapt measures to local conditions, develop characteristic industries, realize the upgrading of regional economic quality and efficiency, and focus on employment opportunities for the local people, actively help the local people to broaden the channels for nearby employment and income, and strengthen the self-survival and development ability of local groups.

Second, the government needs to further increase the construction of financial services in the southwest and central regions, further, enhance the internal function and effect of financial services, attract social forces to actively participate in the construction of rural infrastructure region, and give full play to the multiplier effect of the combination of financial services and infrastructure construction to achieve long-term sustainable poverty reduction. Accordingly, in the central region, the government should step up efforts in financial intervention, and all parties should reasonably adjust the critical point of infrastructure construction to improve supply efficiency and poverty reduction efficiency, in the northwest region, the government should focus on taking advantage of financial tools to create good business tools; and in the southwest region, the government should adjust the use pattern of financial tools to promote local high-quality development.

Third, focusing on the specific situation of deeply impoverished areas and special poverty groups, we will increase government financial investment in poverty alleviation, and strive to improve the effectiveness of sustainable poverty reduction. On the one hand, we should give preference to project approval and resource allocation, and improve the allocation of public financial resources to underdeveloped areas and low-income groups from the macro, and micro levels, so as to improve the efficiency of supply and poverty reduction. In addition, the quantitative conclusion of the mechanism of action shows that fiscal intervention has played a positive incentive role, but the effect is not large, especially in indirectly promoting regional economic growth and individual development, so we should pay attention to guiding the supplementary and regulating role of the third distribution on redistribution, and at the same time support the development of industries that meet the needs of poor areas to improve people’s livelihood and well-being.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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This study was funded by the National Natural Science Foundation of China (grant number: 72174162).

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Lan, F., Xu, W., Sun, W. et al. From poverty to prosperity: assessing of sustainable poverty reduction effect of “welfare-to-work” in Chinese counties. Humanit Soc Sci Commun 11 , 758 (2024). https://doi.org/10.1057/s41599-024-03267-z

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Causes and measures of poverty, inequality, and social exclusion: a review.

Graphical Abstract

1. Introduction

2. literature review: poverty, inequality, and social exclusion.

‘It is the poor person, the “ aporos ”, who is an irritation, even to his own family. The poor relative is considered a source of shame it is best not to bring to light, while it is a pleasure to boast of a triumphant relation well situated in the academy, politics, art, or business. It is a phobia toward the poor that leads us to reject individuals, races, and ethnic groups that in general lack resources and that therefore cannot—or appear unable to—offer anything’. ( Cortina 2022 )

2.1. Poverty

2.1.1. monetary poverty, 2.1.2. multidimensional poverty, 2.2. inequality, 2.2.1. types of inequality.

‘The nation-state is still the right level at which to modernize any number of social and fiscal policies and to develop new forms of governance and shared ownership intermediate between public and private ownership, which is one of the major challenges for the century ahead. But only regional political integration can lead to effective regulation of the globalized patrimonial capitalism of the twenty-first century’. ( Piketty 2014 )

2.2.2. Measuring Inequality

2.3. is the middle class disappearing, 2.4. social exclusion: what is going on with aporophobia, 2.5. the sdgs overview, 3. discussion and future directions, 4. policy implications and conclusions.

• In order to diminish social exclusion and aporophobia, further utilization of poverty and inequality indices for the most needed target groups is necessary.
• Discrepancies between indicators provided by institutions (i.e., the World Bank and the UN) ought to be adjusted in order to have a unique poverty indicator.
• More focus on how to cover Maslow’s hierarchy of needs should be given from governments and international institutions, as it provides a framework for basic needs necessary for a decent life and is in tandem with the proposed indices of World Bank and the UN.
• The diversification of the SDGs not only in their targets, but also in their sub-targets, ought to be conducted.
• There is not a common rule on the acceptance and implementation of a specific poverty or inequality index: the application of two or more indices and their comparison might lead to better interpretation of the extent and depth of poverty or inequalities.
• It is also suggested that inequality measures should be further compared with polarization, as the former measures focus on the tails of a population distribution and the latter polarization index delves into the disappearance of the middle class.

Author Contributions

Informed consent statement, data availability statement, conflicts of interest.

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Halkos, G.E.; Aslanidis, P.-S.C. Causes and Measures of Poverty, Inequality, and Social Exclusion: A Review. Economies 2023 , 11 , 110. https://doi.org/10.3390/economies11040110

Halkos GE, Aslanidis P-SC. Causes and Measures of Poverty, Inequality, and Social Exclusion: A Review. Economies . 2023; 11(4):110. https://doi.org/10.3390/economies11040110

Halkos, George E., and Panagiotis-Stavros C. Aslanidis. 2023. "Causes and Measures of Poverty, Inequality, and Social Exclusion: A Review" Economies 11, no. 4: 110. https://doi.org/10.3390/economies11040110

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• DOI: 10.6007/ijarbss/v11-i15/10637
• Corpus ID: 241764759

Poverty: A Literature Review of the Concept, Measurements, Causes and the Way Forward

• Rusitha Wijekoon , M. Sabri , L. Paim
• Published in International Journal of… 22 July 2021
• Economics, Sociology

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Review article, poverty reduction of sustainable development goals in the 21st century: a bibliometric analysis.

• Institute of Blue and Green Development, Shandong University, Weihai, China

No Poverty is the top priority among 17 Sustainable Development Goals (SDGs). The research perspectives, methods, and subject integration of studies on poverty reduction have been greatly developed with the advance of practice in the 21st century. This paper analyses 2,459 papers on poverty reduction since 2000 using VOSviewer software and R language. Our conclusions show that (1) the 21st century has seen a sharp increase in publications of poverty reduction, especially the period from 2015 to date. (2) The divergence in research quantity and quality between China and Kenya is great. (3) Economic studies focus on inequality and growth, while environmental studies focus on protection and management mechanisms. (4) International cooperation is usually related to geographical location and conducted by developed countries with developing countries together. (5) Research on poverty reduction in different regions has specific sub-themes. Our findings provide an overview of the state of the research and suggest that there is a need to strengthen the integration of disciplines and pay attention to the contribution of marginal disciplines to poverty reduction research in the future.

Introduction

Global sustainable development is the common target of human society. “No Poverty” and “Zero Hunger” are two primary goals of the 2030 Agenda for Sustainable Development (SDGs) , along with important premises in the completion of the goals of “Decent Work and Economic Growth Industry” and “Innovation and Infrastructure.” China has made great efforts in meeting its No Poverty targets. To achieve the goal of eliminating extreme poverty in the rural areas by the end of2020 1 , China has been carrying out a basic strategy of targeted approach named Jingzhunfupin 2 , which refers to implementing accurate poverty identification, accurate support, accurate management and tracking. By 2021, China accomplished its poverty alleviation target for the new era on schedule and achieved a significant victory 3 .

However, the worldwide challenges are still arduous. On the one hand, the recent global poverty eradication process has been further hindered by the COVID-19 pandemic. The World Bank shows that global extreme poverty rose in 2020 for the first time in over 20 years, with the total expected to rise to about 150 million by the end of 2021 4 . People “return to poverty” are emerging around the world. On the other hand, people who got out of income poverty may still be trapped in deprivations in health or education. About 1.3 billion people (22%) still live in multidimensional poverty among 107 developing countries, according to the Global Multidimensional Poverty Index report released by the United Nations 5 . Meanwhile, the issue of inequality became more prominent, reflected by the number of people who are in relative poverty 6 .

In line with the dynamic poverty realities, the focusing of poverty research moved forward as well. Research frameworks have evolved from single dimension poverty to multidimensional poverty ( Bourguignon et al., 2019 ) and from income poverty to capacity poverty ( Zhou et al., 2021 ). Research perspectives concentrate on the macroscopic view, but have now turned to microscopic individual behavior analysis. Cross-integration of sociology, psychology, public management, and other disciplines also helps to expand and deepen the research ( Addison et al., 2008 ). Some cutting-edge researchers are making effort to shed light on the relationships between “No Poverty” and other SDGs. For example, Hubacek et al. (2017) verified the coherence of climate targets and achieving poverty eradication from a global perspective 7 . Li et al. (2021) discussed the impacts and synergies of achieving different poverty eradication goals on air pollutants in China. These novel papers give us insightful inspiration on combining poverty reduction with the resource or environmental problem including aspects like energy inequity, carbon emission. Hence, summarizing the research on different poverty realities and academic backgrounds should provide theoretical and empirical guidance for speeding up the elimination of poverty in the world ( Chen and Ravallion, 2013 ).

Previous review literature on poverty reduction all directed certain sub-themes. For example, Chamhuri et al. (2012) , Kwan et al. (2018) , Mahembe et al. (2019a) reviewed urban poverty, foreign aid, microfinance, and other topics, identifying the objects, causes, policies, and mechanisms of poverty and poverty reduction. Another feature of the review literature is that scholars often synthesize the articles and map the knowledge network manually, which constrains the amount of literature to be analyzed, leading to an inadequate understanding of poverty research. Manually literature review on specific fields of poverty reduction results in a research gap. Analysis delineating the general academic knowledge of poverty reduction is somewhat limited despite the abundance of research. Yet, following the trend toward scientific specialization and interdisciplinary viewpoints, the core and the periphery research fields and their connections have not been clearly described. Different studies are in a certain degree of segmentation because scholars have separately conducted studies based on their countries’ unique poverty background or their subdivision direction. Possibly, lacking communication and interaction will affect the overall development of poverty reduction research especially in the context of globalization. Less than 10 years are left to accomplish the UN sustainable development goals by 2030. It is urgent to view the previous literature from a united perspective in this turbulent and uncertain age.

Encouragingly, with advances in analytical technology, bibliometrics has become increasingly popular for developing representative summaries of the leading results ( Merediz-Solà and Bariviera, 2019 ). It has been widely applied in a variety of fields. In the domain of poverty study, Amarante et al. (2019) adopted the bibliometric method and reviewed thousands of papers on poverty and inequality in Latin America. Given above issues, we expand the scope of the literature and conduct a systematic bibliometric analysis to make a preliminary description of the research agenda on poverty reduction.

This paper presents an analysis of publications, keywords, citations, and the networks of co-authors, co-words, and co-citations, displaying the research status of the field, the hot spots, and evolution through time. We use R language and VOSviewer software to process and visualize data. Our contributions may be as follows. Firstly, we used the bibliometric method and reviewed thousands of papers together, helping keep pace with research advances in poverty alleviation with the rapid growth in the literature. Secondly, we clarified the core and periphery research areas, and their connections. These may be beneficial to handle the trend toward scientific specialization, as well as fostering communication and cooperation between disciplines, mitigating segmentation between the individual studies. Thirdly, we also provided insightful implications for future research directions. Discipline integration, intergenerational poverty, heterogeneous research are the directions that should be paid attention to.

The structure of this article is as follows. Methodology and Initial Statistics provides the methodology and initial statistics. Bibliometric Analysis and Network Analysis offer the bibliometric analysis and network visualization. The remaining sections offer discussions and conclusions.

Methodology and Initial Statistics

Bibliometrics, a library and information science, was first proposed by intelligence scientist Pritchard in 1969 ( Pritchard, 1969 ). It exploits information about the literature such as authors, keywords, citations, and institutions in the publication database. Bibliometric analyses can systematically and quantitatively analyze a large number of documents simultaneously. They can highlight research hotspots and detects research trends by exploring the time, source, and regional distribution of literature. Thus, bibliometric analyses have been widely used to help new researchers in a discipline quickly understand the extent of a topic ( Merediz-Solà and Bariviera, 2019 ).

Research tools such as Bibexcel, Histcite, Citespace, and Gephi have been created for bibliometric analysis. In this paper, R language and VOSviewer software are adopted. R language provides a convenient bibliometric analysis package for Web of Science, Scopus, and PubMed databases, by which mathematical statistics were performed on authors, journals, countries, and keywords. VOSviewer software provides a convenient tool for co-occurrence network visualization, helping map the knowledge structure of a scientific field ( Van Eck and Waltman, 2010 ).

Data Collection

The bibliometric data was selected and downloaded from the Web of Science database ( www.webofknowledge.com ). We choose the WoS Core Collection, which contained SCI-EXPANDED, SSCI, and A&HCI papers to focus on high-quality papers. The data was collected on March 19, 2021.

To identify the documents, we used verb phrases and noun phrases with the meaning of poverty reduction, such as “reduce poverty” and “poverty reduction,” as search terms, because there are several different expressions of “poverty reduction.” We also considered the combinations of “no poverty” and SDGs, “zero hungry” and “SDGs.” Because the search engine will pick up articles that have nothing to do with “poverty alleviation” depending on what words are used in the abstract, we employed keyword matching. Meanwhile, to prevent missing essential work that does not require author keywords, we also searched the title. Specifically, a retrieval formula can be written as [AK = (“search term”) OR TI = (“search term”)] in the advanced search box, where AK means author keywords and TI means title. Finally, we restricted the document types to “article” to obtain clear data. Thus, papers containing search phrases in headings or author keywords were marked and were guaranteed to be close to the desired topic.

A total of 2,551 studies were obtained, with 2,464 articles retained after removing duplicates. Table 1 presents the results for each search term. The phrasing of “poverty alleviation” and “poverty reduction” are written preferences.

TABLE 1 . Information of data collection.

Descriptive Analysis

Figure 1 gives details of each year’s publications during the period 2000–2021. The cut-off points of 2006 and 2015 divide the publication trends into three stages. The first period is 2000–2006, with approximately 40 publications per year. The second period is 2007–2014, in which production is between 80 and 130 papers annually. The third period is 2015–2021, with an 18.31% annual growth rate, indicating a growing interest in this field among scholars. Perhaps this is because 2006 was the last year of the first decade for the International Eradication of Poverty, and 2015 is the year that eliminating all forms of poverty worldwide was formally adopted as the first goal in the United Nations Summit on Sustainable Development. Greater access to poverty reduction plan materials and data is a vital reason for the growth in papers as well.

FIGURE 1 . Annual scientific production.

We can notice that the milestone year is 1995 when we examine the time trend with broader horizons ( Figure 2 ). Before 1995, scant literature touches upon the topic of “poverty alleviation.” This confirms that in the time range we check the majority of the development of academic interest in this issue takes place. Thus, the 21st century has become a period of booming research on poverty reduction.

FIGURE 2 . Annual scientific production in a longer period.

Bibliometric Analysis

In this section, we offer the bibliometric analysis including the affiliation statistics, citation analysis and keywords analysis. Author analysis is not included because some authors’ abbreviations have led to statistical errors.

Affiliation Statistics

From 2000 to 2021, a total of 2,459 articles were published in 979 journals, a wide range. Table 2 lists the top ten journals, which together account for 439 (17.86%) of the articles in our data set. Development in Practice and World Development have the most publications, respectively 121 (4.92%) and 107 (4.35%), followed by Sustainability at 44 (1.8%). The top 10 journals mostly involve development or social issues, with some having high impact factors, including Food Policy (4.189) and Journal of Business Ethics (4.141).

TABLE 2 . Top 10 sources of publications.

Figure 3 presents the geographic distribution of the published articles on poverty reduction. As indicated in the legend, the white part on the map shows regions with zero published articles recorded in WoS. Darker shades indicate a greater number of articles published in the country or region. The US region is darkest on the map, with 593 articles published, followed by England, with 412 papers, and China, with 348 articles. Ranking fourth is South Africa, perhaps because South Africa is a pilot site for many poverty reduction projects. India, for the same reason, is similarly shaded.

FIGURE 3 . Spatial distribution of publication in all countries. Note: the data of all countries is from Web of Science.

Citation Analysis

The number of citations evaluates the influence and contribution of individual papers, authors, and nations. The top 10 countries in total citations are displayed in Table 3 . Consistent with the publication distribution, the leader is the United States (11,861), with the United Kingdom (8,735) and China (1,666) following. However, there is a broad gap between China and England in total citations. The average article citation ranks are quite different from the total citation list. Notably, Kenya takes first place based on its average citations per paper, though its total citations rank seventh, showing that Kenya’s poverty reduction practices and research are of great interest to a large number of scholars. By contrast, China’s average article citation is just roughly one-sixth of Kenya’s. The different pattern of the number of Chinese publications and citations shows that the quality of Chinese research must be improved even as it raises its publication quantity.

TABLE 3 . Top ten countries by total citations.

Table 4 lists the top 10 most cited articles with their first author, year, source, total citations, and total citations per year. Highly cited articles can be used as a benchmark for future research, and in some way signal the scientific excellence of each sub-field. For example, Wilson et al. (2006) reminded the importance of informal sector recycling to poverty alleviation. Daw et al. (2011) discussed the poverty alleviation benefits from ecosystem services (ES) with examples in developing countries. Pagiola et al. (2005) found that Payments for Environmental Services (PES) can alleviate poverty, and explored the key factors of this poverty mitigation effect using evidence from Latin America 8 . These three papers combined the environmental ecosystem with poverty alleviation. Beck et al. (2007) , Karnani (2007) explored the relationships between the SME sector and poverty alleviation and the private sector and poverty alleviation, respectively. Grindle (2004) discussed the necessary what, when, and how for good governance of poverty reduction. Cornwall and Brock (2005) took a critical look at how the three terms of “participation,” “empowerment” and “poverty reduction” have come to be used in international development policy. Adams and Page (2005) examined the impact of international migration and remittances on poverty. In the theory domain, Collier and Dollar (2002) derived a poverty-efficient allocation of aid. Hulme and Shepherd (2003) provided meaning for the term chronic poverty. Even from the present point of view, these scholars’ studies remain innovative and significant.

TABLE 4 . Top 10 papers with the highest total citations.

Keywords Analysis

The keywords clarify the main direction of the research and are regarded as a fine indicator for revealing the literature’s content ( Su et al., 2020 ). Two different types of keywords are provided by Web of Science. One is the author keywords, offered by the original authors, and another is the keywords plus, contrived by extracting from the cited reference. The frequency of both types of keywords in 2,459 papers is examined respectively in the whole sample and the sub-sample hereinafter for concentration and coverage.

Whole Sample

Table 5 lists the Top 10 most frequently used keywords and keyword-plus of total papers. Clearly author keywords are often repetitive, with “poverty,” “poverty reduction,” and “reduction” chosen as keywords for the same paper, but these do not dominate the keywords-plus. Hence, the keywords-plus may be more precise at identifying relevant content. However, we used author keywords for the literature screening.

TABLE 5 . Top 10 author keywords and keywords-plus with the highest frequency.

In addition to the terms “poverty” or “poverty reduction or alleviation,” we note that “China” and “Africa” occur frequently, with “India” and “Bangladesh” following when we expand the list from the Top 10 to Top 20 ( Supplementary Figure S1 ). The appearance of these places coincides with our speculation that the research was often conducted in Africa, East Asia, or South Asia once again, whereas the larger compositions are from developing countries or less developed countries.

The cumulative trend of TOP20 author keywords and keywords-plus is shown in Supplementary Figure S1 . The diagram also gives some information about other concerns bound up with anti-poverty programs, including “microfinance,” “food security,” “livelihoods,” “health,” “Economic-growth” and “income,” as numerous papers are focused on these aspects of poverty reduction.

Further, policy study and impact evaluation may be the core objectives of these papers. Vital evidence can be found in countless documents. Researchers measured the effect of policies or programs from various perspectives. In the study of Jalana and Ravallion (2003) , they indicated that ignoring foregone incomes overstated the benefits of the project when they estimated net gain from the Argentine workfare scheme. Meng (2013) found that the 8–7 plan increased rural income in China’s target counties by about 38% in 1994–2000, but had only a short-term impact 9 . Galiani and McEwan (2013) studied the heterogeneous influences of the Programa de Asignación Familiar (PRAF) program, in which implemented education cash transfer and health cash transfer to people of varying degrees of poverty in Honduran. Maulu et al. (2021) concluded that rural extension programs can provide a sustainable solution to poverty. Some studies also have drawn relatively fresh conclusions or advice on poverty reduction projects. Mahembe et al. (2019a) found that aid disbursed in production sectors, infrastructure and economic development was more effective in reducing poverty through retrospecting empirical studies of official development assistance (ODA) or foreign aid on poverty reduction. Meinzen-Dick et al. (2019) reviewed the literature on women’s land rights (WLR) and poverty reduction, but found no papers that directly investigate the link between WLR and poverty. Huang and Ying (2018) constructed a literature review that included the necessity and the ways of introducing a market mechanism to government poverty alleviation. Mbuyisa and Leonard (2017) demonstrated that information and communication technology (ICT) can be used as a tool for poverty reduction by Small and Medium Enterprises.

Web of Science provides the publications of each journal category ( Figure 4 ). Economics is the largest type of journal, followed by development studies and environmental studies. Education should be regarded as an important way to address the intergenerational poverty trap. However, we note that journals in education are only a fraction of the total number of journals. Psychology journals are in a similar position, though endogenous drivers of poverty reduction have been increasingly emphasized in recent research. The detailed data can be found in the supplementary documents. To investigate the differences between the subdivisions of the research, we chose economic and environmental journals as sub-samples for further analysis.

FIGURE 4 . Visualization of journal category from the web of science.

As Supplementary Figure S2 shows, the TOP10 author keywords in economic sample are similar to the whole sample. We note that microfinance is a real heated research domain both in economic and whole sample. The poor usually have multiple occupations or self-employment in very small businesses ( Banerjee and Duflo, 2007 ). The poor often have less access to formal credit. Karlan and Zinman (2011) examined a microcredit program in the Philippines and found that microcredit does expand access to informal credit and increase the ability against risk. Banerjee et al. (2015a) reported the results of an assessment of a random microcredit scheme in India, which increased the investment and profits of small-scale enterprises managed by the poor.

Several new keywords enter the TOP20 list in the economic field, including “targeting,” “income distribution,” “productivity,” “employment,” “rural poverty,” “access,” and “program.” “Targeting” is an essential topic in the economic field. It concerns the effectiveness of poverty reduction program and social fairness. Hence, an abundance of literature reviews the definitions of poverty that allow individuals to apply for poverty alleviation programs. Park et al. (2002) , Bibi and Duclos (2007) , Kleven and Kopczuk (2011) , discussed the inclusion error and exclusion error in programs’ targeting and identification under the criterion of poverty lines or specific tangible asset poverty agency indicators (e.g., whether households have color televisions, pumps or flooring, and so forth). In practice, Niehaus et al. (2013) tested the accuracy of different agency indicators to allocate Below Poverty Line (BPL) cards in India and found that using a greater number of poverty indicators led to a deterioration in targeting effectiveness while creating widespread violations in the implementation because less qualified families are more likely to pay bribes to investigators. Bardhan and Mookherjee (2005) explored the targeting effectiveness of decentralization in the implementation of anti-poverty projects. He and Wang (2017) assessed the targeting accuracy of the College Graduate Village Officials (CGVOs) project, a unique human capital redistribution policy in China, on poverty alleviation 10 .

The terms “inequality” and “growth” are first and second in the keywords-plus. This may be because inequality and growth are two of the major components in poverty changes in the economic field, which are stressed in the studies of Datt and Ravallion (1992) , Beck et al. (2007) . The ranking may also imply that the economics of the 21st century is more concerned with human welfare than the pursuit of rapid economic growth. Since a growing number of organizations are trying to build human capital to improve the livelihoods of their clients and further their mission of lifting themselves out of poverty. McKernan (2002) showed that social development programs are important components of microfinance program success. Similarly, Karlan and Valdivia (2006) argued that increasing business training can factually improve business knowledge, practice effectiveness, and revenue. Besides, cash transfers are widely adopted to reduce income inequality and improve education and the health status of poor groups ( Banerjee et al., 2015b ; Sedlmayr et al., 2020 ). Benhassine et al. (2015) noted that the Tayssir Project in Morocco, a cash transfer project, achieved an increasing improvement of school enrolment rate in the treatment group, especially for girls 11 .

We combine the journal types of “Environmental Studies” and “Environmental Sciences” into one unit for analysis ( Supplementary Figure S3 ). In the environmental field, the terms “conservation” and “management” are ranked first and second. This field also involves “ecosystem services,” “climate change,” “biodiversity conservation,” and “deforestation,” with rapid growth in recent years. These themes were discussed by Alix-Garcia et al. (2013) , Alix-Garcia et al. (2015) , Sims and Alix-Garcia (2017) in their investigations of the effect of conditional cash transfers on environmental degradation, the poverty alleviation benefits of the ecosystem service payment project, and comparison of the effects in protected areas and of ecosystem service payment on poverty reduction in Mexico. The differences in economic research in poverty reduction and environmental field show the necessity of strengthening cooperation between disciplines.

Network Analysis

Network relationship is established by the co-occurrence of two types of information. It enables mapping of the knowledge nodes with a joint perspective, instead of viewing scientific ideas in isolation. The data is imported into VOSviewer software after removing duplicates by R package. We then provide the co-authorship analysis, co-citation analysis, and co-keywords analysis.

Co-Authorship Analysis

Co-authorship may reflect international cooperation as shown by the country distribution ( Figure 5 ). When the authors of two countries have a cooperative relationship, a line is generated to connect the corresponding countries. The size of nodes reflects the number of countries of origin of the authors. The width of the line represents the cooperative frequency between them, and the different colors mark the partition of the countries.

FIGURE 5 . International networks of co-authorship.

The network includes a total of 1,449 countries, of which 92 meet the threshold of at least five instances of cooperation. The United States, United Kingdom, China, and South Africa have the strongest interlinkage with other countries or regions. Whether countries in each cluster demonstrate international academic cooperation on poverty reduction is sometimes based on geographic location. For example, the red cluster includes the United States, Mexico, Brazil, Chile, and Ecuador. These countries mainly lie in the Americas. The United Kingdom, Kenya, Uganda, and South Africa are in the yellow group, located in Europe and Africa. The green cluster includes China, Malaysia, and Bangladesh, all Asian countries. The distribution of countries on each cluster and the map as a whole show that research on poverty alleviation is usually conducted by developed and developing nations together. This may be due to anti-poverty programs in developed countries usually being subsidized by international non-governmental organizations, as shown by the branch literature devoted to foreign aid and poverty reduction ( Mahembe et al., 2019b ).

Co-Citation Analysis

Co-citation analysis can locate the core classical literature efficiently ( Zhang et al., 2020 ). Pioneering studies of co-citation analysis were performed by Small (1973) . When an article cited two other articles, a relationship of co-citation will be established between these two “cited” articles ( González-Alcaide et al., 2016 ). Since co-citation aims at reference, it targets the knowledge base for the past.

Figure 6 displays the co-citation network of the cited references. The functions of the sizes and colors are the same as in Figure 5 . The most cited papers in the co-citation relationship are the studies of Foster et al. (1984) , Sen et al. (1999) , Dollar and Kraay (2002) , which respectively explore poverty measures, globalization and development, and the growth impact for the poor.

FIGURE 6 . Cited reference network of co-citation.

Figure 7 gives the co-citation heat map of sources, based on their density. We set the threshold at 20, and 78 cited sources remained on the map. Different colors signify different clusters of co-citation. The lighter the color, the more frequently the journals are cited. There are four major categories. World development and the Journal of Development Economics have the largest influence on the red cluster, which mainly contains development and economic studies. The second cluster is green and includes the fields of energy, environment, and ecology, with Ecology Economics as its brightest star. The Journal of Business Ethics and Annals of Tourism Research are the most-cited journals in the third and fourth cluster, which represents the fields of business and tourism. Some psychology studies exist in transitional spaces between business studies and economic studies, suggesting a trend of interdisciplinary work. In the past 10 years, we checked manually that psychology and other interdisciplinary research performed well. Many papers were published in Science or Nature. In the research of Mani et al. (2013) , there was a causal relationship between poverty and psychological function. Poverty reduced the cognitive performance of the poor, because poverty consumes spiritual resources, leaving fewer cognitive resources to guide choices and actions. Another psychology-based experiment in Togo showed that personal proactive training increased the profits of poor businesses by 30%, while traditional training influence was not significant ( Campos et al., 2017 ). In the study of Ludwig et al. (2012) , they revealed that the shift from high-poverty to low-poverty communities resulted in significant long-term improvements in physical and mental health and subjective well-being and had a continuing impact on collective efficacy and neighborhood security.

FIGURE 7 . Cited source density network of co-citation.

Co-Words Analysis

The analysis of co-words was performed after the co-citation analysis. Since it is hard to explain the changes in cluster from year to another in a co-citation map, Callon et al. (1983) proposed co-word analysis to identify and visualize scientific networks and their evolution. Based on our keyword analysis and following the arguments of Zhang et al. (2016) , the knowledge structures of author keywords and keywords plus are similar, but keywords plus can mirror a large proportion of the author keywords when the threshold of the number of instances of a word exceeds 10. The merger of two types of keywords will inflate the total number of words, leaving unique words representing the latest hot spot with little chance to be selected. Therefore, we conduct the co-word analysis using keywords plus to map the structure.

We set the minimum number of occurrences to 15, and 100 words with the greatest link strength are selected from the total of 2,774. As shown in Figure 8 , keywords plus generates 4 clusters. To our delight, each cluster does reflect the research priorities of each region.

FIGURE 8 . Keywords-plus co-occurrence cluster map.

The first cluster (red) reveals studies concerning livelihood, conservation, management, climate change and agriculture. These topics have strong interlinkage to Africa, suggesting that poverty reduction in Africa is often related to basic livelihood and ecology. The poor in Africa rely on the ecological conditions heavily as they are facing a more disadvantaged climate and resources. Therefore, their poverty reduction process is sometimes highly unstable and subject to considerable internal and external constraints. Stevenson and Irz (2009) concluded that the numerous studies presented almost no evidence of aquaculture reducing poverty directly.

The second cluster (green) represents studies focused on economic growth and income inequality, common in China and India. This pattern may imply that papers of this cluster focus on the economic conditions of the poor. Other studies in this cluster are related to migration, health, and welfare. The third cluster (blue) is the poverty reduction strategies on microfinance and empowerment, which are associated with Bangladesh where the Grameen Bank, one of the most notable and intensely researched microcredit programs, was founded ( McKernan, 2002 ). This cluster’s studies are interested in approaches such as business, markets, and education, to help the poor rise from poverty. The fourth cluster (yellow) contains studies of poverty reduction programs on environmental services in Latin America, where the environmental problem is intertwined with poverty traps.

Figure 9 shows the time trend of keywords-plus co-occurrence. Because the keywords plus are extracted from the cited references, they can reflect the changes in hotspots from relatively early to the most recent years. As can be seen, education, technology, and environmental services are the latest keywords in research on poverty reduction.

FIGURE 9 . Keywords-plus co-occurrence time trend map.

There are several limitations to our bibliometric analysis, though we undertake an extensive review of the literature. First, we inevitably lose a fraction of the literature. keywords and title are chosen as the criteria for helping precisely concentrate the search results on our subject. However, the Web of Science core collection on which our study relied is weak in the coverage of literature to some degree. Hence, there is a trade-off between the quantity and the quality of literature. We choose the latter, leading to an unclear restriction of the comprehensiveness of research. Second, we can identify recent research status but are not able to locate the Frontier accurately. Network mapping requires selecting a minimum occurrence threshold for including corresponding authors, keywords, and citations into the network. Because a certain number of citations or new hotspots take several years to be widely used and studied, this threshold may neglect these important data ( Linnenluecke et al., 2020 ). One possible solution is to manually examine the latest published papers in high-quality journals. Third, the mining of subfields is not deep enough. In other words, bibliometrics cannot sort out the main conclusions of literature on poverty reduction. For instance, we do not know whether the conclusion of different studies are consistent for the same poverty alleviation project. Neither do we know the exact mechanism of the anti-poverty program through bibliometric analysis, which limits the possibility of finding research points from controversial conclusions or mechanisms.

However, several points are worth taking into consideration for the future. To start with, poverty reduction is a natural interdisciplinary social science problem. Interdisciplinary has become a major research trend. Except applying cash transfer to ecological programs, associations are raised. We may discuss whether the combination of finance and ecology will bring positive benefits to financial stability, ecological protection, and poverty reduction by the means of capitalization of ecological resources or establishing the ecological bank. Our analysis suggests that some unheeded branch disciplines like human ethology are contributing to poverty reduction research as well. Thus, we need to investigate the interdisciplinary integration and the contribution of marginal disciplines on poverty reduction.

Then, more attention should be paid the intergenerational poverty. It requires researchers to extend the time span of observation and questionnaire investigation. Some work has been done. One example is the research of Hussain and Hanjra (2004) . They reviewed literature and clarified that advances in irrigation technologies, such as micro-irrigation systems, have strong anti-poverty potential, alleviating both temporary and chronic poverty. Another example is the research of Jones (2016) , which indicated that conditional cash transfers (CCTs) could indeed interrupt the intergenerational cycle of poverty through human capital investments. However, there remains a lot of work to be done for preventing the next generation from returning to poverty in this turbulent period. In a related matter, the role of education in isolating intergenerational poverty or returning to the poverty trap should be highlighted. What kind of education would more effectively help families out of poverty, quality education or vocational skill education? How to allocate educational resources effectively? For poor students, what kind of psychological intervention in education is needed to mitigate the impact of native families and help them grow up confidently? Lots of questions waiting for empirical answering, yet we note that the educational journal only took a little fraction of the total journals in Section 4.3.2.

Next, poverty does exist in prosperous conurbations though the focal point obtained from keywords analysis is “rural area”. Nevertheless, both the slums in the center of big cities and circulative flowing refugees are experiencing more relative deprivation, representing a state of instability. Chamhuri et al. (2012) reviewed the objects, causes, and policies of urban poverty. Exploring how to lift a particular small economic low-lying area out of poverty is also of great significance. Follow-up researches should keep up.

Moreover, poverty alleviation needs to be based on individual or group-specific characteristics to some degree. It is not feasible to implement a unified poverty alleviation policy on a large scale. Exquisitely designed randomized controlled trials are used to reveal the heterogeneous influence of poverty alleviation programs. Haushofer and Shapiro (2016) compared the difference between monthly transfers and one-time lump-sum transfers. The subdivision research on the effect of poverty reduction programs should be strengthened. We imagine that a model may be formed to predict the total poverty reduction effects of different policies in the various region to obtain an optimized strategy of “No Poverty” in the future.

Lastly, exploring whether poverty reduction will be contradictory or coordinate with other SDGs might be a popular direction. About the literature review, two aspects can be improved. The first is merging with other databases to compare the loss of the trade-off between quality and quantity. Next, subsequent literature reviews need to explore how to better combine manual literature collation and bibliometrics, especially when the subject is a large topic.

Poverty reduction is one of the objectives of welfare economics and development economics. It is a classic and lasting topic and has recently come into the limelight. Poverty reduction studies in the 21st century are usually based on specific poverty alleviation projects or policies in developing countries. Researchers examine numerous topics, including whether the target audience has been precisely identified and covered in the design and implementation process, whether poverty reduction projects have been proved effective, what mechanisms have contributed to the success of poverty reduction projects, and what caused their failure. The aim of this paper is to summarize the amount, growth trajectory, citation, and geographic distribution of the poverty reduction literature, map the intellectual structure, and highlight emerging key areas in the research domain using the bibliometric method. We use the VOSviewer software and the R language as tools to analyze 2,459 articles published since 2000.

We have several conclusions. First, the 21st century is a period of booming research on poverty reduction, and the number of publications has increased sharply since 2015. Second, in affiliation analysis, Development in Practice and World Development are the top publications. The most frequently cited source of co-citations are World Development , Ecology Economics, Journal of Business Ethics, and Annals of Tourism Research , respectively the centers of the fields of economics, energy, the environment, and ecology, business, and tourism. Third, there are differences in the national and regional distribution of literature, based on the number of publications and citations. The United States led both the publication list and the total citation list, followed by the United Kingdom, China, and South Africa. Yet, there is a huge variation in the number of citations, with the United States and the United Kingdom having almost 5 to 6 times more citations than China and South Africa. In terms of average citations, Kenya is the best performer. The average citation amount in China is low, implying that Chinese scholars need to improve the quality of their literature. Fourth, in the keyword analysis, policy discussion and impact estimation are the two major themes. The keywords related to poverty reduction are different among different disciplines. Economics pays more attention to inequality and growth, while environmental disciplines pay more attention to protection and management. This may suggest that strengthening the cooperation between disciplines will lead to more diversified research perspectives. Fifth, in the co-author analysis, international cooperation is usually related to geographical location. For example, there is a large amount of cooperation between Europe and Africa, within Asia, and between North and South America. At the same time, poverty reduction research often shows the cooperative patterns of developed and developing countries. Last, in the co-keyword analysis, four clusters reflect the research priorities of each region. Poverty reduction in Africa is often related to basic livelihood and ecology. The economic conditions of the poor are the concerns of research in China and India. The South Asia region is also the location of microcredit program experiments. Poverty traps are intertwined with environmental problems in Latin America’s literature.

Our findings also offer inspiration for the future. There may be a need to investigate the interdisciplinary integration. Intergenerational and urban poverty deserve attention. The heterogeneous design of poverty alleviation strategies needs to be further deepened. It might be a popular direction to figure out whether poverty reduction will be contradictory with other SDGs and conduct scenario simulation. We identify shortcomings as well. Finally, precisely identifying research frontiers requires further exploration.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

This work is supported by National Natural Science Foundation of China (NSFC) (grant number 72022009).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2021.754181/full#supplementary-material

1 The extreme poverty criterion set by World Bank is 1.9$ a day in purchasing power parties (PPP), https://www.worldbank.org/en/research/brief/policy-research-note-03-ending-extreme-poverty-and-sharing-prosperity-progress-and-policies . The China poverty alleviation target in 2020 is to eliminate absolute poverty, which is defined as living less than 2,300 yuan per person per day at 2010 constant prices. In addition to living above the absolute poverty line, people who must reached other five qualitative criteria can be calculated getting rid of absolute poverty, which is no worries about food, clothing, basic medical care, compulsory education and housing safety

2 Jingzhunfupin is a general term of Chinese targeted poverty alleviation work model. Opposite to the haploid poverty alleviation, different assistance policies will be formulated according to the different category of poverty, distinctive causes, dissimilar background of poor households and their divergent living environment

3 https://enapp.chinadaily.com.cn/a/202102/26/AP60382a17a310f03332f97555.html . https://www.bbc.com/news/56213271

4 https://www.worldbank.org/en/topic/poverty/overview

5 The global Multidimensional Poverty Index (MPI) is developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) since 2010. It has been published annually by OPHI and in the Human Development Reports (HDRs) ever since. https://ophi.org.uk/multidimensional-poverty-index/

6 Relative poverty is another poverty measurement to reflect the underlying economic gradient. It is induced from the relative deprivation theory. Countries set the relative poverty line at a constant proportion of the country or year-specific mean (or median) income in practice ( https://doi.org/10.1162/REST_a_00127 )

7 This paper mainly found that eradicating extreme poverty, i.e., moving people to an income above $1.9 purchasing power parity (PPP) a day, does not jeopardize the climate target. That is to say, the climate target and no poverty goal is consistent and coordinated

8 This paper indicated that Payments for Environmental Services may reduce poverty mainly by making payments to poor natural resource managers in upper watersheds. The effects depend on how many participants are poor, the poor’s ability to participate, and the amounts paid

9 8–7 plan is the second wave of China’s poverty alleviation program. The Leading Group renewed poverty line and the National Poor Counties list in 1993. Targeted counties received three major interventions: credit assistance, budgetary grants for investment and public employed projects (i.e., Food-for-Work).

10 In the College Graduate Village Officials (CGVOs) program, the government hire outstanding graduates to work in the rural areas, for example as the village committee secretary, to help rural development and alleviate poverty. In this paper, the College Graduate Village Officials assisted eligible poor households to understand and apply for relevant subsidies, which reduced elite capture of pro-poor programs and move forward poverty alleviation process

11 The Tayssir Project was labeled the Education Support Program, sending a positive signal of its educative value

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Keywords: poverty reduction, bibliometric analysis, VOSviewer, sustainable development goals, 21st century

Citation: Yu Y and Huang J (2021) Poverty Reduction of Sustainable Development Goals in the 21st Century: A Bibliometric Analysis. Front. Commun. 6:754181. doi: 10.3389/fcomm.2021.754181

Received: 06 August 2021; Accepted: 01 October 2021; Published: 18 October 2021.

Reviewed by:

Copyright © 2021 Yu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yanni Yu, [email protected] ; Jinghong Huang, [email protected]

† These authors have contributed equally to this work

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The consequences of poverty and inequality for growth have long preoccupied academics and policy-makers. This paper revisits the inequality-growth and poverty-growth links. Using a panel of 158 countries between 1960 and 2010, we find that the correlation of growth with poverty is consistently negative: A 10 p.p. decrease in the headcount poverty rate is associated with a subsequent increase in per capita GDP between 0.5 and 1.2% per year. In contrast, the correlation of growth with inequality is empirically fragile—it can be positive or negative, depending on the empirical specification and econometric approach employed. However, the indirect effect of inequality on growth through its correlation with poverty is robustly negative. Closer inspection shows that these results are driven by the sample observations featuring high poverty rates.

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1 Introduction

What is the effect of poverty on aggregate income growth? And the effect of inequality? Academics and policy-makers have long been concerned with these questions. But they have typically been explored as separate issues. Yet properly answering them requires taking them up jointly, because poverty and inequality are interrelated features of the same income distribution (Bourguignon 2004 ).

This paper attempts to fill that gap by providing an empirical exploration of the growth effects of both poverty and inequality and, in particular, of their respective robustness. The effects of poverty have been analyzed by numerous theoretical papers highlighting a variety of mechanisms through which poverty may become self-perpetuating. But empirical work has been more limited and largely inconclusive. Indeed, a basic implication of the theoretical models of poverty traps—namely, that countries suffering from higher levels of poverty should grow less rapidly than comparable countries with lower poverty—has been largely overlooked. This is the key hypothesis pursued in this paper. It can be viewed as a weak version of the poverty trap hypothesis, in that to support it we do not need to find evidence of multiple equilibria or income stagnation, but just empirical proof that, other things equal, poverty tends to hold back growth.

In contrast, the effects of inequality have attracted massive empirical literature, albeit with sharply conflicting results. The present paper adds to existing work by highlighting a novel angle, namely the indirect effect of inequality on growth accruing through the impact of inequality on poverty: given the poverty line and the overall population’s mean income, an increase in inequality will typically raise poverty, by pushing more individuals below the poverty line. Footnote 1 If poverty affects growth, so will inequality through this indirect channel—in addition to any direct effects that inequality might exert on growth.

To assess the respective growth impacts of poverty and inequality, we estimate a reduced-form growth equation with inequality and poverty added separately and jointly to an otherwise standard set of growth determinants (educational attainment, investment prices, government size, degree of openness, public infrastructures, etc.). For the estimation, we assemble a large panel data set of non-overlapping five-year observations comprising 158 countries over the period 1960–2010. The sample is heavily unbalanced, and its size exceeds by far that found in earlier studies of the poverty-growth link.

Our econometric approach is based on GMM estimation employing internal instruments (Arellano and Bover 1995 ; Blundell and Bond 1998 ; Roodman 2009 ). In our setting, the choice of this approach is dictated by the short time dimension and large cross-sectional dimension of our panel dataset—which makes panel time-series methods unsuitable—and by the potential endogeneity of the regressors—which demands an instrumental variable approach. These issues affect also much of the empirical literature on the links between poverty, inequality and growth, which—like our paper—has to contend with the potential problem of two-way causality between the variables at the core of the analysis.

In this context, GMM represents a natural methodological choice, which we also share with much of the related empirical literature. Footnote 2 Moreover, this common empirical methodology also makes our paper more easily comparable with existing work. Finally, while our use of GMM for growth empirics is not novel, our paper is among the first to examine rigorously, in a system GMM setting, the potential problem of weak instruments plaguing much of the empirical growth literature, as first raised by Kraay ( 2015 ) in the context of the empirical relationship between inequality and growth.

Our main finding is that poverty has a robust negative and significant effect on growth. As for inequality, we find that the sign and significance of its direct effect on growth are fragile. However, its indirect effect (through poverty) is robustly negative. Further inspection reveals the presence of nonlinearities, in that these results are driven by the sample observations featuring high poverty: when poverty is low, its impact on growth is not significant, and the indirect effect of inequality on growth is therefore absent. We reach a similar conclusion when we let the growth impact of poverty differ between developed and developing countries: It is negative and significant for the latter, but not for the former.

Our results survive a battery of robustness checks, including the use of alternative sets of instruments and specifications in the GMM estimation, different poverty lines and poverty measures, alternative poverty data, nonlinear and nonparametric specifications, or the use of alternative sets of control variables. We also find that our preferred GMM specification can address in a satisfactory manner the endogeneity, under-identification and weak instruments problems often encountered in macroeconomic applications of dynamic panel models (Bazzi and Clemens 2013 ).

Our paper is embedded in an extensive literature (recently surveyed by Cerra et al. 2021a ) analyzing the multidirectional links among growth, inequality and poverty. Three strands are especially relevant in our context. They, respectively, focus on the impact of poverty on growth, the impact of inequality on growth, and the contribution of inequality and income growth to poverty. We provide a brief review of these literature works in the next section.

The rest of the paper is structured as follows. As just noted, Sect.  2 is devoted to a selective summary of the literature on the growth-inequality-poverty nexus. In Sect.  3 , we describe the data and we lay out the empirical strategy to test for the effects of poverty and inequality on growth. In Sect.  4 , we report the main empirical results for our baseline specification. Section  5 reports extensive robustness checks on our empirical results. Section  6 analyzes how the links of poverty and inequality with growth might depend on the prevailing degrees of poverty and/or inequality and gauges the direct and indirect effects of inequality on growth. Finally, Sect.  7 concludes.

2 The growth-inequality-poverty nexus: a review

The seminal work of Kuznets ( 1955 ) is the starting point of an extensive literature analyzing the growth-inequality-poverty nexus (see Bourguignon 2004 , and the recent surveys by Cerra et al. 2021a , b ). Our paper relates to several strands of this literature.

First, a long-standing theoretical literature has studied a variety of mechanisms through which poverty may deter economic growth. Its arguments are mostly based on the existence of poverty traps, i.e., mechanisms through which poverty prevents a significant share of the population from helping ignite the growth engine (Azariadis and Stachurski 2005 ; Bowles et al. 2006 ; Haider et al. 2018 ). Under appropriate conditions, those mechanisms may lead to multiple equilibria and make the negative impact of poverty on growth self-reinforcing. In general, the mechanisms highlighted in the literature operate by reducing the incentives and/or abilities of the poor to undertake risky entrepreneurial activities, and/or to accumulate physical and human capital.

A prominent mechanism involves ‘threshold effects’ (Azariadis and Drazen 1990 ), resulting, for example, from indivisibilities or increasing returns to scale. Footnote 3 For example, if poverty is coupled with credit constraints, the result is that below a certain level of income or wealth economic agents may be too poor to afford the investments (in human or physical capital) or the technologies necessary to raise their income (Galor and Zeira 1993 ; Banerjee and Newman 1993 ). Malnutrition provides another example. In developing countries, poverty is associated with high rates of malnutrition (Dasgupta and Ray 1986 ), which impacts cognitive abilities and school absenteeism and is transmitted to the children’s capacity to learn. The resulting educational inequality is also growth-deterring (Galor and Moav 2004 ).

Institutional arrangements that place economic opportunities beyond the reach of the poor can likewise result in reduced income growth (Mookherjee and Ray 2002 ; Engerman and Sokoloff 2006 ). Another poverty-perpetuating mechanism is related to risk aversion (Banerjee 2000 ): Because poorer individuals are typically more risk averse, in the absence of well-functioning insurance and credit markets, they will skip profitable investment opportunities that they deem too risky. Footnote 4 Poverty can also alter the decision-making process of individuals toward less growth-enhancing activities. For instance, the poor devote a significant fraction of their income to satisfying basic needs (Shah et al. 2012 ) and to “temptation” goods (Banerjee and Mullainathan 2010 ) and reduce the resources devoted to education, health and investment. Poor individuals show also lower aspirations, as they anticipate that their current status will impede their future success (La Ferrara 2019 ).

In spite of the diversity of these analytical models, evidence on their empirical relevance remains largely inconclusive. A few papers (see Durlauf 2006 , for a review) have searched for various empirical regularities consistent with those models, such as aggregate non-convexities (Azariadis and Stachurski 2005 ) and convergence clubs (Quah 1993 ). A broader empirical review of different mechanisms advanced in the literature finds little evidence that they may be at work, except perhaps in remote or disadvantaged areas (Kraay and McKenzie 2014 ). More recently, large-scale randomized evaluations, such as the one developed by Bandiera et al. ( 2017 ) in Bangladesh, yield strong evidence that the poor face imperfections in capital markets that keep them in a low asset-low employment poverty trap.

Somewhat surprisingly, just a few papers have taken up the fundamental aggregate implication of the poverty trap literature—that, ceteris paribus, countries with higher poverty should grow more slowly. The list is limited to our working paper version, Marrero and Servén ( 2018 ), plus López and Servén ( 2015 ) and Ravallion ( 2012 ), all of which conclude that poverty is growth-deterring Footnote 5 ; Easterly ( 2006 ) shows a non-significant impact of poverty on growth.

The second strand of literature to which our paper is related is concerned with the impact of inequality on growth. It includes a large number of empirical contributions reaching conflicting conclusions; for overviews, see Voitchovsky ( 2011 ), Berg et al. ( 2018 ), and Cerra et al. ( 2021a ). For example, Alesina and Rodrik ( 1994 ) and Perotti ( 1996 ) found a negative relationship between inequality and growth in cross section data, but subsequently, Li and Zou ( 1998 ) and Forbes ( 2000 ) obtained the opposite result using panel data. Barro ( 2000 ) found that inequality might affect growth in different directions depending on the country’s level of income, while Panizza ( 2002 ) found that results might depend on the model specification and the quality and type of data (see also Deininger and Squire 1998 ). In turn, Banerjee and Duflo ( 2003 ) concluded that the response of growth to inequality changes has an inverted U-shape.

The multiplicity of factors affecting both inequality and growth might explains these contradictory results. For example, rising inequality could be the result of growth-enhancing technological change whose returns are captured by talented individuals at the top of the distribution (Goldin and Katz 2008 ). In contrast, if rent-seeking is the fundamental force behind growing incomes of the rich, the increase in inequality could come along with declining growth (Stiglitz 2012 ).

In this line of enquiry, Galor and Moav ( 2004 ) argue that the replacement of physical capital accumulation by human capital accumulation as a prime engine of economic growth has changed the qualitative impact of inequality on growth. Marrero and Rodríguez ( 2013 ) emphasize that the sign of the effect of inequality on growth depends on the type of inequality considered (i.e., inequality of opportunity or of effort). Voitchovsky ( 2005 ) and, more recently, van der Weide and Milanovic ( 2018 ) argue that the effect of inequality is negative for the income growth of the poor but positive for the income growth of the rich—i.e., inequality tends to be self-reinforcing. The effects of inequality on growth might also depend on the sectoral structure of the economy (Erman and te Kaat 2019 ) and on the degree of intergenerational mobility (Aiyar and Ebeke 2020 ). Footnote 6

In general, different mechanisms affecting growth in opposite directions through different channels act all simultaneously, leading to conflicting inferences. In the empirical literature, an emerging consensus view is that the long-run effect of inequality on growth is significantly negative, and only when looking at relatively short periods of time, the relationship may turn positive (Halter et al. 2014 ; Brueckner et al. 2015 ; Berg et al. 2018 ; Brueckner and Lederman 2018 ). Footnote 7

A third strand of the literature explores the links between growth and inequality, on the one hand, and poverty, on the other. The bulk of this literature, which is quite extensive (Cerra et al. 2021a ), focuses on the poverty-reducing effect of growth and the factors that shape it (Dollar and Kraay 2002 ; Bourguignon 2003 ; Ravallion 2004 ). This angle of the poverty-growth link is the opposite to that pursued in this paper.

Empirically, there is ample consensus that growth reduces poverty—i.e., it is “good for the poor.” Dollar and Kraay ( 2002 ), and the subsequent updates using alternative databases and empirical approaches (Kraay 2006 , Dollar et al. 2016 ) find that the income of the poorest deciles varies in the same proportion as average income, hence fostering aggregate growth is pro-poor (see also Ferreira et al. 2010 , or Loayza and Raddatz 2010 ). Recent work confirms this result (Fosu 2017 ; Bluhm et al. 2018 ; Bergstrom 2020 ). For example, Bergstrom ( 2020 ) finds that, in a large cross-country sample, 90% of the variation in poverty is explained by variation in per capita GDP. However, the reason is that the sample variation in per capita income is much larger than that of inequality; indeed, in most of the sample countries, the estimated inequality elasticity of poverty exceeds the income elasticity of poverty—which suggests that declines in inequality offer a large potential (as yet unrealized) to reduce poverty rates.

Comparatively, the literature has paid less attention to the impact of inequality on poverty (Bourguignon 2003 ; Ravallion 2005 ; Ferreira et al. 2010 ; Kalwij and Verschoor 2007 ). This is precisely the mechanism behind the indirect inequality-to-growth channel analyzed in this paper, and not covered in earlier literature. More recently, Sehrawat and Giri ( 2018 ), the aforementioned Bergstrom ( 2020 ) and Lakner et al. ( 2020 ) find evidence supporting the role of declining inequality for poverty reduction.

3 Growth, inequality and poverty: data and empirical implementation

We turn to the description of our empirical strategy. First we describe the data and then the econometric approach employed in the estimation.

Since our focus is not on cyclical growth fluctuations, we follow the empirical literature on inequality and growth and construct a panel data set of non-overlapping 5-year observations on the three variables of interest: inequality, growth and poverty. We focus on the 1960–2010 period, as done by the recent empirical literature on inequality and growth. Growth is measured as the log difference of real per capita income over the entire 5-year interval, while poverty and inequality are measured at the beginning of the interval. This means we only need to collect poverty and inequality data up to 2005.

We use the Gini index to measure inequality and take the UN-WIID2 (2008) database as our primary source of data on income inequality. It includes 5313 surveys for 154 countries from 1950 to 2006. We complete the WIID2 data with information from PovcalNet, which adds another 122 country-year (16 countries) observations over the 1960–2010 period. In a number of instances, there are multiple surveys referring to the same country-year, but they offer different coverage or use different concepts of income. We restrict our sample to Gini indexes based on nationally representative surveys. Moreover, data are sometimes based on income and other times on expenditure figures; income is net of transfers and taxes in some cases and not in others; the unit of analysis may be the individual or the household, etc. To correct at least in part for this heterogeneity, we adjust the original Gini data following Dollar and Kraay ( 2002 ). Footnote 8

For economic growth, we use national accounts purchasing-power-parity (PPP)-adjusted per capita GDP data from the Penn World Tables 7.1, the same source used by Berg et al. ( 2018 ) and many other studies of inequality and growth, which facilitates comparability with them. Sala-i-Martin ( 2006 ) and Dollar and Kraay ( 2002 ), among many others, emphasize the advantages of using per capita GDP instead of the mean level of income obtained directly from household surveys. The survey mean usually does not match per capita income from the national accounts, because of differences in concepts and methodology, inconsistent data collection methods, misreporting, etc. Additionally, for many of the country-year observations for which we have information on inequality, we do not have matching information on mean income from the same source, which hampers the construction of a large panel dataset. In contrast, national accounts data are reported yearly for all countries, using a homogenous methodology, which, in addition, allows us to compare our empirical results with those of the ample macroeconomic literature on income inequality and growth.

Regarding poverty data, we follow the strategy proposed by Dollar and Kraay ( 2002 ), López and Servén ( 2015 ), Sala-i-Martin ( 2006 ) and Pinkovskiy and Sala-i-Martin ( 2013 ). These authors point out that combining poverty and income growth data from household surveys and national accounts may lead to misleading conclusions, because of the inconsistencies between the two sources just noted. To avoid this problem, they use PWT data to construct both income growth and poverty measures, with the latter computed assuming that household income follows a lognormal distribution. Thus, we construct a set of poverty measures (the headcount ratio P0, the poverty gap P1 and the squared poverty gap P2) using a lognormal approximation on the basis of the observed per capita GDP levels and Gini coefficients. Footnote 9 We also experiment with alternative, widely used poverty lines: US$ 1.25, US$ 2 and US$ 4 per person per day, in 2005 PPP US$ (see Appendix 1 for details).

This approach allows a considerable increase in sample size. Despite the progress made in recent years, mainly through the PovcalNet project, survey-based poverty data are still relatively scarce, at least in comparison with the size of the standard cross-country time-series growth dataset. Using the lognormal approximation, we assemble 746 observations on poverty over non-overlapping 5-year intervals, covering 156 countries between 1960 and 2005 (an average of almost five observations per country). Footnote 10 In contrast, using the January 2020 version of PovcalNet over the same 1960–2005 time span, we can construct a dataset of 383 poverty observations over non-overlapping 5-year intervals for 144 countries, roughly half the size of our sample—i.e., an average of less than 3 observations per country, with data for the vast majority of countries starting in 1990 or later. Footnote 11

As far as we are aware, ours is the largest sample used to date to study the impact of poverty on growth. It exceeds by far the samples used by the two earlier papers analyzing the poverty-growth nexus in a panel regression setting: López and Servén ( 2015 ) assemble a sample comprising 325 observations from 85 countries over 1960–2000, while Ravallion ( 2012 ) uses unbalanced panel data from PovcalNet covering up to 97 developing countries over a shorter time span, 1981–2005.

Table 1 presents summary statistics on annual growth, mean income, inequality and poverty for the common sample of these variables in the unbalanced 1960–2010 panel. The table shows the wide range of per capita income levels (expressed in 2005 US dollars in PPP terms) in the sample—from just over $200 (the Democratic Republic of Congo in the mid-2000s) to about $73,000 (Luxembourg in 2005). The median observation corresponds to Brazil in the mid-1970s, with per capita income about $5500. The overall sample mean is about $9800, much larger than the median, which reflects a world income distribution skewed to the right.

Regarding inequality, both the median and the mean of the Gini coefficient equal 0.4, which matches the values found for the U.S. (in 2000), Burkina Faso (in 1995), Turkey (in 2010) or Singapore (in 1970). The maximum value (above 0.74) corresponds to Zimbabwe in 1995, and the minimum (below 0.16) corresponds to Bulgaria in 1975. Around 80% of the observations fall in the range between 0.28, a value found among Western European countries, and 0.54, a value found among Latin American and Sub-Saharan African countries.

Poverty rises by construction with the poverty line and declines as the poverty measure changes from P0 to P2 (i.e., as one considers more bottom-sensitive measures). For our lognormal poverty estimates, the table shows that median headcount poverty P0 is 0.6% using US$ 1.25 per day as poverty line, but it raises to 2.3% with a US$ 2 poverty line, and to 13% with US$ 4. Likewise, the median P1 ranges from less than 0.1% for US$ 1.25 to about 4% for US$ 4, while the median P2 ranges from less than 0.1% for US$ 1.25 to almost 2% for US$ 4. Although the mean and the median of these poverty measures are relatively small, the heterogeneity in the sample is quite high, since the ranges of the various poverty measures run from a minimum of zero (reflecting the presence of high-income countries in the sample) to a maximum whose value depends on the particular poverty measure and poverty line under consideration. For example, depending on the poverty lines considered, these maximum levels go from 90 to 99% for P0, from 60 to 86% for P1 and from 50 to 75% for P2. The maximum corresponds in all cases to Tanzania. We use headcount poverty P0 (with a poverty line of US$ 2 per day) as our baseline poverty measure for the rest of the paper.

Figure  1 shows the sample correlation between annual per capita growth, the baseline P0 (for US$ 2) and the Gini coefficient. The top graphs plot growth against lagged poverty, and the bottom graphs plot growth against the Gini coefficient. The leftmost graphs show the unconditional correlation, while the center graphs control for lagged income and the rightmost graphs add also regional dummies. The top left scatter, which shows the unconditional correlation between growth and poverty, highlights the degree of heterogeneity in the sample. For instance, there is a wide range of observations with very small poverty rates and very large variation in growth rates (from − 5% to + 10%). At high poverty rates (above 80%, say), the range of variation in growth rates is fairly wide as well. However, once we control for real per capita GDP (top center graph), the correlation turns negative and significant. The result is robust to the addition of regional dummies. Results are different for the growth-inequality scatter plots. The ambiguous relation shown in the leftmost graph turns negative when we control for real per capita GDP. However, it becomes slightly positive (but insignificant) when adding regional dummies.

Growth, poverty and inequality: preliminary cross section evidence. Note Growth is measured as per capita annual GDP growth between 1970 and 2010. The initial period is 1970. For the graphs in the second column, growth, as well as initial poverty P0, and the initial Gini coefficients G0 are the residuals from projecting the respective original variables on initial per capita GDP (in logs). For the graphs in the third column, they are measured as the residuals from projecting the respective original variables on initial per capita GDP (in logs) and a set of regional dummies (North America, Europe and Central Asia, Latin American and the Caribbean, Middle East and North Africa, Sub-Saharan Africa, South Asia and East Asia and the Pacific)

Additional controls used in the empirical exercises described in Sects.  4 and 5 (years of schooling, investment prices, inflation, trade openness, government size, degree of democracy, etc.) come from the Penn Word Tables, the World Development Indicators database, the Barro and Lee ( 2013 ) educational attainment database, and the political risk module of the International Country Risk Database (ICRD).

The controls used are standard in the empirical growth literature (Perotti 1996 ; Forbes 2000 ; Knowles 2005 ; Barro 2000 , among many others). In particular, we consider the price of investment goods relative to that of the USA as a measure of market distortions, so its expected growth impact is negative. As a measure of human capital, we consider the average years of secondary education for males and females in our baseline specification, and the rate of primary and secondary school attainment (as a percentage of the population) in our robustness analysis. The distinction between male and female education is motivated by the finding that the latter appears to be more important than the former in raising labor productivity in developing countries (Owen et al. 2002 ). Human capital is expected to have a positive impact on growth. However, neither the years of education nor the educational attainment measures capture the quality of education (Hanushek 2017 ), which detracts from the significance of their growth contribution (Sianesi and Van Reenen 2003 ). Also, the contribution could be highly nonlinear (Liu and Stengos 1999 ), so that linear regressions could generate misleading conclusions (a question we revisit in Sect.  5.1 ).

We also consider standard policy indicators as control variables: the rate of inflation of the GDP deflator as an indicator of macroeconomic stability, the adjusted ratio of the country’s volume of trade to its GDP as an indicator of the degree of openness of the economy, Footnote 12 and the ratio of public consumption to GDP as an indicator of the burden imposed by the government on the economy. As a measure of public infrastructure, we update the composite index constructed by Calderón et al. ( 2015 ). It comprises the telecommunication sector (the number of main telephone lines per 1000 workers), the power sector (the electricity generating capacity in MW per 1000 workers), and the transportation sector (the length of the road network—in km. per sq. km. of land area). Finally, we consider controls related to institutional quality, such as the degree of democracy, and government stability.

Table 13 in the Appendix 2 describes all the variables used in the paper (source, sample size, mean and standard deviation), either in the baseline estimation (Sect.  4 ) or the robustness checks (Sect.  5 ). In turn, Table 14 reports the pairwise correlation matrix of all controls and core variables in the model (growth, per capita GDP, poverty and inequality). Correlations are shown for the full sample, and they are calculated using the variables transformed as they enter in the regressions (i.e., per capita GDP in logs; poverty and the Gini coefficient in levels; adjusted openness and government size in logs, etc.).

In general, the correlations show the expected signs. In the case of growth, they anticipate the signs of the coefficient estimates obtained below: positive for the education variables, openness, infrastructure, democracy and government stability; negative for investment prices, inflation, and government size, as well as poverty and inequality. In turn, the pairwise correlations between the control variables are generally small except for the human capital variables, ruling out potential collinearity concerns for the regression analysis.

3.2 Empirical strategy

To explore the links between growth, inequality and poverty, we use a specification adding suitable measures of poverty to an otherwise standard empirical growth regression (López and Servén 2015 ; Ravallion 2012 ):

where \(lny\) is the log of per capita income, \(\alpha_{i}\) and \(\gamma_{t}\) are country- and time-specific effects, p is a measure of poverty, x represents a set of control variables, which we shall discuss shortly, and \(\varepsilon\) is an i.i.d error term. Likewise, we estimate the standard inequality-growth regression (Forbes 2000 ; Berg et al. 2018 ),

where \(g\) is the Gini coefficient. The parameters \(\delta_{0}\) and \(\varphi_{0}\) in ( 1 ) and ( 2 ) capture the impacts on growth of poverty and inequality, respectively, given lagged per capita income.

It is important to note that, even if inequality has no direct effect on growth—as assumed in Eq. ( 1 ), which omits the Gini coefficient—it can still affect growth indirectly through poverty. The reason is that inequality and poverty are related. This is not due to our assumption of lognormality when constructing the poverty data, but just a general consequence of the very definition of poverty as the share of the population whose income lies below the poverty line. Given the poverty line and the overall population’s mean income, an increase in inequality (more precisely, a mean-preserving spread of the income distribution) must bring more individuals below the poverty line, and therefore raise the poverty rate. Footnote 13 Hence, poverty and inequality are positively correlated in general, a fact that also applies to our dataset, as can be confirmed from the pairwise correlations reported in Table 14 . If poverty has a negative effect on growth (i.e., if \(\delta_{0}\) in ( 1 ) is negative), it follows that an increase in inequality would raise poverty and reduce growth through this indirect channel. We return to this issue in Sect.  6 .

Furthermore, if inequality does have a direct effect on growth, the poverty coefficient estimate from a regression equation like ( 1 ) that erroneously omits inequality will be biased, and—other things equal—the bias will be greater the larger the correlation between poverty and inequality. To avoid such problem, we also estimate a model including both lagged inequality and lagged poverty as explanatory variables:

Estimation of ( 3 ) merits comment. In principle, \(\delta_{1}\) captures the impact on growth of a shock to poverty holding constant inequality and average income, along with the other regressors. Thus, identifiability of \(\beta_{2}\) , \(\delta_{1}\) and \(\varphi_{1}\) in a linear regression setting requires that poverty not be an almost exact linear combination of \(\ln y\) and \(g\) —otherwise, the estimating equation would feature (nearly) perfect collinearity. In our data set, panel regressions of poverty on per capita income and the Gini coefficient account for less than half of the sample variation in poverty. Footnote 14 Thus, collinearity does not prevent identification of \(\beta_{2}\) , \(\delta_{1}\) and \(\varphi_{1}\) in ( 3 ), as more than half of the sample variation in poverty can be attributed to shocks uncorrelated with average income or inequality. Footnote 15

We turn to the set of controls included in x . Rather than adding to the already huge variety of empirical growth models contributing yet another idiosyncratic set of regressors, we opt for considering alternative growth specifications found in the literature, in order to explore the sensitivity of our results to the specific choice of control variables. We use the four models described next as our baseline specifications, leaving for Sect.  5 a robustness check on the inclusion of additional controls.

First, we consider a skeleton model of growth (M1), which includes only lagged income, poverty and the Gini coefficient as regressors. In this setting, the estimated parameters capture the direct impacts of poverty and inequality on growth, as well as potential indirect effects due to other variables omitted from the model (Galor 2009 ). Our second model (M2) is taken from the empirical literature on inequality and growth (Perotti 1996 ; Forbes 2000 ). It comprises a measure of market distortions (the domestic price of investment goods relative to that of the USA) and a measure of human capital, given by the average years of secondary education of the male and female populations, considered separately. Our third model (M3) focuses on standard policy indicators (Barro 2000 ). It includes the rate of inflation of the GDP deflator (macroeconomic stability), the adjusted ratio of the country’s volume of trade to its GDP (the degree of openness), and the ratio of public consumption to GDP (government size). Lastly, the fourth model (M4) is taken from López and Servén ( 2015 ). It includes the inflation rate, the average years of secondary female education, and a lagged composite index of public infrastructure.

Our empirical strategy has to confront two endogeneity concerns. On the one hand, the joint determination of income, poverty and inequality could result in biased estimates. The fact that poverty and inequality are pre-determined in ( 1 )–( 3 ) should help alleviate, even if not necessarily eliminate, this concern. On the other hand, the country-specific unobservable α i may be correlated with the regressors in ( 1 )–( 3 ).

Dealing with endogeneity requires an instrumental variable estimation approach. However, we have no obvious candidates for suitable external instruments—i.e., exogenous variables correlated with poverty and/or inequality but not with growth. Thus, following common practice in the empirical literature on the effects of inequality on growth, we opt for using GMM panel estimators employing “internal instruments,” that is, instruments based on lagged values of the explanatory variables. To build such instruments, we assume that the explanatory variables (including poverty and inequality) are weakly exogenous. In other words, they can be affected by current and past realizations of the growth rate—e.g., today’s poverty or inequality may depend on past growth—but must be uncorrelated with future realizations of the time-varying growth shock. This assumption does not seem particularly restrictive; furthermore, we can statistically examine its validity through several specification tests, as explained below.

Specifically, we take first differences in ( 1 )–( 3 ) to remove the country-specific unobservable α i . This leaves us with the first-differenced time-varying residual (e.g., \(\varepsilon_{it} - \varepsilon_{it - 1}\) ) in the transformed equations. Under the assumption that the original regressors are weakly exogenous, so that for any regressor \(z\) we have \(E\left[ {z_{is} \varepsilon_{it} } \right] = 0\) for \(s < t\) , the levels of the regressors lagged two or more periods become valid instruments for GMM estimation of the parameters of the first-differenced equations, because \(E\left[ {z_{it - s} \left( {\varepsilon_{it} - \varepsilon_{it - 1} } \right)} \right] = 0\) for \(s > 1\) . Using these instruments, we can consistently estimate the parameters of interest, namely β, δ, and φ , even though the dependent variable of the first-differenced equations is the change in the growth rate, rather than the growth rate itself. Footnote 16

However, working only with the model in first differences may lead to major finite sample biases if the variables are highly persistent, because their lagged levels become weak instruments for the first-differenced regressors (Blundell and Bond 1998 ). Under the additional stationarity assumption that \(E\left[ {\left( {z_{it} - z_{is} } \right)\alpha_{i} } \right] = 0\) for all \(t\) and \(s\) , differences of the regressors lagged one or more periods become valid instruments for the original level Eqs. ( 1 )–( 3 ) (Blundell and Bond 1998 ). This allows building the so-called system GMM estimator, which estimates the parameters of interest combining the first-differenced equation and the original levels equation.

The GMM estimator is consistent as long as the underlying instruments are valid. Their validity can be tested using Hansen’s J test of over-identification. We also report results for the Difference-in-Hansen statistic, which tests the validity of the subset of instruments employed in the level equation of the system GMM estimation.

A problem often encountered in GMM estimation is the excessive proliferation of instruments, which biases downward the estimated standard errors and weakens the power of the over-identification tests (Roodman 2009 ). To remedy this, we apply the Windmeijer ( 2005 ) correction to the variance–covariance matrix and also reduce the number of instruments employed in the estimation (Roodman 2009 ). Specifically, we limit the number of lags in the matrix of instruments, and/or collapse the matrix of instruments and create one instrument for each variable and lag distance, rather than one instrument for each lag distance, time period and variable as commonly done in the system GMM approach.

Although the GMM estimators attempt to deal with the endogeneity of regressors typical of dynamic panel data models like ( 1 )–( 3 ), when the cross-sectional dimension of the sample is not large relative to its time dimension—a common situation with macroeconomic panel data—these GMM estimators can behave poorly (Bun and Sarafidis 2015 ). In this setting, it is not obvious that GMM should be preferred to more conventional estimation methods, such as OLS with time and/or country dummies. Our sample should not be affected by this problem, since its cross-sectional dimension is much larger than its time dimension. Nevertheless, in the next section, we report both sets of estimates, which helps also assess the robustness of the results.

4 Empirical results: baseline model and specification

We next report the main empirical results and assess the robustness of the poverty-growth and inequality-growth relationships. Tables 2 and 3 present pooled-OLS and within-group (WG) estimates, respectively. We use P0 (with a poverty line of US$ 2) as our baseline measure of poverty. Footnote 17

It can be seen that the estimated coefficients of poverty do not change significantly when also including inequality in the model. A quick look at Tables 2 and 3 shows that the coefficient on poverty is negative and significant in all cases. Its magnitude is larger in absolute value in the WG regressions than in the pooled-OLS regressions, but it is in all cases economically significant. Other things equal, a one-standard deviation decline in poverty (24.7 p.p., Table 1 ) is associated with an increase in income growth between 0.8% and 2.1% per annum. In contrast, results are not robust regarding the inequality-growth relationship. The estimated coefficients on the Gini index are uniformly negative and significant when using pooled-OLS, but uniformly positive in the WG estimation, and significantly so when poverty is also included in the regression (except for model M3, where the Gini index is also significant when poverty is omitted).

The coefficients of the other controls are generally consistent across estimation methods. Lagged income carries negative and significant coefficients in most cases. The market distortions proxy (in model M2) and inflation (M2 and M3) both carry significant negative coefficients. Trade openness (in M3) and the infrastructure index (in M4) carry positive and significant coefficients (Calderón et al. 2015 ). In contrast, the effects of male and female secondary education depend on model specification. Female education carries a positive and significant coefficient in M4, but turns insignificant in M2, while the coefficient of male education is generally positive. Similarly, among the policy variables, the coefficient of government size is generally negative, but it is significant only for the WG estimates.

Table 4 shows estimation results for first-difference GMM, while Table 5 shows the results for the baseline system GMM specification (limiting the instrument matrix to two lags). In Appendix 3 (Tables 15 and 16 ), we report results under alternative approaches to reducing the dimension of the system GMM instrument set: collapsing the matrix of instruments while using all lags as instruments (Table 15 ), and limiting them to two lags and collapsing the instruments at the same time (Table 16 ). For first-difference GMM (Table 4 ), we use three lags in the matrix of instruments so as to have the same number of orthogonality conditions as in the baseline system GMM estimation, thus making the results more easily comparable. Footnote 18 The p values of the Hansen tests suggest that in virtually every case, the null of joint validity of all instruments cannot be rejected. Moreover, the Difference-in-Hansen test results, whose p values always exceed 0.10, point toward the superiority of system GMM over first-difference GMM.

The parameter estimates of the variables of interest follow the same pattern found earlier. The coefficient on the poverty headcount is consistently negative and highly significant, regardless of the choice of model and specification. In contrast, the coefficient of the inequality variable varies in sign and significance depending on the GMM approach and the controls used in the estimation. It is always positive and in one case significant for first-difference GMM, consistent with our results for the WG estimates in Table 3 and part of the earlier literature (e.g., Forbes 2000 ). However, it is negative and, in some cases, significant for system GMM, consistent with our results for pooled-OLS and another strand of the literature (e.g., Berg et al. 2018 , and references therein). The negative effect of poverty on growth is robust to changes in model specification and estimation method, while the effect of inequality on growth, which has been the focus of a massive literature, is not.

The theoretical model outlined in López and Servén ( 2015 ) and explored in Marrero and Servén ( 2018 ) helps rationalize our empirical results. In that model, poor individuals—i.e., those whose initial endowment is below a minimum consumption level—do not save and do not contribute to the economy’s aggregate growth. In the absence of financial markets, the model shows that poverty is unambiguously growth-deterring, while inequality can affect growth directly, through the savings of the non-poor, and indirectly, through its effect on poverty. While the indirect effect is negative, the direct effect is ambiguous (as found by the empirical literature), and so is the overall impact of inequality on growth.

As a further diagnostic check on the GMM estimates of Tables 4 , 5 , 15 , 16 , we inspected the residuals for cross-sectional dependence, using Pesaran’s ( 2021 ) CD test, and focusing on the model versions including both poverty and inequality. Results are shown in Table 17 (Appendix 4 ). In the majority of cases, the test results are supportive of the empirical specification. This is particularly the case for the models including policy variables (models M3 and M4 in the aforementioned tables), for which the test fails in all cases to reject the null of cross-sectional independence. For the stripped-down model M1, which omits all controls, results are more mixed, as the test fails to reject the null at the conventional 5% level in some exercises (those in Tables 4 and 5 ) but rejects it in others (those in Tables 15 , 16 ). The exception is model M2, for which the test consistently finds significant evidence of cross-sectional dependence. Footnote 19 Overall, we take these results as supporting the view that models M3 and M4 are correctly specified. However, the presence of residual cross-sectional correlation in model M2—first explored by Perotti ( 1996 ) and Forbes ( 2000 ), suggests that the model’s estimated standard errors may be incorrect. Footnote 20

4.1 Weak instruments analysis

Bazzi and Clemens ( 2013 ) have raised the potential problem of weak instruments when using system GMM estimation in growth regressions. Weak identification arises when the instruments are only weakly correlated with the endogenous regressors, and its consequence is that estimators perform poorly (Nelson and Startz 1990 ). To assess the strength of the instruments employed in our system GMM estimations—in particular, the identification of the poverty and inequality parameters—we use tools designed for settings featuring multiple endogenous regressors. We follow Sanderson and Windmeijer ( 2016 ) (SW hereafter), who propose a conditional F statistic based on Angrist and Pischke ( 2009 ) to test whether, in a multivariate setting, a particular endogenous regressor is weakly instrumented. For each such regressor, a conditional test is constructed by “partialing-out” linear projections of the remaining endogenous regressors. SW show that the conditional F statistic can be assessed against the Stock and Yogo critical values, and the weakness can then be expressed in terms of the size of the bias of the IV (or 2SLS) estimator relative to that of the OLS estimator. The null hypothesis is that the instruments are weak. It is rejected if the conditional F statistic exceeds the corresponding critical value, and we use a critical value allowing for a 30 percent maximal relative bias. We also perform a Chi-square under-identification test separately for each regressor. Here, the null hypothesis is that the matrix of coefficients from the first‐stage conditional regressions is not full rank, signaling a complete failure of identification. Thus, rejection of the null supports identification, although not necessarily the absence of weak identification (Kleibergen and Paap 2006 ).

These tests have been originally designed for use with external instruments in IV or 2SLS settings; no suitable equivalents exist for system GMM at present. Thus, to apply the tests to our system GMM setting, we follow Bun and Windmeijer ( 2010 ) and construct the exact instrument matrix for the difference and level equations of each system GMM estimator, and then apply the standard 2SLS regressions and tests to each case.Table 6 reports the results of the SW tests for all models estimated under our baseline system GMM specification (Table 5 ). For lagged poverty and inequality, we present the Chi-square under-identification test, and the weak instruments F statistic.

The Chi-square tests indicate that under-identification of the coefficients on poverty and the Gini index is not a major problem in any of the models and specifications considered, neither for the level equation nor for the difference equation. As for the SW weak instruments F test, the null hypothesis that lagged poverty and weakly instrumented is rejected, as the conditional F statistic exceeds the Stock and Yogo critical value for both the first-difference and the level equations in all cases. In contrast, the null that lagged inequality and weakly instrumented is not rejected for the first-difference equation in models M1, M2 and M4, while it is rejected in all other situations.

Overall, the results of these tests suggest that instrument weakness is not a major problem with our estimates. Further, we also conclude that including the level equation in the GMM estimation helps alleviate potential problems of weak instruments, especially when estimating the effect of inequality on growth. This points to system GMM as the preferred estimation approach.

5 Estimation results: robustness analysis

We next perform an extensive set of robustness checks, along five dimensions. First, we allow for nonlinearities using a nonparametric approach. Second, we consider alternative poverty measures. Third, we replace our poverty data with the PovcalNet data. Fourth, we assess additional control variables. And fifth, we consider alternative econometric specifications.

5.1 Nonlinearities: nonparametric analysis

One potential concern with our linear regression analysis is that the estimated effect of poverty on growth could be partly capturing nonlinearities in the relationship between growth and other controls. Following Liu and Stengos ( 1999 ), we use the Baltagi and Li’s ( 2002 ) semiparametric fixed-effects regression estimator to assess this question. This approach considers a linear fixed-effects model such as our Eqs. ( 1 )–( 3 ) allowing for a nonparametric specification for one particular regressor. Footnote 21

Figure  2 depicts the nonlinear nonparametric estimates of the effects of lagged poverty and the lagged Gini index. To save space, we only show results for model M1, but results using models M2, M3 and M4 are qualitatively similar. They are consistent with our findings using the conventional specification: First, lagged poverty is negatively correlated with growth, and more strongly so for high poverty levels; second, the lagged Gini index is weakly correlated with growth, which echoes the lack of robustness found with the conventional specification. When the Gini index is nonparametrically adjusted, the estimated linear coefficient for poverty is still negative and significant at 5%.

Poverty, inequality and growth: nonlinear nonparametric estimates. Note Estimations made using the Baltagi and Li’s ( 2002 ) semiparametric fixed-effects regression estimator. We estimate Eq. ( 3 ) for model M1 allowing for a nonparametric (approximated by a spline interpolation) specification for one particular regressor at a time. In this case: lagged poverty (left graphic) and lagged Gini index (right graphic)

Next, we use the same procedure to assess nonlinearities for all the other regressors in models M2, M3 and M4, taken one at a time. Table 18 in Appendix 5 reports the resulting coefficient estimates on inequality, poverty, and lagged income obtained in this manner. These estimates can be compared with the WG estimates in Table 3 and the first-difference GMM estimates in Table 4 .

Our conclusion that poverty is growth-deterring does not change when using nonlinear nonparametric specifications for any regressor. Moreover, like in our WG and first-difference GMM specifications, the parameter estimates on the Gini coefficient are positive and significant in the majority of the cases. In Fig. 4 in Appendix 5 , we graph the estimates of the nonlinear components for average years of male and female education, the two variables for which we find a significant nonlinear relationship, as in Liu and Stengos ( 1999 ). For female education, there is a clear positive nonlinear relationship: after 2 years of average female education, the effect on per capita GDP growth turns positive and keeps rising until the 4–5 years mark; for average years of male education, the nonlinear relationship is more concave than for female education, and the slope is positive for almost all years, with the exception of some observations above the 6-year mark. For comparison, we report also the results obtained with government size, which yields a close to linear negative slope, and the infrastructure index, which yields a close to linear positive slope.

5.2 Alternative poverty measures and poverty lines

To assess the robustness of our results to the use of alternative poverty measures and poverty lines, we re-estimate the empirical growth equations using the poverty gap (P1) and the squared poverty gap (P2), and considering alternative poverty lines: US$ 1.25, $2 and $4 per person per day. The poverty rates based on alternative poverty lines and poverty measures exhibit high, but not perfect, pairwise correlation (ranging from 0.74 to 0.99). As the different poverty measures capture different dimensions of poverty, the robustness analysis can be informative about potential differences in their respective effects on growth.

Table 7 reports system GMM estimates of all specifications that this strategy yields, with the matrix of instruments defined as in Table 5 . Regarding the poverty coefficient, 72 out of 72 estimates are negative, and 71 out of 72 are also significant, regardless of the choice of poverty measure, poverty line, and set of control variables employed. In general, the absolute value of the poverty coefficient rises as we move from P0 to P2. In turn, while all 72 estimates of the inequality coefficient are negative (recall that the positive estimates arise from the within-country dimension of the data, Tables 3 , 4 ), only 36 of them are significant at the 10 percent level or better. Finally, the Hansen tests do not show evidence against the validity of the instruments, and the p values of the Hansen-difference test (omitted from the table to save space) exceed 0.1 in all cases.

5.3 Alternative poverty data

As explained in Sect.  3.1 , our use of lognormal-based poverty data is driven by the intent to achieve sample coverage as large as possible. However, one may wonder if that choice has a significant effect on our empirical results. To address this concern, we next re-compute our system GMM estimates using only the PovcalNet poverty data. However, as already noted, the small size (especially in the time dimension) of the raw PovcalNet sample would pose a major obstacle to our estimation approach. Thus, to expand the sample size, we use the interpolated poverty series provided in PovcalNet, as discussed in Sect.  3.1 . The main difference between our lognormal poverty measures and those from PovcalNet is not the lognormal approximation, but the reference average income used. In PovcalNet, poverty is directly computed from the income distribution of the household surveys, hence the reference point is the mean level of household income obtained from the survey. In our lognormal approach, average income is given by the 2005 PPP-adjusted GDP per capita from the national accounts. We already discussed the advantages of using this approach in Sect.  3.1 .

The poverty headcount values from the interpolated PovcalNet series are fairly similar to those from our constructed P0 with a US$ 2 poverty line (see Table 1 above): While the PovcalNet poverty median is higher (6.8%), the sample average and standard deviation (19% and 24%, respectively), and the minimum and maximum values are similar to those in our baseline data. Moreover, the two poverty series are closely correlated: Over the common sample, the correlation is 0.89 (see Table 14 ). Further inspection reveals that the correlation is higher for the more recent data, reaching 0.93 in 2005 and 0.96 in 2010.

Table 8 shows estimation results for models M1, M2, M3 and M4 using the PovcalNet interpolated poverty series and our preferred system GMM specification. Comparison with Table 4 reveals that the results are robust to the use of this alternative source of poverty data: Poverty consistently carries a negative coefficient, significant in all cases but one. In turn, the coefficient on inequality is also negative in most instances, but insignificant in three out of eight cases.

5.4 Additional controls

Next, we assess the robustness of our results to the use of alternative controls. We focus on two extensions. First, we consider alternative measures of education to proxy for human capital. Second, we consider a set of institutional quality variables. Results are shown in Table 19 in the Appendix 6 .

In model M2, we added male and female education separately, following Perotti ( 1996 ) and Owen et al. ( 2002 ). Here, we estimate several variants of model M2, using average years of schooling, on the one hand, and the percentage of the population with at least primary or secondary education, on the other hand (first and second columns in Table 19 ).

In turn, we consider two of the most widely used measures of the quality of institutions (see also Table 13 in Appendix 2 ): an index of democratic accountability (“democracy”), and an index of government stability (“stability”), information taken from the political risk module of the International Country Risk Database. Footnote 22 Columns 3, 4 and 5 of Table 19 extend models M2, M3 and M4 with these institutional variables; column 6 reports the estimation results when jointly including all the variables from M2, M3 and M4.

Finally, and just for illustrative purposes, we report (in the last column of the table) estimates of a model including all the controls. They should be taken with caution, however, given the sharp reduction in sample size (by almost half relative to columns 1–2) and the high degree of collinearity among the regressors.

Estimated coefficients for the percentage of population with primary and secondary education are positive and significant. In the extended specifications with institutional variables, the coefficients of both the quality of democracy and government stability are positive and, in most cases, significant, confirming that the quality of institutions is positively correlated with growth. More importantly, the baseline estimation results for poverty (consistently negative) and inequality (its sign and significance depends on the particular specification) are robust to the inclusion of all these additional controls.

5.5 Alternative econometric specifications

We also performed a number of other robustness checks concerning the empirical specification and estimation approach. To save space, we just provide a brief summary here (results are available upon request). First, we modified the system GMM estimation employing different lag structures—e.g., using y it−s , p it−s , g it−s and x it−s , for s  ≥ 4 for the first-difference equation and Δ y it− 4 , Δ p it− 4 , Δ g it− 4 and Δ x it− 4 for the level equation—or using 1-step instead of 2-step estimates. We also experimented with a modified version of the basic empirical equation including a quadratic term in the Gini coefficient. The main conclusion is that the significantly negative effect of poverty on growth is quite robust to all these variations in specification and estimation approach, while the inequality-growth relationship is highly fragile.

Finally, we also re-estimated the models in a pure cross section of countries, with the variables expressed as averages over the entire sample period, capturing what could be viewed as the long-run relationship between them. The estimated poverty coefficient remains uniformly negative and significant, although its precision declines somewhat relative to the panel estimates. In turn, inequality tends to show a negative and significant coefficient, more frequently than in the panel estimates, consistent with recent evidence (e.g., Halter et al 2014 ; Berg et al. 2018 ) that inequality exerts a negative long-run impact on growth.

6 Poverty regimes

6.1 the effect of poverty and inequality on growth.

The nonparametric analysis in the preceding section hinted at possible nonlinear effects of poverty and inequality on growth. To take a deeper look, we estimate alternative versions of Eqs. ( 1 )–( 3 ) allowing for different coefficients on lagged poverty and lagged inequality depending on whether the lagged value of P0 lies above or below the sample median (2.7% for our baseline P0, see Table 1 ). We follow the same strategy conditioning instead on the lagged level of inequality, and estimate Eqs. ( 1 )–( 3 ) allowing for different coefficients on poverty and inequality depending on whether the lagged Gini coefficient lies above or below its sample median (39.8%, see Table 1 ). Table 9 reports estimates distinguishing whether poverty is above or below the median—what we shall label the ‘high poverty regime’ and ‘low poverty regime,’ respectively. In turn, Table 10 reports the estimates distinguishing whether inequality is above or below the median—the ‘high inequality regime’ and ‘low inequality regime,’ respectively. In both cases, we use the baseline system GMM specification (Table 5 ).

Table 9 shows that, under the low poverty regime, the impact of poverty on growth is negative but statistically insignificant. However, it is negative and highly significant under the high poverty regime. In turn, the estimated coefficient on the Gini index is in most cases negative, but it turns significant only for high poverty rates and for the M1 and M3 model specifications. Thus, like with the unconditional estimates, while the result for poverty is robust, the result for inequality is not. In contrast, Table 10 shows that, when we condition on the lagged level of inequality, the estimated coefficients on poverty and inequality exhibit very little variation across inequality regimes. In effect, they are very similar to the unconditional estimates from Table 5 .

As a final exercise, we allow the growth effects of poverty and inequality to vary across countries according to their level of development. Specifically, we divide the sample countries into two groups, developed and developing, with the distinction drawn according to the World Bank classification. The “developed” group comprises countries classified as upper-middle income, high-income non-OECD, and high-income OCDE; the “developing” group comprises those classified as low-income and lower-middle income. The estimation results, reported in Table 11 , are similar to those obtained in Table 9 when the two groups are drawn according to the median headcount poverty rate: The negative impact of poverty on growth is larger and more significant for developing countries than for developed ones; indeed, for the latter, the effect is insignificant in most cases. The same applies to the coefficient estimate of the Gini index.

6.2 The indirect effects of inequality on growth

The coefficient \(\varphi_{1}\) in ( 3 ) reflects the direct effect of inequality on growth, for given lagged poverty and per capita income levels. However, the overall impact of inequality on growth also depends on how inequality affects poverty. Thus, from ( 3 ), Footnote 23

We next examine the indirect effect of inequality on growth, as defined by the second term in the right-hand side of ( 4 ), across alternative regimes. Specifically, we only consider the values of \(\delta_{1}\) from different poverty regimes (Table 9 ) because, as Table 10 shows, conditioning on high and low inequality yields estimates of \(\delta_{1}\) very similar to the unconditional ones. In the same spirit, to evaluate \(\partial p/\partial g\) in ( 4 ), we estimate the following equation:

both for the entire sample and splitting the sample in two depending on whether poverty is above or below the median.

We estimate ( 5 ) using the WG estimator. To take care of the potential bias arising from simultaneity between poverty and income, we instrument the log of income in ( 5 ) with past values of the saving rate (Acemoglu et al 2008 ). Results are shown in Table 12 : The left panel reports the within-group estimates and the right panel reports the instrumental variable (IV) estimates. In both cases, we include time dummies and country fixed effects in the regressions. For the IV estimation, we report the first-stage Kleibergen–Paap F statistic to test for the weakness of our set of instruments. In all cases, the test statistic is above the Stock and Yogo ( 2005 ) critical values. Thus, we can reject the null of weak instruments. The p value of Hansen’s J test of over-identification exceeds 0.1 in all cases, suggesting that we cannot reject the hypothesis that the instruments are valid. Both estimation strategies lead to similar conclusions. The poverty-inequality slope is positive but relatively flat for low poverty rates (i.e., when P0 is below the sample median), and strongly positive for high poverty rates (when P0 is above the sample median). The implication is that inequality changes have a strong effect on poverty when poverty is high, but not when it is low.

To illustrate numerically the indirect effect of inequality on growth, we combine these estimates of \(\partial p/\partial g\) with the estimates of \(\delta_{1}\) from Table 9 . Since when P0 is below the median the estimate of \(\delta_{1}\) is statistically insignificant, the indirect effect of inequality on growth is negligible in this situation. Thus, we focus on the high poverty subsample. Figure  3 shows the estimated indirect effect of inequality on growth (expressed in percent per year) for models M1, M2, M3 and M4, and two alternative sets of estimates: the unconditional estimates, ignoring the prevailing poverty regime, and the estimates obtained when poverty is above the median. Specifically, the figure illustrates the consequences of a one-standard deviation increase in the Gini coefficient (i.e., by 0.10 according to Table 1 ). In all cases, we use estimation results from Eq. ( 3 ). Footnote 24

Inequality and growth under different poverty regimes. Indirect effect on growth of a 1-standard deviation (0.10) increase in the Gini coefficient from its median (0.40). Note The indirect effect is given by the second term in ( 4 ). For the entire sample, we use \(\delta_{1}\) from Table 4 , along with the poverty-inequality coefficient in Table 12 (the IV estimates). For the case of P0 > Median, we use \(\delta_{1}\) from Table 9 . for P0 > Median, along with the corresponding poverty-inequality coefficient from Table 12 (for the IV approach). The sample is divided according with the sample median of P0, which is 2.7% for our baseline P0 with poverty line of 2US$. M1, M2, M3 and M4 denote the alternative sets of control variables included in ( 1 )–( 3 )

To compute the unconditional indirect effect of inequality on growth, we combine the estimated \(\partial p/\partial g\) from Table 12 for the IV case (equal to 0.307) with the unconditional estimates of \(\delta_{1}\) in ( 3 ) from the baseline system GMM (Table 5 ). The indirect effect of inequality (through poverty) on growth is always negative. Specifically, a 10-point increase in the Gini coefficient generates, through the indirect effect, a decrease in annual growth by about 0.20 (model M4) to 0.40 (model M1) percentage points. For the high poverty case (P0 above the median), we employ the estimates of \(\delta_{1}\) applicable to that regime from Table 9 , and the estimated \(\partial p/\partial g\) of 0.725 from Table 12 for the IV approach. In this scenario, the indirect impact of inequality on growth is uniformly negative, and larger than the unconditional one, i.e., a 10-point increase in the Gini coefficient reduces growth on average by 0.6 percentage points (model M3) or 0.9 percentage points (model M1) per year.

7 Conclusions

This paper has examined two issues that have received limited attention in the otherwise extensive empirical literature of growth, inequality and poverty. First, the paper provides an empirical assessment of the impact of poverty on growth. Second, the paper also highlights the indirect effect of inequality on growth accruing through poverty.

The paper uses a large panel dataset including 804 observations covering 158 countries and spanning the years 1960–2010. The empirical strategy involves including inequality and poverty indicators among the explanatory variables in an otherwise standard empirical growth equation. On the whole, the results reveal a consistently negative and strongly significant correlation of poverty with subsequent growth. Its magnitude is economically significant too: A 10 percentage point decrease in the headcount poverty rate is associated with a rise in annual per capita real growth of 0.5% to 1.2%. However, further analysis reveals that the significance of the effect depends on the prevailing level of poverty. Specifically, when the level of poverty is low (below the sample median), the growth effect of poverty is not statistically significant. In contrast, when the level of poverty is high, changes in the poverty headcount rate do show a significantly negative association with subsequent growth.

In contrast, we find that the link between inequality and growth is fragile. It can take either sign depending on the particular model and econometric approach employed. Consistent with previous results in the literature, we find a positive (significant in some specifications) sign when using the within dimension of the data, and a negative one (also significant at times) when using the cross-country dimension. Still, the indirect effect of inequality (through poverty) on growth is robustly negative, especially when the level of poverty is above the sample median. Its magnitude is also economically significant, e.g., a 10-percentage point decrease in the Gini coefficient is associated with an increase in per capita growth ranging between 0.2% and 0.4% in the full sample, and over twice as large in the above-median poverty subsample.

More broadly, our findings underscore the potential growth cost of adverse shocks to poverty, triggered by events such as drops in income or surges in inequality. Because poverty deters growth, a shock that causes poverty to rise may lead to a subsequent growth slowdown. The COVID-19 pandemic provides a relevant example. The growth collapse triggered by the pandemic is estimated to have raised extreme poverty in developing countries by some 120 million individuals in 2020 (or, equivalently, more than 20% over the pre-pandemic trend), with an even larger increase expected for 2021 (Lakner et al. 2021 ). However, these figures are biased downward because they assume no change in inequality, and the evidence shows that pandemics typically raise inequality (Furceri et al. 2020 ). Rising inequality adds indirectly to the poverty surge. In fact, while reliable data are not yet available, some rough estimates (e.g., IMF 2020 ) suggest that the COVID-19 pandemic is leading to a substantial increase in inequality in emerging and developing countries, especially poorer ones, which will result in a further increase in poverty rates and a major setback to the fight against global poverty. Our results imply that, in addition, the poverty rise may act as a drag on future growth, potentially triggering a vicious circle of stagnating incomes and rising poverty.

From the policy perspective, the finding that poverty tends to deter growth has potentially major implications. Supporting the incomes of poor households in the face of adverse shocks—such as COVID-19—through expanded social assistance (e.g., cash transfers, food stamps and in-kind nutrition) and enhanced social protection (e.g., relaxing eligibility criteria for unemployment insurance, expanding sick pay), as well as improved access to education and health care, can help contain the impact on aggregate poverty and the knock-on effect on growth. More broadly, our results suggest that these kinds of policies may be indicated not only for reasons of social equity and fairness, but also from the point of view of overall growth and prosperity.

The consequences of inequality for poverty are highlighted for example by Bourguignon ( 2003 , 2004 ) or Ravallion ( 2005 ). Marrero and Servén ( 2018 ) provide numerical simulations illustrating the magnitude of the effect of inequality on poverty, for given average income.

Empirical analyses of the links between aggregate growth, poverty and inequality commonly use an instrumental variable approach. A few papers feature external instruments—e.g., Brueckner et al. ( 2015 ), assessing the effect of GDP growth on inequality—but GMM using internal instruments (given by suitably lagged and transformed regressors) is much more commonly used: for example, by Partridge ( 1997 ), Forbes ( 2000 ), Panizza ( 2002 ), or Berg et al. ( 2018 ), all of which are concerned with the opposite direction of causality, from inequality to growth.

Poverty traps arising from threshold effects have often been offered as a rationale for a ‘big push’ approach to policy. In particular, when large aid programs are coordinated in a multi-faceted way, a ‘big push’ can be effective to engineer growth takeoffs (Banerjee et al., 2015 ). However, in a cross-country dataset, Easterly ( 2006 ) finds that takeoffs are rare and, in general, they are not associated with ‘big push’ strategies.

The argument that risk aversion leads to underinvestment goes back to Stiglitz ( 1969 ). See also Agenor and Aizenman ( 2011 ), who argue that aid volatility could induce poverty traps in poor countries through a similar mechanism.

Easterly ( 2006 ) investigates (and rejects) a more extreme hypothesis, namely that high poverty countries should show no growth.

Erman and te Kaat ( 2019 ) show that higher inequality increases growth in physical capital-intensive industries, while it harms grow in industries using skilled labor intensively.

A more limited literature has examined the inequality-growth link from the opposite perspective, assessing how income growth affects inequality. Its results are mostly inconclusive, however. For instance, while Brueckner et al. ( 2015 ) and Blau ( 2018 ) find that GDP growth reduces inequality, Krusell et al. ( 2000 ) and Aghion et al. ( 2019 ) reach the opposite conclusion.

Specifically, we pool the sample and regress the Gini coefficient on a constant, regional dummies and dummy variables indicating whether the survey is stated in terms of gross income or consumption (the omitted category is income net of taxes and transfers). We then subtract the estimated mean difference between these two alternatives and the omitted category to arrive at a set of Gini indices that notionally correspond to the distribution of income net of taxes and transfers. The results of these adjustment regressions are available upon request, but they show similar conclusions as in Dollar and Kraay ( 2002 ).

The UN-WIID2 Gini index is not always available for the first year of each 5-year interval. In such cases, we allocate the available observation(s) to the closest starting year of a 5-year interval, with a limit of 2 years of difference. When more than one observation is available within the 2-year limit, we take the average. Because of the strong inertia of inequality and poverty time series, using a 1-year limit instead of 2 years, or not using means, yields very similar results (Dollar and Kraay 2002 ).

Our data comprise 121 data points corresponding to 32 low-income countries, 180 to 41 lower-middle income countries, 240 to 44 upper-middle income, 57 to 11 high-income non-OECD, and 206 to 30 high-income OCDE countries. The sample includes 18 observations (2 countries) from North America, 248 (48 countries) from Europe and Central Asia, 159 (28 countries) from Latin American and the Caribbean, 53 (12 countries) from Middle East and North Africa, 144 (40 countries) from Sub-Saharan Africa, 56 (9 countries) from South Asia and 126 (19 countries) from East Asia and the Pacific.

This sample size would be too small for many of our exercises, and thus for the robustness tests using PovcalNet data reported in Section V below, we resort to the interpolated PovcalNet series, which allows increasing the sample size to 556 observations. These interpolated information start in 1981 and are reported every three years. Thus, to construct a non-overlapping 5-year panel data similar to the one used in our baseline specification and match the timing of poverty data with that of the other variables (growth and other controls), we use a “closest” criterion or take the average if two poverty observations are one year above and one below the assigned year. We should also note that the current PovcalNet series uses a poverty line of 1.90 2011 US$, which replaces its previous line of 1.25 2005 US$ (see Ferreira et al. 2016 , for more details).

We use the residuals of a regression of the openness index on country size and two dummies indicating whether the country is landlocked and oil exporter (Loayza et al. 2005 ).

There are two exceptions to this rule, although they represent extreme cases of limited empirical relevance: First, when poverty is extremely high (so nearly everyone is poor), a sufficiently large increase in inequality will push some individuals above the poverty line and thereby reduce poverty; second, when everyone is very rich (so poverty is close to zero), a sufficiently small increase in inequality will fail to push anyone below the poverty line, leaving poverty unchanged (see Marrero and Servén, 2018 , for further elaboration and some numerical simulations illustrating this issue).

See Table 7 for the baseline case of headcount poverty with a US$ 2 poverty line. For alternative poverty lines and measures, the \({\mathrm{R}}^{2}\) ranges from 0.303 for P2 (US$-1.25) to 0.582 for P0 (US$-4).

The distinction between uncorrelatedness and independence is important here. Our measure of poverty is constructed as a (exact) nonlinear function of log-income and the Gini. This implies that the data do not contain shocks to poverty independent from (as opposed to uncorrelated with) inequality and average income—indeed, the sample variation in poverty is not due to shocks correlated with income and inequality is necessarily due to shocks correlated with various nonlinear functions of these variables. Although we can estimate a linear equation such as (3), the interpretation of the resulting coefficient estimates requires some caution. For this reason, the discussion below focuses primarily on the empirical estimates of (1) and (2), and we view the estimation of (3) as a robustness check.

We initially constructed the instrument matrices using the second and higher lags of the variables ( s  > 1), as outlined in the text. However, the test of second-order serial correlation of the first-differenced residuals (the m2 -test of Arellano and Bond 1991 ) rejected the null of no serial correlation in most specifications. Hence, we opted for lagging the instruments one more period, so that they are valid even in the presence of second (but no higher)-order serial correlation of the first-differenced residuals. To check this assumption, we report a test of third-order serial correlation of the first-differenced residuals.

Following López and Servén ( 2015 ), we drop Nigeria and Swaziland because of the poor quality of their GDP data. We found three big outliers for investment prices and six for inflation. These observations affect the Hansen tests of system GMM, bringing them closer to rejection in some cases, but have only minor incidence on the estimations.

Data for the infrastructure index included in M4 are available for only 88 countries under system GMM and 79 under the first-difference GMM specification. Using two lags as instruments to estimate this model would result in the number of instruments exceeding the cross section dimension of the data. Thus, we limit the number of instruments to just one lag.

The robustness exercises in section V follow the same pattern regarding cross-sectional dependence tests: The residuals of models M3 and M4 show no evidence of dependence, while in most cases, those of model M3 yield the opposite conclusion. Model M1 again yields mixed results.

The absence of cross-sectional dependence in models M3 and M4 (and, to a lesser extent, M1) may seem surprising given that short-term growth fluctuations typically display significant international comovement. However, our use of 5-year averages greatly mitigates the comovement usually found at annual (or higher) frequency. In addition, the inclusion of time dummies in our empirical specifications also helps soak up common factors affecting growth in multiple countries. Lastly, the presence of statistically significant policy variables in models M3 and M4 likely helps soak up any remaining cross-sectional correlation in these specifications, unlike in models M1 and M2.

The nonparametric part is approximated by a spline interpolation (Newson 2000 ), which yields similar results to the classical Epanechnikov-kernel-weighted local polynomial fit, but is recommended to approximate complex nonlinear shapes.

There are other institutional dimensions, such as the control of corruption, the military in power, the degree of international conflicts, or the Polity2 variable (from the Polity IV project). Including all these dimensions/variables simultaneously would introduce serious problems of collinearity in the estimated model.

The model in Marrero and Servén ( 2018 ) provides a theoretical foundation for these two simultaneous effects. In the model, the first term in (4) is associated with the direct impact of inequality on growth due to changes in the investment of the non-poor, whose sign is shown to be ambiguous—it depends on the degree of concavity of the production function. The second term represents the indirect impact of inequality on growth channeled through poverty, which is the focus of this section.

We obtain similar results when using \({\updelta }_{0}\) from (1).

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Appendix 1: Lognormal approximation of alternative poverty measures

Following Dollar and Kraay ( 2002 ), López and Servén ( 2015 ) or Pinkovskiy and Sala-i-Martin ( 2013 ), we construct a set of poverty figures (the headcount ratio, P0, the poverty gap, P1 and the squared poverty gap, P2) using a lognormal approximation on the basis of the observed per capita income levels and Gini coefficients, which are available much more widely than survey-based poverty data.

The use of the lognormal approximation to the distribution of income dates back to Gibrat ( 1931 ). The literature employs also other functional forms, such as the Pareto, the gamma or the Weibull distribution, but the lognormal is the more widely used. Indeed, López and Servén ( 2006 ) compare the quintile income shares generated by a lognormal distribution with their observed counterparts using data from over 1000 household surveys and find the lognormal approximation fits the data extremely well, so that they are unable to reject the null hypothesis that per capita income follows a lognormal distribution.

Under lognormality, given the Gini coefficient ( g ), the standard deviation (σ) of the log of income is given by \(\sigma = \sqrt {\Phi^{ - 1} \left( {\frac{1 + g}{2}} \right)}\) , where \(\Phi ( \cdot )\) is the standard normal cumulative distribution function. Using this expression and the log of per capita income ( y ), we can compute the FGT family of poverty measures for a given poverty line z as:

Appendix 2: Data description and cross-correlations

See Tables 13 , 14 .

Appendix 3: Alternative system GMM estimation results

See Tables 15 , 16 .

Appendix 4: Residual cross-sectional dependence tests

See Table 17 .

Appendix 5: Nonlinearities and nonparametric results

See Table 18 and Fig. 4 .

Per capita GDP growth and several key controls (nonparametric estimates). Note Estimations made using the Baltagi and Li’s ( 2002 ) semiparametric fixed-effects regression estimator. We estimate Eq. ( 3 ) for model M2, M3 and M4 allowing for a nonparametric (approximated by a spline interpolation) specification for one particular regressor at a time. In this example, we show the cases of male and female education (model M2), government size (model M3) and the Infrastructure index (model M4)

Appendix 6: The use of additional controls

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Marrero, G.A., Servén, L. Growth, inequality and poverty: a robust relationship?. Empir Econ 63 , 725–791 (2022). https://doi.org/10.1007/s00181-021-02152-x

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A Review of Consequences of Poverty on Economic Decision-Making: A Hypothesized Model of a Cognitive Mechanism

This review focuses on the issue of poverty affecting economic decision-making. By critically evaluating existing studies, the authors propose a structural model detailing the cognitive mechanism involved in how poverty negatively impacts economic decision-making, and explores evidence supporting the basis for the formation of this model. The suggested mechanism consists of a relationship between poverty and four other factors: (1) cognitive load (e.g., experiencing negative affect and stress); (2) executive functions (e.g., attention, working memory, and self-control); (3) intuition/deliberation in decision-making; and (4) economic decision-making (e.g., time-discounting and risk preference), with a final addition of financial literacy as a covariate. This paper focuses on shortfalls in published research, and delves further into the proposed model.

Introduction

Poverty is a global socio-cultural phenomenon usually examined from an economic perspective. In behavioral studies, research on poverty largely focuses on the familial and social aspects of the background of people experiencing poverty, with the majority of research being carried out on children. Behavioral focuses of poverty research have included the psychological determinants of poverty, as well as the consequences of poverty on the mental health and cognitive functions of individuals (see Džuka et al., 2017 ). In a recent study, Haushofer and Fehr (2014) pointed out the existing need to direct attention toward the currently neglected issue of poverty perpetuation, which has generally been overlooked in favor of assessing the poverty from a solely economical (or even macroeconomical) perspective (see e.g., Semmler and Ofori, 2007 ; Naschold, 2012 ; McKay and Perge, 2013 ). Thus, it is clearly of importance to further examine the factors that may potentially help to unveil underlying reasons for poverty perpetuation. These factors include specific aspects of an individual’s perception of issues, personal experiences, behaviors, and individual abilities, which can either contribute to, or attenuate poverty. According to Mani et al. (2013) , poverty perpetuation is likely the outcome of the interplay of various forms of non-productive behaviors such as inappropriate economic decision-making, or lack of own healthcare. These factors, in particular those related to economic decisions, are often labeled as causes of poverty. In this paper, we suggest that a circular relationship might exist between the causes and consequences of poverty, with the consequences of poverty (e.g., negative affect, stress, or impeded cognitive functions) simultaneously acting as poverty triggers, thus creating a poverty cycle also known as a poverty trap.

Based on the aforementioned research conducted by Mani et al. (2013) , and Haushofer and Fehr (2014) , it is possible to determine that examining the relationship between poverty and economic decision-making (as a consequence of poverty) is necessary to explain the underlying psychological aspects of poverty perpetuation. At the same time, efficient research on this issue should take care to pay heed to other variables not discussed here, which may have the potential to influence the poverty-economic decision-making relationship. Thus, the aim of this review is to propose a theoretical framework for the poverty-economic decision-making relationship, and to further explore economic decision-making as a consequence of poverty on four basic levels. Namely: (1) the effect of poverty on cognitive load experience (negative affect and stress); (2) the effect of poverty on executive functions (attention, working memory capacity, self-control capacity); (3) intuition/deliberation in decision-making; and (4) the effect of poverty on economic decision-making (time-discounting, and risk preferences related to reward/loss). Note that these levels are not mutually exclusive, but affect each other in different ways. Following this literature review, a proposed structural model integrating the aforementioned levels into one complex system will be laid out.

Poverty Definition and Assessment

Our analysis of mainly psychological literature revealed that poverty is primarily regarded as an economical construct, and subsequently a psychological (or socio-behavioral) one. The exact operational (or conceptual) definition of poverty remains undecided upon, with most literature lacking a precise definition of the construct. As a result, the most currently relevant definitions of poverty are those proposed by global organizations. The United Nations (1995) , for instance, defines general poverty as a complex construct of factors such as income insufficiency, lacking resources to ensure dignified living, experiences of hunger, aggravated health and poor healthcare, limited access to education, improper housing conditions, and social discrimination. The World Bank (in Haughton and Khandler, 2009 ) further defines poverty in a similar manner but goes on to delineate the psychological aspect of poverty by discussing matters of subjective well-being. However, these definitions remain rather ambiguous and open to questioning. For example, how might one define “dignified living”? What exactly might improper housing constitutes? Where might the line be drawn between the availability of food being accessible or limited? Poverty, therefore, appears to be a multidimensional construct which presents itself with various aspects that can be assessed both on an individualistic (subjective) level, as well as objectively, based on more general predefined criteria.

In practice, researchers tend to assess poverty according to various objective poverty lines (e.g., household incomes being lower than 60% of the country median or the income-to-needs ratio). However, poverty lines are not representative of whether or not individuals consider themselves to be poor (see Ravallion, 2016 ), a psychological aspect of poverty that should be heeded. Mani et al. (2013) found that a subjective experience of poverty is associated with deprived cognitive capacities to a greater extent than objective poverty indicators. Therefore, in order to assess poverty as a multidimensional construct (see: Smeeding, 2015 ), it is apparent that traditional assessments via economic indicators should be enriched by the inclusion of subjective evaluations of psychosocial measures of poverty such as subjective well-being poverty ( Shams, 2015 ) or subjective social status ( Diemer et al., 2013 ). Furthermore, as poverty is not a one-off state and is subject to temporal changes ( Cooper et al., 2012 ; Dutta et al., 2012 ; Bresson and Duclos, 2014 ), it is necessary to measure individuals’ perceptions of the length of poverty duration, and the frequency of poverty reoccurrence across a lifespan.

Poverty and Cognitive Load in the Form of Experiencing Negative Affect and Stress

Cognitive load refers to the presence of a burden on the cognitive system of an individual. An increase in cognitive load can occur when dealing with a problem and focusing attention on certain stimuli, thus leading to a reduced ability to attend to other stimuli ( Paas and Van Merrienboer, 1994 ; Sweller et al., 1998 ). From the point of poverty research, an increase in cognitive load has been found to be associated with negative experiences related to long-term poverty ( Shah et al., 2012 ). Moreover, a study by Haushofer and Fehr (2014) found that people living in poverty are more likely to experience cognitive load in the form of stress and negative affect, due to protracted exposures to adverse economic and social phenomena. Hence, negative affect and stress could be the bridging factor between poverty and its effect on economic decision-making ( Haushofer and Fehr, 2014 ). From an economic context, cognitive load can arise from a person living in poverty having to deal with constant uncertainties in current and future economic situations. As coping with the resulting negative affect reduces one’s cognitive resources, this can lead to a deterioration of executive functions, thus causing an individual to become enmeshed in a cycle of focusing on poverty-related problems (see Shah et al., 2012 ).

Negative affect and stress are consequences of both persistent financial pressure and associated economic vulnerability ( McLeod and Kessler, 1990 ) as well as social dimension of poverty. For instance, people living in poverty may lack financial and social resources to cope with acute and chronic problems. This can lead to individuals having to deal with negatively skewed affective perceptions of situations on top of the negative situations themselves ( Haushofer and Fehr, 2014 ). Evidence from longitudinal studies ( Lorant et al., 2003 ; Najman et al., 2010 ) further reveal a direct connection between poverty and depression. Similarly, Kim et al. (2013) found that growing up poor leads to increased negative emotional experiences in adulthood.

The relationship between poverty and stress can be evaluated on two levels: (1) short-term, where poverty diminishes one’s ability to respond to threatening and unpredictable events (i.e., economic aspects such as loss of work) and (2) long-term, where an individual deals with an allostatic load (i.e., constant thinking about the financial situation). In both cases, cortisol production, a biological indicator of stress, ( Blair et al., 2011 ), has been found to increase after as little as 1 year of living in poor financial conditions ( Butterworth et al., 2011 ). Haushofer and Fehr (2014) further list several examples of experimental situations (see Fernald and Gunnar, 2009 ; Baird et al., 2013 ), which provide evidence for a causal effect of poverty on stress via indicators such as subjective evaluations, or cortisol levels.

As has been mentioned, poverty is highly correlated with the experience of both negative affect and stress across short- and long-term situations. This is also associated with cognitive load, and potentially with ego-depletion. While emotional well-being or cognitive evaluation of situations are directly related to poverty, we argue that the resulting cognitive load can impede crucial executive processes, specifically attention, working memory, self-control, and decision-making.

Poverty and Executive Functions

The majority of studies addressing poverty and cognitive/executive functions have traditionally been administered to children (e.g., Evans et al., 2005 ; Ayoub et al., 2009 ; Dickerson and Popli, 2016 ; Kaya et al., 2016 ), with only a few authors ( Shah et al., 2012 ; Mani et al., 2013 ) focusing on adults. Based on existing studies, we have isolated 3 executive functions that may play a crucial role in the mechanism linking poverty and economic decision-making. Namely, (1) attention, (2) working memory and (3) self-control (self-regulation) capacity. In respect to the presented findings, these executive functions are associated with not only poverty but also with its consequences on cognitive load.

Attention is the ability to select and focus on relevant information in the environment, whilst ignoring other information of lesser task-related importance ( Kastner and Pinsk, 2004 ; Lui and Tannock, 2007 ). The effect of poverty on attention has been examined in a series of experiments conducted in both simulated (e.g., by inducing resource restriction and a sense of poverty in games; Shah et al., 2012 ; Mani et al., 2013 ), as well as in real-world environments (e.g., in pre- and post-harvest measures of cognitive functions of Indian farmers; Mani et al., 2013 ). As Shah et al. (2012) discovered, people deprived of resources less engaged in games, were fatigued, and took longer to make a decision, while also scoring worse on an attention test than controls. They argued, therefore, that the scarcity of any kind of resource can lead to an excessive degree of engagement with a task. This focusing of attention on certain problems (e.g., states of deprivation like hunger, or task-related time-pressures) can lead to attentional neglect of other stimuli. Specifically, in a difficult economic situation, this narrowing of attention may lead to problematic decision-making, such as the incautious borrowing of money (e.g., people living in poverty often make use of short-term high-interest loans), late bill payments, and even making heedless purchases.

One’s attention can also be impaired by cognitive load. For instance, Mani et al. (2013) found that (1) experiencing financial pressures can lead to higher exhibited stress levels, (2) cognitive functions (i.e., attention and intelligence) are significantly lower before (temporarily) resolving financial difficulties, (3) cognitive performance is negatively correlated with the severity of financial difficulties experienced, and (4) these results hold true even when factors such as physical exertion, anxiety, nutrition, or learning effects on test performance are controlled for. According to the authors, the mechanism of highly focused attentional capture caused by poverty is, therefore, the most significant factor in the reduction of cognitive performance. The authors also highlight the importance of distinguishing long-term poverty from short-term scarcity. While long-term poverty affects cognitive load due to the chronic experience of negative emotional states, scarcity is best qualified as an acute dearth of resources leading to a temporal increase in cognitive load by tempting one to immediately satisfy a need, while disregarding future costs ( Shah et al., 2012 ).

Besides this, attention can be also influenced by stress. Despite the finding that stress does not fully explain the observable decline of cognitive functions, Mani et al. (2013) identify the mechanism of poverty with a broader concept of stress. The authors claim that aspects of scarcity become the key focus of individuals’ attention, leading to obsessive thoughts and an eventual reduction of mental resources. Meanwhile, Braunstein-Bercovitz (2003) argues that cognitive load also increases selective attention to stressors, amplifies stress levels, and is detrimental to the ability to diffuse attention to other relevant issues. It is plausible, that stress affects attention in different ways (in certain cases it can help to focus on relevant stimuli, see Chajut and Algom, 2003 ) relative to its attributes such as a concrete type of stressor or the duration of its exposure.

Working Memory

Working memory is the ability ‘to hold information in mind and mentally work with, while this information is not accessible by a sensory apparatus at that moment’ ( Diamond, 2013 , p. 142). The majority of research on working memory and poverty has been conducted on children, revealing that living in poverty causes significantly worse working memory ( Tine, 2014 ; Pavlakis et al., 2015 ; Rowe et al., 2016 ). From a biological perspective, this may be the result of reduced hippocampal development often associated with low socioeconomic status ( Pavlakis et al., 2015 ). Engel de Abreu et al. (2014) further propose two psychological explanations. Mainly, poverty diminishes working memory due to insufficient cognitive stimulation. Moreover, the lower test scores of children in poverty compared to financially secure children may result from standardized tests being inappropriate to their social-cultural background. When ‘culture-fair’ tools (e.g., digit-span test) are applied, differences are often abolished.

A longitudinal study by Evans and Schamberg (2009) revealed that childhood poverty is correlated with decreased working memory in young adults, with stress (allostatic load) acting as a mediator of the relationship. The authors suggest a causal relationship in this case, as working memory was not found to be a significant mediator of the poverty-allostatic load relationship in an alternative model. Evans and Fuller-Rowell (2013) further confirmed that poverty and stress influence working memory, but argue that this effect is driven by self-regulation. Although short-term stress exposure (linked with task demands and duration) can facilitate working memory ( Yuen et al., 2009 ), the effect does not apply to long-term exposure (e.g., poverty; Joëls et al., 2006 ).

The relationship between working memory and negative affect has been also examined. Brose et al. (2012) conclude that working memory is not a stable disposition, and fluctuates depending on negative affect (e.g., increased negative affect is related to diminished working memory performance), reduced control of attention, and motivation. The authors explain this with the allocation model ( Ellis and Ashbrook, 1988 ), under which people experiencing negative affect end up focusing their attention on it, with subsequent attempts on self-regulation further limiting their mental capacities.

Despite the fact that ruminating on financial difficulties impairs performance, and requires intensive working memory involvement, it is possible that it may not necessarily impair cognitive functions related to proceduralized processes ( Dang et al., 2015 ). In a recent study, Dang et al. (2016) showed that financial demands and consequent distractions diminish the cognitive functions of poor individuals. They argue that this impairment results from an overwhelmed working memory due to economic concerns. However, in certain conditions, these distractions can improve proceduralized processes such as learning ( Markman et al., 2006 ). Dang et al. (2016) propose that these findings support the notion of learning through repetition and conditioning in poor people. Yet, it is unclear how effective this would be in real-world conditions of poverty (e.g., during economic decision-making). Without proper external control, it is possible that this process could easily facilitate inadequate economic behaviors instead.

Self-control capacity

Diamond (2013) defines self-control as an individual’s ability to regulate attention, thoughts, behaviors, and emotions, by resisting temptations and impulsive behaviors (note: a broader concept is self-regulation; McCullough and Willoughby, 2009 ). Psychological theory offers several models of self-control, two of which we have selected as possible frameworks to explain the deterioration of self-control in relation to poverty. The Resource Model ( Baumeister et al., 1994 ) describes self-control as an inner capacity-limited resource that can be exhausted when controlling one’s own behavior. Resisting one temptation, therefore, increases the chance of succumbing to a subsequent desire ( Hofmann et al., 2012 ; Vohs, 2013 ). On the other hand, the Process Model ( Inzlicht and Schmeichel, 2012 ) questions the existence of inner depletable resources. Instead, self-control is considered as a value-based decision-making process, with failures in self-control occurring due to shifts in motivational orientation, and attentional reorienting toward indications of potential rewards. Furthermore, Inzlicht and Berkman (2015) define the depletion of mental resources as a form of mental fatigue that prevents individuals from being able to motivate themselves to produce more effort.

Cognitive load can have a negative impact on self-control capacity. As poor individuals are constantly exposed to economic pressures (and must thus make extensive compromises in satisfying their desires, e.g., while shopping and during leisure time), their self-control capacity is correspondingly decreased. A persistent regulation of basic needs can thus lead to reduced self-control ( Hofmann et al., 2012 ; Vohs, 2013 ). In addition to cognitive load, self-control is driven by attention and working memory. Baumeister et al. (1994) argue that directing attention away from oneself to the environment can lead to a loss of self-control. Mann and Ward (2007) claim that limited attentional resources cause individuals to focus on their acute needs and neglect more distal stimuli. Hence, such behavior does not correspond with optimal goals of self-regulation. Paradoxically, when urgent needs are associated with control and restriction, narrowed attention can lead to better self-control, with high working-memory capacity also enhancing self-regulation.

The depletion of mental resources for self-control can lead to impulsive and intuitive behaviors that eventually cumulate producing poor economic decisions, thus leading to a vicious cycle of poverty-inducing behaviors ( Vohs, 2013 ). In contrast to previous research (see Heatherton and Wagner, 2011 ; Kurzban et al., 2013 ; Inzlicht et al., 2014 ), Dang et al. (2015) criticize the limited-resource model of self-control ( Vohs, 2013 ), positing that self-regulation failures are due to motivation-based reasons instead of limited mental resources. Fundamentally, people become more sensitive to reward when financially deprived, thus stimulating a need for reward in other domains (e.g., making budget-exceeding purchases). At the same time, Tuk et al. (2015) conducted a meta-analysis of their own results and revealed that self-control in one domain may result in increased self-regulation in other potentially unrelated domains. Despite this strong evidence, however, they suggest that self-control may be dependent on the nature of the stimulus or task being dealt with. Thus, results from short-term interventions are likely not applicable to conditions of poverty, which may be chronic and/or episodic. Further investigation on the effect of poverty and its direct consequences in the form of negative affect and stress, together with the effect of attention and working memory on the self-control capacity would, therefore, be beneficial to further explore this topic.

On the whole, we believe that the Resource Model is more appropriate to explaining improper economic behaviors in the context of poverty. This is due to the fact that (1) it posits that mental capacity can be exhausted; this is applicable to circumstances of poverty, which impair working memory and attention; (2) we consider poverty perpetuation to be the result of a series of events rather than a failure in one’s motivation to expand more effort. Nonetheless, according to the latest evidence ( Lindner et al., 2017 ), both self-control models explain the reduction of performance in subsequent tasks equally well.

To summarize, research has shown that poverty impacts executive functions directly, and indirectly via cognitive load in the form of negative affect and stress. In order to accomplish the goals of this review, three executive functions (self-control, attention, and working memory) gleaned from scientific literature were selected for further examination. Based on existing studies, we suggest that these executive functions have effect on economic decision-making. While the nature of their relationship remains unclear, we propose several alternative mechanisms of these relationships: (1) self-control depends on attention and working memory; (2) attention and working memory are dependent on self-control, (3) self-control, working memory and attention covary on the same hypothesized level, or (4) the functions reciprocally affect each other, with self-control being the most closely linked to economic decision-making.

Intuition/Deliberation As A Determinant of Economic Decision-Making

Another process that influences economic decision-making is an individual’s intuitive/deliberative decision-making style. This intuition/deliberation dichotomy represents two distinct systems of thinking based on Dual process theory (the theory of two disparate reasoning processes; Evans, 2003 ; Kahneman, 2003 , 2011 ). Kahneman (2011) defines the intuitive system of thinking as fast, implicit and heuristic-based, while the deliberative system is slow, rational and logical.

However, the capacity to make rational decisions does not translate to their actually being carried out ( Starcke and Brand, 2012 ; see also Epstein et al., 1996 ; Kahneman, 2003 ). For instance, exposure to stress causes one to rely on simpler, more primitive automatic decision-making preferences ( Porcelli and Delgado, 2009 ). The use of heuristics may be beneficial, as they are fast, accessible, and require less effort and resources ( Hafenbrädl et al., 2016 ). In particular, the framing effect heuristic, or the manner in which one’s decisions are affected by the way that alternative choices are presented, may be more influential when under stress ( Starcke and Brand, 2012 ). Evidently, stress impairs deliberative processes, reducing one’s ability to evaluate pros and cons of alternative choices ( Simonovic et al., 2016 ). Moreover, Cui et al. (2015) propose that emotional experiences and stress pose high demands on working memory, resulting in poor decision-making abilities. The effect of stress on the use of intuition/deliberation in decision-making is presented by Yu (2016) in a stress-induced deliberation-to-intuition (SIDI) model. A meta-analysis by Fields et al. (2014) supports the existence of moderate to strong relationships between stress and impulsive decision-making. Furthermore, a study by Masicampo and Baumeister (2008) provides evidence that people under stress tend to opt for automatic instead of controlled processes when making decisions.

The relationship between self-control (self-regulation) and decision-making was examined by Pocheptsova et al. (2009) . The authors suggest that self-control and decision-making share a mental capacity, and provide evidence that participants utilize simpler and more intuitive decision-making strategies following self-regulation (which depletes mental resources). Similarities also exist between the two systems of self-control described by De Ridder et al. (2012) , and the theory of two processes of reasoning during decision-making. According to De Ridder et al. (2012 , p. 78), self-control (self-regulation) follows either (1) a ‘cool’ pragmatic system or (2) a ‘hot’ feeling system. The pragmatic system determines one’s behavior based on rational evaluation (“do it if it makes sense”) and is associated with high self-control and low impulsive decision-making. The ‘Hot’ system, however, is regulated by a feeling principle (“do it if it feels good”) and is linked with low self-control and higher impulsivity.

According to research by Evans (2010) , deliberative (analytical) processes depend on working memory (which is tightly linked to attention and executive functions), while intuitive processes are independent of it. Likewise, Travers et al. (2016) propose that deliberative reasoning depends on working memory capacity and self-control, and is influenced by mathematical abilities and dispositional factors. Contrarily, intuitive reasoning is independent of these factors.

When making a decision, people living in poverty must take into consideration a broad spectrum of compromises related to the economic and social aspects of poverty. These decisions tend to be intuitive, impulsive and poorly thought out. As resisting temptation deprives one of self-control resources ( Vohs, 2013 ), this causes a chain reaction of future inappropriate decisions (see Shah et al., 2012 ). Therefore, we can conclude that intuition/deliberation in decision-making has the potential to mediate the relationship between executive functions induced by poverty and economic decision-making.

Poverty and Economic Decision-Making

Behavioral economics offer several alternative forms of economic decision-making assessments, extending to the perspective of psychological research. For the purpose of this review, we focus on three pertinent aspects: (1) time-discounting; (2) risk-taking for potential reward; and (3) risk-taking with potential loss. In order to best assess the economic dimension of these aspects, it appears necessary to take individuals’ financial literacy into consideration. Controlling for financial literacy allows us to determine if economic preferences are either (1) a consequence of a cognitive mechanism of the effect of poverty, or (2) a consequence of financial literacy and the ability to utilize mathematical abilities during fundamental economic events.

Time-Discounting

Time-discounting (similar terms: intertemporal choice, temporal discounting, delay discounting, delay of gratification) refers to decision-making which involves compromises between costs and benefits occurring at different times ( Frederick et al., 2002 ), and is also a demonstration of self-control and will ( Shamosh and Gray, 2008 ). According to Mishra and Lalumière (2016 , p. 769), increased time-discounting indicates a ‘preference for smaller immediate rewards over larger, distal rewards.’

A study by Brown et al. (2015) describes the determinants of time-discounting. The authors found that people prefer larger delayed rewards if they have a higher income, are not liquidity constrained and are healthier/have longer life expectancy. Similarly, Carvalho et al. (2016b) found that a sense of financial stability in related to a willingness to wait for a higher reward, and to improved self-control. Furthermore, Liu et al. (2012) note that the choice of reward is influenced by psychological dimensions of poverty rather than by the objective socioeconomic status. They claim that the preference of a smaller immediate reward is due scarcity of resources faced by poor people, who are often at risk and have reduced self-control and display impulsive behavior. The authors hypothesize that immediate rewards are chosen to level their playing field with richer people, if even for an instance (see also Hoel et al., 2016 ).

Research on cognitive load and time-discounting yields fairly consistent results. Experiencing sadness (or negative affect) is associated with the preference of immediate, lower rewards ( Lerner et al., 2013 ; Liu et al., 2013 ), whereas positive emotional states lead to the choice of higher, delayed rewards ( Ifcher and Zarghamee, 2011 ; Liu et al., 2013 ). Neutral affect has no effect on the preference of the type of reward ( Liu et al., 2013 ). Initially, stress was found to not have any effect on time-discounting. For example, Haushofer et al. (2013) induced a stress event in a laboratory setting, and observed no effect of stress on intertemporal choice. However, other studies ( Cornelisse et al., 2013 ; Moreno, 2015 ) have since confirmed the impact of stress on a tendency to choose smaller and earlier rewards. Haushofer and Fehr (2014) explain this by postulating that: (1) stress leads to the favoring of habitual behaviors, and (2) earlier rewards come with higher satisfaction levels than delayed ones.

Research on time-discounting and working memory, however, has yet to reach a consensus. Shamosh et al. (2008) and Basile and Toplak (2015) report a positive correlation between time-discounting and working memory, with a decreased willingness to wait for a larger reward being related to diminished working memory. Contrarily, no such significant relationship was found by Steinberg et al. (2009) . Similar issues can also be found with self-control. Waegeman et al. (2014) argue that a preference for larger, delayed reward is related to higher self-control. This is further supported by the findings in Basile and Toplak’s (2015) study which assessed the correlation between time-discounting and the ability to consider future consequences of decisions (a construct similar to the concept of self-control). Conversely, Carvalho et al. (2016a) state that despite scarce resources leading to inadequate economic behaviors, the expenditures of people shortly before and after payday do not differ. This, therefore, indicates that apparent self-control related differences are possibly due to liquidity constraints, rather than low self-control. Furthermore, according to Kidd et al. (2013) , the willingness to wait for a larger reward depends on the perceived stability of one’s environment, instead of solely on self-control. Additionally, Michaelson et al., 2013 ) found that people tend to wait for a reward when others in the environment appear to be trustworthy. In other words, when a person believes the social context of a situation to be reliable, the likelihood of delayed gratification increases.

Studies on the relationship between deliberation or impulsivity in decision-making and time-discounting produce more consistent results. According to Frederick (2005) , people who display higher deliberative reasoning in decision-making are more patient and thus prefer a higher reward, with this phenomenon being even stronger among women. However, the overall correlation was lower when the reward was only accessible after a long wait period (e.g., 10 years). Higher levels of deliberative reasoning in decision-making also predict better results in cognitive tasks, and reduce heuristic use and cognitive biases, while being linked to a preference for larger, delayed rewards ( Travers et al., 2016 ). In line with this, Stanovich (2010) found that individuals’ intuitive/deliberative style of thinking predicts their performance in decision-making tasks (including reward preference) independent of other cognitive skills. Mishra and Lalumière (2016) postulate that the choice of smaller, earlier reward is correlated with the inability to control impulses (e.g., increased impulsivity, lower self-control, attraction to risky financial investments or vulnerability to gambling risks), with people living in poverty showing greater sensitivity to such behavior. And as noted by Wittmann and Paulus (2009) , impulsive economic decisions are not generally sustainable in the long run.

We thus conclude that prior research provides evidence supporting the existence of a mechanism by which poverty induces cognitive load, impedes executive functions, and hence affects time-discounting. It appears evident that poor people favor immediate and smaller rewards that provide short-term satisfaction but are not economically beneficial from the long-term perspective. Since time-discounting is not the only aspect of economic decision-making, it is important to focus on risk preference as another factor.

Risk Preference in Economic Decision-Making

Living in poverty is associated with an increased prevalence of risky behavior and potential negative consequences in areas such as health care ( Shankar et al., 2010 ), sexual behaviors ( McBride Murry et al., 2011 ), criminality ( Hay et al., 2006 ), substance abuse ( Datta et al., 2006 ; Mulia et al., 2008 ), and gambling ( van der Maas, 2016 ). However, does this tendency apply to economic decision-making? Are people living in poverty more eager to accept the certainty of a smaller reward or might they willingly take the risk of losing a potential bigger reward? Finally, does a similar mechanism also work in the case of financial loss?

Andersen et al. (2008) claim that, in general, people naturally tend to avoid economic risk. Haushofer and Fehr (2014) further argue that the tendency to avoid risks related to financial rewards is even more pronounced in people influenced by poverty, as reward certainty can attenuate acute liquidity constraints. This decrease susceptibility to risk regardless of intrinsic risk preference. Experiments on people living in poverty conducted by Carvalho et al. (2016b) show that participants with bank accounts savings engaged in lottery risks (with potential financial rewards) more often. According to the authors, people with savings recognize the benefits of accumulating more money for future use. Finally, Carvalho et al. (2016a) did not find differences in reward related risk-taking between the groups of poor individuals before and after payday. They thus hypothesize that long-term (rather than short-term) financial stability may increase people’s willingness to take economic risks.

From the perspective of cognitive load, one can assume that exposure to naturally occurring negative emotions (fear and anxiety) might increase risk aversion in the case of reward ( Heilman et al., 2010 ). Findings of the effect of stress on risk-taking are more ambiguous. Shah et al. (2012) claim that stress can lead to riskier economic decision-making. Similarly, Starcke and Brand (2012) state that stress (both experimentally induced and chronic) promotes risk-seeking preference for both reward and loss. However, other studies show that acute stress enhances conservative decisions when facing a potential reward ( Porcelli and Delgado, 2009 ; Moreno, 2015 ), but also increases the chance of risk-taking in the case of potential loss ( Porcelli and Delgado, 2009 ). Contrarily, Moreno (2015) found chronic stress to be virtually uncorrelated with risk preference in economic decision-making, while Kandasamy et al. (2014) found that induced chronic stress does contribute to risk-aversion.

A willingness to take risks is also linked with intuitive/deliberative style of thinking. Frederick (2005) found that individuals with more deliberative reasoning (higher cognitive reflection), were more inclined to risk-taking, especially when the potential reward was high. This was found to be more pronounced in men, with additional gender differences found – women with high scores in deliberative thinking were as prone to risk-taking as men with low scores in deliberative thinking. In the case of loss, individuals exhibiting deliberative thinking style were more willing to withstand a smaller loss compared to risking a larger loss. In contrast, people with a tendency for intuitive thinking were more inclined to take risks in case of a potential loss rather than a reward. Such behavior corresponds with the Prospect Theory (people are more sensitive to a loss than to a reward, risking more to avoid it; Kahneman and Tversky, 1979 ). This indicates that people do not consider risks in isolation, but also look at the profits ( Cueva et al., 2016 ). Thus, people living in poverty prioritize ‘here and now’ rewards while simultaneously trying to avoid potential loss despite the fact that this can backfire, leading to even more negative consequences. This assumption can be explained in light of the influence of stress. Mather and Lighthall (2012) argue that acute stress initiates behaviors that have been rewarded in the past. However, when under stress, individuals’ perceptions of past negative experiences may be biased toward more positive evaluations. The willingness to take risks also differs across genders, with men being riskier ( Mather and Lighthall, 2012 ; Cueva et al., 2016 ).

Therefore, we can conclude that the interacting mechanism of poverty, cognitive load and intuitive decision-making can lead to a tendency of the poor to risk less for a potential gain and simultaneously to risk more in case of a potential loss. According to Haushofer and Fehr (2014) , risk aversion to rewards in poor people is separated from their intrinsic preference for risk-taking. Thus, the poor prefer guaranteed profits that help them to decrease liquidity constraints and compensate for frequently occurring negative events. In order to avoid financial loss, however, the poor may prefer to take risks. This has been explained by Mather and Lighthall (2012) , who suggest that people under stress tend to avoid negative experiences and seek to prevent further negative consequences. Besides these factors, economic decision-making is also linked with financial literacy.

Financial Literacy

Financial literacy is defined as an ‘ability to process economic information and make informed decisions about financial planning, wealth accumulation, debt, and pensions’ ( Lusardi and Mitchell, 2013 , p. 6). Moreover, it consists of a combination of apprehension, abilities, attitudes and behavior associated with economic aspects of life ( OECD INFE, 2011 ).

Despite the fact that financial literacy directly affects economic decisions ( Lusardi, 2011 ) and results in individuals with higher financial literacy being in better financial situations ( Meier and Sprenger, 2013 ), education in this field is often neglected. Lusardi (2011) argues that illiteracy or ignorance toward basic financial concepts leads to incautious borrowing, or poor investments (e.g., in purchasing securities). Generally, people are unable to make simple economic calculations, lack knowledge about interest, cannot distinguish between a real and nominal product value, are not familiar with options of risk allocation, and have even less knowledge about more complex concepts. Lusardi and Mitchell (2011) and French and McKillop (2016) studied the financial management abilities and numerical skills of people in debt living in socially disadvantaged environments. This led to the discovery that wealth inequality is largely caused by deficiencies in financial management. However, mathematical abilities only had a marginal effect on financial situations. It was therefore concluded that better financial management abilities (as a component of financial literacy) result in a behavioral, rather than cognitive, benefit in helping individuals to cultivate a habit of borrowing less and avoiding high interest rates, thus decreasing overall debt.

The effect of financial literacy on economic decision-making has been described in several studies. Gathergood (2012) found that excessive financial demands and the inability to repay debts are correlated with impairments in self-control and, crucially, financial literacy. Moreover, Meier and Sprenger (2013) outlined a relationship between time-discounting and financial literacy, demonstrating that individuals with higher financial literacy prefer larger, delayed rewards.

In summary, poverty and economic decision-making are closely linked, with studies showing that poverty and cognitive load have an impact on economic decision making. Additionally, economic decision-making is associated with self-control and intuitive/deliberative style of thinking. In general, the evidence supports the notions that (1) individuals living in poverty are inclined toward smaller, earlier rewards due to higher cognitive load, lower self-control (higher impulsivity), and a tendency to utilize intuitive decision-making processes; (2) these characteristics are associated with a reluctance to take risks for a reward; (3) the same characteristics are related to a willingness to take risks associated with losses; (4) conversely, people who favor larger, delayed rewards are more willing to take risks associated with rewards and are more cautious in regards to potential loss; they also have higher self-control and/or more deliberative thinking style. These findings allow us to propose a complex conceptual model, which reflects the consequences of poverty on economic decision-making via a cognitive mechanism that rationalizes these relationships.

The Proposal of Two Models Integrating Poverty, Cognitive Load, Executive Functions, Intuitive/Deliberative Style of Thinking and Economic Decision-Making

Based on the reviewed literature, we propose two models of cognitive factors that contribute to the perpetuation of the poverty cycle by negatively affecting people’s ability to make sound economic decisions. The first model consolidates all the presented prior findings (see Figure ​ Figure1A 1A ). Since this model is too complex, we propose our own simplified version, which lays out the most probable causality of relationships in a comprehensive and parsimonious manner ( Figure ​ Figure1B 1B ). This model details a mechanism explaining difficulties faced in attempting to break out of the cycle. However, currently available empirical evidence is inconsistent, and the relationship between some variables remains to be assessed. Therefore, it remains necessary to build partial models and to examine their validity and parameters.

Two models of how poverty affects economic decision-making via cognitive mechanism. (A) The model integrating all the presented findings. (B) The proposed structural model of a cognitive mechanism of poverty perpetuation. + and - signs indicate the direction of the effect. E.g., the higher the self-control, the lower the tendency of intuitive style of thinking.

The mechanism of the predicted model ( Figure ​ Figure1B 1B ) can be explained as follows. Living in poverty, as defined by objective (e.g., person’s income, household income, wealth), and subjective indicators (e.g., subjective assessment of economic well-being and social status), is causally related to persistent, repeated and more prevalent states of negative affect and stress. In other words, living in poverty or having limited resources creates a heavy cognitive load in the form of negative psychological states such as shame, guilt, sadness, misfortune, fear, hostility toward others, uncertainty, worries, and distress. This mental pressure severely limits working memory capacity, and focuses attention on situations and needs that cannot be met because of poverty. This prevents these executive functions from being used on other problems, and causes these functions to seem deteriorated. Focusing on the emerging issues related to poverty, a person is constantly forced to choose which needs will and will not be satisfied, thus, leading to self-control being affected by the need to make compromises and resist temptations. This can result from a depletion of mental resources and/or the need to shift one’s attention and motivation from one task to another, thereby increasing vulnerability to impulsive behaviors for instant gratification. When a person is exhausted, deliberative processes are often neglected in favor of intuitive ones. This intuitive thinking style may be more beneficial in the short run, as it is automatic, based on heuristics, and requires little mental effort for decision making. Apart from conserving mental processes, intuitive thinking might also afford a hedonistic experience, as it provides an instant reward regardless of the potential consequences. The economic decision-making effect of intuitive thinking can also be seen in time-discounting tasks, compelling an individual to select a smaller immediate reward over a larger delayed larger one. This pattern of behavior could be explained in light of the fact that poverty and the cognitive mechanisms that result from it, encourage individuals to satisfy their emerging needs whenever possible. In risk preference tasks, the proposed mechanism can also result in cautious behaviors in regards to potential gain, and increased risky behaviors to moderate potential loss. This willingness to engage in risks when a potential losses are greater could be due to the fact that individuals in a period of poverty may already be facing significant issues, leading to any slight chance of loss being perceived as being disproportionately severe, and the possibility of no loss at all being seen as subjectively more beneficial. Altogether, these behaviors demonstrate the difference between psychological and economical rationality in decision-making. From an economic perspective, it is clearly more beneficial to wait for a larger reward. However, in the case of poverty, a long-run economic advantage may often be neglected in favor of satisfying present urgent needs which may otherwise be difficult to meet. From an evolutionary perspective, this pattern of behavior is justifiable, as primal motivations dictate that fundamental acute problems have to be met before long-term actions can be decided upon. However, this does not translate well to poverty alleviation, which requires the making of economic decisions that are focused not only on the “here and now,” but also take into consideration future consequences of decisions. As both time-discounting and risk preference have strong economic foundations, they are likely influenced by financial literacy. Therefore, it is necessary to control for its effect on economic decision-making. Although the proposed model focuses on cognitive mechanisms that arise from poverty, the outcomes of economic decision-making also create a feedback loop on future financial situations, leading to a cycle of poverty perpetuation.

Potential Limits of the Presented Model

Validating the suggested model may run into several limitations, however, with the first being related to the methods of assessing poverty. Considering that poverty can be examined in our model from various perspectives, e.g., (1) through a focus on its subjective experience; (2) through a focus on its objective indicators; (3) by multidimensional approach combining psychological and economic indicators (likely the best solution) or (4) by categorically dividing people across a poor/not poor threshold (e.g., based on household income), with poverty becoming a moderator. However, such dichotomization omits subjective indicators crucial for the function of the overall model and could potentially reduce the proposed mechanism as early as the relationship between poverty and cognitive load.

One other issue is that of causality. Despite the fact that experimental evidence for the causes of poverty exists, the majority of existing research is based on statistical correlations between poverty and the aforementioned factors. Current research therefore only provides a tenuous hypothetical account for the causalities underlying poverty. To ensure that the model (and emerging partial models) are valid, the proposed mechanism is based on a factor of causality that we believe is the more likely, based on the presented literature. Namely, our proposed model suggests that poverty is the causal factor for the development of cognitive mechanisms underlying poor economic decision-making. However, an alternative hypothesis treats poverty as a consequence instead of the cause of different poverty-related processes, including those discussed in the text. For instance, cognitive abilities can affect economic outcomes, with higher intelligence being related to better jobs and higher incomes ( Gottfredson, 1997 ). As we are aware that any kind of model merely attempts to simplify and reflect real-life events taken from a complex reality, we believe that the alternative models of the whole mechanism and of its parts should be tested as well.

Moreover, the results of previous studies are not always consistent. The most pertinent issue appears to be that of the relationship between cognitive load and executive functions. Currently, there is a lack of strong evidence as to how attention, working memory and self-control affect each other in situations of stress or negative affect (note: the description of the whole model includes the most plausible alternative). In order to clarify this system of relationships, it is thus necessary to test different partial models. We propose testing four alternatives: (1) working memory and attention as mediators of the relationship between cognitive load (negative affective and stress) and self-control; (2) self-control as a mediator between cognitive load and working memory with attention; (3) attention, working memory and self-control are at the same level, mutually covaried, and depend on cognitive load; and (4) attention, working memory and self-control affect each other reciprocally (creating non-recursive relationships) and depend on cognitive load.

Taking into account the various aspects of economic decision-making, time-discounting and risk preference/aversion to reward or loss into consideration, can also be problematic. Choi et al. (2014) provide evidence that individuals with limited mental resource capacity make inconsistent decisions. However, this can be overcome with the use of appropriate measurement tools (see Falk et al., 2016 ). Successfully distinguishing between economic and psychological rationality in financial decision-making (see Simon, 1993 ) can also be another potential issue. By its nature, economic decision-making is based on the principle of achieving maximum profit while minimizing potential loss. Hence, the result of such decisions can be evaluated mathematically. Conversely, psychological aspects of decision-making cover a wider range of decision-based contexts. For example, it is economically more rational to select the larger but delayed reward when given a choice between a reward of 100 now or 200 in a month. Nonetheless, such a conclusion simplifies and neglects the psychosocial aspects of decision making. For instance, one might choose an immediate reward to satisfy an urgent need such as purchasing food. Reducing economic decision-making to the simplified pursuit of economic advantage while neglecting more complex perspectives, therefore, becomes inadequate for the purposes of explaining and interpreting observed relationships or potential causalities.

In the presented overall model as well as in the partial ones, other variables may play an important role – e.g., gender, duration of poverty spell (eventually the number of poverty cycles in the lifetime) or type of social unit focused on when assessing poverty (individual vs. family). It would, therefore, be of benefit to at least control for participants’ social backgrounds, even in the case of research on the level of the individual.

Although we aimed to present a highly complex model, it was impractical to assess and analyze all possible variables that might plausibly interfere with the presented mechanism. Therefore, the model does not include variables such as: (1) intelligence, which is associated with time-discounting ( Shamosh and Gray, 2008 ; Steinberg et al., 2009 ) and economic decision-making in general (see Rustichini, 2015 ); (2) time perception, which is also related to time-discounting ( Soman et al., 2005 ); (3) the ego-depletion effect, which differs from cognitive load ( Maranges et al., 2017 ), and might only occur under specific circumstances; and also a broader concept of fatigue; (4) motivation influencing self-control ( Dang et al., 2015 ); (5) macroeconomic and political expectations toward future ( Brown et al., 2015 ), or (6) metacognitive abilities, for example, feeling of rightness of judgment, which may determine the tendency of intuitive/deliberative decision-making ( Thompson et al., 2011 ). Implementation of these variables into models can, therefore, be explored in future research.

In respect to existing research, this model is applicable in the context of poverty, as well as for evaluating the broader issue of (socio)economic status. However, the model might not be relevant in specific regions or countries affected by extreme poverty.

Poverty is a serious long-term, pervasive issue in society. Although research on poverty has primarily been conducted from the perspective of economic science, present attention has shifted to more psychological aspects, focusing on the causes and consequences of poverty. As stated by Haushofer and Fehr (2014) , the examination of such aspects can be a key to unraveling the processes leading to poverty persistence. Till recently, research has only been carried out in partial studies with distinct goals, inadvertently overlooking the role of relationships between different aspects across a broader framework. We thus propose a comprehensive holistic mechanism, detailing the manner in which poverty affects economic decision-making via cognitive load, executive functions and intuitive/deliberative style of thinking. Testing this model can thus be an initial step in attempting to explain the self-perpetuating nature of the poverty cycle.

This review contributes to current literature by bridging the gaps of missing connections between various aspects, which taken together as a system, can be used to examine the economic decision-making style of an individual. At the same time, further analysis of specific relations between poverty, cognitive load, executive functions, and economic decision-making can contribute to an understanding of events related to individuals and poverty. In a broader context, a greater understanding of the workings of specific poverty-related mechanisms also carries with it the potential to better craft and improve intervention programs focused on poverty alleviation.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank the reviewers for their rigorous feedback, which substantially helped to improve the manuscript into the present form.

Funding. This work was supported by the Slovak Research and Development Agency [project number APVV-15-0404].

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International Journal of Academic Research in Business and Social Sciences

Open access journal.

ISSN: 2222-6990

Poverty: A Literature Review of the Concept, Measurements, Causes and the Way Forward

Rusitha wijekoon, mohamad fazli sabri, laily paim.

• Pages 93-111
• Received: 09 May, 2021
• Revised: 30 May, 2021
• Published Online: 22 Jul, 2021

http://dx.doi.org/10.6007/IJARBSS/v11-i15/10637

Open access

In spite of the fact that there is some lucidity within the field of poverty with respect to the concept, measurements, causes, and the way forward, those exterior to the field are confronted with an apparently complex poverty literature, overlapping terminology, and several published measures. Therefore, the objective of this review was to give an overview of concepts, measurements, causes and the way forward on poverty. A systematic literature review was performed by searching websites, and electronic databases from January 2000 to September 2020, and the selected articles were then analyzed thematically. Twenty research articles, and 23 website articles were incorporated in the analysis. In the current paper authors try to develop a guidance to the academicians and policy makers who are looking to use poverty in their work. Further, it gives an outline of the various conceptualizations of poverty, and afterward give a set of recommendations for researchers, and practitioners with respect to the most appropriate measures of poverty for a scope of various purposes, and policy implications for the way forward the poverty.

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In-Text Citation: (Wijekoon et al., 2021) To Cite this Article: Wijekoon, R., Sabri, M. F., & Paim, L. (2021). Poverty: A Literature Review of the Concept, Measurements, Causes and the Way Forward. International Journal of Academic Research in Business and Social Sciences, 11(15), 93–111.

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I am an undergraduate student at Case Western Reserve University in the combined Bachelor’s and Master’s program, studying Neuroscience, Bioethics, and Health Communication. I’m interested in neuropsychiatry and health equity research, with the goal of uplifting patients who have systemically fallen through the cracks of the healthcare system. This summer, I am excited to be working with Dr. Ava Kikut-Stein on Youth Action Participatory Research (YPAR) in the context of cancer prevention, and promoting post-traumatic growth in young adult cancer survivors. I am looking forward to learning from everyone at the Lee Kum Sheung Center for Health and Happiness and getting involved in community-based action research!

Subodh Potla

My primary research interests, broadly speaking, are investigating the various factors that impact health and well-being in underrepresented or disadvantaged populations. My current research involves studying opioid abuse prevention strategies and health promotion techniques in the Hispanic population using a family-based approach which examines the roles of parents and adolescents. This summer, I am excited to work under the mentorship of Dr. Susan Peters on her Thriving Workers, Thriving Workplaces study, which aims to explore how work experiences and working conditions affect the mental, physical, and social well-being of workers. Through our collaboration this summer, I hope to gain valuable skills and insights on improving partnership recruitment outreach, learning implementation strategies, and applying translational research principles.

Nikita Rohila

I am an undergraduate student at Vanderbilt University studying Psychology and Medicine, Health, and Society. I am passionate about researching about how health inequity affects minoritized and vulnerable groups. I hope my research can transform health policy and work to address healthcare disparities. I am excited to be interning in Dr. K. “Vish” Viswanath’s lab under the mentorship of Dr. Dhriti Dhawan, on a project partnering with the Salaam Bombay Foundation. In this project, our lab will be investigating how school and community connectedness and health promotion behaviors support the health and well-being of schoolchildren in Mumbai, India. I am looking forward to strengthening my qualitative research skills and contributing to project development. Through this internship, I will build a greater understanding of how evidence-based strategies support the well-being and resilience of vulnerable populations. Ultimately, I will learn how applied research is actively shaping people’s lives.

Talyn Steinmann

I am a fourth-year undergraduate at the University of Virginia studying Public Health and Kinesiology. I am especially interested in chronic disease, intervention development, and implementation, health behaviors, and mental wellbeing and resiliency and their interrelation with physical health. With all of the research I undertake, I prioritize conducting intentional, community-informed research; and ensuring that research goes beyond a paper, and informs policy or initiatives that are effectively and meaningfully implemented. This summer, I am excited to work with Dr. Christopher Celano through the Cardiac Psychiatry Research Program (CPRP) at Massachusetts General Hospital. I’ll be primarily working on an NIH study (BEHOLD) which examines a novel psychological-behavioral intervention to promote physical activity in patients with type 2 diabetes. The intervention is a combined positive psychology/ motivational interviewing program. I look forward to engaging with the CPRP team, deepening my knowledge in the topic area, and advancing the work of the project.

Michael Tang

My main goal in public health research is to contribute to the conceptualization, development, and implementation of interventions that improve health and well-being for everyone in an equitable manner. This summer, I will be working with Dr. Gloria Pedersen and my fellow intern Lekha Amin in the Mental Health for All Lab. I will assist with the preparatory phase of a randomized clinical trial for a community-based psychosocial rehabilitation program designed for schizophrenia care in rural India, that also involves a digital mental health app. Through this program, I hope to learn more about global mental health issues and how to design and conduct effective and equitable interventions for these issues. I also hope to further develop my research skills (e.g., conducting literature reviews, preparing protocol documents) and my scientific thinking skills.

Shriya Thakkar

I am a medical sociologist and mixed-methods researcher, with strong interests in health inequalities and a particular focus on disasters, gender, and aging. I study health disparities in gender, race, rural-urban contexts, and social capital, situating them within the expansive realm of the region’s socio-political economy. Currently, my regional focus is the United States and South Asia (primarily India). This summer, I will be collaborating with Dr. Ashley Whillans of the Harvard Business School to investigate how alleviating time poverty can lead to sustained psychological, economic, and health benefits for girls and women in Rajasthan, India. Through this internship, I seek to broaden my networks and collaborations while honing my quantitative skills within an enriching interdisciplinary space.

My research interests encompass well-being, information processing, and mental health. I am currently collaborating with Dr. Marciano on an NIH-funded project that examines loneliness and social media use behaviors among adolescents. In addition, we are involved in other projects focus on well-being, biomarkers, and media-related addiction behaviors. Through this collaboration, I can gain valuable experience in data collection for human experiments, systematic review writing, and brainstorming research directions. Working with Dr. Marciano has provided me with an enriching learning environment and various opportunities for professional growth in the field of psychological and public health research.

‘White Poverty’ and the limits of progressive empathy

A new book by a black civil rights leader urges his allies to acknowledge the plight of poor white people — and build a multiracial coalition for justice..

The Reverend William J. Barber II is chasing an old dream.

A dream that traces back at least as far as Reconstruction. A dream that animated Martin Luther King Jr. at the end of his life: bridging the racial gap that splits America’s poor and building a powerful movement for change.

Barber, a charismatic North Carolina preacher who may be the most important civil rights leader of our time, has chased the dream in church basements, community meetings, and legislative hearings.

And now he’s chasing it in a slender, highly readable polemic called “ White Poverty .”

But Barber’s book is not just the continuation of a noble tradition.

Amid all the strange and unsettling shifts in American politics these past few years — left and right — it’s a reclamation project, too. A reminder to a nation adrift — and to a certain kind of identity-politics-driven ally who has abandoned the inclusive politics of old — about what they should be reaching for. About their moral obligation to all poor people.

Including the white ones.

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Barber was born to activism . His mother, he likes to recall, went into labor on the day of the 1963 March on Washington.

And when he was a child, the Barbers moved to a small town in North Carolina to help with a family friend’s school integration project; he was one of the first Black students in the local elementary.

When he was a young man, Barber thought he might become a civil rights lawyer. But he went into the ministry instead. And he started winning national attention a decade ago with his “Moral Mondays” campaign, which drew tens of thousands of people — white, Black, and Latino; Christian, Muslim, and Jewish; gay and straight; wealthy and poor — to weekly protests at North Carolina’s conservative state Legislature.

The effort contributed to the defeat of Republican Governor Pat McCrory in 2016.

And a stemwinder at the Democratic National Convention that same year brought him more national acclaim.

All along, he preached that a multiracial coalition was the best path to power.

And as he writes in his new book, building such a coalition requires moving past the heavily freighted idea that poverty is a Black problem.

The largest bloc of poor people in this country, Barber reminds his readers, is white. And focusing on “white poverty’s wounds,” he suggests, can focus the nation’s attention on all that is wrong with our highly stratified economic system.

“White Poverty” is not just a strategy document, though. It’s also a plea to an increasingly identitarian left to see the humanity in a population that is too often dismissed as unworthy of its attention.

After telling the story of a young white woman he met in an impoverished part of Kentucky, Barber feels compelled to remind his readers, in one of the most remarkable passages in the book, “that white folks hurt like every other human being. Their stomachs growl like everyone else’s when they are hungry; their bones ache when they are sick; their muscles tense and tremble when they are left out in the cold without shelter.”

In another section, he recalls visiting a church in San Francisco’s Tenderloin district and hearing a parade of speakers decry the effects of racism. He is sympathetic, but when it is his turn to speak, he feels called to summon a group of mostly white homeless people at the back of the sanctuary to the front. “We’ve got to be real about what poverty looks like, y’all,” he said. “We can’t challenge the government to tell the truth and not tell the truth ourselves.”

These parts of the book are less a throwback attempt at building solidarity than an of-the-moment plea for the social justice movement to rediscover its soul.

The book is less convincing on the other brewing threat to a multiracial coalition of the dispossessed: the populist right.

Barber insists that the Tea Party’s and MAGA movement’s appeal “did not, in fact, grow out of the ‘economic anxiety’ of hardworking people or the ‘traditional values’ of everyday Americans” but from the “well-funded propaganda campaigns of elites” interested in keeping “white people in the bondage of an increasingly unequal society.”

Big-money interests certainly played an important role in fueling these movements. But Barber doesn’t give enough agency, here, to the white working-class people he spends most of the book humanizing. Their economic anxiety is real. Many of them take traditional values seriously.

And while Barber wants his protagonists to be partial to social justice politics — he points to Joe Biden’s solid performance with lower-income voters in the 2020 election, suggesting there were more poor white people backing the Democrat than is often imagined — exit polls show Donald Trump had an overwhelming advantage among downscale white voters.

Even more worrisome, Trump is making significant incursions this election season into the heart of the multiracial alliance Barber envisions. A recent Pew poll showed Black support for Trump has more than doubled since 2020 — with about 1 in 5 Black voters, college educated and non-college educated alike, backing the former president.

A half century after King’s Poor People’s Campaign, the foundation for a broad working-class political movement is shakier than social justice advocates may have hoped.

But the need for such a movement is as great as ever. Democracy is under threat and, for too many, the post-industrial economy is a desperate grind.

Barber’s analysis may fall short in places.

But “White Poverty,” bristling with moral energy, reaffirms what many who have encountered the preacher have come to believe: He may be the country’s best hope for building the movement that could redeem it.

David Scharfenberg can be reached at [email protected] . Follow him @dscharfGlobe .

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Desperately Seeking Answers on How to Raise Boys

Ruth Whippman had three sons and a lot of questions. In her memoir “BoyMom,” she hopes to offer parents some of the reporting she gathered on the road to understanding her children.

Sarah Palmer, a photographer, culled images from her personal collection to illustrate this story. The image above features tender moments between Ms. Palmer and her boys, as well as her friends and their sons. Credit... Photo illustration by Sarah Palmer for The New York Times; Source images by Trudy Chan; Saran Simmons; Gina Stulman; Sarah Palmer

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By Casey Schwartz

Casey Schwartz is a contributing reporter with one small son of her own.

• Published June 6, 2024 Updated June 11, 2024

When the British American writer Ruth Whippman decided to thaw one final embryo, she was 42 years old. She and her husband had two sons, Solly, then 6, and Zephy, 3. Their remaining embryos all had XY chromosomes, too.

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As her pregnancy became visible, most people assumed she was trying for a girl. When she told them she was having a boy, people treated her “as this object of pity,” Ms. Whippman said in a recent interview from her home in Berkeley, Calif. “There was this real sense that boys were somehow disappointing.”

Even her mail carrier expressed her sympathy.

It was 2017. Ms. Whippman, a self-described liberal feminist, was watching the #MeToo movement explode all around her. She felt as though men had become the enemy, which made bringing another one into the world a different kind of challenge from what she already faced at home with two rambunctious little boys.

But she was conflicted. “While the feminist part of me yelled, ‘Smash the patriarchy! ’ the mother part of me wanted to wrap the patriarchy up in its blankie and read it a story,” she writes in her new book, “BoyMom,” out this week.

The title of the book borrows from the social media phenomenon known as #BoyMom, a hashtag that has become a full-blown trend in recent months and has as many interpretations as a Rorschach test.

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