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article review of economic

  • 03 Sep 2024
  • Cold Call Podcast

How the US Government Is Innovating in Its Efforts to Fund Semiconductor Manufacturing

In February 2023, US Commerce Secretary Gina Raimondo was deciding whether or not to sign off on a Notice of Funding Opportunity (NOFO) for $39 billion in direct semiconductor manufacturing incentives. But this NOFO had several unconventional provisions: a pre-application (pre-app) to the actual application, upside sharing provisions to align incentives, and funding milestones so that only awardees making progress would receive additional funds. The funding had been made available through the US Department of Commerce by the CHIPS (Creating Helpful Incentives to Produce Semiconductors) and Science Act passed a few months earlier. Raimondo’s team had proposed additional measures that would help the US regain technological leadership while protecting taxpayer funds. Should Raimondo move forward with the “innovative” NOFO, despite the risks? Harvard Business School professor Mitch Weiss explores the issue of risk-taking and innovation in government in his case, “The CHIPs Program Office.”

article review of economic

  • 29 Aug 2024
  • Research & Ideas

Shoot for the Stars: What to Know About the Space Economy

Outer space has come a long way since the 1960s. Matthew Weinzierl explains the current state of the space economy, highlighting the various opportunities for businesses hidden among the stars.

article review of economic

  • 22 Aug 2024

Reading the Financial Crisis Warning Signs: Credit Markets and the 'Red-Zone'

While fears about slowing economic growth have roiled stock markets in recent weeks, credit markets remain stable and bullish, and a recession hasn't materialized as some analysts predicted. Robin Greenwood discusses the market conditions that are buoying the economy—and risk signals to watch.

article review of economic

  • 05 Aug 2024

Watching for the Next Economic Downturn? Follow Corporate Debt

Rising household debt alone isn't enough to predict looming economic crises. Research by Victoria Ivashina examines the role of corporate debt in fiscal crashes since 1940.

article review of economic

  • 23 Jul 2024

Forgiving Medical Debt Won't Make Everyone Happier

Medical debt not only hurts credit access, it can also harm one's mental health. But a study by Raymond Kluender finds that forgiving people's bills—even $170 million of debt—doesn't necessarily reduce stress, financial or otherwise.

article review of economic

  • In Practice

The New Rules of Trade with China: Navigating Tariffs, Turmoil, and Opportunities

Trade tensions between the US and China have continued well beyond the Trump Administration's tariffs. Harvard Business School faculty offer insights for leaders managing the complexities of doing business with the world's second-largest economy.

article review of economic

  • 18 Jun 2024

Central Banks Missed Inflation Red Flags. This Pricing Model Could Help.

The steep inflation that plagued the economy after the COVID-19 pandemic took many economists by surprise. But research by Alberto Cavallo suggests that a different method of tracking prices—a real-time model—could predict future surges better.

article review of economic

  • 28 May 2024

Job Search Advice for a Tough Market: Think Broadly and Stay Flexible

Some employers have pared staff and reduced hiring amid mixed economic signals. What does it mean for job seekers? Paul Gompers, Letian Zhang, and David Fubini offer advice for overcoming search challenges to score that all-important offer.

article review of economic

  • 21 May 2024

What the Rise of Far-Right Politics Says About the Economy in an Election Year

With voters taking to the polls in dozens of countries this year, could election outcomes lean conservative? Paula Rettl says a lack of social mobility and a sense of economic insecurity are some of the factors fueling far-right movements around the world.

article review of economic

  • 11 Apr 2024

Why Progress on Immigration Might Soften Labor Pains

Long-term labor shortages continue to stoke debates about immigration policy in the United States. We asked Harvard Business School faculty members to discuss what's at stake for companies facing talent needs, and the potential scenarios on the horizon.

article review of economic

  • 01 Apr 2024

Navigating the Mood of Customers Weary of Price Hikes

Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.

article review of economic

  • 29 Jan 2024

Do Disasters Rally Support for Climate Action? It's Complicated.

Reactions to devastating wildfires in the Amazon show the contrasting realities for people living in areas vulnerable to climate change. Research by Paula Rettl illustrates the political ramifications that arise as people weigh the economic tradeoffs of natural disasters.

article review of economic

  • 10 Jan 2024

Technology and COVID Upended Tipping Norms. Will Consumers Keep Paying?

When COVID pushed service-based businesses to the brink, tipping became a way for customers to show their appreciation. Now that the pandemic is over, new technologies have enabled companies to maintain and expand the use of digital payment nudges, says Jill Avery.

article review of economic

  • 17 Aug 2023

‘Not a Bunch of Weirdos’: Why Mainstream Investors Buy Crypto

Bitcoin might seem like the preferred tender of conspiracy theorists and criminals, but everyday investors are increasingly embracing crypto. A study of 59 million consumers by Marco Di Maggio and colleagues paints a shockingly ordinary picture of today's cryptocurrency buyer. What do they stand to gain?

article review of economic

  • 15 Aug 2023

Why Giving to Others Makes Us Happy

Giving to others is also good for the giver. A research paper by Ashley Whillans and colleagues identifies three circumstances in which spending money on other people can boost happiness.

article review of economic

  • 13 Mar 2023

What Would It Take to Unlock Microfinance's Full Potential?

Microfinance has been seen as a vehicle for economic mobility in developing countries, but the results have been mixed. Research by Natalia Rigol and Ben Roth probes how different lending approaches might serve entrepreneurs better.

article review of economic

  • 23 Jan 2023

After High-Profile Failures, Can Investors Still Trust Credit Ratings?

Rating agencies, such as Standard & Poor’s and Moody's, have been criticized for not warning investors of risks that led to major financial catastrophes. But an analysis of thousands of ratings by Anywhere Sikochi and colleagues suggests that agencies have learned from past mistakes.

article review of economic

  • 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

article review of economic

  • 21 Nov 2022

Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income Shoppers

More consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers.

article review of economic

  • 01 Sep 2022
  • What Do You Think?

Is It Time to Consider Lifting Tariffs on Chinese Imports?

Many of the tariffs levied by the Trump administration on Chinese goods remain in place. James Heskett weighs whether the US should prioritize renegotiating trade agreements with China, and what it would take to move on from the trade war. Open for comment; 0 Comments.

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American Economic Review

ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)

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Building better bureaucracy

The effects of the Pendleton Act on the quality of public services in the United States

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Measuring the value of public–private partnership

  • Josef Jilek

Redistributive effects of tax-benefit policies in the EU. Simulation of reform proposals

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Victim’s identification and social categorization: first- and second-order effects on altruistic behavior

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Three times more than money: generativity, relational goods and life satisfaction

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Working but hungry: precarious employment and household food insecurity in Ghana

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Building Joyful cities: is urbanization always pave a path to happiness in Africa?

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Remote work and the effects on secondary childcare

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Examining the relationship of healthcare expenditures and socio-economic factors with population health in Russia

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Vilfredo Pareto 1923–2023: a special issue

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Individual and regional determinants of women’s participation in the European labour market: a Labour Force Survey empirical study

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article review of economic

Key underlying concepts of shared prosperity: insights from a literature review

Impact of mobile financial services on financial inclusion: empirical insights from kenya.

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article review of economic

Sociological analysis: a cornerstone of Pareto’s political theory?

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Pareto’s legacy in the Italian tradition: the case of mathematical economics

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The influence of firm life cycle on firm risk-taking: evidence of Vietnam

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article review of economic

A capabilitarian behavioral economics: what behavioral economics can learn from the capability approach

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Industry or civil society? Role of institutions in COVID-19 crisis management

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article review of economic

Pareto and pure economics: analyses subsequently accepted and others neglected

Vilfredo pareto’s sociologia in relation to adam smith’s the theory of moral sentiments.

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Gender differences in old-age poverty in 14 EU countries: exploring the role of household structure

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Pareto and probability distributions

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Analyzing inequalities: a multifaceted perspective of OECD welfare regimes during the Great Recession and the Pandemic

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The subjective treatment effects of COVID-19 on child well-being: evidence from Luxembourg

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The impact of education costs on income inequality

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The dark side of the moon? Fintech and financial stability

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Survival of the luckiest

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Commemorative stamps as a recognition tool: a cross-sectional analysis

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Human capital consumption of households in a generational economy: evidence and implications for India

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Tax morale: a global scoping review from the cultural approach to economics

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Effect of Ramadan on purchasing behavior: a panel data analysis

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Causality between stock indices and cryptocurrencies before and during the Russo–Ukrainian war

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Financial Resilience, Financial Ignorance, and their impact on financial well-being during the COVID-19 pandemic: evidence from Brazil

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The power of economics to explain and shape the world

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Photo of Abijit Banerjee and Esther Duflo standing side-by-side against a blurred background

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Nobel Prize-winning economist Esther Duflo sympathizes with students who have no interest in her field. She was such a student herself — until an undergraduate research post gave her the chance to learn first-hand that economists address many of the major issues facing human and planetary well-being. “Most people have a wrong view of what economics is. They just see economists on television discussing what’s going to happen to the stock market,” says Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics. “But what people do in the field is very broad. Economists grapple with the real world and with the complexity that goes with it.”

That’s why this year Duflo has teamed up with Professor Abhijit Banerjee to offer 14.009 (Economics and Society’s Greatest Problems), a first-year discovery subject — a class type designed to give undergraduates a low-pressure, high-impact way to explore a field. In this case, they are exploring the range of issues that economists engage with every day: the economic dimensions of climate change, international trade, racism, justice, education, poverty, health care, social preferences, and economic growth are just a few of the topics the class covers. “We think it’s pretty important that the first exposure to economics is via issues,” Duflo says. “If you first get exposed to economics via models, these models necessarily have to be very simplified, and then students get the idea that economics is a simplistic view of the world that can’t explain much.” Arguably, Duflo and Banerjee have been disproving that view throughout their careers. In 2003, the pair founded MIT’s Abdul Latif Jameel Poverty Action Lab, a leading antipoverty research network that provides scientific evidence on what methods actually work to alleviate poverty — which enables governments and nongovernmental organizations to implement truly effective programs and social policies. And, in 2019 they won the Nobel Prize in economics (together with Michael Kremer of the University of Chicago) for their innovative work applying laboratory-style randomized, controlled trials to research a wide range of topics implicated in global poverty. “Super cool”

First-year Jean Billa, one of the students in 14.009, says, “Economics isn’t just about how money flows, but about how people react to certain events. That was an interesting discovery for me.”

It’s also precisely the lesson Banerjee and Duflo hoped students would take away from 14.009, a class that centers on weekly in-person discussions of the professors’ recorded lectures — many of which align with chapters in Banerjee and Duflo’s book “Good Economics for Hard Times” (Public Affairs, 2019). Classes typically start with a poll in which the roughly 100 enrolled students can register their views on that week’s topic. Then, students get to discuss the issue, says senior Dina Atia, teaching assistant for the class. Noting that she finds it “super cool” that Nobelists are teaching MIT’s first-year students, Atia points out that both Duflo and Banerjee have also made themselves available to chat with students after class. “They’re definitely extending themselves,” she says. “We want the students to get excited about economics so they want to know more,” says Banerjee, the Ford Foundation International Professor of Economics, “because this is a field that can help us address some of the biggest problems society faces.”   Using natural experiments to test theories

Early in the term, for example, the topic was migration. In the lecture, Duflo points out that migration policies are often impacted by the fear that unskilled migrants will overwhelm a region, taking jobs from residents and demanding social services. Yet, migrant flows in normal years represent just 3 percent of the world population. “There is no flood. There is no vast movement of migrants,” she says. Duflo then explains that economists were able to learn a lot about migration thanks to a “natural experiment,” the Mariel boat lift. This 1980 event brought roughly 125,000 unskilled Cubans to Florida over a matter a months, enabling economists to study the impacts of a sudden wave of migration. Duflo says a look at real wages before and after the migration showed no significant impacts. “It was interesting to see that most theories about immigrants were not justified,” Billa says. “That was a real-life situation, and the results showed that even a massive wave of immigration didn’t change work in the city [Miami].”

Question assumptions, find the facts in data Since this is a broad survey course, there is always more to unpack. The goal, faculty say, is simply to help students understand the power of economics to explain and shape the world. “We are going so fast from topic to topic, I don’t expect them to retain all the information,” Duflo says. Instead, students are expected to gain an appreciation for a way of thinking. “Economics is about questioning everything — questioning assumptions you don’t even know are assumptions and being sophisticated about looking at data to uncover the facts.” To add impact, Duflo says she and Banerjee tie lessons to current events and dive more deeply into a few economic studies. One class, for example, focused on the unequal burden the Covid-19 pandemic has placed on different demographic groups and referenced research by Harvard University professor Marcella Alsan, who won a MacArthur Fellowship this fall for her work studying the impact of racism on health disparities.

Duflo also revealed that at the beginning of the pandemic, she suspected that mistrust of the health-care system could prevent Black Americans from taking certain measures to protect themselves from the virus. What she discovered when she researched the topic, however, was that political considerations outweighed racial influences as a predictor of behavior. “The lesson for you is, it’s good to question your assumptions,” she told the class. “Students should ideally understand, by the end of class, why it’s important to ask questions and what they can teach us about the effectiveness of policy and economic theory,” Banerjee says. “We want people to discover the range of economics and to understand how economists look at problems.”

Story by MIT SHASS Communications Editorial and design director: Emily Hiestand Senior writer: Kathryn O'Neill

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Prof. Esther Duflo will present her research on poverty reduction and her “proposal for a global minimum tax on billionaires and increased corporate levies to G-20 finance chiefs,” reports Andrew Rosati for Bloomberg. “The plan calls for redistributing the revenues to low- and middle-income nations to compensate for lives lost due to a warming planet,” writes Rosati. “It also adds to growing calls to raise taxes on the world’s wealthiest to help its most needy.”

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Research: Articles

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Select Faculty Journal Publications since 2014

Econometric Theory

Yanqin Fan and Xuetao Shi (2023). An Interval Arithmetic Linear Model and Inequality-Constrained Inference.  Econometric Theory . 39, 27-69.

Yanqin Fan and Marc Henry (2023). Vector Copulas.  Journal of Econometrics.  234, 128-150.

Yanqin Fan and Xuetao Shi (2023). Wald, QLR, and Score Tests When Parameters are Subject to Linear Inequality Constraints.  Journal of Econometrics . 235, 2005-2026.

Yanqin Fan , Xuetao Shi, and Jing Tao (2023). Partial Identification and Inference in moment Models with Incomplete Data.  Journal of Econometrics.  235, 418-443.

Review of Economic Dynamics

Brian Greaney and Conor Walsh (2023). Deleveraging, Demand, and Growth.  Review of Economic Dynamics .

Laurent Bossavie, yoonyoung Cho, and Rachel Heath (2023). The Effects of International Scrutiny on Manufacturing Workers: Evidence from the Rana Plaza Collapse in Bangladesh.  Journal of Development Economics.  163: 103107.

Manuel Angelucci, Rachel Heath , and Eva Noble (2023). Graduate Programs targeting Women: Evidence from the Democratic Republic of Congo.  Journal of Development Economics . 164: 103146.

Journal of Economic Theory and Econometrics

Chang-Jin Kim and J. Kim (2023). Non-Markovian Regime-Switching Models.  Journal of Economic Theory and Econometrics . Vol. 34, No. 4, 115-148.

Yingyao Hu, Yuya Sasaki, Yuya Takahashi , and Yi Xin (2023). Dynamic Discrete Choice Models with Incomplete Data: Sharp Identification.  Journal of Econometrics . 236(1): 105461.

Jing Tao , Zhentong Lu, and Xiaoxia Shi (2023). Semi-Nonparametric Estimation of Random Coefficients Logit model for Aggregate Demand.  Journal of Econometrics . 235(2023): 2245-2265.

Journal of Macroeconomics

Stephen Turnovsky and Y. Gokan (2023). Taylor Rules: Condequences for Wealth and Income Inequality.  Journal of Macroeconomics.  77: 103544.

Stephen Turnovsky , S. Lim, and M. Morshed (2023). Endogenous Labor Migration and Remittances: Macroeconomics and Welfare Consequences.  Journal of Development Economics . 163: 103110.

Journal of Labor Economics

Alan Griffith (2022). Name Your Friends but Only Five? The Importance of Censoring in Peer Effects Estimates using Social Network Data.  Journal of Labor Economics.  40(4): 779-805.

Journal of Public Economic Theory

Alan Griffith (2022). A Continuous model of Strong and Weak Ties.  Journal of Public Economic Theory.  24(6): 1519-1563.

Journal of Public Economics

Alan Griffith and Thomas Noonen (2022). The Effects of Public Campaign Funding: Evidence from Seattle's Democracy Voucher Program.  Journal of Public Economics.  211:104666.

Journal of Human Resources

Rachel Heath , Ghazala Mansuri, and bob Rijkers (2022). Labor Supply Responses to Health Shocks Evidence from High-Frequency Labor Market Data from Urban Ghana.  Journal of Human Resources.  57(1): 143-177.

Journal of Economics and Management Strategy

Alex Henke, Fahad Khalil , and Jacques Lawarree (2022). Honest Agents in a Corrupt Equilibrium.  Journal of Economics and Management Strategy.  31/3 (Fall) 762-783.

Macroeconomic Dynamics

Chang-Jin Kim and Jaeho Kim (2022). Trend-Cycle Decompositions of Real GDP Revisited: Classical and Bayesian Perspectives on an Unsolved Puzzle.  Macroeconomic Dynamics.  26(2), 394-418.

International Economic Review

Taisuke Otsu, Martin Pesendorfer, Yuya Sasaki, and Yuya Takahashi (2022). Estimation of (Static or Dynamic) Games Under Equilibrium Multiplicity.  International Economic Review . 63(3): 1165-1188.

Economic Inquiry

Stephen Turnovsky , S. Chatterjee, and M. Kelly (2022). Foreign Aid, Public Investment, and the Informal Economy.  Economic Inquiry.  60: 174-201

Journal of Economic Dynamics and Control

Stephen Turnovsky , F. Carneiro, and O. Tourinho (2022). Economic Growth and Inequality Tradeoffs under Progressive Taxation.  Journal of Economic Dynamics and Control.  143, 104513.

Journal of International Economics

Fabio Ghironi and Matteo Cacciatore (2021). Trade, Unemployment, and Monetary Policy. Journal of International Economics . 103488

Journal of Money, Credit and Banking

Fabio Ghironi, Matteo Cacciatore, Romain Duval, and Giuseppe Fiori (2021). Market Reforms at the Zero Lower Bound. Journal of Money, Credit and Banking , 53, 745-777.

Fabio Ghironi, Alessandro Barattieri and Matteo Cacciatore (2021). Protectionism and the Business Cycle. Journal of International Economics , 129, 103417.

The World Bank Economic Review

Clara Delavallade, Alan Griffith and Rebecca Thornton (2021). Effects of a Multi-Faceted Education Program on Enrollment, Learning and Gender Equity: Evidence from India. The World Bank Economic Review .

American Economic Journal: Microeconomics

Yael Jacobs , Aaron M. Kolb and Curtis R. Taylor (2021). Communities, Co-ops, and Clubs: Social Capital and Incentives in Large Collective Organizations. American Economic Journal: Microeconomics .

The Rand Journal of Economics

Indranil Chakraborty, Fahad Khalil and Jacques Lawarree (2021). Competitive Procurement and Ex Post Moral Hazard. The Rand Journal of Economics , https://doi.org/10.1111/1756-2171.12366 .

Prospects for Economic Growth in the United States

Stephen J Turnovsky (eds. John W. Diamond and George R. Zodrow) (2021). Economic Growth and Income Inequality: Insights from the Representative Consumer Theory of Distribution. Prospects for Economic Growth in the United States .

Journal of Human Capital

Sokchea Lim, A. K. M. Mahbub Morshed and Stephen J Turnovsky (2021). Migrant Labor and Remittances: Macroeconomic Consequences and Policy Responses. Journal of Human Capital , 15, 128-173.

Journal of Mathematical Economics

Manoj Atolia, Chris Papageorgiou and Stephen J Turnovsky (2021). Re-opening after the lockdown: Long-run aggregate and distributional consequences of COVID-19. Journal of Mathematical Economics , 93, 102481.

Macroeconomic Dynamics

Manoj Atolia, Chris Papageorgiou and Stephen J Turnovsky (2021). Taxation and public health investment: policy choices and tradeoffs. Macroeconomic Dynamics , 25, 426-461.

Open Economies Review

Jorge Rojas-Vallejos and Stephen J Turnovsky (2021). Differential Tariffs and Income Inequality in the United States: Some Evidence from the States. Open Economies Review , 32, 1-35.

Journal of Economic Theory

Quan Wen and Marco Mariotti (2021). A Non-Cooperative Foundation of the Competitive Divisions for Bads. Journal of Economic Theory . https://doi.org/10.1016/j.jet.2021.105253 .

Environmental Modeling and Assessment

Quan Wen and Miguel Aramendia (2021). Symmetric Renegotiation-Proof Climate Agreements. Environmental Modeling and Assessment . https://doi.org/10.1007/s10666-021-09751-z .

JMet

Yanqin Fan, Fang Han, Wei Li, and Xiao-Hua Zhou (2020). On Rank Estimators in Increasing Dimensions. Journal of Econometrics , 214, 379-412.

Journal of European Economic Association

Rachel Heath and Xu Tan (2020). Intrahousehold bargaining, female autonomy, and labor supply: Theory and evidence from India, Journal of the European Economics Association, 18, 1928-1968.

JMet

Fahad Khalil, Jacques Lawarree and Alexander Rodivilov (2020). Learning from failures: Optimal contracts for experimentation and production. Journal of Economic Theory , https://doi.org/10.1016/j.jet.2020.105107 .

Theoretical Economics

Wei Li and Xu Tan (2020). Locally Bayesian learning in networks. Theoretical Economics, 15, 238-278.

Journal of Macroeconomics

Iñaki Erauskin and Stephen J Turnovsky (2020). Financial globalization and its consequences for productive government expenditure. Journal of Macroeconomics , 66, 103244.

Yoseph Y. Getachew and Stephen J Turnovsky (2020). Redistribution, inequality, and efficiency with credit constraints: Implications for South Africa. Economic Modelling , 93, 259-277.

International Economic Review

Evangelos V. Dioikitopoulos, Stephen J Turnovsky, and Ronald Wendner (2020). Dynamic Status Effects, Savings, and Income Inequality. International Economic Review , 61, 351-382.

Quan Wen and Xu Tan (2020). Information Acquisition and Voting with Heterogeneous Experts. Rand Journal of Economics , 51(4), 1063-1092. https://doi.org/10.1111/1756-2171.12350 .

GBE

Quan Wen and Miguel Aramendia (2020). Myopic Perception in Repeated Games. Games and Economic Behavior , 119, 1-14. https://doi.org/10.1016/j.geb.2019.10.003 .

Yanqin Fan and Heng Chen (2019). Identification and Wavelet Estimation of Weighted ATE under Discontinuous and Kink Incentive Assignment Mechanisms. Journal of Econometrics , 212, 476-502.

JMet

Yanqin Fan , Ming He, Liangjun Su, and Xiahua Z hou (2019). A Smoothed Q-learning Algorithm for Dynamic Treatment Regimes. Scandinavian Journal of Statistics , 46, 446-469.

American Economic Journal: Macroeconomics

Fabio Ghironi, Florin O. Bilbiie and Marc J. Melitz (2019). Monopoly Power and Endogenous Product Variety: Distortions and Remedies. American Economic Journal: Macroeconomics , 11, 140-174.

Fahad Khalil , Doyoung Kim, and Jacques Lawarrée (2019). Use It or Lose It. Journal of Public Economic Theory, https://doi.org/10.1111/jpet.12391 .

IntEconReview

Marcelo Arbex, Dennis O'Dea , and David Wiczer (2019). Network search: Climbing the job ladder faster. International Economic Review, 60, 693-720.

 Journal of International Economics

Iñaki Erauskin and Stephen J Turnovsky (2019). International financial integration and income inequality in a stochastically growing economy. Journal of International Economics , 119, 55-74.

OER

Inaki Erauskin and Stephen J. Turnovsky (2019). Financial globalization and the increase in the size of government: Are they related? Open Economies Review, 30, 219-253.

Evangelos Dioikitopoulos, Stephen J. Turnovsky , and Ronald Wendner (2019). Public policy, dynamic status preferences, and wealth inequality. Journal of Public Economic Theory, 21, 923-944.

JIE

Inaki Erauskin and Stephen J. Turnovsky (2019). International financial Integration, volatility, and inequality in a stochastically growing open economy. Journal of International Economics, 119, 55-74.

 Journal of International Money and Finance

Stephen J Turnovsky (2019). Demographic structures, savings, and international capital flows. Journal of International Money and Finance , 98, 102062. https://onlinelibrary.wiley.com/doi/abs/10.1111/iere.12356 .

Yangguang Huang and Quan Wen (2019). Auction-lottery hybrid mechanisms: Structural model and empirical analysis. International Economic Review , 60, 355-385.

JIMF

Yu-chin Chen and Dongwon Lee (2018). Market power, inflation targeting, and commodity currencies. Journal of International Money and Finance , 88, 122-139.

ScanJoStats

Yanqin Fan , Ming He, Liangjun Su, and Xiao-Hua Zhou (2018). A smoothed Q-learning algorithm for estimating dynamic treatment regimes. Scandinavian Journal of Statistics, 46, 446-469.

Yanqin Fan and Ruixuan Liu (2018). Partial identification and inference in censored quartile regression. Journal of Econometrics, 206, 1-38.

Yanqin Fan , Lei Hou, and Karen Yan (2018). On the density estimation of air pollution in Beijing. Economics Letters, 163, 110-113.

Oxford Review of Economic Policy

Fabio Ghironi (2018). Macro Needs Micro. Oxford Review of Economic Policy , 34, 195-218.

Journal of Political Economy

Rachel Heath (2018). Why Do Firms Hire Using Referrals? Evidence from Bangladeshi Garment Factories. Journal of Political Economy , 126(4): 1691-1746.

JDE

Rachel Heath and Xu Tan (2018). Worth fighting for: Daughters improve their mothers' autonomy in South Asia. Journal of Development Economics, 135, 255-271.

AEA PP

Elaina Rose (2018). Gender peer effects in a predominantly male environment: Evidence from West Point.  American Economic Association Papers and Proceedings, 108, 392-95.

Vladimir Dashkeev and Stephen J. Turnovsky (2018). Balanced-budget rules and risk-sharing in a fiscal union. Journal of Macroeconomics, 57, 277-298.

Journal of Economic Dynamics and Control

S. F. Schubert and Stephen J. Turnovsky (2018). Growth and unemployment: Short-run and long-run tradeoffs. Journal of Economic Dynamics and Control, 91, 172-189.

Santanu Chatterjee and Stephen J. Turnovsky (2018). Remittances and the informal economy. Journal of Development Economics, 133, 66-83.

Jorge Rojas-Vallejos and Stephen J. Turnovsky (2018). The distributional consequences of trade liberalization: Consumption tariff versus investment tariff reduction. Journal of Development Economics , 134, 392-415.

Scandinavian Journal of Economics

Harold Houba, Evgenia Motchenkova, and Quan Wen (2018). Legal principles in antitrust enforcement. Scandinavian Journal of Economics, 120, 859-893.

JMath

Jesse Schwartz and Quan Wen (2018). A subsidized Vickrey auction for cost sharing. Journal of Mathematical Economics, 77, 32-38.

Econ Th Bulletin

Jesse Schwartz and Quan Wen (2018). Robust trading mechanisms with budget surplus and partial trade. Economic Theory Bulletin. https://doi.org/10.1007/s40505-018-0138-7

Econometric Reviews

Yanqin Fan and Carlos A. Manzanares (2017). Partial Identification of the treatment effect on the treated in difference-in-differences framework with repeated cross-sectional data. Econometric Reviews, 36, 1057-1080.

Yanqin Fan, Emmanuel Guerre, and Dongming Zhu (2017). Partial identification and confidence sets for functionals of the joint distribution of “potential outcomes”. Journal of Econometrics 197, 42-59.

Amalavoyal Chari, Freeha Fatima, Rachel Heath , and Annemie Maertens (2017). The causal effect of maternal age at marriage on child wellbeing: Evidence from India. Journal of Development Economics, 127 , 42-55.

Rachel Heath (2017). Fertility at work: Children and women's labor market outcomes in urban Ghana. Journal of Development Economics, 126, 190-214.

Jorge Rojas-Vallejos and Stephen J. Turnovsky (2017). Tariff reduction and income inequality: Some empirical evidence. Open Economies Review, 1-29.

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David Oxborrow and Stephen J. Turnovsky (2017). Closing the small open economy model: A demographic approach. Review of International Economics 25, 44-75.

European Economic Review

Charis Christofides, Theo S. Eicher, and Chris Papageorgiou (2016). Did established early warning signals predict the 2008 crises? European Economic Review, 90, 103-114.

Theo S. Eicher and David Kuenzel (2016). The elusive effect of trade on growth. Canadian Journal of Economics, 49, 264-95.

Heng Chen, Yanqin Fan , and Ruixuan Liu (2016). Inference for the correlation coefficient between the potential outcomes in Gaussian Switching Regime Models. Journal of Econometrics 195, 255-270.

Frontiers Econ China

Yanqin Fan , Ruixuan Liu, and Dongming Zhu (2016). Inference for optimal split point in conditional quantiles. Frontiers of Economics in China 11, 40-59.

adv metrics

Yanqin Fan and Emmanuel Guerre (2016). Multivariate local polynomial estimators: Uniform boundary properties and asymptotic linear representation. Advances in Econometrics, 36, 489-537.

jMetMethods

Yanqin Fan,  Robert Sherman, and Matthew Shum (2016). Estimation and inference in an ecological inference model. Journal of Econometric Methods, 5, 17-48.

Yanqin Fan and Ruixuan Liu (2016). A direct approach to inference in nonparametric and semiparametric quantile models. Journal of Econometrics, 191, 196-216.  

Matteo Cacciatore, Fabio Ghironi, and Yurim Lee (2016). Financial market integration, exchange rate policy, and the dynamics of business employment in Korea. Journal of the Japanese and International Economies, 42 , 79-99.

Matteo Cacciatore, Romain Duval, Giuseppe Fiori, and Fabio Ghironi (2016). Market reforms in the time of imbalance. Journal of Economic Dynamics and Control, 72, 69-93.  

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Brian Greaney , Joseph P. Kaboski and Eva Van Leemput (2016). Can Self-Help Groups Really Be "Self-Help"?. Review of Economic Studies .

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Elisa Gamberoni, Rachel Heath,  and Emily Nix (2016). Bridging the gender gap: Identifying what is holding self-employed women back in Ghana, Rwanda, Tanzania, and the Republic of Congo. World Bank Economic Review, 30, 501-521.

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Yunjong Eo and Chang-Jin Kim (2016). “Markov-switching models with evolving regime-specific parameters: Are post-war booms or recessions all alike? Review of Economics and Statistics, 98, 940-949.

Marcelo Arbex, Sidnet Caetano, and Dennis O'Dea (2016). The implications of labor market network for business cycles. Economics Letters, 144, 37-40.

GEB

Xu Tan (2016). Information revelation in auctions with common and private values. Games and Economic Behavior, 97, 147-65.  

Journal of Demographic Economics

Oksana Leukhina and Stephen J. Turnovsky (2016). Push, pull, and population size effects in structural development: Long-run tradeoffs. Journal of Demographic Economics, 84, 423-457.

Murat Koyuncu and Stephen J. Turnovsky (2016). The dynamics of growth and income inequality under progressive taxation. Journal of Public Economic Theory 18, 560-588.

AEJ-Macro

Oksana Leukhina and Stephen J. Turnovsky (2016). Population size effects in the structural development of England. American Economic Journal: Macroeconomics 8, 195-229.

Matteo Cacciatore, Giuseppe Fiori, and  Fabio Ghironi (2015). Market deregulation and optimal monetary policy in a monetary union. Journal of International Economics, 99 , 120-137.  

Econometrics Journal

Yanqin Fan, Sergio Pastorello, and Eric Renault (2015). Maximization by parts in extremum estimation. Econometrics Journal, 18, 147-171.

ER

Yanqin Fan and Ruixuan Liu (2015). Symmetrized multivariate k-NN estimators. Econometric Reviews, 34, 827-847.

Matteo Cacciatore, Giuseppe Fiori, and  Fabio Ghironi  (2015). The domestic and international effects of Euro area market reforms. Research in Economics, 69 , 555-581.

Fabio Ghironi, Jaewoo Lee, and Alessandro Rebucci (2015). The valuation channel of external adjustment. Journal of International Money and Finance, 57, 86-114.

International Journal of Central Banking

Matteo Cacciatore, Fabio Ghironi, and Stephen J. Turnovsky (2015). Inflation targeting and economic reforms in New Zealand. International Journal of Central Banking, 11  (Supp. 1), 145-198.

Matteo Cacciatore, Fabio Ghironi, and Viktors Stebunovs (2015). The domestic and international effects of interstate US banking. Journal of International Economics, 95 , 171-187.

Rachel Heath and A. Mushfiq Mobarak (2015). Manufacturing growth and the lives of Bangladeshi women. Journal of Development Economics, 115, 1-15.

Fahad Khalil, Jacques Lawarrée , and Troy Scott (2015). Private monitoring, collusion, and the timing of information. RAND Journal of Economics, 46, 872-890.

Journal of Business and Economic Statistics

Chang-Jin Kim and Jaeho Kim (2015). Bayesian inference in regime-switching ARMA models with absorbing states: The dynamics of the ex-ante real interest rate under structural breaks. Journal of Business and Economic Statistics, 33 , 566-578.

Kaihua Deng and Chang-Jin Kim (2015). Predicting stock returns: The information contents at different horizons. Annals of Financial Economics, 10 , xx-xx.

MacroDyn

Chang-Jin Kim , James Morley, and Jeremy Piger (2015). Introduction to special issue on the empirical analysis of business cycles, financial markets, and inflation: Essays in honor of Charles Nelson. Macroeconomic Dynamics, 19 (4).

Stephen J. Turnovsky (2015). Economic growth and inequality: The role of public investment. Journal of Economic Dynamics and Control, 61, 204-221.  

Yoseph Getachew and Stephen J. Turnovsky (2015). Productive government spending and its consequences for the growth-inequality tradeoff. Research in Economics, 69, 621-640.

Cecilia García-Peñalosa and Stephen J. Turnovsky (2015). Income inequality, mobility, and the accumulation of capital. Macroeconomic Dynamics, 19, 1332-1357.

Jorge Rojas-Vallejos and Stephen J. Turnovsky (2015). The consequences of tariff reduction for economic activity and inequality. Open Economies Review, 26, 601-631.

Miguel Aramendia and Quan Wen (2015). Repeated Cournot Model with justifiable punishments. Economics Letters, 136, 171-174.

Yu-chin Chen , Stephen J. Turnovsky , and Eric Zivot . (2014). Forecasting inflation using commodity price aggregates. Journal of Econometrics, 183, 117-134.

Theo S. Eicher, Alex Lenkoski and Adrian Raftery (2014).Bayesian model averaging and endogeneity under model uncertainty: An application to development determinants. Econometric Reviews ,  33, 122-151.

Econometrica

Yanqin Fan, Robert Sherman, and Matthew Shum (2014). Identifying treatment effects under data combination. Econometrica, 82, 811-822.

Annual Review of Economics

Yanqin Fan and Andrew Patton (2014). Copulas in econometrics. Annual Review of Economics, 6, 179-200.

Heng Chen, Yanqin Fan, and Jisong Wu (2014). A flexible parametric approach to estimating switching regime models and treatment effect parameters. Journal of Econometrics, 181, 77-91.  

Yanqin Fan and Sangsoo Park (2014). Nonparametric inference for counterfactual means: Bias correction, confidence sets, and weak instruments. Journal of Econometrics, 178 , 45-56.  

Journal of Monetary Economics

Florin O. Bilbiie, Ippei Fujiwara, and Fabio Ghironi (2014). Optimal monetary policy with endogenous entry and product variety. Journal of Monetary Economics, 64, 1-20.

Rachel Heath (2014). Women’s access to labor market opportunities, control of household resources, and domestic violence. World Development, 57, 32-46.

JMCB

Chang-Jin Kim, Pym Manopimoke, and Charles R. Nelson (2014). Trend inflation and the nature of structural breaks in the new Keynesian Phillips Curve. Journal of Money, Credit, and Banking, 46, 253-266.

Review of Economic Design

Qiang Gong, Xu Tan , and Yiqing Xing (2014). Ordering sellers in sequential auctions. Review of Economic Design, 18, 11-35.

Journal of Population Economics

Jochen Mierau and Stephen J. Turnovsky (2014). Capital accumulation and the sources of demographic change. Journal of Population Economics, 27, 857-894.

Economic Theory

Jochen Mierau and Stephen J. Turnovsky (2014). Demography, growth, and inequality. Economic Theory, 55, 29-68

Miguel Aramendia and Quan Wen (2014). Justifiable punishments in repeated games. Games and Economic Behavior, 88, 16-28.  

Harold Houba and Quan Wen (2014). Backward induction and unacceptable offers. Journal of Mathematical Economics, 54, 151-156.

Peter Fuleky and Eroc Zivot (2014). Indirect inference based on the score. Econometrics Journal, 17, 383-393.

Natural Resource Modeling

Nina Sidneva and Eric Zivot (2014). Evaluating the impact of environmental policy on the trend behavior of U.S. emissions of nitrogen oxides and volatile organic compounds. Natural Resource Modeling, 27, 311-337.

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Peer-reviewed

Research Article

The impact mechanism and empirical analysis of financial efficiency of science and technology empowering regional real economy growth

Roles Conceptualization, Writing – original draft

Affiliation School of Economics, Hebei GEO University, Shijiazhuang, China

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Roles Conceptualization, Formal analysis, Writing – review & editing

* E-mail: [email protected]

  • Tao Zhang, 

PLOS

  • Published: September 13, 2024
  • https://doi.org/10.1371/journal.pone.0307497
  • Reader Comments

Table 1

With the aim of exploring the impact mechanism of scientific and technological financial efficiency on regional real economy growth in the context of ecological civilization construction, this study introduces environmental regulation as a mediating factor. By analyzing changes in science and financial efficiency of science and technology, we provide an effective basis for regional real economy development. To achieve this goal, we define concepts such as science and financial efficiency of science and technology and regional real economy, measure data from 2012 to 2021, analyze the impact of science and financial efficiency of science and technology on economic growth using intermediary models, test mediation effects with bootstrap methods, and identify significant differences between regions. It indicates that enhancing science and financial efficiency of sci-tech benefits China’s regional real economy growth, but there’s unbalanced development across regions. Additionally, environmental regulation serves as a crucial intermediary in the relationship between sci-tech finance and economic growth. There exist regional disparities in the mediation effects of environmental regulation, with eastern regions demonstrating stronger effects compared to central and western regions.

Citation: Zhang T, Tian J (2024) The impact mechanism and empirical analysis of financial efficiency of science and technology empowering regional real economy growth. PLoS ONE 19(9): e0307497. https://doi.org/10.1371/journal.pone.0307497

Editor: Yuantao Xie, University of International Business & Economics, CHINA

Received: November 30, 2023; Accepted: July 6, 2024; Published: September 13, 2024

Copyright: © 2024 Zhang, Tian. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In terms of the new normal of China’s economic development, it is imperative to comprehensively grasp the scientific connotation of high-quality development in order to facilitate the transformation towards enhanced quality, efficiency, and power. This can be achieved through robust power reforms that drive changes in economic development quality and efficiency, as well as by fostering scientific and technological innovation to enhance output quality and efficiency. Scientific and technological innovation takes precedence in promoting economic development and facilitating high-quality industrial growth. It serves as the fundamental driving force behind achieving high-quality development while simultaneously bolstering overall innovative capabilities. The significance of scientific and technological innovation has garnered increased attention with more profound discussions taking place on this subject matter; it has become a topic of great interest for society at large. The latest Chinese government work report explicitly emphasizes the need to strengthen support for scientific and technological innovation—an aspect particularly crucial within China’s context where such innovation plays an irreplaceable role in advancing industrial economy, national economy, and regional real economy.

In the context of scientific and technological innovation, the deep integration of science, technology, and finance has facilitated the emergence of "scientific and technological finance". Scientific and technological finance serves as a fundamental component within both national innovation systems and financial systems. The government’s financial sector takes the lead in guiding numerous financial institutions and intermediary service agencies to continuously innovate financial products, establish corresponding service platforms, optimize service models, thereby promoting the development of the real economy. The efficiency of technological financing operations has always been an urgent issue that needs to be addressed–specifically how to maximize output from investments in scientific and technological achievements. This disparity between regions can impact the efficiency of science and technology finance output while diminishing its substantive promotion effect on regional real economies. Taking Shanghai as an example, the city has established 17 scientific and technological financial service stations, serving enterprises more than 2,000 times a year, effectively promoting the docking between the government, financial institutions, and scientific and technological innovation enterprises.

Consequently, it becomes evident that enabling regional entity growth through efficient sci-tech finance is crucial. The higher the input-output efficiency of sci-tech finance, the more significant its positive impact on regional economic growth. This study aims to investigate this positive relationship by examining existing mechanisms influencing it as well as exploring how science and technology finance empowers regional economic growth. Additionally, we introduce environmental regulation’s role in enabling such growth while laying a solid foundation for future developments within regional economies.

Literature review

The research on technology finance can be traced back to the mid to late 20th century. Schumpeter clarified the advantages of the integration of finance and technology in his book “Economic Development Theory” and explained the supporting role of credit capital for technological innovation. Ang believes that technological innovation cannot do without the liberalization of financial development [ 1 ]. Peter also pointed out in his book “Technology Revolution and Financial Capital” that the new paradigm formed by the integration of technology and finance is more conducive to promoting the development of regional economy [ 2 ]. Guarnieri points out from the enterprise level that the integration between public financial investment and technological innovation promotes the development of technological innovation [ 3 ]. Similarly, Sasidharan also believes that the development of technology finance has played an important economic support role in enterprise product innovation [ 4 ]. Zetsche believes that the advantages of technology finance lie in improving business quality, improving risk management level, reducing transaction costs, and having good financial inclusivity, which can provide more credit support for the development of small and medium-sized enterprises and consumers in the regional real economy [ 5 ]. Guariglia’s research found that high-tech industries dominated by technological innovation are more susceptible to financial influences in the early stages of development, indicating that technology finance has a significant impact on local high-tech industries to a large extent [ 6 ]. Lenong’s discussion on the economic value and impact of technology finance indicates that technology finance is based on the integration of technology, innovation, and capital [ 7 ]. The vast majority will view “technology finance” as a result of the connection between technology and finance, which can have a positive impact on regional economy, industrial development, and personal credit. As Seoh believes, technology finance can promote national economic development on the one hand, and effectively improve the level of technological innovation on the other hand, thereby enhancing the competitiveness of national technology research and development [ 8 ]. At present, the academic community has conducted a detailed and in-depth analysis of technology finance, but has not provided a clear definition of it. Moreover, the mechanism of technology finance also needs further optimization.

Regarding the efficiency of technology finance, it generally refers to the efficiency of resource allocation in technology finance, especially under the role of innovation driven development strategies, the development scale of technology finance is increasingly expanding, and the efficiency of resource allocation in technology finance is more crucial due to limited financial resources [ 9 ]. The measurement and analysis of the efficiency of technology finance generally adopts frontier analysis, such as stochastic frontier production function, data envelopment analysis method, Malmquist index method and its improved model. After Charnes and Cooper proposed the DEA method, Banker made improvements to the DEA method and proposed the BBC model method [ 10 ]. Kundi used DEA models with constant and variable returns to scale to measure the efficiency of financial support. Huang used the DEA Malmquist index method to calculate the efficiency of technology finance, in order to analyze the differences in financial efficiency of science and technology between different regions [ 11 ]. Similarly, Yi reached similar conclusions using the DEA-BCC model and Gini coefficient analysis method [ 12 ].

By measuring the efficiency of technology finance, we have further analyzed the relationship between technology finance and regional real economic growth. As Rjoja and Valev pointed out in their research, the development of the financial industry has a strong stimulating effect on the real economy, and there are significant regional differences [ 13 ]. Adusei validated the relationship between financial development and economic growth in his research on economic and financial development in South Africa, indicating that the financial industry has a certain one-way promoting effect on economic development [ 14 ]. Peng analyzed the impact of environmental regulations on the efficiency of technology finance from empirical evidence from provincial-level administrative regions in China, and constructed a model to measure the efficiency of technology finance. Based on the CCR model, the BCC model was improved to achieve the measurement of financial efficiency of science and technology [ 15 ]. Based on the measurement of the efficiency of technology finance, the impact mechanism of technology finance on regional real economic growth can be effectively analyzed. For example, Adusei analyzed the economic and financial development of South Africa, and based on the Granger causality test method, verified the impact relationship between financial development and economic growth. The results showed that the development of the financial industry can promote regional economic growth [ 14 ]. Peng pointed out that the impact mechanism between technology finance and real economic growth is largely influenced by environmental regulations, which is closely related to the impact of environmental regulations on the efficiency of technology finance [ 16 ]. Wasi uses DEA method to measure energy efficiency, which can also be applied to measure the financial efficiency of science and technology [ 17 – 19 ]. In addition, the inline study used data envelopment analysis (DEA-SBM) to measure the energy efficiency of Chinese provinces from 2004 to 2017. Mann-Whitney U test was used to explore whether there are significant differences in energy efficiency levels between China’s energy security policy and energy conservation and emission reduction policy. Meta-frontier analysis was used to further investigate the regional heterogeneity of production technology gap in east, middle and west China, in which DEA-SBM was used to measure efficiency [ 20 – 25 ]. In general, there are abundant researches on the evaluation of financial efficiency of science and technology. However, the current researches still face some challenges and limitations. First of all, although there are various models and methods used to measure the financial efficiency of science and technology, each method has its specific applicable conditions and limitations, so it needs to be selected and adjusted according to the specific situation in practical application. The evaluation of financial efficiency of science and technology is a complex and important research field, which needs constant exploration and innovation. Future research should focus on the improvement of influencing factors, mechanism and evaluation methods of S&T finance efficiency, so as to provide strong support for promoting the healthy development of S&T finance in our country.

To sum up, the existing research has made some progress in exploring the mechanism of technological finance’s influence on regional economic growth, but there are still some deficiencies and gaps. First of all, although the measurement model of technical finance efficiency has been established, most studies are still focused on the macro level, and there are few discussions on the micro level of technical finance efficiency. Secondly, the existing research on the relationship between technical finance and regional economic growth often ignores the differences between regions. Although studies have mentioned the impact of environmental regulations on the relationship between technological finance and real economic growth, research in this area is still weak. This study presents a comprehensive overview of the implications of technology finance and enhances its theoretical framework from a deeper perspective, aiming to further enhance and optimize China’s technology finance service system, thereby contributing to the development of the regional real economy. The interconnection between the technology finance system and the regional real economy is highly significant. This study is based on empirical evidence from various regions in China, utilizing statistical data analysis to conduct a thorough examination of how technology finance influences the development of the regional real economy. It explores the impact of technological advancements and financial efficiency on real economic growth within an environmental regulatory context. Moreover, it proposes corresponding solutions and suggestions for addressing challenges encountered during this developmental process, with an ultimate goal to elevate the level of regional real economic development while providing valuable insights for research conducted in other provinces and cities.

Empirical analysis

Measurement of variables, efficiency of technology finance..

The financial efficiency of science and technology can be measured using the DEA model, which constructs a production frontier to evaluate the relative distance between each DMU and the frontier, thereby obtaining an efficiency value. The DEA model demonstrates robustness and flexibility, making it adaptable to different scales and types of datasets. Given the challenges in data collection and processing within the field of science and technology finance, selecting a DEA model that is more adaptable with relatively low data requirements is crucial for accurately evaluating financial efficiency of science and technology. To begin with, constructing an efficient index system for science and technology finance is necessary. This index system focuses on the input and output aspects of science and technology finance. Specifically, investments in science and technology finance encompass bank credit investment in science and technology, VC/PE capital investment, investment in science and technology capital markets, government financial investments in science and technology, as well as R&D personnel investments. As for outputs related to science and technology finance, they include scientific research papers published, valid patent numbers obtained, as well as industrial added value generated by science-technology enterprises.

How to choose the above indicators is because as an important means to promote the development of science and technology and economic growth, the measurement of its efficiency is very important. Through the application of DEA model, the efficiency of science and technology finance can be quantitatively analyzed, so as to better understand its operating mechanism and existing problems, further reveal the inherent laws and potential problems of science and financial efficiency of science and technology, and provide a scientific basis for optimizing the allocation of science and technology financial resources and improving the efficiency of science and technology finance. As shown in Table 1 , it is the efficiency index system of science and technology finance.

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https://doi.org/10.1371/journal.pone.0307497.t001

As illustrated in Table 1 above, venture capital and private equity fund investments are pivotal concepts within the contemporary financial market, playing a crucial role in fostering scientific and technological innovation, facilitating the growth of small and medium-sized enterprises, and driving economic expansion [ 26 ]. Venture capital represents a specialized form of capital operation that focuses on investing in nascent or expanding stage enterprises with the aim of attaining substantial returns. Typically targeting innovative small to medium-sized enterprises exhibiting high growth potential, venture capital investments often involve significant technical barriers and possess considerable market prospects [ 27 ]. Private equity encompasses an unpublicized approach for raising funds from a limited number of investors to invest in non-publicly listed companies through equity investment, aiming to capitalize on enterprise growth-induced appreciation.

There is a scarcity of data on technology credit in the relevant literature pertaining to indicators for technology finance investment. The financing of Chinese technology-based enterprises predominantly relies on indirect funding. Therefore, the year-end debt of innovative technology industries in each province is selected as the primary measure for assessing technology credit. The quantification methods and data sources for other indicators are presented in Table 2 .

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https://doi.org/10.1371/journal.pone.0307497.t002

To address the efficiency issue of technology finance, it is imperative to establish a robust evaluation index system and methodologies for assessing the financial efficiency of science and technology. Therefore, this study has opted to employ the Data Envelopment Analysis (DEA) method for implementation. DEA is a mathematical programming approach that enables an assessment of input-output indicators to evaluate the performance of similar departments, units, or types [ 28 ]. This method can further evaluate multiple input and output decision units of the same department, unit, and type [ 29 ]. Simultaneously, the DEA method is a non-parametric statistical approach that treats each decision-making unit as an evaluated entity. It identifies relevant issues by evaluating other decision-making units and constructs appropriate data models to analyze relative efficiency, determine potential input-output combinations within the production frontier, measure the distance between each decision-making unit and the production frontier, assess the effectiveness of DEA for each unit, and ultimately derive an evaluation ranking. Generally speaking, within the model-defined production possibility set, it is required to either maintain inputs while increasing outputs or maintain outputs while reducing inputs. Previous research has extensively employed DEA models in various industries primarily for assessing relative effectiveness of multiple inputs and outputs in social and economic domains; particularly suitable for analyzing benefits and efficiency in cultural industries and government sectors.

This study considers different provinces in the technology and finance industry as different types of decision-making units. For each decision-making unit, there are m types of inputs and p types of outputs. Therefore, the input vector can be represented by Formula ( 1 ), and the output vector can be represented by Formula ( 2 ).

article review of economic

https://doi.org/10.1371/journal.pone.0307497.t003

Measurement of regional real economy indicators.

Currently, there is no universally accepted definition of the real economy; however, numerous studies have been conducted in this area. This study proposes that the real economy encompasses economic activities facilitated through social capital. Specifically, it includes direct economic activities associated with material and financial production as well as cultural service consumption. Considering the volatility of China’s real estate market, the regional real economy discussed in this study refers to the residual portion obtained after subtracting the added value contributed by both the real estate industry and financial sector. The growth of regional real economy is defined as actual GDP minus the added value from both real estate and financial industries, with this remaining portion serving as a dependent variable. Regional indicators for real economic growth are calculated based on actual regional GDP and nominal GDP.

Measurement of environmental regulation.

The efficiency of technology finance in relation to capital flows may be influenced by environmental regulations, which can vary depending on market volatility and subsequently impact regional real economic growth. Therefore, this study considers environmental regulation as a key explanatory variable, specifically focusing on the intensity of such regulation. The intensity is measured by the ratio of regional pollution control investment to regional GDP.

Analysis of the impact of technology and financial efficiency on regional real economic growth

Construction of mediation effect model..

Hierarchical regression analysis is a widely employed approach for examining mediating effects. By assessing the significance of regression coefficients from independent variables to dependent variables, independent variables to intermediate variables, and intermediate variables to dependent variables, progression to the next step is contingent upon each test being statistically significant. The presence of statistical significance across all steps indicates a significant mediating effect. In cases where non-significant findings arise during testing, subsequent evaluation using Bootstrap or Sobel tests is conducted to further ascertain the significance of the mediating effect [ 30 ]. The specific method steps are shown in Fig 1 .

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https://doi.org/10.1371/journal.pone.0307497.g001

In the above figure, the first step, verify a × b significant (Bootstrap method), if a × b is significant, and the intermediate test result does not include 0 at the 95% confidence interval. If the intermediate path exists, perform the first step of the test. Conversely. If a × b is not significant, then the intermediate test result contains 0 at the 95% confidence interval, and the intermediate path does not exist, skip to the third step;

The second step is to test whether c’ is significant. If c’ is not significant, it is a complete mediating effect. If c’ is significant, it is a partial mediating effect;

Step 3, if a × b is not significant, then the mediation is not established. Continue to test whether c’ is significant. If c’ is significant, then there is only a direct effect. If c’ is not significant, then it has no effect.

In this study, the direct and intermediate effects were tested using the hierarchical regression method, and the regression equation constructed is shown in Formulas ( 3 )–( 5 ).

article review of economic

Among them, coefficient c refers to the total effect of the independent variable on the dependent variable, that is, the total effect of the efficiency of technology and finance on the growth of regional real economy. The coefficient a refers to the effect of the independent variable on the environmental regulation of the intermediary variable; The coefficient b refers to the effect of the mediating variable environmental regulation on the regional real economic growth of the dependent variable after controlling for the influence of the independent variable; The coefficient c’ refers to the direct effect of technology and finance efficiency on the growth of regional real economy after controlling for the influence of intermediary variables. e1—e 3 refers to the regression residual. If there is a mediating effect in the model, then the mediating effect is a × b, so the total effect is c = c’ + a × b.

In the mediation effect test, considering the impact of factors such as urbanization level (Urban), degree of openness to the outside world (Open), and fiscal expenditure (Fis) on environmental regulation intensity and regional real economic growth in the survey sample, the above variables are used as control variables. Among them, Urban refers to the proportion of urban population in total labor force; Open refers to the proportion of the total import and export volume of each region to GDP; Fis refers to the proportion of the total fiscal expenditure of each province to GDP. The data source is the “China Statistical Yearbook” and “Provincial Statistical Yearbook” from 2012 to 2021, and relevant adjustments are made based on local price levels.

Robustness test.

As for the robustness of this model analysis, the Tobit model is selected for testing in this study. Its principle and hypothesis are shown in the following Eqs ( 6 ) and ( 7 ).

article review of economic

Multiple regression model was used to replace the Tobit model and the results were shown in Table 4 .

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https://doi.org/10.1371/journal.pone.0307497.t004

As can be seen from Table 4 , after replacing the model, the contribution degree and influence direction of the above five independent variables to the dependent variables did not change significantly. Among the control variables, the influence of Fis is very significant. Even after replacing the model, the influence of the core explanatory variables on the explained variables in this study did not change significantly, and only the correlation coefficient changed. Therefore, the model estimation adopted in this study was robust.

Analysis of empirical results.

Based on the financial efficiency of science and technology index TE, environmental regulation intensity ERI, and real economy development index Y measured in the previous text, the mediation effect model is used to analyze the mediation effect between the three variables. This study analyzed the mediating effect of optimizing environmental regulations on the efficiency of technology finance in 30 provinces of China on regional real economic growth, and tested the experimental results. Considering the potential regional heterogeneity in technology finance, environmental regulation, and real economy development, this study divides 30 provinces into three regions: Eastern, central, and western. Conduct an empirical analysis on the mediating effects of technology and finance efficiency, environmental regulation intensity, and regional actual economic growth in three regions.

Firstly, conduct a regression analysis on the efficiency of technology finance and regional real economic growth, and obtain a regression coefficient c to test the significance between the two; Secondly, regression analysis is conducted on the impact of technology and finance efficiency on the regional real economic growth as an intermediary variable, and a regression coefficient a is obtained to test the significance between the two; Finally, a hierarchical regression analysis is conducted on the effects of technological and financial efficiency and environmental regulation as mediators on regional real economic growth, from which the regression coefficients are obtained b and c’ . To test the significance between the three variables. The results are shown in Tables 5 – 7 .

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https://doi.org/10.1371/journal.pone.0307497.t005

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https://doi.org/10.1371/journal.pone.0307497.t006

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https://doi.org/10.1371/journal.pone.0307497.t007

From the data in the eastern part of Table 5 , it can be seen that the efficiency of technology finance has a significant positive predictive effect on regional real economic growth (c = 0.325); The efficiency of technology finance also has a significant positive predictive effect on environmental regulation (a = 0.168); The mediating variable environmental regulation also has a significant positive predictive effect on regional real economic growth (b = 0.222); When environmental regulations are included as mediating variables in the model, the positive predictive effect of financial efficiency of science and technology on regional real economic growth becomes less significant (c’ = 0.213).

Similarly, in the central data of Table 6 , it can be seen that the efficiency of technology finance has a significant positive predictive effect on regional real economic growth (c = 0.313); The efficiency of technology finance also has a significant positive predictive effect on environmental regulation (a = 0.172); The mediating variable environmental regulation also has a significant positive predictive effect on regional real economic growth (b = 0.322); When environmental regulations are included as mediating variables in the model, the positive predictive effect of financial efficiency of science and technology on regional real economic growth becomes less significant (c’ = 0.257).

From the western data in Table 7 , it can be seen that the efficiency of technology finance has a significant positive predictive effect on regional real economic growth (c = 0.209); The efficiency of technology finance also has a significant positive predictive effect on environmental regulation (a = 0.211); The mediating variable environmental regulation also has a significant positive predictive effect on regional real economic growth (b = 0.222); When environmental regulations are included as mediating variables in the model, the positive predictive effect of financial efficiency of science and technology on regional real economic growth becomes less significant (c’ = 0.213).

On the previous text, c represents the regression coefficient between X and Y (when there is no intermediate variable M in the model), which is the total effect; A represents the regression coefficient between X and M, b represents the regression coefficient between M and Y, and a × b is the product of a and b, which is the mediating effect; 95% Boot CI represents the 95% confidence interval obtained from Bootstrap sampling calculation. If the interval does not include 0, it indicates significance; C’ represents the regression coefficient between X and Y (when there is an intermediate variable M in the model), which is the direct effect; If a and b are significant, and c ‘is not significant, then it is a complete mediator; If a and b are significant, and c ‘is significant, and a × b is the same sign as c’, then it is a partial mediating effect;

If at least one of a and b is not significant, and the 95% Boot CI of a × b includes the number 0 (not significant), then the mediating effect is not significant. If at least one of a and b is not significant, and the 95% Boot CI of a × b does not include the number 0 (significant), and c ‘is not significant, then it is a complete mediator; If at least one of a and b is not significant, and the 95% Boot CI of a × b does not include the number 0 (significant), and c’ is significant, and a * b is signed with c’, then it is a partial mediating effect.

Based on this, the mediating effect of this study was tested, and the results are shown in Table 8 .

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https://doi.org/10.1371/journal.pone.0307497.t008

From the above table, it can be seen that the efficiency of technology and finance in the eastern, central, and western regions has a significant and documented positive impact on regional real economic growth. The efficiency of technology and finance has a significant positive impact on environmental regulation, and environmental regulation also has a significant positive impact on regional real economic growth. This indicates that the mediating effect of technology finance on regional real economy growth through environmental regulation in various regions is significant, and the mediating effect in the eastern region is greater than that in the western region. There are regional differences in the mediating effect of technology finance on regional real economy growth through environmental regulation.

Similarly, the mediating effect of this study was tested, and the results are shown in Table 9 .

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https://doi.org/10.1371/journal.pone.0307497.t009

In summary, it is not difficult to see that environmental regulations play a partial intermediary role between the efficiency of technology finance and the growth of regional real economy. That is to say, the efficiency of technology finance has a significant positive effect on environmental regulation, and environmental regulation has a significant positive effect on regional real economic growth. Therefore, environmental regulation has a mediating effect between financial efficiency of science and technology and regional real economic growth. However, the efficiency of technology finance also plays a significant positive role in the growth of regional real economy, which makes the mediating role played by environmental regulations not entirely mediating, but partially mediating. The relationship between the efficiency of technology finance and the growth of regional real economy is not only influenced by environmental regulations, but also by many other factors. For example, it is influenced by factors such as Uran, Open, Fi, and industrial structure [ 18 ]. This to some extent weakens the chain effect of financial efficiency of science and technology and environmental regulation on regional real economic growth, resulting in a direct relationship between financial efficiency of science and technology and regional real economic growth, with environmental regulation playing a partial intermediary role between the two.

Based on the current situation of science and technology finance, environmental regulation, and regional real economy development in China, this paper analyzes the theoretical relationship among these three factors. It then utilizes data from 2012 to 2021 based on 2011 to construct an index system for each factor, measure their respective indices, and build an intermediary effect model. This study examines the mediating effect of environmental regulation intensity on the influence of science and technology financial efficiency on real economic growth. Additionally, it investigates the mediating effect of industrial structure optimization on the impact of science and technology financial efficiency on real economic growth in three regions. The research findings are as follows: (1) The enhancement of science and technology financial efficiency has a significant positive impact on China’s regional real economy growth. Over the past decade, there has been a general increase in science and technology financial efficiency across China’s 30 provinces and autonomous regions; however, there exists an imbalance in its development between central/western regions compared to other areas. (2) Science and technology financial efficiency significantly affects environmental regulation intensity, with environmental regulation playing a crucial intermediary role in linking science and financial efficiency of science and technology to real economic growth. (3) There are regional disparities regarding the intermediary effect of science and technology finance on real economic growth through increased environmental regulation intensity; specifically, this effect is more pronounced in eastern regions compared to central or western ones. Therefore, enhancing both the development and efficacy of sci-tech finance is essential for promoting real economic growth.

In order to further promote science and technology finance to empower regional real economic growth, policy recommendations that can be adopted:

  • We will strengthen financial support for local governments. Chinese government departments should be able to formulate highly targeted legal provisions to effectively implement fiscal and tax policies into specific green spending practices such as energy conservation, pollution reduction, and carbon reduction.
  • Increase the intensity of fiscal investment in science and technology, and establish a mechanism for the growth of fiscal green spending on science and technology. Strengthen the role of government green guidance funds, vigorously develop government guidance funds, and increase the amount of venture capital for regional real economy enterprises.
  • Optimize the government-guided venture capital mechanism. Through the establishment of special channels, improve the level of internal management, and increase the proportion of total government investment.
  • Vigorously support and build a science and technology financial service platform, and formulate a coordinated development strategy for the coupling of environmental regulation and science and technology finance according to different local economic development levels and ecological civilization construction requirements.
  • The central and western regions should give priority to the development of science and technology finance, and strengthen the research and development institutions within leading financial institutions and large industrial enterprises.
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  • Published: 11 September 2024

The synergetic effect of economic complexity and governance on quality of life: policy thresholds

  • Eslam A. Hassanein   ORCID: orcid.org/0000-0002-5690-3335 1 , 2 ,
  • Nagwa Samak 2 , 3 &
  • Salwa Abdelaziz 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1185 ( 2024 ) Cite this article

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  • Development studies

This study aims to bridge the empirical research gap in governance-modulating effects on the link between a country’s productive structure and individuals’ well-being. In doing so, this study utilizes the economic complexity index to quantify a country’s productive structure and the social progress index to measure quality of life. The empirical strategy relies on the system-GMM approach, covering 75 developing countries from 2011 to 2021. The following conclusions were drawn from the empirical analysis. (1) Economic complexity and governance consistently and unconditionally improve quality of life. (2) Governance substantially modulates economic complexity to enhance quality of life, generating an overall positive net effect. (3) The results remain robust and consistent across several GMM specifications, regardless of whether the six governance indicators compiled by the World Bank were clustered using principal component analysis into four categories (i.e., general, political, economic, and institutional) or used individually. (4) Of the six governance indicators, government effectiveness, the rule of law, and control of corruption were found to be particularly significant, as were economic and institutional governance. (5) An additional threshold analysis was implemented to identify the critical governance levels that further improve quality of life. The thresholds for complementary policies are then established as follows: 0.8435, 1.846, and 1.717 for government effectiveness, rule of law, and corruption control, respectively, and 5.59, 3.14, and 3.32 for general, institutional, and economic governance, respectively. Consequently, economic complexity and governance are necessary and sufficient to improve well-being below these thresholds. Complementary policies are, however, necessary to sustain the overall positive impact beyond these thresholds. The findings of this study provide insights into complementary policies for leveraging economic development to improve the well-being of developing countries.

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Introduction.

Gross domestic product (GDP) has traditionally been the standard measure of economic development (Giannetti et al., 2015 ). However, GDP has been heavily criticized for its limited focus on the quantitative dimensions of development while neglecting qualitative dimensions (Nguea and Noumba, 2024 ). Economic development extends beyond mere GDP growth and includes non-tradeable capabilities such as technological advancements, human capital, and institutional developments (Hidalgo and Hausmann, 2009 ), which are crucial for determining a nation’s productivity level. Likewise, the conventional method of evaluating production structure based on the contributions of agriculture, industry, and services to GDP does not adequately reflect the sophistication of a country’s production capabilities (Hartmann et al., 2017 ) and, by extension, its overall development level.

The limitations of these traditional measures have prompted calls for more refined measures of economic development (Hartmann et al., 2017 ), culminating in the introduction of the economic complexity index (ECI) by Hidalgo and Hausmann ( 2009 ). ECI Footnote 1 effectively captures the productive capabilities embedded within a nation’s economic structure. It is rooted in the idea that development involves transforming a country’s economic structure toward producing and exporting more complex products (Felipe et al., 2012 ).

Since its inception, the implications of ECI have been extensively researched. Several studies have examined its influence on various developmental dimensions, including income (Tabash et al., 2022 ; Zhu and Li, 2017 ), sustainable development (Ferraz et al., 2021 ), inequality (Gómez‐Zaldívar et al., 2022 ; Sepehrdoust et al., 2021 ), health outcomes (Vu, 2020 ), human capital (Hartmann et al., 2017 ; Lapatinas, 2016 ; Lee and Vu, 2019 ), and more recently, social welfare and quality of life (QoL) (Nguea and Noumba, 2024 ). A burgeoning body of research has demonstrated that economic complexity affects multiple facets of human capabilities (Constantine and Khemraj, 2019 ; Hartmann et al., 2017 ), implicitly assuming that greater complexity leads to enhanced well-being.

The literature has identified several channels through which economic complexity can influence well-being. In addition to its well-documented favorable effects on income (Tabash et al., 2022 ; Hoeriyah et al., 2022 ), which subsequently enhances public infrastructure and welfare expenditures (Nguea and Noumba, 2024 ), a nation with a high level of useful knowledge is more likely to produce high-tech and diversified goods. This, in turn, fosters the development of new skills and the accumulation of human capital, which are essential in determining wages and living standards (Ferrarini and Scaramozzino, 2016 ).

Empirical evidence also suggests that complex economic structures are associated with a more equitable income distribution (Ghosh et al., 2023 ; Sepehrdoust et al., 2021 ), higher employment rates, and increased opportunities for various occupations (Arif, 2021 ). Another mechanism through which ECI influences well-being is its role in attracting foreign direct investment (FDI) (Nguyen et al., 2023a ). Advanced workforce and robust infrastructure in high-ECI nations make them appealing to investors seeking opportunities in sophisticated industries. FDI inflows result in capital, technology, and expertise transfers, leading to job creation, infrastructure improvements, and improvements in overall well-being (Osinubi and Ajide, 2022 ). Moreover, ECI reduces vulnerability to external shocks, thus reducing the negative effects of economic crises on well-being (Güneri and Yalta, 2020 ). However, the question persists: Do more complex countries inevitably have better well-being?

An opposing perspective contends that economic complexity can detrimentally affect well-being by exacerbating income inequality through disparities in access to resources and opportunities (Chu and Huang, 2020 ), rendering some capabilities obsolete while elevating the importance of others (i.e., the creative destruction process), and harming the environment through increased economic activities and energy consumption (Aydin et al., 2024 ; Khezri et al., 2022 ).

The divergence in literature findings regarding the ECI-QoL link can be attributed to their reliance on single indicators, such as health outcomes, education, and income, to express well-being. Therefore, a comprehensive approach is required to better understand the relationship between ECI and QoL. Furthermore, existing studies often focus on the linear link between the two variables, overlooking the nuanced and potentially dependent nature of the ECI-QoL link with other macroeconomic factors (Lapatinas, 2016 ).

To the best of our knowledge, only a few studies have explored the nonlinear association between ECI and QoL. For example, Nguea and Noumba ( 2024 ) investigate the impact of economic complexity on social welfare across 27 African countries, uncovering the positive effect of increasing complexity on social welfare. They also validated the moderating effects of GDP per capita and globalization on this relationship. Similarly, Le Caous and Huarng ( 2020 ) found that the positive relationship between human development and economic complexity is partially mediated by income inequality in 88 developing countries. No prior study, however, has considered the role of governance (GOV) in influencing the ECI-QoL link.

Theoretical perspectives posit that GOV can modulate the effects of ECI on QoL via multiple mechanisms. Well-functioning institutions typically enhance economic complexity, which ultimately has implications for well-being (Vu, 2022 ; Yalta and Yalta, 2021 ). Specifically, effective institutions facilitate investment in innovative activities (e.g., R&D, technological advancements, and entrepreneurship) by enforcing contracts, securing property rights, and so forth. This innovation-supporting environment stimulates productive activities and increases economic complexity (Hoang and Chu, 2023 ), thereby amplifying its beneficial impact on well-being, at least by improving income (Lapatinas, 2016 ).

Another noteworthy mechanism through which GOV moderates the ECI-QoL link is the human capital channel. Good governance promotes efficient resource allocation (Olaniyi and Adedokun, 2022 ) and human capital development (Vu, 2022 ; Hoang and Chu, 2023 ), which largely determine the sophistication of the economy, and consequently, higher wages and overall well-being (Nguea and Noumba, 2024 ). Likewise, higher levels of economic complexity are linked to improved institutional performance (Hartmann, 2014 ). According to Djeunankan et al. ( 2023 ), higher levels of economic complexity improve governance performance in African countries by increasing human capital, FDI, and income inequality. These enhanced governance structures are indispensable for achieving better living standards (Asongu and Nwachukwu, 2016 ).

Despite the compelling rationale for the role of institutions in the ECI-QoL link, there is a notable absence of research examining how governance influences the contribution of economic complexity to well-being. Consequently, this study aims to fill this literature gap by investigating the moderating impact of governance on the ECI-QoL link in a large sample of developing countries, which is particularly relevant for evaluating the tripartite relationship between ECI, GOV, and QoL.

Developing countries often encounter challenges in diversifying their economies because of their restricted capabilities (Harding and Javorcik, 2011 ), compelling them to specialize in simple raw products with minimal knowledge requirements (Hausmann and Hidalgo, 2011 ). Dependence on raw commodity exports and production renders these countries susceptible to global shocks and price fluctuations (Abbas et al., 2023 ; Sepehrdoust et al., 2019 ), leading to unstable and sluggish economic growth (UN, 2023 ). As a result, developing countries occupy the lowest ranks in the ECI as per the OEC data. Among the 75 developing nations analyzed in the current study, only a few attained positive ECI scores (see Fig. 1 ), with the overall sample average standing at only −0.411. Low-scoring ECI countries are typically poor (Olaniyi and Odhiambo, 2023 ), as relying on exports of a limited range of commodities often leads to limited access to essential public services and, thereby, a low level of well-being. For instance, in 2020, 20 nations with limited access to electricity heavily relied on a single commodity for 90% of their exports. Moreover, in 2021, 29 of the 32 developing countries classified as having low human development in the UNDP’s human development index (HDI) were reliant on a few commodities, with a specific commodity accounting for 82% of their exports (UN, 2023 ).

figure 1

Economic complexity in selected developing countries.

Specializing in basic economic activities has also led to the rise of authoritarian institutions in these countries, negatively affecting people’s well-being in various ways (Sokoloff and Engerman, 2000 ). As discussed in the development literature, developing countries are destitute because of their weak institutions (Asongu and Nwachukwu, 2016 ). Given this context, it is essential to investigate the interplay among weak institutions, underdeveloped economic complexity, and their impact on well-being in developing countries.

Various empirical studies indicate that economic complexity significantly enhances the well-being of developing countries on several levels, including economic growth (Hoeriyah et al., 2022 ; Wang et al., 2018 ), employment (Adam et al., 2021 ; Arif, 2021 ), income inequality (Ncanywa et al., 2021 ; Sepehrdoust et al., 2019 ), and others. This study contributes to this research strand by examining the nonlinear relationship between economic complexity and well-being, addressing the oversight of neglecting nonlinearity in previous studies, which could have affected their policy implications.

Depending on the Generalized Methods of Moments approach, the following inferences were drawn: (1) There is an unconditional positive effect of ECI and GOV on QoL in developing countries; these findings hold authentic whether we use composite or individual governance indicators; and (2) Generally, a positive net effect from the synergy between ECI and GOV is evident when considering the indirect impact. This effect, however, is contingent on particular governance thresholds for complementary policies. The thresholds for complementary policy were computed accordingly. A fundamental implication of our research is that adjustments in governance settings must be coupled with economic complexity to enhance the quality of life, particularly at early development stages. Other policies, however, should be implemented to complement governance and sustain the overall positive impact. A particular focus should be placed on three key governance dynamics: corruption control, the rule of law, and government effectiveness, due to their remarkable ability to alter the ECI-QoL relationship direction.

This paper is arranged into four sections: section “Introduction” delivers the literature framework, section “Literature review and hypotheses development” describes the data and methods, the section “Data and methods” displays the primary estimation results, and the section “Empirical results and discussion” presents the discussion. Finally, the section “Robustness check” concludes the paper with policy proposals.

Literature review and hypotheses development

Quality of life and economic complexity.

Literature has long debated the relationship between a country’s productive structure and its patterns of economic development and well-being. Contrary to mainstream theories, Structuralists asserted in the late 1950s that industrialization and structural change determine the pace and trajectory of development rather than capital accumulation or income per capita (Justman and Teubal, 1991 ). They ventured even further, arguing that countries cannot improve employment, productivity, and income, thereby enhancing QoL, without industrialization (Gala et al., 2018 ). A recently developed theoretical thread reiterated several decades later that structural change and economic complexity, measured by the ECI, are intertwined with various human capabilities (Hartmann, 2014 ; Hausmann et al., 2014 ). This culminated in an explosion of studies suggesting that QoL is more dependent on a country’s productive structure and level of diversification than on its economic growth (Hartmann et al., 2017 ). Diversification and complexity are implicitly assumed to affect QoL positively (Hartmann and Pyka, 2013 ). Nevertheless, empirical evidence has drawn two opposing perspectives on the impact of ECI on QoL: one advocates its positive effect, whereas the other asserts its detrimental effect.

According to the first approach, a higher ECI entails a series of beneficial attributes that can boost social welfare, such as increasing the income share of workers (Arif, 2021 ), reducing greenhouse gas emissions (ElMassah and Hassanein, 2023 ), enabling the production of medicines and health equipment (Le Caous and Huarng, 2020 ), and providing individuals with better infrastructure and education to live longer and healthier lives (Vu, 2020 ; Ferraz et al., 2018 ). A higher ECI also offers the country a wider choice of ideas, lifestyles, and opportunities, honing its ability to adapt to people’s needs (Hartmann et al., 2017 ) and facilitating individuals’ freedom of choice (Ali and Cantner, 2020 ; Le Caous and Huarng, 2020 ), thereby propelling their QoL.

By examining 36 countries using cluster analysis and integral assessment, Gryshova et al. ( 2020 ) empirically proved that countries with a progressive industrial structure exhibit higher QoL. Ali and Memon ( 2019 ) examined the effect of diversification, as proxied by export diversification, on social welfare in South Asia from 1996 to 2011, revealing that diversification benefits human development. A similar conclusion was drawn by Ali and Cantner ( 2020 ) in the context of European countries, yet the diversification impact was achieved after a significant lag (i.e., in the long run). Along the same line, Ferraz et al. ( 2018 ), using Data Envelopment Analysis, proved that more complex countries in Asia and Latin America are more effective in generating human development. Arica and Kurt ( 2021 ) observed similar results in 24 OECD countries. Afanasiev ( 2022 ) discovered that economic complexity had a positive impact on well-being in Russia through regional development projects that focused on boosting employment and material prosperity.

Vu ( 2020 ) provided solid evidence that countries exporting complex products have better health outcomes than those exporting unsophisticated products. Hartmann et al. ( 2017 ) also established that complex countries can more effectively provide citizens with broader labor market alternatives and adequate access to health and education systems. Analogously, Zhu and Li ( 2017 ) reported that complexity improved both secondary and higher education across a panel of 210 countries.

Studies have also documented that countries with a high ECI enjoy a wide range of job opportunities, particularly in sophisticated and intermediate industries (Arif, 2021 ; Gala et al., 2018 ). A structural transformation can shift workers into productive sectors, providing them with better conditions and wages as per Andreoni et al. ( 2021 ). Hernández Umana and Laguna ( 2023 ) identified a negative relationship between unemployment and product sophistication in both panel and cross-country settings, suggesting that countries producing more sophisticated products generally have lower unemployment rates. Arif ( 2021 ), Gala et al. ( 2018b ), and Soyyigit et al. ( 2019 ) reached similar conclusions in various contexts. However, recent research reports a positive association between economic complexity and unemployment. Basile et al. ( 2019 ) found that economic complexity increased spatial productivity inequalities in both the short- and long-term, and polarized regional productivity levels in Italy over the long-term. Can and Doğan ( 2017 ) also observed that economic complexity in South Korea and Japan led to lower employment in agriculture and manufacturing. Hajimoladarvish and Mozaffaripour ( 2021 ) revealed a nonlinear relationship between unemployment and economic complexity, noting that once an innovation index threshold—specifically between 0.456 and 0.493—was reached, robots replaced human labor.

Hartmann et al. ( 2017 ) pioneered a research stream showing the ECI was a significant and negative predictor of income disparities, prompting subsequent studies to validate their conclusions. For instance, Ghosh et al. ( 2023 ) report that higher levels of structural transformation are associated with a fairer income distribution. The authors also note that the relationship demonstrates country heterogeneity, with the poorest countries experiencing the most significant influence on income inequality. Gómez‐Zaldívar et al. ( 2022 ) established a similar pattern in Mexican states, illustrating that states with more diverse and sophisticated economic structures experience more equitable income distribution. Ncanywa et al. ( 2021 ) find that economic complexity can reduce income disparities in Sub-Saharan Africa. In a cross-country sample, Lee and Vu ( 2019 ) observed a negative correlation between the ECI and income inequality; however, they also noticed that the ECI and inequality within a country increased over time. Sepehrdoust et al. ( 2021 ) demonstrate that of several macroeconomic variables, ECI has the largest share in explaining variations in income inequality. They further illustrate that as the ECI increases, income inequality initially increases and then decreases once a particular threshold is reached. Ultimately, Yusuf et al. ( 2021 ) found that during the industrialization period, structural transformation was associated with a reduction in income inequality.

The second approach posits that ECI has dubious implications for QoL (Lapatinas, 2016 ; Hartmann and Pyka, 2013 ). Despite exerting a beneficial effect on QoL by expanding people’s choices and increasing their demand for numerous QoL aspects, economic complexity can also spark economic polarization across countries and regions (Lapatinas, 2016 ). Scholars intuitively assume that innovative and economically diverse countries have higher freedom of choice, capacities, and, thus, well-being. However, more choices do not always mean better because of decision paralysis and unrealistic expectations (Ali and Cantner, 2020 ). Evidence also suggests that complex economies opt to offshore undesirable products (i.e., low wages and high pollution) and instead focus on sophisticated products that involve networks of specialized labor and more inclusive institutions, thereby widening the social welfare gaps between and within countries and regions (Hartmann and Pinheiro, 2022 ).

Mounting inequality has also been noted in some scholars’ work as an unfortunate consequence of complexity (Balland et al., 2022 ). For instance, Zhu et al. ( 2020 ) proposed that export complexity exacerbates the rural-urban divide in Chinese regions, as it is only related to reduced income inequality in urban areas. Contrary to previous studies, Lapatinas ( 2016 ) demonstrated that ECI does not influence QoL. He explained that this relationship depends on multiple factors, such as the type and extent of diversification and institutional frameworks.

The above literature clarifies that an increase in ECI does not necessarily improve the QoL. Following the approach supporting the positive impact of ECI on QoL, we hypothesized the following:

H1: A positive unconditional impact of economic complexity on the quality of life exists in developing countries

Quality of life, economic complexity and governance

Governance and economic complexity.

Recently, a growing body of research has established that governance is crucial for explaining the persistent variations in economic complexity between nations (Olaniyi and Odhiambo, 2023 ; Nguyen et al., 2023b ). Vu ( 2022 ), using a large dataset of 115 countries, found that countries with strong institutional settings have a greater capacity to produce and export sophisticated products than those with weak institutional frameworks. This finding is attributed to the positive impact of institutions on human capital accumulation and innovation incentives. Nguyen et al. ( 2023b ) replicated these findings in a panel of 89 countries. Similarly, Hoang and Chu ( 2023 ) examined the ECI-GOV relationship using 16 years of data from 98 countries and observed a positive association between the variables investigated. Interestingly, they note that each institutional component has a different influence on economic complexity, with these impacts manifesting in heterogeneous patterns across countries in various subgroups. Vu ( 2020 ) argued that the equalizing effect of economic complexity is mediated by the presence of inclusive institutions such as favorable labor market conditions.

Across 116 countries, Njangang and Nvuh-Njoya ( 2023 ) find that governance increases economic complexity by enhancing human capital, innovation, and financial development. Conversely, Sivak et al. ( 2011 ) argue that bureaucratic systems can constrain firms from engaging in innovative activities, whereas corruption hinders R&D investment. Additionally, they find that well-functioning and effective courts can inhibit innovation. In the same line of thought, a study by Yu and Qayyum ( 2023 ) found that institutional quality can hinder economic complexity. Contrary to the reviewed studies, Djeunankan et al. ( 2023 ) argue that more complex countries exhibit better institutional settings. This favorable impact of ECI on institutions channels through improvements in human capital, foreign direct investment, and income.

Governance and quality of life

Scholars and policymakers widely believe that countries with inclusive institutions tend to be economically prosperous (Ferraz et al., 2021 ) and socially developed (Fehder et al., 2019 ). Intuitively, effective institutions improve QoL in multiple ways, such as by increasing the resources available to meet non-economic societal needs (Porter et al., 2014 ) and connecting people to economic opportunities that address many social concerns (Fehder et al., 2019 ). Using a four-year panel across 121 countries, Carvalho et al. ( 2021 ) assessed the link between institutions and the SPI as a measure of QoL. They realized that institutions are among the most influential factors in boosting competitiveness and QoL. Most recently, Peiró-Palomino et al. ( 2024 ) demonstrated that a well-functioning institutional framework is indispensable for long-run social progress. Huang et al. ( 2023 ) showed that bolstering good GOV positively and considerably impacts social progress: a one-unit increase in the governance index raises social progress by about 13 units. Arshed et al. ( 2021 ) reached identical conclusions for 33 Asian nations. In the same vein, Agarwal and Samanta ( 2006 ) proved that governance is strongly linked to social progress rather than GDP per capita. Carvalho et al. ( 2016 ) investigated the impact of macro-level institutional and infrastructure reforms on the HDI in 25 transition economies, concluding that these reforms resulted in an economic benefit in other aspects of human development.

Similar investigations in developing countries have consistently reported beneficial effects of GOV on QoL. For instance, Mohammed ( 2022 ) demonstrates a significant and positive relationship between economic freedom and democracy on the HDI in his analysis of sub-Saharan Africa. Similarly, Hassan et al. ( 2020 ) found a negative association between governance indicators and poverty across 37 developing countries. A comparable finding was reported by Workneh ( 2020 ) in sub-Saharan Africa. Lastly, Sarkodie and Adams ( 2020 ) reveal that a favorable governance environment reduces income inequality, thereby enhancing human development in sub-Saharan Africa.

Consequently, institutions play a crucial role in modulating the relationship between ECI and QoL through several channels. To name a few, a robust institutional framework facilitates the accumulation of human capital and inventive activities, resulting in the creation of new knowledge and increased productivity (Vu, 2022 ; Hoang and Chu, 2023 ). Good institutions are also essential for securing a competitive labor market (Betcherman, 2012 ), encouraging research and development, and stimulating patenting (Kafka et al., 2022 ). Sturdy institutional settings also help allocate financial resources efficiently by directing credit to activities that support economic complexity. These frameworks also help reduce opportunistic behavior and rent-seeking within the financial system (Olaniyi et al., 2023 ). Moreover, an easier-to-do business environment and protected intellectual property rights are likely to encourage innovative entrepreneurial activities (Nguyen et al., 2021 ). In turn, this will lead to the development of a diverse set of high-tech/complex products, which will result in improved health, education, and overall quality of life (Hartmann et al., 2017 ).

The prominent role of GOV in shaping the impact of ECI on well-being dimensions has lately attracted scholarly attention. Bedemo Beyene ( 2024 ) utilized the SYS-GMM method to investigate the moderating effect of governance on the ECI-inequality relationship in 24 African countries from 2000 to 2018. His findings reveal that despite the exacerbating effect of ECI on income inequality in the region, effective governance can mitigate this negative impact.

The literature review outlined above highlights a prevalent tendency among studies to focus on the bilateral relationships between ECI, GOV, and QoL, underscoring the need for further research on the dynamic interactions between these variables. Consequently, while we recognize the direct impact of ECI on well-being, it could also exert an indirect effect through governance channels. Hence, we propose the following hypotheses:

H2: The impact of economic complexity on quality of life is contingent on governance settings

H3: The governance thresholds matter in determining the impact of economic complexity on the quality of life in developing countries.

Quality of life and complexity research in developing countries

A substantial body of research has established a strong correlation between economic complexity and the various aspects of well-being in developing countries. Given that many developing countries face persistently low per capita incomes, research has primarily focused on the impact of ECI on income. Using the GMM method, Hoeriyah et al. ( 2022 ) concluded that the ECI positively affects economic growth in 86 developing countries. Similarly, Wang and Taghvaee ( 2023 ) found that economic complexity and industrialization had a positive impact on the economic growth of 21 MENA countries from 1971 to 2017. Comparable findings were reported by Wang et al. ( 2023 ) and Zhu and Li ( 2017 ).

However, similar investigations of the ECI-inequality link in developing countries have produced mixed results. Ncanywa et al. ( 2021 ) used an ARDL model to examine the ECI impact on the Gini index-measured inequality in sub-Saharan African countries and found that economic complexity can reduce income disparities. Siddiq ( 2020 ) reported that Indonesia’s success in diversifying its economy reduced income inequality, while Venezuela’s dependence on oil exports increased inequality despite redistributive policies. Conversely, Bedemo Beyene ( 2024 ) finds that economic complexity exacerbates income inequality in 24 African countries. Also, Sepehrdoust et al. ( 2022 ) demonstrate that the ECI initially increases income inequality before reversing after a certain threshold across a panel of middle-income countries.

Analogously, studies on ECI’s impact on employment in developing countries have presented mixed results. Adam et al. ( 2021 ) analyzed data from OECD and non-OECD (developing) countries and found that higher ECI levels lowered unemployment rates and increased employment opportunities in both groups. In a similar context, Arif ( 2021 ) reported that ECI enhances employees’ negotiating power and increases labor share, while Bhorat et al. ( 2019 ) reported a trade-off between building complexity and job creation in South Africa.

Gnangnon ( 2021 ) examined the ECI-poverty nexus in 84 developing countries and found that greater economic complexity was associated with lower poverty headcount rates, particularly in countries with higher economic growth rates, lower income inequality, and diminished economic growth volatility. Most recently, Erumban and de Vries ( 2024 ) investigated the said relationship in 42 developing countries from 1990 to 2018, demonstrating that poverty reduction is significantly linked to productivity growth, especially in the manufacturing sector, and structural changes. These findings are consistent with those of Atta-Ankomah and Osei’s ( 2021 ) study in Ghana.

Developing countries’ specialization in specific sectors and reliance on a limited range of export products make them vulnerable to output volatility and external shocks, which exacerbate poverty and overall living standards (Gnangnon, 2021 ). Studies, such as Breitenbach et al. ( 2022 ), propose that enhancing economic complexity can reduce output volatility. Güneri and Yalta ( 2020 ) provided robust empirical evidence that increased economic complexity reduces output volatility across a large sample of developing countries. According to Gnangnon ( 2021 ), producing and exporting complex products can help developing countries mitigate the effects of output and economic growth volatility, thereby reducing the negative impact of external shocks on poverty.

In a limited number of studies that have adopted a broader approach to investigate the connection between ECI and QoL in developing countries, Nguea and Noumba ( 2024 ) examined the impact of ECI on social welfare across 27 African countries. Utilizing several variables to capture social welfare, including the HDI, infant mortality rate, life expectancy at birth, household consumption expenditure, and human capital, the authors uncovered a positive effect of increasing complexity on social welfare. Additionally, they validated the moderating effects of GDP per capita and globalization on the ECI-social progress nexus. Similarly, Le Caous and Huarng ( 2020 ) employed hierarchical linear modeling to investigate the ECI-HDI nexus and found that the positive relationship between human development and economic complexity was partially mediated by income inequality in 87 developing countries.

Research gaps and contributions

While several research suggests a connection between a country’s complexity level and QoL, empirical investigations are scarce (Ali et al., 2024 ), and those that exist have shown inconsistent findings (Le Caous and Huarng, 2020 ). Furthermore, existing ECI-QoL research has been criticized for insufficient use of robust panel data and advanced econometric methods (Pugliese et al., 2017 ; Lapatinas, 2016 ). Also, studies that focus on specific aspects of well-being, such as income, inequality, and employment, may be misleading, as are those that rely solely on the HDI, which heavily depends on economic rather than social indicators (Hsu et al., 2013 ). Once basic needs are met, the HDI lacks a broader range of indicators to guide further progress (Comim, 2016 ). The impact of innovation and complexity on QoL may go beyond the mere provision of essential needs (Carvalho et al., 2021 ), which may explain why some studies have reported no effect of ECI on QoL when using the HDI (e.g., Lapatinas, 2016 ). Furthermore, the relationship between ECI and QoL may not be straightforward or linear. It can vary based on different institutional settings, a factor that has often been overlooked. These gaps and deficiencies motivated this study.

Accordingly, this study aims to investigate the effect of the interaction between ECI and GOV on QoL in developing countries. By doing so, this research contributes to the existing knowledge in several novelties: (1) It is the first empirical attempt to examine the relationship between ECI and QoL while considering the moderating effect of GOV in a large dataset of developing countries. (2) Unlike previous work that addressed a single element of QoL, we employ the SPI as an exhaustive measure of QoL. (3) The study is confined to developing countries with crucial ECI, QoL, and GOV levels. (4) Prior research has mainly condensed the linear relationship between ECI and QoL; in contrast, this study explores the nonlinear relationships between the variables, employing advanced econometric techniques. (5) In recognition of the distinct effects of each institution type on the ECI, this study categorizes governance indicators compiled by the World Bank into four categories.

Data and methods

This section empirically probes the joint effect of governance and ECI on QoL, while controlling for significant macroeconomic variables. The underlying intuition is straightforward: complex productive structures improve quality of life (Hypothesis 1), such improvements are more pronounced in the presence of robust governance (Hypothesis 2), and the thresholds of governance are crucial in determining the direction of the impact of ECI on QoL (Hypothesis 3) (Fig. 2 ).

figure 2

Study design.

Data and variables

The study’s empirical strategy relies on the SYS-GMM approach using data spanning 2011–2021 from 75 developing countries (825 observations). The entire list of countries is provided in the Appendix (Table A-1 ). The availability of data guides sample selection and periodicity. A complete description of the main variables follows:

The outcome variable

The social progress index is used to measure QoL, aligning with previous studies (Pritchett, 2022 ; Huang et al., 2023 ). The SPI was developed to address the limitations of economic-prosperity-oriented measures, such as GDP, in capturing comprehensive development. However, it complements GDP rather than modifying or replacing it (Fehder et al., 2019 ). The SPI enables cross-country comparisons by evaluating a country’s performance across various social and environmental dimensions relevant to different stages of economic development (Peiró-Palomino et al., 2024 ). The index consists of three non-economic pillars: Basic Human Needs, Foundations of Well-being, and Opportunity. Each pillar is further divided into four equally weighted components defined by a comprehensive list of indicators, totaling 60 sub-indicators and covering 169 countries (Social Progress Imperative, 2023 ).

Main regressors

In this study, the economic complexity index is employed as the primary independent variable to reflect a nation’s productive structure, building upon a growing body of literature in this field (Adam et al., 2023 ; Sepehrdoust et al., 2019 ). The ECI provides insights into a country’s productive structure by examining the diversity and ubiquity of its export basket products. A complex country exports and produces a diverse range of non-ubiquitous goods (Hidalgo and Hausmann, 2009 ). In contrast to traditional measures of development, the ECI considers not only economic aspects but also the social and institutional factors critical for economic development (Aslam et al., 2023 ). Contemporary research has suggested that economic complexity diversity among countries explains their divergence in development levels (Shahmoradi et al., 2024 ; Saad et al., 2023 ; Olaniyi and Odhiambo, 2023 ).

Governance indicators, originally developed by Kaufmann et al. ( 1999 ) and produced by the World Bank (ranging from −2.5 to 2.5, with higher values signifying stronger governance), are incorporated as policy variables modulating the ECI-QoL link. Following the methodology of Bekana ( 2023 ) and (Ofori et al., 2023 ), this study employs principal component analysis (PCA) to aggregate the six governance indicators into four distinct components: political, economic, institutional, and general governance. Specifically: (1) political governance pertains to the processes by which governments are selected, monitored, and replaced, as well as the capacity of the government to formulate and implement sound policies; (2) economic governance concerns the quality of economic policies and the government’s ability to effectively implement them and provide essential public services; (3) institutional governance refers to the extent to which citizens and the state respect the institutions that govern economic and social interactions (Ofori et al., 2023 ), and (4) general governance integrates all six indicators into a comprehensive measure.

Control variables

Consistent with previous research, this study controls for FDI, government final consumption expenditure (EXP), and information and communication technology (ICT), which are sourced from the World Development Indicators published by the World Bank. It is anticipated that government expenditure will have a detrimental impact on well-being because these expenditures are not typically directed towards activities that promote well-being in developing countries (Aloui, 2019 ). Furthermore, government spending in these countries rarely meets the basic needs of citizens; however, the SPI surpasses this level by capturing higher QoL aspects in its second and third dimensions.

Similarly, FDI is expected to have a negative impact on well-being, as the benefits of FDI are often not evenly distributed and the types of FDI received by developing countries usually lack diversification (Nam and Ryu, 2023 ; Emara and Mohamed, 2023 ). In contrast, ICT is expected to have a positive impact on well-being due to its indirect impact on innovation enhancement, cost reduction, quality improvement, and direct impact on consolidating payment facilities and lowering unemployment rates, among other factors, which ultimately improve QoL (Khan et al., 2019 ). A detailed description of the variable definitions, measurements, and summary statistics is provided in Table 1 .

As shown in Table 1 , throughout the study course, the SPI value in the sampled countries averaged 59.586, indicating that these countries fall within the low tiers of the index and hence have a low level of QoL. This pattern is echoed in the ECI average of −0.411, indicating low levels of sophistication. Similarly, the average values of all individual governance indices are negative, reflecting weak institutional frameworks. Concerning the control variables, the average FDI net inflows constituted 3.8% of GDP in developing countries during the study period. Government consumption expenditure made up 14.58% of GDP on average. As for ICT, which encompasses both mobile and telephone penetration, the average penetration rate exceeded 100%.

Scatter plots in Fig. 3 , indicate that as developing countries enhance their economic complexity (or governance), their well-being improves. However, it is essential to empirically validate this relationship while considering the various macroeconomic indicators that may affect it as well as the impact of the interplay between ECI and GOV on QoL.

figure 3

Social progress index vs. economic complexity and governance.

The principal component analysis (PCA)

Using the PCA technique, four composite indices of governance were constructed, namely general, political, institutional, and economic governance, to avoid high collinearity among individual governance variables (Sabir et al., 2019 ) (see Table A-2 in the Appendix) and the ensuing bias in the results (Henri and Larissa, 2018 ; Ali and Gninigue, 2022 ). A pairwise correlation was conducted (found in Table A-3 in the Appendix) to confirm the existence of a minimum 0.30 correlation coefficient among the variables. The findings revealed that the minimal value of the association between variables is 0.38, validating the application of PCA (Table 2 ). The first component was selected for each independent PCA because all eigenvalues were greater than one (Ofori et al., 2023 ; Jolliffe, 2002 ).

Table 2 exemplifies that ISGOV has an eigenvalue of 1.925 and covers over 96% of the variance in all input variables in PC1. ECGOV’s first PC also has an eigenvalue of 1.879, effectively capturing 94% of the variation in the input variables. Similarly, the first PC of POGOV exhibits a 1.419 eigenvalue and is responsible for 71% of the explained variation. Finally, the GOV has an eigenvalue of 4.440 and explains 74% of the variation.

Empirical strategy

Given the causal relationship between ECI and QoL, which runs in reverse, with well-being-related factors, such as health, education, and human capital, substantially determines ECI, and ECI itself influences QoL (Nguea and Noumba, 2024 ). The potential for endogeneity between these variables is evident. Consequently, to address the prevalent econometric concerns associated with traditional panel data estimation methods, including pooled ordinary least squares (OLS), fixed effects (FE), and random effects (RE) models, which can often result in inaccurate parameter estimates, particularly in the presence of non-stationarity and endogeneity issues (Jahanger, 2022 ), omitted variable biases, measurement errors (Teixeira and Queirós, 2016 ), and heterogeneity, we opt for the generalized method of moments (GMM) approach.

The GMM was first introduced by Arellano and Bond ( 1991 ) and refined by Arellano and Bover ( 1995 ) and Blundell and Bond ( 1998 ). The GMM approach is divided into different GMM and system GMM (SYS-GMM). SYS-GMM provides more robust and precise outcomes than difference-GMM (Hakimi and Inglesi-Lotz, 2020 ), as the typical difference-GMM may suffer from a lack of precision and weak instruments in finite samples (Teixeira and Queirós, 2016 ). Moreover, the difference-GMM approach generates biased and inefficient estimates when the dependent variable displays persistence and closely resembles a random walk, particularly as the lag of the dependent variable approaches 1 (Blundell and Bond, 1998 ). This limitation is particularly salient in datasets with a short time-series. Notably, our analysis (as evidenced in row 1 of Tables 4 – 7 ) underscores the persistence of our dependent variable.

In contrast to one-step SYS-GMM, two-step SYS-GMM estimates offer more consistent and asynchronous values, particularly in cases of autocorrelations and heteroscedasticity (Jahanger, 2022 ). Furthermore, our sample met all three criteria for SYS-GMM: a sample size ( N ) exceeding the time-series length ( T ), the dependent variable (QoL) exhibiting persistence due to a strong correlation between the level series and its first lag (Asongu and Odhiambo, 2020 ), and the panel dataset revealing cross-country variation.

The high correlation between the dependent variable (QoL) and its lagged value justified the inclusion of the lagged dependent variable as an explanatory variable in the model. Incorporating the lagged dependent variable into the model may result in a correlation with the fixed effects in the error term, leading to a dynamic panel bias when estimating using traditional methods (Nickell, 1981 ), further supporting the use of the SYS-GMM specification. In the same line, the theoretical and empirical considerations preclude the use of traditional methods, FE and RE models (Table 3 ), given that the number of cross-sections ( N ) exceeds the time-series ( T ) length, and T is relatively short (Mulusew and Mingyong, 2023 ). Therefore, SYS-GMM is particularly suitable for our dataset.

Accordingly, the study adopts the two-step SYS-GMM to effectively estimate the dynamic model described below at level (Eq. 1 ) and the difference (Eq. 2 ) as follows:

Where \({{\rm{QoL}}}_{{\rm{it}}}\) is the quality of life measured by the SPI; \({{\rm{QoL}}}_{{\rm{it}}-1}\) is the lagged value of \({{\rm{QoL}}}_{{\rm{it}}}\) ; ECI is the economic complexity index; GOV is the governance indicators; X is a vector of additional regressors incorporated as control variables containing FDI inflows, General government final consumption expenditure, and ICT. Additionally, γ describes the time-invariant constant, i denotes the cross-section (75 developing countries), and t symbolizes the time dimensions (2011–2021). ω refers to the error term, and η is the time-invariant constant. μ is the country’s fixed effects, and τ defines the auto-regression level/degree, which is presumed to be one. Hence, \({{\rm{QoL}}}_{{\rm{it}}-2{\rm{\tau }}}\) denotes the second lag of the dependent variable, which reflects the year before the previous year’s performance. All variables are used in their original forms.

To test the second and third hypotheses, this study employs a methodology aligned with the contemporary literature on moderating analysis through interactive regression and net effects computation (Asongu et al., 2024 ; Tadadjeu et al., 2023 ; Ofori et al., 2023 ). However, to avoid the drawbacks of interactive terms, this study follows the recommendations of Brambor et al. ( 2006 ), which include incorporating all constitutive terms (GOV and ECI) alongside their interaction term (ECI × GOV), refraining from interpreting the estimates as in linear additive models (i.e., through the coefficients). Instead, we consider both conditional and unconditional effects to fully capture the impact of the moderating variables and compute meaningful marginal/net effects. These recommendations have been widely acknowledged and cited in similar research contexts, with over 7200 citations, according to Google Scholar. Although other threshold techniques based on balanced panel data, such as Hansen ’s ( 1999 ), do not necessarily require interactive regressions, the study’s unbalanced panel dataset and constraints in data availability necessitate the use of interactive regressions for threshold computations (Asongu et al., 2024 ; Ngassam et al., 2024 ).

Accordingly, the subsequent model with interaction terms is proposed to capture the hypothesized moderating effect of GOV, signifying that higher levels of GOV will either increase or decrease the impact of ECI on QoL.

In Eq. ( 3 ), \({\gamma }_{1}\) and \({\gamma }_{2}\) represent the direct impact of GOV and ECI, while \({\gamma }_{3}\) captures the indirect effect of GOV through which ECI affects QoL. Thus, differentiating Eq. ( 3 ) with respect to the ECI gives:

Equation ( 4 ) demonstrates that a unit change in QoL depends on the sign and magnitude of the interaction term. The outcome of this term may lead to a net effect contingent upon the signs and coefficients involved. Precisely, a net effect arises when both coefficients are statistically significant, but exhibit opposite signs. Conversely, no net effect occurs when both coefficients share the same sign, or when at least one of them is statistically insignificant (Achuo et al., 2022 ).

Empirical results and discussion

Tables 3 – 7 present the results of the SYS-GMM estimations of the conditional and unconditional effects of economic complexity and governance variables on quality of life. Conditional regressions allow us to assess the synergistic (joint) effects of ECI and general, political, economic, and institutional governance on QoL, while the unconditional specification analyzes the direct effects of ECI and GOV measures on QoL.

The direct/unconditional impact

Table 3 presents the direct impact of GOV and ECI on QoL using OLS and FE models Footnote 2 . These initial results provide a foundational understanding of the relationships between the variables. However, relying solely on these static specifications could potentially lead to econometric issues, especially considering the nature of our data (Khan, 2022 ).

Tables 4 and 5 show the direct impacts of GOV and ECI on QoL. Table 4 details the individual governance indicators, while Table 5 presents the governance dimensions. As per the findings of Table 4 , SPI exhibits a strong dependence on its prior level. In addition, all governance indicators have a positive impact on the SPI. Among these indicators, government effectiveness and voice and accountability are the most influential, with a 1% increase in these indicators resulting in 0.499% and 0.468% improvements in SPI, respectively. In contrast, corruption control is the least impactful, causing a 0.01358% change in the SPI for every 1% increase in the index. Therefore, it is essential to improve the governance structure, particularly by enhancing the efficiency and quality of public services, strengthening public officials’ accountability, monitoring and evaluating government performance, promoting transparency and freedom of expression, and empowering civil society organizations while safeguarding individuals’ rights to voice their opinions in order to improve QoL.

These findings are consistent with the notion that good governance drives social progress and development, as reported in relevant empirical studies (e.g., Arshed et al., 2021 ; Fehder et al., 2019 ; Arshed et al., 2021 ). According to Huang et al. ( 2023 ), a one-unit increase in institutional quality enhances SPI by nearly 13 units. Sarpong and Bein ( 2021 ) arrived at a similar conclusion by using the HDI in selected Sub-Saharan African nations. Peiró-Palomino et al. ( 2024 ) also discovered that a well-functioning institutional framework is crucial for long-term social progress in a recent study.

Moreover, the ECI consistently improved the SPI across all six models. For instance, a 1 unit increase in ECI improves SPI by 0.507% and 0.545% in models (1) and (2), respectively. This finding is in line with the growing body of literature suggesting that higher levels of economic complexity enhance social welfare. Previous studies have documented numerous positive externalities of higher ECI, including improved health and educational outcomes (Vu, 2020 ; Hartmann et al., 2017 ; Zhu and Li, 2017 ), diverse job opportunities, better working conditions and wages (Arif, 2021 ; Gala et al., 2018 ; Andreoni et al., 2021 ; Adam et al., 2021 ), and reduced inequality (Hartmann et al., 2017 ; Ghosh et al., 2023 ; Gómez‐Zaldívar et al., 2022 ; Ncanywa et al., 2021 ), all of which collectively contribute to enhanced overall well-being (Le Caous and Huarng, 2020 ; Ferraz et al., 2018 ). In a similar manner, ICT exhibited a consistently positive effect on SPI, with a one-unit increase in ICT resulting in a 0.015% increase in SPI in Model (1) and a 0.012% increase in Model (2). Only in Model (2) do FDI and EXP have a significantly negative impact on SPI, with one-unit increases in FDI and EXP reducing SPI by 0.012% and 0.02%, respectively.

The six governance indicators are then clustered into four additional governance dimensions for a deeper empirical inspection. Models (7)–(10), shown in Table 5 , portray the unconditional effects of the ECI and GOV dimensions (i.e., GOV, POGOV, ISGOV, and ECGOV) on SPI. The SPI (L) coefficient is still positive and highly significant at the 1% level, indicating a solid dependency of the SPI on its prior level. The ECI retains its positive and significant impact on SPI, permitting us to accept the first hypothesis (H1) and confirm our previous findings. As illustrated, a 1% increase in ECI increases SPI by 0.377%, 0.518, 0.464%, and 0.337% across the four models. Similarly, GOV and its three dimensions positively and significantly impact SPI. Additionally, the relationship between ICT and SPI is consistently positive across the four models. It is only in Models (7) and (8) that FDI has a significantly negative effect on SPI.

The impact of the interplay between ECI and GOV on QoL (Indirect/Contingent impact)

Tables 6 and 7 below show the conditional impacts of ECI and GOV dynamics on QoL, incorporating an interactive term for each governance dimension/indicator and ECI to capture the moderating effect. Table 6 displays the governance dimensions, and Table 7 shows the governance indicators.

From Table 6 , we draw the following conclusions: (a) Economic complexity and governance continue to have a consistent positive unconditional impact on QoL, as illustrated by the variables’ coefficients. (b) The nexus between economic complexity and quality of life is moderated by governance, leading us to validate the second hypothesis (H2), which is compatible with (Hartmann and Pyka, 2013 ; Lapatinas, 2016 ) theoretical pointers, arguing that several factors, particularly governance structures, modulate the impact of ECI on QoL. For instance, our findings partially concur with those of Lapatinas ( 2016 ) regarding how other forces can affect the impact of ECI on human development, as the relationship between the two variables depends on several variables, including institutional arrangements; and (c) Generally, ISGOV, ECGOV, and GOV negatively moderate the ECI-QoL nexus, whereas POGOV is insignificant.

Concerning the control variables, the findings are concordant across the different specifications and the control variables have the expected signs. FDI has a consistent negative impact on QoL, which is reminiscent of (Nam and Ryu, 2023 ; Emara and Mohamed, 2023 ; Sharma and Gani, 2004 ) findings. According to Bayar and Gunduz ( 2020 ), FDI inflows have a relatively negative effect on human capital development in 11 transitional EU economies. In contrast, this finding contradicts those of (Djokoto and Wongnaa, 2023 ; Ha et al., 2023 ), who found that FDI favorably influences human development, particularly in developing countries. In addition, government spending has a significant adverse effect on QoL, which is in agreement with recent studies (Djokoto and Wongnaa, 2023 ; Masduki et al., 2022 ; Qureshi, 2022 ; Ranjan and Panda, 2022 ) but contrary to Adegboye and Akinyele ( 2022 ) and Iheoma ( 2014 ), who established government spending as a contributory factor in boosting social welfare. The adverse impact of government expenditures on well-being may be attributable to the diversion of these expenditures from the activities that promote them. Among the control variables, ICT had the most significant positive impact on QoL. Numerous empirical studies have demonstrated that ICT has a positive effect on individual well-being (Karaman Aksentijević et al., 2021 ; Khan et al., 2019 ; Nevado-Peña et al., 2019 ). Sabbagh et al. ( 2012 ) asserted that ICT has a significant and beneficial effect on well-being, with countries that are highly digitized being more likely to experience a higher level of well-being.

A robustness check for the above findings is provided in Table 7 , verifying that our results remain robust and congruent when considering individual GOV indicators. ISGOV’s two input variables show a negative moderating impact on the ECI-QoL relationship, while the POGOV input variables are insignificant. Ultimately, GE negatively moderates the attendant nexus between the two ECGOV input variables. Among the three significant indicators, government effectiveness appears to have the highest negative moderating impact.

A battery of post-diagnostic criteria is formulated to validate our models: (1) the absence of first- and second-order autocorrelations of the residuals. This is indicated by the probability values of AR1 being less than 10% and AR2 being greater than 10% for the first- and second-order autocorrelations, respectively, across all specifications; (2) verifying the validity of the instruments through the Hansen test of over-identification; and (3) ensuring that there are no more instruments than groups (i.e., countries) for each specification.

Multicollinearity tests were also conducted using the variance inflation factor (VIF). A threshold of less than five VIFs indicates a low concern for multicollinearity. The results, depicted in Tables A-4 to A-7 in the Appendix, revealed that the models had VIF values of less than five, proving that the models do not suffer from multicollinearity. Moreover, the correlation matrix, presented in Appendix Table ( A-2) , displays the correlations among the variables below the threshold of 0.8, further reducing the risk of multicollinearity (Gujarati and Porter, 2009 ). While high correlations among governance indicators are typically accepted in the literature, employing PCA helps alleviate any potential impacts stemming from such correlations (Hair et al., 2006 ). Additionally, each governance indicator presents a distinct parameter (Kaufmann et al., 1999 ) and is used as a standard measure of governance quality. Therefore, these correlations are unlikely to affect the findings.

Net effect and threshold calculations

Nevertheless, the sign alteration between the conditional and unconditional impacts of the governance suggests the existence of net effects, motivating the calculation of net effect, and the corresponding thresholds as follows.

Drawing on recent contributions that follow Brambor et al.’s ( 2006 ) criteria for calculating the net effects of interactive specifications (Asongu and le Roux, 2023 ; Nkemgha et al., 2023 ), we compute the net effect as follows:

\({{\rm{\rho }}}_{1}\) signifies the magnitude of the direct effect and ρ is the mean value of the modulatory variable and \({\omega }_{i}\) is the magnitude of the indirect effect. When \({{\rm{\alpha }}}_{1}\) \({\rm{and}}\) \({\beta }_{i}\) have the same sign or \({{\rm{\rho }}}_{1}\) or \({\omega }_{i}\) is insignificant, the net effect cannot be computed. Consequently, the corresponding threshold can be computed as the absolute term for dividing the unconditional coefficient of the independent variable by the conditional coefficient, as follows:

The threshold value here is defined as the moderator value at which the slope of the predictor variable is zero (Nye and Witt, 1995 ). In the current context, the threshold is the governance value that results in a zero slope for the relationship between ECI and QoL. An example illustrating the net effect and corresponding threshold computations for general governance (GOV) follows.

The interplay between governance and economic complexity on QoL consistently displays a negative interaction. Nonetheless, the estimated net effect indicates an overall (net) positive impact of governance. Subsequently, an extended threshold analysis is executed to delineate critical thresholds beyond which increasing governance effectively enhances the quality of life.

Considering a net positive effect, a threshold of (5.59) implies that the direct positive impact dominates the negative indirect impact until the computed threshold, suggesting that the positive impact of the interaction between governance and economic complexity diminishes until it vanishes entirely when governance is equal to 5.59. Simply put, higher (tighter) governance standards over a certain threshold (5.59) may yield a zero net impact on quality of life and undermine the favorable impact of economic complexity on QoL, thus validating our third hypothesis (H3). In this scenario, when policy variables exceed crucial thresholds, potentially triggering adverse macroeconomic consequences, comparable research on interactive regression has highlighted the need for complementary policies to sustain a positive overall effect (Asongu et al., 2021; Ofori et al., 2021 ; Asongu and Odhiambo, 2020 ).

The analysis was repeated for the economic and institutional governance dimensions and the three significant governance indicators to compute the net effect and thresholds, as shown in Eqs. ( 5 ) and ( 6 ). Overall, our findings indicate a positive net effect.

 = 0.471+ (−0.105\(\ast\)0) = 

\({\bf{Threshold}}=\left|\frac{0.471}{-0.105}\right|={\mathbf{4}}{\boldsymbol{.}}{\mathbf{48}}\)

 = 0.349+ (−0.105\(\ast\)0) = 

\({\bf{Threshold}}=\left|\frac{0.349}{-0.105}\right|={\mathbf{3}}{\boldsymbol{.}}{\mathbf{32}}\)

 = 0.248+ (−0.294\(\ast\)−0.316) = 

\({\bf{Threshold}}=\left|\frac{0.248}{-0.294}\right|={\mathbf{0}}{\boldsymbol{.}}{\mathbf{843}}\)

 = 0.395+ (−0.214\(\ast\)−0.427) = 

\({\bf{Threshold}}=\left|\frac{0.395}{-0.214}\right|={\mathbf{1}}{\boldsymbol{.}}{\mathbf{846}}\)

 = 0.371+ (−0.216\(\ast\)−0.461) = 

\({\bf{Threshold}}=\left|\frac{0.371}{-0.216}\right|={\mathbf{1}}{\boldsymbol{.}}{\mathbf{717}}\)

Therefore, improving the structures and frameworks of GOV, ISGOV, and ECGOV above the thresholds of 5.59, 3.14, and 3.32, respectively, and for GE, RL, and CC above the thresholds of 0.8435, 1.846, and 1.717, respectively, is necessary but not sufficient to maintain the positive impact. In other words, while enhanced governance is necessary, it is not sufficient on its own, and additional measures must be taken once certain thresholds have been reached. This is economically intuitive because governance facilitates business operations and fosters innovation at early stages, but is insufficient for business continuation, and this is supported by various studies as well. For instance, Kramer ( 2015 ) found that lower-quality institutional environments have a significantly higher impact on productivity and economic development. Further, he revealed that greater governance standards may undermine a country’s ability to leverage spillovers from productivity growth and impede the development of domestic firms. Similarly, Rodríguez-Pose and Zhang ( 2020 ) found that institutional factors have a more significant impact on the early phases of innovation than on the overall share of innovation in a company’s revenue. This is because the influence of local institutions on a firm’s revenue from innovation diminishes when it surpasses the innovation threshold. A similar conclusion was reached in the European region by Barbosa and Faria ( 2011 ) and in the OECD region by Bourlès et al. ( 2010 ), concerning the adverse impact of heavy regulation on productivity and innovation.

From a theoretical perspective, our findings are supported by the Growth-Enhancing Governance Capabilities approach proposed by Khan ( 2010 ). This approach underpins the critical role of governance capabilities in promoting early development in developing countries by addressing property rights instability, fostering technological catch-up, and preserving political stability. However, these capabilities are second-order criteria in the sense that governance capacities are insufficient and eventually unsustainable in the absence of other state capacities that facilitate sustainable growth. Additionally, the finding accords with Weill’s ( 1990 ) definition of “conversion effectiveness,” in which governance confines how effectively resources, in our case complex productive structures gains, are transformed into productivity measures (e.g., better quality of life).

It is noteworthy that the calculated thresholds are policy-relevant as they lie within the statistical ranges (minimum to maximum) of the calculated PCAs and governance variables (see Table 1 ) (Ofori et al., 2021 ). Owing to the non-significance of at least one estimated coefficient, POGOV’s underlying net effects cannot be established (Asongu and Odhiambo, 2020 ). Given the obtained net positive effect, the insignificance of political governance in moderating the ECI-QoL nexus is supported by the fact that economic governance goals are more pertinent to the early industrialization phases than political governance and offer better development outcomes, as reported in the pertinent literature (Asongu and Nwachukwu, 2016 ; Anyanwu and Erhijakpor, 2014 ).

Robustness check

We conduct three types of robustness tests to ensure the comparability and robustness of our findings. First, we introduce an alternative methodology, the Limited Information Maximum Likelihood (LIML) method, to scrutinize our results. Second, we utilized an alternative measure of QoL, namely the HDI, which is commonly employed in similar research contexts. Finally, we addressed potential outliers by excluding them from the analysis and re-estimating the models and corresponding GOV thresholds.

Robustness to an alternative estimation method: LIML

The LIML method results detailed in Table 8 are largely consistent with those obtained from the SYS-GMM. The positive effect of ECI and the negative moderating effect of governance were consistently observed, thereby reinforcing our results. However, apart from POGOV, general governance and the other two dimensions (ECGOV and ISGOV) exhibited a negative effect on SPI, in contrast to our initial findings.

An Anderson-Rubin test was conducted to assess the validity of our instruments. Across all four models, the test produced p-values greater than the significance level of 0.05, indicating insufficient evidence to reject the null hypothesis, and consequently, that the instruments are not weak. The Basmann test of over-identification also yielded p-values greater than 0.05, further indicating that the models are not over-identified.

LIML method, although designed to alleviate endogeneity, may still display bias in particular situations. In such circumstances, GMM is a more effective approach to address endogeneity issues as it provides more stable and unbiased estimators than instrumental variable (IV) regression, particularly when the availability of strong and persuasive instruments is scarce (Khan, 2022 ).

Robustness to an alternative QoL measure: HDI

Employing the SYS-GMM methodology, we examined the relationship between QoL and ECI, while integrating HDI as an alternative variable of interest. Our analysis, outlined in Table 9 , underscores the enduringly favorable impact on ECI across all four models. Although the favorable influence of governance remains evident in the contexts of GOV and ECGOV, it fails to attain statistical significance for ISGOV and POGOV. Furthermore, the findings emphasize the adverse moderating effect of governance on the ECI-QoL link across all four governance dimensions. This robustness check confirms that the inclusion of HDI does not alter the main findings of our analysis.

Robustness through outlier removal: a sensitivity analysis

A sensitivity analysis was also performed (Table 10 ), involving the identification and exclusion of outliers, particularly those comprising the top and bottom 2%, based on the ECI and QoL metrics. This analysis consistently reaffirms our initial findings. An additional examination of the interplay between GOV and ECI revealed a positive net effect. Subsequent re-estimation of the thresholds confirmed their policy relevance as they fell within the moderator’s value range.

Concluding remarks and implications

In recent years, scholarly attention has increasingly focused on investigating the impact of productive structures on QoL, yielding divergent findings. This study aims to advance this ongoing discourse by empirically examining the moderating role of governance in this relationship. The ECI was used to measure the nation’s productive structure, whereas SPI was used to measure QoL. The six governance indicators published by the World Bank were also incorporated as policy variables designed to influence the ECI-QoL relationship. These governance indicators were further categorized into four dimensions: institutional, economic, political, and general governance. This classification is intended to dissect the distinct impacts of different governance measures, determine the most pertinent ones for policymaking, and identify specific thresholds at which additional complementary policies for each indicator should be enacted.

Based on macro data from 75 developing countries spanning the period between 2011 and 2021, an empirical analysis relying on the system GMM approach revealed that both ECI and governance exert unconditional and consistent positive effects on QoL in developing countries. Furthermore, the findings provide robust evidence that governance significantly moderates the relationship between these two variables. However, contrary to the positive direct impact, the joint effect of GOV and ECI on QoL displayed a consistently negative interaction effect. In light of this, further investigation was conducted to determine the net effect and corresponding thresholds of governance that would further improve QoL.

The interplay between GOV and ECI was found to yield a positive net effect, which is economically rational and aligns with the theoretical predictions. This suggests, however, that increasing governance above certain thresholds may lead to a zero net impact on QoL if complementary policies are not implemented. The thresholds for these complementary policies are then established as follows: 0.8435:1.846 and 1.717 for government effectiveness, the rule of law, and corruption control, respectively; and 5.59, 3.14, and 3.32 for general, institutional, and economic governance (out of the maximum values of 7.73, 5.456, and 5.247, respectively). These thresholds are crucial to the formulation of policies as they delineate the points below which governance is necessary and sufficient for the modulation of ECI to have positive outcomes on QoL. However, complementary measures are required to maintain the intended positive impact above these points. Our findings are robust and consistent across the various GMM specifications.

Considering the synergistic relationship between ECI and GOV, developing countries should prioritize robust governance measures, especially in the early stages of development, to maximize the positive spillovers of structural transformation to QoL. This involves creating a pro-development institutional environment that fosters business innovation and growth, with a special emphasis on government effectiveness, the rule of law, and corruption control, owing to their particular significance in amplifying the favorable impact of ECI on QoL at the early development stage. Measures such as strengthening regulatory frameworks, improving the efficiency of government operations, establishing a well-defined property rights system, mitigating crime and violence risks, ensuring quality contract enforcement, and ensuring fair taxation are, therefore, imperative.

Complementary policies to support governance in maintaining the ECI’s favorable impact should emphasize enhancing both national and individual capacities to tackle complex productive structures. Developing countries typically rely heavily on labor-intensive industries, and a significant proportion of this labor force lacks formal education or sufficient skill training. This educational and skills deficit presents a substantial challenge in fostering economic complexity while ensuring that individuals have an equal opportunity to benefit from development and increased complexity.

To address these challenges, governments of developing countries can strategically intervene by implementing policies that target the expansion of specific industries, particularly capital-intensive ones, and facilitating the entry of foreign firms by promoting vertical FDI. Foreign firms bring advanced technologies, management practices, and expertise, which are essential for developing complex export sectors. These export sectors, being more complex, can significantly contribute to the advancement of a country’s economic complexity, while simultaneously enhancing individuals’ well-being by creating higher-value jobs, increasing productivity, and broadening the availability of consumption options.

However, achieving this requires the concurrent enhancement of human capital, particularly through reskilling and upskilling the workforce to manage the implications of ECI in the labor market. By improving the quality of education, individuals can more effectively seize the diverse opportunities presented by an increasingly complex economy. Additional crucial measures include reducing trade policy barriers, enhancing trade facilitation, advancing technological capabilities, and promoting private sector involvement in the industrialization process. These policies can serve as buffers to alleviate the adverse effects of further economic and institutional reforms on well-being.

Future inquiries should prioritize the examination of cases from diverse regions and stages of development to enhance policy relevance. It is equally crucial to conduct country-specific analyses because panel and cross-country studies may not adequately capture the unique characteristics of each nation. Additionally, the nonlinear relationship between ECI and QoL remains largely unexplored. Moreover, classifying countries into low-income, lower-middle-income, and upper-middle-income categories may provide another dimension of empirical outcomes and policy perspectives, especially in defining the most relevant institutional setups for each group of countries. A moderation analysis that considers several factors, including financial development, ICT, FDI, among others, may offer valuable insights. One potential limitation of this study is the restricted period of analysis, which spans only 11 years of available data from 2011 to 2021.

Data availability

The authors confirm that data will be made available upon reasonable request.

Concisely clarified in the section on data and methods.

The selection of fixed effect models is guided by the Hausmann test.

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Hassanein, E.A., Samak, N. & Abdelaziz, S. The synergetic effect of economic complexity and governance on quality of life: policy thresholds. Humanit Soc Sci Commun 11 , 1185 (2024). https://doi.org/10.1057/s41599-024-03577-2

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The enactment of policies that bolster new research and development (R&D) institutions stands as a pivotal strategy to catalyze urban innovation and development. Adopting a strategic lens of innovation chain management and employing the Differences-in-Differences (DID) method to scrutinize panel data from 43 Chinese cities spanning 2008 to 2019, this study probes the efficacy and underlying mechanisms of policies designed to support nascent R&D institutions in facilitating urban innovation. Empirical findings reveal that policies in support of new R&D institutions have markedly enhanced the three integral stages of the urban innovation chain: research and development, transfer, and application. Furthermore, the innovation ambiance within cities and the innovative activities of enterprises emerge as significant mediators between support policies and the output of urban innovation across these stages. A regional heterogeneity analysis unveils that the impact of support policies on the output of urban innovation diverges across regions, with a notably more pronounced effect observed in the eastern region compared to central and western regions. An objective appraisal of the policy's impact on urban innovation not only aids in delving into the profound implementation effects of policy instruments but also furnishes policy-makers with decision-making references for optimizing the utilization of support policies for new R&D institutions to advance the edification of the national innovation system.

Human factor inputs are key to promoting agricultural modernisation. Traditional economic theory suggests that improving human capital is the key to boosting agricultural productivity, but can this conclusion hold in China's dualistic economic structure? We use Chinese provincial panel data from 2000-2017 to examine the impact of rural human capital inputs on agricultural total factor productivity. We find that, on the whole, rural human capital inputs have a negative effect on agricultural total factor productivity, and that there is a "rural human capital trap". Mechanism analysis reveals that rural human capital inputs, on the one hand, cause labour loss and reduce the quality of agricultural workers, while on the other hand, they may promote the application of mechanization, which has both positive and negative effects on agricultural total factor productivity. Heterogeneity analysis finds that this negative effect is more pronounced in the central region of China because of the serious loss of rural labour. This study provides new policy insights for further improving the structure of rural education inputs and promoting human capital accumulation in agriculture.

The long-term low level of residents' property income is not only detrimental to the wealth accumulation of farmers, but also aggravates the urban-rural income imbalance. This paper uses an empirical approach to explore the mechanism of digital literacy's impact on farm households' property income. The main findings of this paper are as follows: (1) Digital literacy and its sub-dimensions can significantly increase the level of farmers' property income, with the strongest contribution of digital information acquisition literacy. (2) Digital literacy has a greater boosting effect on the property income of farm households with low education level heads, low physical capital households, and villages with better economic development. (3) Digital literacy increases the property income of farm households mainly through expanding social capital and improving risk preferences. The innovation of this paper is to construct a digital literacy evaluation index system based on a micro perspective that fits the behavioral characteristics of farmers, and reveal the impact and mechanism of digital literacy and its sub-dimensions on farmers' property income. This study expands the theoretical research related to digital literacy and farmers' property income, and provides a scientific basis for the choice of strategies to enhance the digital literacy of the majority of farmers, which is important for promoting wealth accumulation of farmers and achieving common prosperity.

  • Open access
  • Published: 17 October 2023

The economic impact of endemic respiratory disease in pigs and related interventions - a systematic review

  • Marloes Boeters 1 ,
  • Beatriz Garcia-Morante 2 , 3 , 4 ,
  • Gerdien van Schaik 1 , 5 ,
  • Joaquim Segalés 3 , 4 , 6 ,
  • Jonathan Rushton 7 , 8 &
  • Wilma Steeneveld 1  

Porcine Health Management volume  9 , Article number:  45 ( 2023 ) Cite this article

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Understanding the financial consequences of endemically prevalent pathogens within the porcine respiratory disease complex (PRDC) and the effects of interventions assists decision-making regarding disease prevention and control. The aim of this systematic review was to identify what economic studies have been carried out on infectious endemic respiratory disease in pigs, what methods are being used, and, when feasible, to identify the economic impacts of PRDC pathogens and the costs and benefits of interventions.

By following the PRISMA method, a total of 58 studies were deemed eligible for the purpose of this systematic review. Twenty-six studies used data derived from European countries, 18 from the US, 6 from Asia, 4 from Oceania, and 4 from other countries, i.e., Canada, Mexico, and Brazil. Main findings from selected publications were: (1) The studies mainly considered endemic scenarios on commercial fattening farms; (2) The porcine reproductive and respiratory syndrome virus was by far the most studied pathogen, followed by Mycoplasma hyopneumoniae , but the absence or presence of other endemic respiratory pathogens was often not verified or accounted for; (3) Most studies calculated the economic impact using primary production data, whereas twelve studies modelled the impact using secondary data only; (4) Seven different economic methods were applied across studies; (5) A large variation exists in the cost and revenue components considered in calculations, with feed costs and reduced carcass value included the most often; (6) The reported median economic impact of one or several co-existing respiratory pathogen(s) ranged from €1.70 to €8.90 per nursery pig, €2.30 to €15.35 per fattening pig, and €100 to €323 per sow per year; and (7) Vaccination was the most studied intervention, and the outcomes of all but three intervention-focused studies were neutral or positive.

The outcomes and discussion from this systematic review provide insight into the studies, their methods, the advantages and limitations of the existing research, and the reported impacts from the endemic respiratory disease complex for pig production systems worldwide. Future research should improve the consistency and comparability of economic assessments by ensuring the inclusion of high impact cost and revenue components and expressing results similarly.

Respiratory disease, referred to as the porcine respiratory disease complex (PRDC) when multiple pathogens and non-infectious factors are involved, is regarded as one of the most serious health problems in contemporary pig production. In Europe, pneumonia and pleuritis are the most frequent lung lesions observed at the slaughterhouse, with prevalence up to 69% and 48%, respectively [ 1 , 2 , 3 , 4 , 5 ]. In the United States, results from the 2012 National Animal Health Monitoring System indicated that respiratory problems were the main cause of deaths in weaned (47.3%) and grower/finisher pigs (75.1%) [ 6 ]. Besides increasing mortality, the PRDC is believed to induce production losses through reduced growth rates and a lower feed conversion efficiency [ 7 , 8 ]. Consequently, respiratory disease remains one of the main reasons for antimicrobial usage in both nursery and growing/finishing pigs [ 9 , 10 , 11 ].

The PRDC term was coined to emphasise the complexity of events leading to respiratory disease development, including the involvement of (several) viral and bacterial pathogens as well as environmental, management, and genetic factors [ 12 , 13 ]. Pathogens involved in the PRDC vary considerably in different countries, regions, and herds over time [ 14 , 15 ]. The most common primary viral agents include porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus 2 (PCV-2), and swine influenza virus (SIV) [ 12 , 13 , 16 ]. Other primary pathogens such as pseudorabies virus and porcine respiratory coronavirus are reported but they have less impact on today’s porcine health [ 17 ]. The bacterial species involved in this disease complex are traditionally distinguished between primary or initiators, such as Mycoplasma ( M. ) hyopneumoniae , and Actinobacillus ( A. ) pleuropneumoniae , and secondary or follower pathogens (e.g., Pasteurella multocida , Streptococcus suis and Bordetella bronchiseptica ) [ 12 , 13 , 16 ]. The presence of various infectious agents in cases of PRDC leads to complex and potentially synergistic interactions that can increase the severity and duration of clinical signs and lesions, as well as the economic consequences [ 17 ].

As economic margins on pig farms are generally small [ 18 ], it is valuable to understand costs caused by endemically prevalent individual and co-existing pathogens within the PRDC, as there may be opportunities to increase farm profitability by controlling or preventing these infections. Therefore, estimates of costs and benefits of mitigation measures, can support decision-making regarding disease control at farm, integration system, regional and national levels.

Although one would expect the economic impact of respiratory disease to be well studied for the abovementioned reasons, no review or meta-analysis exists that maps the current state of economic research in this field. The economic implications of pathogens involved in the PRDC are likely to be heavily impacted by the variety in production systems and endemically prevalent strains of different pathogens across countries, as well as by the applied economic methods. These methods are defined by both the type of economic analysis (e.g. basic cost computations, partial budget analysis, cost-benefit analysis) and the cost components considered in this analysis (e.g. labour costs, feed costs, veterinary costs). Thus, the aim of this systematic review was to identify what economic studies have been carried out on infectious endemic respiratory disease in pigs, what economic methods are being used, and, when feasible, to identify the economic impacts of specific or co-existing PRDC pathogens and the costs and benefits of interventions.

Materials and methods

A systematic literature review was conducted to identify relevant economic research on infectious endemic respiratory disease in pigs and related interventions. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed [ 19 ], without the use of risk-of-bias analysis (e.g. assessing the selection bias, reporting bias per study).

Literature search

The search for suitable references was conducted in PubMed®, Scopus and CAB Abstracts. We restricted the search to studies published after January 1, 1980, and to peer-reviewed original research in English only. The search strings consisted of three parts (topic, population and focus), which were all required to be present in the title or abstract for a study to be included (for the full search strings, please refer to Supplementary file S1 ). The terms related to respiratory disease (topic) included terminology for both respiratory disease at syndrome level and for specific respiratory pathogens. The pathogens included were the most common infectious agents within the PRDC that are considered endemic in large parts of the world: the viral agents PRRSV, SIV and PCV-2, and the bacterial agents M. hyopneumoniae and A. pleuropneumoniae . The systematic search was lastly updated on January 23, 2023.

Selection of studies

The abstracts obtained from the search were screened by two independent reviewers (co-authors MB and BGM). Studies were excluded when their main focus was not on respiratory disease in pigs and/or when no mention was made of an impact on either production parameters (e.g. average daily gain, mortality or feed conversion ratio) or on costs or revenues. The two reviewers compared and merged their findings and created a list of studies for the full text review, which was likewise carried out independently by MB and BGM. At this stage, only studies were included when the full text was available, when the report provided a full (e.g., farm budget analysis, cost-benefit analysis) or partial financial evaluation (e.g., cost analysis, basic calculation of medication costs), and when all calculated changes in economic outputs could be attributed to respiratory disease or to the interventions aiming to reduce or control the disease.

In addition, all open-access issues from the Journal of Swine Health and Production (JSHAP) were manually checked, as this journal is not indexed in a number of databases. Studies that met the screening and eligibility criteria were included. Lastly, reference lists and citations of all selected studies were examined for additional studies that met all inclusion criteria (literature snowballing).

Data extraction

Data from the eligible studies were manually extracted by MB and BGM (not independently) and an online spreadsheet for data entry was used. These metadata included study characteristics related to publication (authors, year of publication, country and journal), study focus (syndrome or pathogen level, disease or intervention, unit of interest, farm type and animal age-group), study design (observational, experimental or simulation model) and economic methodology (type of economic evaluation, cost/revenue components, reported economic outcomes and currency). Additionally, we registered the origin of the data used in each study (e.g. primary data collected by the authors, expert opinion, data from scientific literature) and whether the paper mentions the testing of or accounting for the presence of other PRDC pathogens. All collected data are summarized in the text and provided in the Supplementary Files. Where we provide economic outcomes from the included studies, we adjusted the reported study outcomes for inflation using an online tool ( https://in2013dollars.com/ ) and converted the original currency to euros using a currency converter tool ( https://cuex.com/en/ ) (last applied on September 29, 2023). Where applicable, simple calculations were performed to reach a common unit to express the study results, such as the economic impact per fattening pig.

Overview of the included studies

The combination of search terms in the selected databases resulted in 1,940 studies (Fig.  1 ). In total, 651 non-duplicate citations were screened, and those that did not meet our previously defined screening criteria were excluded, leaving a total of 114 studies. After the final selection, 58 studies were deemed eligible for the purpose of this systematic review, including results from snowballing and JSHAP. The full list of references obtained from the systematic search is available in Supplementary file S2 .

figure 1

Flow diagram illustrating the systematic search strategy for identifying relevant studies. *JSHAP = Journal of Swine Health and Production

Characteristics of included studies

Detailed characteristics of the studies included in this review are presented in Tables  1 and 2 . Overall, the studies were classified into those focused on the economic impact of the disease (23/58; Table  1 ) and those assessing economics of interventions to prevent and/or control disease (33/58; Table  2 ). Two studies analysed both the impact of disease and of interventions [ 20 , 21 ]. Most intervention-focused studies investigated the effects of vaccination (24/35). Of these studies, seventeen evaluated the costs and benefits of vaccination for a short time period (i.e. in one cycle or one year), while seven evaluated the impact for a longer period. After vaccination, the most studied interventions related to elimination strategies (8/35; i.e. depopulation and repopulation, test and removal, herd closure), for all of which the impacts were studied for a long time period (> 1 year). Other interventions that were studied include animal management-related measures (4/35; no mixing of litters, early weaning, selection of pathogen-free gilts, separate housing), medication (3/35), biosecurity (3/35), alternative diet or feed regimen (2/35), and installation of air filtration systems (1/35). Eight of the intervention-focused studies investigated and compared the effects of several interventions.

The studies were conducted in 23 different countries. Twenty-six studies used data derived from European countries, 18 from the US, 6 from Asia, 4 from Oceania, and 4 from other countries, i.e., Canada, Mexico, and Brazil. Considering the period of 1980 until now, we found that over half of the studies (33/58) were published in the last ten years (2013–2022) and, of those, 61% (20/33) focused on PRRSV. Overall, half of the included studies (29/58) analysed the economic impact of PRRSV associated disease and/or its interventions, followed by M. hyopneumoniae (13/58). For the remaining pathogens the number of indexed studies was low: three for PCV-2, two for A. pleuropneumoniae , and one for SIV. Only in ten of all studies focusing on one specific pathogen, the absence or presence of other specific endemic respiratory pathogens was verified or accounted for. Then, six studies targeted co-infection scenarios (e.g., PRDC). In three of these studies, the co-infection of M. hyopneumoniae and PCV-2 was studied, whereas in the remaining studies different combinations of at least three of the primary pathogens (i.e. PRRSV, SIV, PCV-2, M. hyopneumoniae, A. pleuropneumoniae) were investigated. Lastly, four studies did not specify the respiratory pathogens involved, instead, they assessed the economic impact of lung lesions. Since the pathogens included in the present review are predominantly endemic worldwide, the economic analyses were mainly applied for endemic scenarios, although 24% (14/58) of the studies also included epidemic (i.e., outbreak) episodes in their analyses.

Most of the studies were conducted in commercial herds (54/58), with only two Asian studies of smallholder farms with less than 20 sows or 100 fattening pigs [ 22 , 23 ] and two studies conducted in research facilities [ 24 , 25 ]. The number of farms (owned by one or more pig producers) from which primary data were collected on production performance or health ranged from 1 to 162, with a single farm being investigated in 16 of the studies. Studies on the growing phase (33/58), including weaners and fatteners, predominated over the breeding phase (11/58), although several studies assessed economics in both production phases (14/58). Regarding the expression of economic outcomes, 17 different units of analysis were identified (e.g. pig, herd, farm, Kg meat). In 66% (38/58) of the studies, a singular unit was used, whereas the remaining 34% (20/58) used several units to express economic results. The growing pig was the most extensively used unit of analysis (28/58).

Methodology applied in included studies

In most of the disease-focused studies (16/25), an observational study design was used in which data were collected cross-sectionally, longitudinally, or retrospectively, with no intervention except for the collection of the data. Of these observational study designs, the cross-sectional study design (7/16) and the historical control study design (6/16), dominated over cohort (2/16) and case-control (1/16) study designs. Across all disease-focused studies, only one controlled trial was performed [ 26 ]. The remaining eight studies calculated economic impacts through modelling (8/25); five models were stochastic, one deterministic, one study described the use of a systems dynamics model [ 27 ] and one study applied the Cobb-Douglas production function [ 28 ]. In three of the modelling studies, input parameters were based on primary data on production performance or health collected on farms. In the remaining five, only secondary data (from scientific literature, grey literature, expert opinion or personal communication) were used. All modelling studies that used secondary data only, performed sensitivity analysis on uncertain input parameters.

Modelling was part of a large share of intervention-focused research, as 11 studies relied on simulation modelling exclusively. Of these studies, four collected primary production data from farms to be used in their models, whereas seven used secondary data only. As before, the modelling studies that used only secondary data performed sensitivity analysis on uncertain input parameters. Additionally, in three intervention-focused studies, controlled trial [ 29 ], cohort [ 30 ], or cross-sectional [ 31 ] study designs were combined with an economic deterministic model. Furthermore, fourteen studies collected data solely by means of a controlled trial and six by means of observational study designs (five historical control studies and one cohort study). One study, by Dee and Molitor [ 32 ], entailed a case report describing the attempt of PRRSV elimination on one farm. For detailed information on the study designs per included study, refer to Tables  1 and 2 .

Economic methods that were applied in the eligible studies, ranged from basic cost computations to more comprehensive methods commonly used in animal health economics (Table  3 ). The most utilised methods were basic cost computations (15/58) and cost analysis (14/58), followed by partial budget analysis (12/58). As expected, methods built for comparing the profitability of on-farm changes, i.e. the partial budget and cost-benefit analysis, were almost exclusively applied in intervention-focused studies. In five modelling studies, multiple economic methods were applied [ 33 , 34 , 35 , 36 , 37 ].

Seven studies provided estimates of the economic burden at a national level, of which only two studies included price effects across the industry or looked at changes in consumer and producer surplus due to decreased pork production [ 28 , 38 ]. The remaining five studies extrapolated farm-level estimates without accounting for additional macro-economic effects. Thus, most studies investigated the financial losses at the farm-level, rather than economic losses. However, to keep the terminology simple, we will keep referring to the calculated impacts as the economic impact.

To calculate the on-farm economic impact, a wide range of cost components were considered across all papers (for detailed information of the components per study, please refer to Supplementary file S3 ). Studies using the same economic method or focusing on the same disease, often included different cost and revenue components in their calculations (Fig.  2 ). Overall, the most frequently used cost components were veterinary costs (49/58 studies), feed costs (39/58), and labour costs (26/58); whereas the most frequently used revenue components were reduced carcass value (24/58), fewer growing pigs sold (19/58) and fewer piglets weaned/sold (19/58). The modelling studies that considered the most cost components [ 33 , 36 , 37 , 39 ] all reported that feed costs and the reduced revenue from fewer sold piglets or fattening pigs were the costliest components. Although most studies included these components, 19 out of the 58 studies did not consider feed costs, and 24 did not calculate lost revenues due to fewer piglets weaned or fattening pigs sold.

figure 2

Cost and revenue components considered in economic analyses of studies on PRRSV. * Other components include penalties, subsidies/compensation and industry effects

Pathogen specific costs

Despite the variety in units of analysis, the economic outcomes per study could be converted to a common unit for 17 out of 25 disease-focused studies (Fig.  3 a-c). This figure serves as an illustration for the range in reported economic impacts, but it should be noted that study outcomes cannot be merged directly due to the variety in study characteristics and methods of calculation.

figure 3

Economic impact of disease caused by endemic respiratory pathogens. The economic impact is expressed in decreased profit (in euros) per sow-year ( a ), per nursery pig ( b ), and/or, per fattening pig ( c ). Circles indicate a single reported outcome, whereas boxplots represent a range of economic outcomes from one study (e.g. when different scenarios with varying disease severity were considered, or when economic losses were reported for multiple farms separately). Reported outcomes were adjusted for inflation up until the year 2023 and converted to euros as a common currency. Studies that are marked with an *, did not include feed costs as a component in their economic analysis

Since most intervention-focused studies analysed the benefits of vaccination, the main economic outcomes for these studies are summarised in Table  4 . It is evident from this table that there is no common method for expressing the main economic impact of vaccination. Overall, most of the intervention-focused studies (24/35) reported a positive economic impact due to the implementation of the respective intervention, while three reported a negative impact [ 21 , 32 , 40 ] and four a neutral impact [ 30 , 41 , 42 , 43 ]. In the remaining four intervention-focused studies, the effects of different interventions were compared with each other rather than with a control group [ 20 , 25 , 29 , 44 ]. For all outcomes from both disease-focused and intervention-focused studies in their original currency, please refer to Supplementary file S4 .

An economic perspective is key to understand the impacts of disease and the intervention options available, and, therefore, to improve decision-making regarding animal health and welfare. This is especially important when endemic diseases are concerned, since their effects are often not easily quantified [ 45 ]. The present systematic review is the first in the field aiming to identify the economic impacts of specific or co-existing pathogens involved in the porcine respiratory disease complex (PRDC), and the costs and benefits of interventions. This work additionally reveals the economic evaluation methods that were applied across included studies, including the cost and revenue components that were considered in their calculations.

In an ideal scenario, an estimated disease impact should be completely attributable to the disease that is being analysed. However, often endemic respiratory diseases are multifactorial, and the impact of the disease can be influenced by multiple non-infectious risk factors. In addition, pig herds are often burdened with more than one endemic respiratory disease at the same time under the umbrella of the PRDC [ 12 , 13 ]. If the whole complex is not carefully studied, this could result in flawed estimates. Consequently, studying the effects of a specific pathogen where multiple disease-causing factors are involved is rather difficult, if not impossible in many cases. Most studies in the present review focused on one respiratory pathogen, and the presence or absence of other pathogen(s) was often not established. Therefore, the reported economic outcomes may not fully be the result of one specific respiratory pathogen only, but will be the product of a complex interaction between infectious agents, management conditions, environment, and genetics [ 12 , 13 ].

In total, 58 peer-reviewed studies were included within this systematic review. Most of these studies analysed the effects of an intervention, of which nearly half focused on vaccination. With fairly low numbers of studies on PCV-2, A. pleuropneumoniae and SIV, the PRRSV was by far the most studied pathogen, followed by M. hyopneumoniae . However, it should be noted that most studies on PRRSV were from the United States, thus outcomes were based on the effects of PRRSV-2 genotypes, which tend to be considered more virulent than PRRSV-1 ones, predominantly present in Europe [ 46 ]. However, others could not confirm that PRRSV-2 genotypes are more virulent than PRRSV-1 [ 47 , 48 ]. Nevertheless, estimates of PRRSV impact might be overestimated due to the overrepresentation of studies based on PRRSV-2. Although the difference in strain virulence of PRRSV-1 and PRRSV-2 genotypes shows perhaps the most clear difference in disease impact due to differentiated virus species, many studies have shown a variety of genotypes for a respiratory pathogen circulating and evolving within continents, countries, and even within the same swine operation over time [ 49 , 50 , 51 , 52 ]. The evolution of genotypes may influence not only their virulence, but also their resistance to treatments and vaccine efficacy [ 53 , 54 ].

An additional factor adding to the variation in economic impact is the variety in production systems and the overall industry structure across countries. Comparing the production losses on a commercial fattening farm in the United States [ 55 ] to the losses for a smallholder breeding farm in Vietnam [ 22 ] provides an evident example, but even within a continent or country vast differences may exist due to, among others, varying genetics of the pigs (e.g. differing productivity or disease resilience), the internal and external climate, the farm’s biosecurity or health status, access to high quality raw materials and veterinary services, differing target weights for selling and the size of the farm. External factors such as the amount of international import and export and governmental subsidies or other incentives can also lead to differences in economic losses suffered by the industry due to endemic respiratory disease. As this review covers research from a period of nearly 40 years, the evolution of pig production systems and industries regarding these aspects should be considered when drawing conclusions. It should be stressed that, although the described variation may complicate comparing or merging of study outcomes by means of a meta-analysis [ 56 ], this variation in research is essential to understand the range in economic impact from endemic respiratory disease at a global level.

When translating production impact into financial consequences, various limitations arise regarding the applied economic methodology. We observed over seven different economic evaluation methods with a large variety in cost and revenue components used to calculate economic outcomes. With the exception of one study [ 57 ], in which the farmers’ willingness to pay for a vaccine was estimated, the studies included in this review did not include non-monetary costs (e.g. environmental, social or welfare effects). The methods applied in the eligible studies varied from basic cost calculations to more comprehensive methods such as a farm budget analysis. Even after grouping eligible studies by their applied economic method, it was rare that the same cost and revenue components were used. Although we assume that for most studies, the authors included the components that were most relevant for the specific farms under study, a highly varying level of detail in calculations impacts the comparability of economic outcomes from each study. For instance, while increased feed costs and reduced revenue from fewer weaned or sold pigs were identified as the most important components [ 33 , 36 , 37 , 39 ], over a third of all studies did not include one or both components. Although these studies do not provide a specific reason for not including these components, it is recognised that in a number of them calculating the economic impact of a disease or an intervention was not the primary objective. Leaving out these important cost components may, therefore, be suitable for their respective study aims, but referring to the results as true economic impact estimates will lead to biased conclusions and comparisons with other study outcomes, as the total costs are underestimated. Additionally, the amount of feed costs per kg of carcass can differ greatly between countries, especially between continents [ 58 ]. This fact additionally holds for revenues per kg of carcass and the costs of medicines and vaccines [ 58 , 59 ]. Moreover, the prices of feed and raw materials are volatile and particularly rising in Europe during the last few years [ 60 ], which further impacts the comparability of economic outcomes estimated during different time periods.

While keeping the differences in economic evaluation methods, their level of detail and the differences in prices across countries and time in mind, most outcomes from the disease-focused studies could be converted to an economic impact in euros per pig, which gives a very rough impression of the range in economic impact of the PRDC syndrome. The median economic impact of one or several co-existing respiratory pathogen(s) as extracted from all studies, ranged from €1.70 to €8.90 per nursery pig, €2.30 to €15.35 per fattening pig, and €100 to €323 per sow per year. Excluding the studies in which feed costs were not considered, increases the minimum reported costs to €2.90 per nursery pig, €2.80 per fattening pig, and €195 per sow per year. Due to the low numbers of studies on pathogens other than PRRSV, the ranges mainly reflect the significant worldwide impact of PRRSV. It is, therefore, unfeasible to compare and rank the various pathogens according to their economic importance. Furthermore, converting absolute economic outcomes to a single currency complicates the interpretability and comparability of the study outcomes, as differences exist in the relative importance of the economic losses suffered by farmers from countries of different income levels. Preferably, outcomes would be reported in a relative manner, such as the percent decrease in profits due to disease. However, most often information on farm profits in a non-diseased scenario is lacking.

Nearly all studies reported neutral or positive impacts of implementing an intervention. This suggests that for a wide range of production systems and disease scenarios, implementing an intervention on a farm with endemic respiratory diseases increases farm profits. There may be an outcome reporting bias, with only the favourable interventions reported that can undermine the validity of systematic reviews [ 61 ]. However, we have no evidence that this is the case in our systematic review. Apparently, most studies looked at the effects of vaccination, with very few studies considering long-term sustainable interventions. Where several countries are making efforts to eliminate endemic respiratory diseases completely [ 62 , 63 ], economic research on long-term interventions (such as improvement of management practices, housing conditions or biosecurity measures) would provide valuable information for countries starting with or expanding the elimination of endemic respiratory pathogens. Besides the low number of studies on an intervention other than vaccination, comparison and ranking of interventions was also made unfeasible by the variation in the expression of results. Future research should use more standardised approaches for economic analyses of interventions with similar outcome metrics. For instance, in human health economics, comparison of control programs is mainly done by determining the cost-effectiveness (e.g. costs per disability-adjusted life-year) or cost-benefit ratio [ 59 ]. In the case of interventions requiring a large initial investment, calculations of the payback period or return on investment might be preferred [ 59 ].

Although the benefits from a standardised approach seem clear from discussing the limitations in the existing research, developing such an approach poses a challenge. The choice for a specific economic method is often dependent on the data available for the study, as well as the purpose of the study outcomes and the nature of the decision (whether researchers estimate the economic impact at the micro-scale or macro-scale, and for a short- or long-term, etc.). Consequently, the richness in methods could be an advantage, rather than only a limitation, as it will allow better alignment of the studies to the decision process required. It would therefore be of great interest to investigate why different methods or outcomes were chosen over others. Moreover, the industry-level economic burden of respiratory diseases in pigs is not limited to the direct costs, but also includes indirect costs, such as costs suffered by non-affected farms due to biosecurity investments or fluctuations in availability and prices of inputs and outputs. Most studies included in this systematic review focused on farm-level economic impacts, whereas methods well suited to study industry effects, such as the partial equilibrium analysis and econometric models, have not yet been explored. Likewise, economic analyses of the impact of policies to control PRDC pathogens were not found through the search. Therefore, there is currently no clarity on which indirect cost and revenue components from the PRDC seem to be most impactful at industry level. An approach that enhances the understanding of the economic burden of endemic respiratory disease for the entire industry would ideally include a range of economic methods, that captures both the economic impact on the farm, and on the (national or international) industry. Such an approach is being taken by the Global Burden of Animal Diseases programme and is being tested in different parts of the world [ 62 , 63 ].

Lastly, restricting the review to only peer-reviewed English literature ensures a certain quality of the work but can also narrow the scope of the review and the results. Including “grey” literature during the search, such as conference abstracts and industry reports, would mostly provide additional cost estimations by non-academic organisations or companies. This could assist with reducing publication bias, but it is important to ensure that the study is relevant to the research question and that it is of sufficient quality to be included in the review [ 64 ]. In this case, several non-peer-reviewed sources were identified, but oftentimes these entailed works in progress, pilot studies, or works that did not contain adequate or complete information (e.g. explicit information on cost or revenue components). This, together with the fact that searching for abstracts is resource-intensive and availability is usually compromised, advocated for the inclusion of peer-reviewed records only.

In conclusion, respiratory diseases represent a significant economic burden in pig production, as highlighted by the range in economic impact provided in this systematic review. Future research should improve the consistency and comparability of economic assessments by ensuring the inclusion of high impact cost and revenue components and expressing results similarly. Regardless, the outcomes from this systematic review provide insight in the variation in studies, their methods, their advantages and limitations, and the reported impacts from the endemic respiratory disease complex for pig production systems worldwide.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. All created tables supporting the conclusions of this article are included within the article and its supplementary files.

Abbreviations

Actinobacillus pleuropneumoniae

Journal of Swine Health and Production

Mycoplasma hyopneumoniae

Porcine circovirus 2

Porcine respiratory disease complex

Porcine reproductive and respiratory syndrome virus

Swine influenza virus

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Supplementary file S1.

Terms used to build the full search strings. - File provides a table of the terms that were used in the search for eligible literature

Supplementary file S2.

List of eligible studies. - File provides the full list of studies included in the systematic review

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Economic methods and cost components per study. - File provides full details on which economic method was applied and which cost components were considered per study

Supplementary file S4.

Reported economic outcomes per study. - File provides the economic outcomes in their original valuta as reported in each disease-focused and intervention-focused study. The file additionally includes information on the evaluation period for intervention-focused studies and on whether the economic analysis accounts for the presence/absence of other PRDC pathogens

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Boeters, M., Garcia-Morante, B., van Schaik, G. et al. The economic impact of endemic respiratory disease in pigs and related interventions - a systematic review. Porc Health Manag 9 , 45 (2023). https://doi.org/10.1186/s40813-023-00342-w

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Harris gives first solo interview as Democratic nominee

article review of economic

Kamala Harris has conducted the first solo interview of her White House campaign since she took up the baton as the Democrats' presidential candidate nearly two months ago.

The US vice-president sat down with a local ABC News station in the key swing state of Pennsylvania to discuss the economy, a political vulnerability, and gun control.

Reacting to the interview, her Republican rival, Donald Trump, said she "had a very hard time yesterday answering the simplest of questions".

Harris and Trump are in a dead heat in Pennsylvania and other battleground states ahead of November's White House election, according to opinion polls.

During Friday's 11-minute sit-down in Johnston, Harris was asked how she would bring down prices for Americans. Inflation has been receding since it surged early in the Biden administration to a 40-year high, even as unemployment fell to historic lows.

She said she would give small-business owners a $50,000 (£38,000) tax deduction to start their enterprises and a $25,000 down payment for first-time homebuyers. The price-tag for the plan and who might qualify are unclear.

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Last month Harris proposed the "first-ever federal ban on price gouging on food", though she did not mention that idea in Friday’s interview. It had provoked criticism from economists and business groups, and Trump likened it to Soviet-style price controls.

Harris also told the local ABC station on Friday that she supports constitutional gun rights, but wants to see semi-automatic weapons prohibited.

"I feel very strongly that it is consistent with the Second Amendment and your right to own a gun to also say we need an assault weapons ban," she said. "They're literally tools of war."

Harris went on to take a swing at Trump, saying the American people need someone to bring the country together, unlike her rival, who she said was "trying to have us point our fingers at each other".

She was also asked how she differed from the current US president, who suspended his campaign on 21 July and endorsed her. Harris repeated a line from the debate, saying she is "obviously not Joe Biden".

She also said she would "offer a new generation of leadership".

Trump posted on his social media platform Truth Social on Saturday morning that the interview was “a world salad, a real mess!”

Harris’ campaign was boosted this week when opinion polls suggested she won her first debate on Tuesday with Trump. The Republican has since said he will not debate her again, claiming he was the winner.

Amid calls for her to grant more media access, Harris last month sat down on CNN for her first interview since becoming the nominee, joined by her running mate Tim Walz.

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Kamala harris once again touts goldman sachs’ review of her economic plan – after firm’s ceo dismissed it.

WILKES-BARRE, Pa. — Vice President Kamala Harris played up a Goldman Sachs review of her economic plan on Friday after the firm’s CEO noted that the report actually showed her policies would have minimal impact on the economy. 

“Independent economists like Goldman Sachs have said my plan would grow our economy and [former President Donald Trump’s] plan would shrink the economy, reignite inflation and send us into a recession by the middle of next year,” Harris claimed at a rally in Wilkes-Barre, Pa. 

The vice president leaned on the investment bank’s report during her Tuesday debate against Trump as well, prompting Goldman Sachs CEO David Solomon to call out Harris for blowing the analysis out of proportion. 

Kamala Harris

“I think this blew up into something that’s bigger than what it was intended to be,” Solomon told CNBC on Wednesday.

The Wall Street heavyweight also noted that the report “came from an independent analyst” – not someone employed at the firm – and that Harris left out key details – including that the difference between her plan and Trump’s was “about two-tenths of 1%.”

“What the report did is it looked at a handful of policy issues that have been put out by both sides, and it tried to model their impact on GDP growth,” Solomon explained. “The reason I say a bigger deal has been made of it is what it showed is the difference between the sets of policies that they’ve put forward is about two-tenths of 1%.”

The Trump campaign accused Harris of “lying” about the report’s findings. 

At the campaign event, Harris also misrepresented Trump’s positions on several policy issues, claiming the GOP nominee for president “intends to cut Social Security and Medicare” and will use the Heritage Foundation’s Project 2025 as the blueprint for his administration. 

The Trump campaign’s official platform states that the 45th president would “fight for and protect Social Security and Medicare with no cuts, including no changes to the retirement age.”

Trump, 78, has also repeatedly disavowed Project 2025 stating that he has no intention of even reading about the think-tank’s policy suggestions.

Kamala Harris

In a new campaign promise, Harris pledged to remove “unnecessary degree requirements” for federal jobs, which is already an ongoing government initiative. 

“As president, I will get rid of the unnecessary degree requirements for federal jobs to increase jobs for folks without a four-year degree,” she told her Keystone State supporters. “Understanding that requiring a certain degree does not necessarily talk about one’s skills.” 

“And I will challenge the private sector to do the same,” she vowed. 

Catch up on The Post’s debate coverage

  • Kamala Harris’ dismissive laugh on full display in first presidential debate with Trump
  • Taylor Swift endorses Kamala Harris minutes after presidential debate: ‘I’ve made my choice’
  • Read Trump’s and Harris’ closing statements from their first presidential debate
  • Harris campaign calls for second debate after Trump’s performance is panned by critics
  • Trump refers to Kamala Harris’ 2020 viral ‘I’m speaking’ moment in presidential debate: ‘Does that sound familiar?’
  • Harris tries to pin Project 2025 on Trump during first minutes of presidential debate
  • Trump claims Harris supports abortions in the ‘ninth month — and she doesn’t deny it’ during presidential debate
  • Everything to know about the Donald Trump-Kamala Harris presidential debate

The US Office of Personnel Management’s federal jobs portal states that “except for certain professional and scientific positions, a college education may not be necessary” to apply for a slew of government positions.  

“You can qualify for many federal jobs based on job-related work experience,” the website notes.

The White House also announced in April that federal information technology jobs would move to a skills-based hiring process, removing educational requirements for certain tech and cybersecurity positions. 

The Harris campaign did not respond to The Post’s request for comment. 

Kamala Harris Pennsylvania rally

The vice president’s speech was interrupted by multiple anti-Israel protester, at least one of whom was removed from the event. 

“I respect your voice. But right now, I am speaking,” Harris said during one boisterous disruption. 

Mail-in voting for the presidential election starts on Monday in Pennsylvania, earlier than in any other state. The battleground state has 19 Electoral College votes up for grabs in November. 

“It’s great,”  Bridget Kosierowski, a 53-year-old, Democratic state representative from Scranton told The Post about the Keystone State’s voting process. “The earlier we [allow] people to get out and vote, give them time and access to such, is very important.”

“I think it’s a fair, legal process,” she added, noting that she intends to vote on Election Day but her children will vote by mail. 

Kamala Harris is not in the swing of things…

Swing . . . and a miss

In the topics that matter most in three key swing states, Kamala Harris showed that she was out of touch in Thursday’s interview :

Top issue: Immigration

58% of Arizonans, of either party, think that the United States does not have control over its border — a reality they see every day as a border state, according to a Redfield and Wilton Strategies poll.

Kam’s response:  CNN’s Dana Bash claimed Harris was put in charge of “root causes” — avoiding the term used at the time, “border czar” — and even then Harris corrected her, saying she was only tasked with dealing with “Northern Central America.” So she dodged all responsibility on the flood of migrants from Venezuela and other South America nations (and maybe Nicaragua? What counts as “Northern?”) Harris insisted the biggest problem was that a recent border bill didn’t pass, while she has been in office for three-and-a-half years without any action.

Top issue: The auto industry

Just 20% of Michiganders, home of much of America’s auto industry, back an electric vehicle mandate , the lowest of any state surveyed, according to Morning Consult.

Kam’s response:  “You mentioned the Green New Deal. I have always believed, and I have worked on it, that the climate crisis is real, that it is an urgent matter to which we should apply metrics that include holding ourselves to deadlines around time.” Harris has previously said those deadlines include getting rid of gas cars.

PENNSYLVANIA

Top issue: Energy and fracking

83% of Pennsylvanians believe drilling for more for gas and oil in the US would lower costs, 86% say it would improve national security, according to Morning Consult.

Kam’s response:  “There is no question I’m in favor of banning fracking,” she said in 2019. In the interview, she claimed she no longer wanted to ban fracking, but insisted, “My values have not changed.” Harris dubiously said she still favored the Green New Deal but would make an exception for fracking.

Kosierowski said she was “excited” about Harris’ “enthusiasm” and “empathy” and “her focus on women’s health, reproductive rights, child care, the workforce, health insurance, protecting the Affordable Care Act, protecting access to care.”

“She gets what people are thinking about,” the state rep said. 

James Ayrton, a 42-year-old higher education administrator from Blandon, Pa., told The Post that “policy-wise” he likes that Harris has strongly supported keeping the Affordable Care Act in place and her stance on the Israel-Hamas war.   

“I like holding on to Obamacare. I like her ideas in the Middle East, finding solutions to problems. And economically, having a fair tax system,” he said. 

When asked about Harris’ debate performance, Ayrton said he didn’t believe it helped the VP all that much.

“Unfortunately, I don’t know that it helps a lot,” he said. “Maybe Trump’s bad performance actually helps more than her good performance.”

The latest RealClearPolitics average of polls shows Harris with a razor-thin 0.1 percentage point advantage over Trump in Pennsylvania. 

Kamala Harris

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DA not keen on rice tariff hike in upcoming EO 62 review

article review of economic

The Department of Agriculture (DA) will not be recommending a higher tariff rate for imported rice in the upcoming periodic review of President Ferdinand Marcos Jr.'s Executive Order (EO) 62, which slashed the tariff rate for the grain to 15% from 35%.

The EO 62 mandates a periodic review of the tariff rates every four months upon the order's effectivity in early July.

"Sa November kasi ang review… the Department did not see the need na baguhin pa ang existing tariff in the existing EO," DA Assistant Secretary and spokesperson Arnel de Mesa said at a news forum in Quezon City.

Agriculture Secretary Francisco Tiu Laurel Jr. on Tuesday said his department would recommend raising the tariff rate for rice imports should retail rice prices go down significantly, at a price range between P42 per kilo and P45 per kilo, in time with EO's periodic review in November.

De Mesa said the EO 62 is still in the early stages of implementation and the impact of lowering the tariff on rice imports on retail prices would take a while to be felt by the consumers.

The DA official said that about 2.2 million metric tons of imported rice came into the country in the first six months of 2024 and that these were purchased "at a higher tariff regime of 35%."

"Kailangan ma-dispose mo 'yan (These need to be disposed)," De Mesa said.

Meanwhile, since the order took effect, there were about 440,000 MT of imported rice, which arrived in the country under a lower tariff regime.

De Mesa said that in July alone, about 150,000 MT of rice were imported while 260,000 MT came in August.

The National Economic and Development Authority (NEDA) had earlier said that it may take a little longer before Filipinos can feel the impact of the executive order lowering the tariff on rice imports.

In August, rice inflation eased to 14.7% from 20.9% in July due to the combined base effects and impact of reduced tariff rates.

Based on the DA's price monitoring in Metro Manila, as of Sept. 13, the prices of different imported commercial varieties rice are as follows:

  • Special — P55-P68 per kilo
  • Premium — P54-P60 per kilo
  • Well-milled — P45-P55 per kilo
  • Regular milled — P42-P50 per kilo

Prices of local commercial rice varieties in Metro Manila markets, on the other hand, are as follows:

  • Special — P59-P63 per kilo
  • Premium — P50-P58 per kilo
  • Well-milled — P47-P55 per kilo
  • Regular milled — P45-P51 per kilo

A petition seeking for the issuance of a temporary restraining order against EO 62 has been filed before the Supreme Court by Industriya ng Agrikultura (SINAG), the Federation of Free Farmers (FFF), the United Broiler Raisers Association, the Sorosoro Ibaba Development Cooperative, and former Magsasaka Party-list representative Argel Cabatbat. — VDV, GMA Integrated News  

rice tariff, rice tariffication law, agriculture, department of agriculture

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Biden Poised to Approve Ukraine’s Use of Long-Range Western Weapons in Russia

The topic will be on the agenda Friday as Britain’s new prime minister, Keir Starmer, visits the White House.

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Silhouette of a police officer wearing a helmet, with a damaged high-rise apartment building in the background.

By David E. Sanger Helene Cooper and Eric Schmitt

Reporting from Washington

President Biden appears on the verge of clearing the way for Ukraine to launch long-range Western weapons deep inside Russian territory, as long as it doesn’t use arms provided by the United States, European officials say.

The issue, which has long been debated in the administration, is coming to a head on Friday, as Britain’s new prime minister, Keir Starmer, visits the White House.

Britain has already signaled to the United States that it is eager to let Ukraine use its “Storm Shadow” long-range missiles to strike at Russian military targets far from the Ukrainian border. But it wants explicit permission from Mr. Biden in order to demonstrate a coordinated strategy with the United States and France, which makes a similar missile. American officials say Mr. Biden has not made a decision, but will hear from Mr. Starmer on Friday.

If the president approves, the move could help Ukraine hold the line after it seizes Russian territory, as it did during its surprise incursion into Russia’s Kursk region. But Mr. Biden has hesitated to allow Ukraine to use American weapons in the same way, particularly after warnings from American intelligence agencies that Russia could respond by aiding Iran in targeting American forces in the Middle East.

On Thursday, White House officials insisted there was no imminent decision on the use of the American-made surface-to-surface Army Tactical Missile Systems — known as ATACMS. But Mr. Biden himself has signaled that a loosening of restrictions is coming. He was asked on Tuesday whether he was ready to grant the increasingly insistent requests from President Volodymyr Zelensky of Ukraine.

“We are working that out right now,” he said.

If Mr. Biden permits the British and French to go ahead, and if he follows in coming weeks by allowing the use of the ATACMS, it could well be his final acceleration of the military aid to Ukraine.

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Volume 91, Issue 5, October 2024

Fixed effects and the generalized mundlak estimator.

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Unequal Expenditure Switching: Evidence from Switzerland

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Informality, Consumption Taxes, and Redistribution

Repayment flexibility and risk taking: experimental evidence from credit contracts, exploiting growth opportunities: the role of internal labour markets, evaluating the accuracy of counterfactuals: heterogeneous survival expectations in a life cycle model, multi-dimensional screening: buyer-optimal learning and informational robustness, bargaining as a struggle between competing attempts at commitment, contingent thinking and the sure-thing principle: revisiting classic anomalies in the laboratory, how credit constraints impact job finding rates, sorting, and aggregate output, dollar safety and the global financial cycle, path dependency in physician decisions, competition and career advancement, single-crossing differences in convex environments, incorporating diagnostic expectations into the new keynesian framework, capital regulation and shadow finance: a quantitative analysis, endogenous uncertainty and credit crunches, correction to: how credit constraints impact job finding rates, sorting, and aggregate output, email alerts.

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