## 1. The Neoclassical Growth Model ## Source details **Canonical URL:** [1. The Neoclassical Growth Model](https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021068-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2021/english/wpiea2021068-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2021/english/wpiea2021068-print-pdf.pdf.json) --- ### I. Introduction — framing the growth–inequality–povererty nexus - GDP measures aggregate economic production and affects payments to factors of production such as capital and labor. - Welfare gains from growth depend on distribution; representative-agent analysis suffices only if everyone benefits proportionately. - Metrics of inclusiveness used: poverty rate (World Bank threshold of $3.20 per day in PPP terms) and the Gini coefficient of income distribution. - Data limitations: poverty and inequality data are infrequent, sometimes unreliable, and sensitive to assumptions; multiple endogenous channels complicate causal inference. - Structure of the paper: stylized facts and trends (Section II); channels linking growth, poverty, and inequality (Sections III and IV); conclusions and policy implications (Section V). ### II. Trends in inequality, poverty, and growth — key stylized facts - Inequality - Market-based income inequality rose steadily in advanced economies in each decade from the 1980s through the 2010s. - Income inequality for EMDEs as a group has been broadly unchanged since the 1980s. - Market inequality in advanced economies has surpassed that of EMDEs on average in recent decades. - Cross-country variation: emerging markets and low-income countries show considerably more variation; top-decile to bottom-decile income ratios exceed 20 in several countries. - Redistribution - Fiscal redistribution through taxes and transfers reduces income inequality, especially in advanced economies. - Example: for advanced economies, disposable-income (net) Gini drops to an average of 30 points from nearly 50 points (market Gini). - Redistribution is limited in emerging markets and low-income countries due to smaller tax bases and fewer resources. - Poverty - Poverty rates are low and stable in advanced economies. - Most dynamics in poverty reduction since the 1970s concentrated in emerging markets and low-income countries. - Poverty measure: $3.20 per day (2011 PPP). - Growth - GDP per capita growth has been slowing in advanced economies every decade since the 1980s. - Growth accelerated in emerging markets and low-income countries, particularly since the 2000s. - Potential drivers of slowdown in advanced economies include aging and generalized slowdown in productivity growth. - Selected country and group comparisons (Inequality and Poverty in the 2010s vs 1980s; figures preserved exactly) - Brazil: Initial Gini (1980s) 60.9; Final Gini (2010s) 55.2; Change in Gini -5.8. Initial Poverty (1980s) 37.5; Final Poverty (2010s) 8.6; Change in Poverty -28.9. - Canada: Initial Gini 40.7; Final Gini 45.5; Change 4.7. Initial Poverty 0.4; Final Poverty 0.4; Change 0.0. - China: Initial Gini 30.2; Final Gini 41.4; Change 11.2. Final Poverty 15.2. - France: Initial Gini 48.2; Final Gini 49.0; Change 0.8. Initial Poverty 1.6; Final Poverty 0.1; Change -1.5. - Germany: Initial Gini 42.5; Final Gini 51.9; Change 9.4. Final Poverty 0.1. - India: Initial Gini 42.1; Final Gini 49.0; Change 6.9. Initial Poverty 84.9; Final Poverty 61.7; Change -23.2. - Indonesia: Initial Gini 39.6; Final Gini 42.6; Change 3.1. Initial Poverty 91.1; Final Poverty 33.9; Change -57.2. - Italy: Initial Gini 43.9; Final Gini 49.3; Change 5.4. Initial Poverty 0.8; Final Poverty 1.9; Change 1.1. - Japan: Initial Gini 37.8; Final Gini 45.6; Change 7.8. Final Poverty 0.6. - Mexico: Initial Gini 46.8; Final Gini 47.2; Change 0.4. Initial Poverty 19.0; Final Poverty 10.2; Change -8.8. - Russia: Initial Gini 35.3; Final Gini 45.6; Change 10.4. Final Poverty 0.5. - South Africa: Initial Gini 65.7; Final Gini 68.5; Change 2.8. Final Poverty 36.4. - Turkey: Initial Gini 44.4; Final Gini 43.1; Change -1.3. Initial Poverty 13.2; Final Poverty 2.6; Change -10.6. - United Kingdom: Initial Gini 46.4; Final Gini 52.9; Change 6.5. Initial Poverty 1.2; Final Poverty 0.3; Change -0.9. - United States: Initial Gini 44.7; Final Gini 50.8; Change 6.1. Initial Poverty 0.7; Final Poverty 1.2; Change 0.5. - Aggregates: - Advanced economies: Initial Gini 42.6; Final Gini 46.9; Change 4.3. Initial Poverty 0.8; Final Poverty 0.5; Change -0.3. - Emerging markets: Initial Gini 44.9; Final Gini 45.1; Change 0.2. Initial Poverty 34.7; Final Poverty 9.0; Change -25.7. - Low-income developing countries: Initial Gini 46.2; Final Gini 44.9; Change -1.2. Initial Poverty 62.3; Final Poverty 46.4; Change -16.0. - World average: Initial Gini 44.3; Final Gini 45.5; Change 1.2. Initial Poverty 29.1; Final Poverty 12.1; Change -16.9. ### III. How does growth affect poverty and inequality? — empirical evidence and channels - Empirical regularities - Income of the poor is strongly correlated with GDP per capita in levels and growth rates; when average GDP per capita rises, income in the lowest decile also increases and poverty falls. - Dollar and Kraay (2002): share of income of the first quintile varies proportionally with average incomes; correlation coefficient not statistically different from one. - Kraay (2006): in short term, growth in average income accounts for 70 percent of variation in poverty changes; in long term it accounts for 97 percent. - Bergstrom (2020): for extreme poverty (PPP $1.90) in 135 countries 1974–2018, 90 percent of variation in poverty rates explained by changes in GDP per capita; remainder largely due to changes in inequality. - Dollar, Kleineberg, and Kraay (2015, 2016): income in poorest deciles varies in equal proportions with average incomes across large samples; GDP per capita correlates with welfare at 0.98 (Jones and Klenow 2016). - Growth versus inequality - No significant systematic relationship between a country’s income level and its market inequality in the cited cross-country evidence; no support for a Kuznets inverse U-shape in this sample. - Per capita GDP growth is uncorrelated with contemporaneous changes in market Gini. - Changes in inequality depend on relative growth across deciles (growth incidence curve); correlation coefficients between per capita GDP growth and growth of each income decile range from 0.6 to nearly 1.0. - Short summary: historically, growth is the dominant force in poverty reduction; impact on inequality is ambiguous and depends on the distribution of income growth across the population. ### IV. How Does Poverty and Inequality Affect Growth? - Empirical evidence that initial inequality relates to subsequent growth - Bénabou (1996): countries with lower initial inequality exhibited higher growth rates in the following decades in comparative samples (e.g., East Asia vs Latin America). - Many cross-country studies find inequality (Gini) enters with a negative and statistically significant sign in growth regressions; reported quantitative range: "a one-standard-deviation decrease [in] inequality raises the annual growth of GDP in the range of 0.5 percentage points to 0.8 percentage points." - Heterogeneity: Barro (2000) finds negative relationship for poor countries and positive relationship for rich countries; Forbes (2000) finds higher inequality raises growth in the short/medium term in some panels; Cingano (2014) and Berg et al. (2018) find negative effects in other specifications. - Nonlinearities and mobility: Banerjee and Duflo (2003) propose an inverted U-shaped relationship; Aiyar and Ebeke (2020) emphasize role of intergenerational mobility. - Synthesis: mixed evidence arises from methodological differences (cross-country vs panel), country coverage, sample period, time horizon, model specification, and econometric method. ### IV.A Channels by which Inequality Can Boost Growth - Incentives: inequality can incentivize work, saving, and investment; compensation schemes that reward performance can encourage skill acquisition and effort. - Savings: Kaldor (1957) hypothesis—richer individuals save more, so higher inequality can raise national saving and investment; U.S. evidence (Dynan, Skinner, and Zeldes 2004) supports positive correlation between saving rates and income level. ### IV.B Channels by which Inequality and Poverty Can Depress Growth - Poverty traps and human capital - Poverty undermines accumulation of human capital via poor nutrition, lower school attendance, larger learning gaps, and weaker public provision of health and education. - Public spending per capita on health and education is lower for countries with high poverty rates; poverty headcount ratio uses $3.20 per day (2011 PPP). - Development-stage effect (Galor and Moav 2004): early-stage economies may benefit from some inequality; human-capital-driven growth benefits from greater equality. - Credit market imperfections - Financial frictions prevent the poor from borrowing to invest in human or physical capital (Galor and Zeira 1993; Banerjee and Newman 1993); redistribution can enable investment and stimulate growth. - Demand and structural transformation - Inequality shapes demand composition; for goods with large fixed costs, sufficient concentrated demand may be required to trigger production; broad distribution of income is needed for spillovers and mass production. - Behavioral and decision-making channels - Risk aversion, attention scarcity, lower aspirations, temptation/self-control issues reduce investment and productive decisions among the poor (Banerjee 2000; Dercon 2005; Banerjee and Mullainathan 2010; Shah, Mullainathan, and Shafir 2012). - Political economy - Redistribution channel: inequality pressures for redistribution can raise distortionary taxation and lower investment (Alesina and Rodrik 1994; Persson and Tabellini 1994). - Institutional channel: elites may capture institutions to their benefit, weakening property rights and competition and harming growth (Glaeser, Scheinkman, and Shleifer 2003; Acemoglu and Robinson 2019). - Sociopolitical unrest - Inequality increases political instability, social unrest, and violence, which reduce investment and growth (Alesina and Perotti 1996; Rodrik 1999). - Gender inequality - Gender disparities depress growth via lower female participation, higher fertility, and associated human-capital effects (Galor and Weil 1996; Kremer and Chen 2002; Cuberes, Newiak, and Teignier 2017). Examples: gender inequality in labor markets leads to income losses of 15.5 percent in OECD countries and 17.5 percent in non-OECD countries (Cuberes, Newiak, and Teignier 2017). ### V. Neoclassical model: core predictions and limitations - Core model: Y = F(A, K, L) where A = TFP, K = capital, L = labor. - Investment accumulates capital, raising marginal product of labor and wages; TFP raises marginal products of both capital and labor. - Diminishing returns to capital imply capital-poor countries expected to grow faster and converge. - Policy implication under one-sector representative-agent model: improve incentives for investment to reach a higher steady-state capital stock. - Limitation: representative-agent assumption ignores heterogeneity in capital ownership and labor supply; model centers on aggregate growth rather than distributional outcomes. ### V.A Role of Government: public goods and redistribution - Growth raises fiscal capacity; higher tax ratio facilitates provision of pro-poor public goods (health, education). - Effect depends on societal preferences and composition/efficiency of public spending (Wagner’s Law cited). - Redistribution through taxes and transfers reduces inequality; composition and incidence of taxes/transfers matter (e.g., energy subsidies often benefit the rich more). - Cross-country evidence: higher-income countries engage in more redistribution (difference between Gini before and after taxes and transfers). ### V.B Factors and markets: employment, technology, and market structure - Employment and labor markets - Short- and medium-term underemployment and recessions can generate spikes in unemployment and inequality; scarring can depress earnings by as much as 20 percent 15 to 20 years after a layoff. - Okun’s law: unemployment varies inversely with cyclical growth. - Average adult worker in a low-income country works 50 percent more hours than adult workers in high-income countries (Bick, Fuchs-Schündein, and Lagakos 2018). - Skill-biased technical change and labor heterogeneity - Technological progress has complemented skilled workers more than unskilled, increasing wage skill premium and labor-income inequality (Autor 2014; Autor, Katz, and Kearney 2008). - Since late 1980s, labor-market polarization increased: demand rose for skilled and unskilled at expense of middle-skill jobs (Goldin and Katz 2007). - Cross-country IV evidence (Brueckner, Dabla Norris, and Gradstein 2015): a 1 percent increase in real GDP per capita reduces the Gini coefficient by around 0.08 percentage points on average; education proxies reduce the importance of national income in explaining inequality. - Market structure and rising markups - Evidence of increasing market concentration and markups across advanced economies and industries (Diez, Leigh, and Tambunlertchai 2018; De Loecker and Eeckhout 2018). - U.S. markups nearly tripled between 1980 and 2016: from 21 percent above marginal cost to 61 percent (De Loecker, Eeckhout, and Unger 2020). - High markups → lower labor share, lower investment, reduced aggregate demand for labor, negative implications for wages and inequality (Autor et al. 2020). ### VI. Unbalanced growth: sectoral composition, capital intensity, technology, trade, and finance - Sectoral composition - Agriculture often most effective at reducing poverty, followed by construction and manufacturing (Loayza and Raddatz 2010). - Mining, utilities, and some services do not have statistically significant impacts on poverty alleviation. - Sectoral effects vary by country (e.g., China, India, Indonesia cases cited). - Capital intensity and returns to capital - Growth concentrated in capital- or innovation-intensive sectors can yield higher returns to capital than to labor; labor share of output has fallen across economies (Dao, Das, and Koczan 2019). - Piketty (2015): when return on capital (r) exceeds growth rate of GDP (g), inequality can increase. - Technology and innovation - ICT adoption raised demand for highly educated workers at expense of middle-educated workers (Michaels, Natraj, and Van Reenen 2014). - Robot adoption increased productivity and wages but often reduced employment shares of low-skilled workers (Graetz and Michaels 2018; Acemoglu and Restrepo 2020); other studies find mixed or positive net employment effects (Aghion et al. 2020). - Trade and financial liberalization - Stolper-Samuelson implications differ by factor abundance: trade liberalization can raise unskilled wages in unskilled-labor-abundant developing countries but lower them in skilled-labor-abundant developed countries. - FDI tends to flow to high-skilled sectors, potentially raising the skill premium; other capital flows have ambiguous impacts. - Financial integration can improve intermediation but increase crisis frequency; institutional quality mediates distributional impacts. ### VII. Conclusions and policy implications - Two main conclusions on growth’s impact on inclusion: - Consensus: growth reduces poverty. Growth in emerging and low-income economies has been a first-order driver of poverty reduction. - Impact on inequality is ambiguous and depends on growth sources and sectoral composition (e.g., skill-biased technological change can raise inequality). - Two main conclusions on inclusion’s impact on growth: - Poverty impedes growth by reducing the poor’s ability and incentives to accumulate human and physical capital, access markets and public services, and behave in forward-looking ways. - Inequality’s effect on growth is complex: can provide incentives for investment and saving but can also harm growth through political economy, institutional capture, social conflict, and demographic channels. - Policy recommendations (preserve framing from source) - Prioritize policies to promote growth because growth helps reduce poverty; growth is necessary for lifting incomes and expanding access to nutrition, health, education, and high-quality jobs. - Adopt policies across five broad categories identified by the Growth Report: accumulation of human and physical capital; innovation and technology adoption; efficient allocation of resources; macroeconomic stabilization; social inclusion. - Evaluate distributional consequences as well as growth consequences of policy interventions. - Implement public measures to ensure losers from economic transformation can transition to better jobs and receive social protection during transitions. - Differentiate between inequality of outcomes and inequality of opportunities; remove barriers based on initial income/wealth/place of birth/race/ethnicity/sexual orientation/disability to improve both growth and equity. - Expand access to health care, education, safety, justice, social protection, and finance to improve growth and equity simultaneously. *Source: wpiea2021068-print-pdf — https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021068-print-pdf.pdf* ### 1. The Neoclassical Growth Model ............................................................................. 15 ### 1. The Neoclassical Growth Model ............................................................................. 15 ### Major sections and themes - 2. The Government: Public Goods and Redistribution.................................................. 16 - 3. Factors and Markets................................................................................................ 18 - 4. Unbalanced Gr o w th ................................................................................................ 21 - 5. Empirical Estimates of Multiple Drivers of Growth and Inequality ........................... 25 ### IV. How Does Poverty and Inequality Affect Growth? .......................................................... 26 - A. Empirical Estimates of th e Imp act of Poverty and Inequality on Growth ...................... 26 - 1. From Poverty to Gro w th ......................................................................................... 26 - 2. From Inequality to Growth...................................................................................... 27 - B. Channels from Poverty and Inequality to Growth ........................................................ 30 - 1. Channels by which Inequality Can Boost Growth .................................................... 30 - 2. Channels by which Inequality and Poverty Can Depress Growth .............................. 31 ### V. Conclusions and Policy Implications............................................................................... 38 *Source: wpiea2021068-print-pdf - 1. The Neoclassical Growth Model — https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021068-print-pdf.pdf* ### References ............................................................................................................. ### wpiea2021068-print-pdf - References ..................................................................................................... ### I. Introduction — framing the growth–inequality–povererty nexus - GDP measures aggregate economic production and is an estimate of how aggregate income increases over time; GDP growth affects payments to factors of production such as capital and labor. - Whether growth improves individual welfare depends on the distribution of growth across society; representative-agent analysis suffices only if everyone benefits proportionately. - This paper focuses on two metrics of inclusiveness: the poverty rate (using the World Bank threshold of $3.20 per day in PPP terms) and the Gini coefficient of income distribution. - Data limitations: poverty and inequality data are infrequent, sometimes unreliable, and sensitive to assumptions (e.g., capital gains, untaxed income). Multiple endogenous channels complicate causal inference between growth, poverty, and inequality. - Structure: Section II presents stylized facts and trends; Sections III and IV review channels linking growth, poverty, and inequality; Section V concludes with takeaways and policy implications. ### II. Trends in inequality, poverty, and growth — key stylized facts - Inequality - Market-based income inequality has risen steadily in advanced economies in each decade from the 1980s through the 2010s. - Income inequality for emerging market and developing economies (EMDEs) as a group has been broadly unchanged since the 1980s. - Result: market inequality in advanced economies has surpassed that of EMDEs on average in recent decades. - Variation across countries: - Emerging markets and low-income countries show considerably more cross-country variation in inequality than advanced economies. - For emerging markets and low-income countries, the ratio of income of the top decile relative to the bottom decile exceeds 20 for several countries. - Redistribution - Fiscal redistribution through taxes and transfers reduces income inequality, especially in advanced economies. - Example magnitude: for advanced economies, disposable-income (net) Gini drops to an average of 30 points from nearly 50 points (market Gini). - Redistribution is limited in emerging markets and low-income countries due to smaller tax bases and fewer resources for redistribution. - Poverty - Poverty rates are low and stable in advanced economies. - Most dynamics in poverty reduction since the 1970s concentrated in emerging markets and low-income countries, with emerging markets seeing the largest reductions. - Poverty measure used: $3.20 per day (2011 PPP). - Growth - GDP per capita growth has been slowing in advanced economies every decade since the 1980s. - Growth has accelerated in emerging markets and low-income countries, particularly since the 2000s. - Potential drivers of slowdown in advanced economies include aging and generalized slowdown in productivity growth. - Selected country and group comparisons (Inequality and Poverty in the 2010s vs 1980s; preserve numbers exactly) - Brazil: Initial Gini (1980s) 60.9; Final Gini (2010s) 55.2; Change in Gini -5.8. Initial Poverty (1980s) 37.5; Final Poverty (2010s) 8.6; Change in Poverty -28.9. - Canada: Initial Gini 40.7; Final Gini 45.5; Change 4.7. Initial Poverty 0.4; Final Poverty 0.4; Change 0.0. - China: Initial Gini 30.2; Final Gini 41.4; Change 11.2. Initial Poverty ...; Final Poverty 15.2; Change ... - France: Initial Gini 48.2; Final Gini 49.0; Change 0.8. Initial Poverty 1.6; Final Poverty 0.1; Change -1.5. - Germany: Initial Gini 42.5; Final Gini 51.9; Change 9.4. Initial Poverty ...; Final Poverty 0.1; Change ... - India: Initial Gini 42.1; Final Gini 49.0; Change 6.9. Initial Poverty 84.9; Final Poverty 61.7; Change -23.2. - Indonesia: Initial Gini 39.6; Final Gini 42.6; Change 3.1. Initial Poverty 91.1; Final Poverty 33.9; Change -57.2. - Italy: Initial Gini 43.9; Final Gini 49.3; Change 5.4. Initial Poverty 0.8; Final Poverty 1.9; Change 1.1. - Japan: Initial Gini 37.8; Final Gini 45.6; Change 7.8. Initial Poverty ...; Final Poverty 0.6; Change ... - Mexico: Initial Gini 46.8; Final Gini 47.2; Change 0.4. Initial Poverty 19.0; Final Poverty 10.2; Change -8.8. - Russia: Initial Gini 35.3; Final Gini 45.6; Change 10.4. Initial Poverty ...; Final Poverty 0.5; Change ... - South Africa: Initial Gini 65.7; Final Gini 68.5; Change 2.8. Initial Poverty ...; Final Poverty 36.4; Change ... - Turkey: Initial Gini 44.4; Final Gini 43.1; Change -1.3. Initial Poverty 13.2; Final Poverty 2.6; Change -10.6. - United Kingdom: Initial Gini 46.4; Final Gini 52.9; Change 6.5. Initial Poverty 1.2; Final Poverty 0.3; Change -0.9. - United States: Initial Gini 44.7; Final Gini 50.8; Change 6.1. Initial Poverty 0.7; Final Poverty 1.2; Change 0.5. - Country classification aggregates: - Advanced economies: Initial Gini 42.6; Final Gini 46.9; Change 4.3. Initial Poverty 0.8; Final Poverty 0.5; Change -0.3. - Emerging markets: Initial Gini 44.9; Final Gini 45.1; Change 0.2. Initial Poverty 34.7; Final Poverty 9.0; Change -25.7. - Low-income developing countries: Initial Gini 46.2; Final Gini 44.9; Change -1.2. Initial Poverty 62.3; Final Poverty 46.4; Change -16.0. - World average: Initial Gini 44.3; Final Gini 45.5; Change 1.2. Initial Poverty 29.1; Final Poverty 12.1; Change -16.9. ### III. How does growth affect poverty and inequality? — empirical evidence and channels - Empirical regularities on growth and the poor - Income of the poor is strongly correlated with GDP per capita in levels and growth rates: when average GDP per capita rises, income in the lowest decile also increases and poverty falls ("rising tide lifts all boats"). - Dollar and Kraay (2002): in a sample of 92 countries from 1950 to 1999, the share of income of the first quintile varies proportionally with average incomes; correlation coefficient not statistically different from one. - Kraay (2006): in a panel of 80 countries, decomposing poverty changes shows that in the short term, growth in average income accounts for 70 percent of the variation in poverty changes; in the long term, it accounts for 97 percent. - Dollar, Kleineberg, and Kraay (2016): using 151 countries from 1967 to 2011, income in the poorest deciles varies in equal proportions with average incomes; shares for the poorest 20th and 40th percentiles are fairly stable over time. - Bergstrom (2020): analyzing extreme poverty (PPP $1.90) in 135 countries 1974–2018, 90 percent of the variation in poverty rates can be explained by changes in GDP per capita; much of the remainder is accounted for by changes in inequality. A 1 percent decline in inequality (std. dev. of log income) reduces poverty more than a 1 percent increase in GDP per capita for most countries, but mean growth changes have been substantially larger historically. - Dollar, Kleineberg, and Kraay (2015): across 151 countries over five decades, most variation in welfare is driven by average income growth; inequality plays a relatively minor role because changes in inequality have been small and generally uncorrelated with growth. GDP per capita correlates with welfare at 0.98 (Jones and Klenow 2016). - Growth versus inequality relationship - No significant systematic relationship between a country’s income level and its market inequality; cross-country evidence does not support a Kuznets inverse U-shaped relationship in this sample. - Per capita GDP growth is uncorrelated with contemporaneous changes in inequality (market Gini). - Reason: strong correlation between per capita GDP growth and growth of each income decile (correlation coefficients between 0.6 to nearly 1.0); changes in inequality depend on relative growth across deciles (growth incidence curve), which can be ambiguous when bottom and top deciles grow faster than middle deciles. - Short summary: growth is the dominant force in poverty reduction historically; the impact on inequality is ambiguous and depends on the distribution of income growth across the population. Reductions in inequality can have a strong poverty-reducing effect, but observed changes in mean income have typically been larger than changes in inequality. *Italic: Source — wpiea2021068-print-pdf (References and selected sections).* ### 1. The Neoclassical Growth Model ### 1. The Neoclassical Growth Model ### Neoclassical model: core predictions and limitations - Output defined as Y = F(A, K, L) where A = total factor productivity (TFP), K = capital, L = labor. - Investment → capital accumulation → increases marginal product of labor and wages. - Increases in TFP raise marginal products of both capital and labor and therefore their income payments. - Higher investment and/or higher technological progress imply higher production and higher incomes for everyone in the economy (per the model). - Diminishing returns to capital imply capital-poor countries are expected to grow faster and converge to capital-rich countries. - Policy implication under the one-sector representative-agent model: focus on improving incentives for investment to grow and converge to a higher steady-state capital stock. - Limitation: model assumes no heterogeneity in capital ownership and labor supply within a country and centers on aggregate growth rather than distributional issues. ### Growth diagnostics (Hausmann, Rodrik, and Velasco 2005) within the neoclassical framework - Growth depends on three elements: the returns to capital accumulation, their private appropriability, and the cost of financing capital investment. - Distortions that lower returns to capital include: high taxes or expropriation risk; large negative externalities; low productivity; insufficient infrastructure or human capital investment. - Distortions that increase cost of financing include: underdeveloped domestic financial markets (lack of banking competition, poor regulatory framework); impediments to international financing (high country-risk premium, excessive capital-account regulation, external debt vulnerabilities). - Limitation: relies on representative agent approach and does not illuminate distributional impacts of growth policies. ### Channels through which relaxing neoclassical assumptions generates distributional effects - Key assumptions relaxed: presence of government redistribution; full employment; fixed and undifferentiated labor supply; competitive market structure; balanced growth (no differential growth across sectors/regions/firms). --- ### 2. The Government: Public Goods and Redistribution - Public goods and services - Growth increases aggregate resources and the public sector’s capacity to collect taxes. - A higher tax ratio facilitates provision of public goods (health and education) that can be pro-poor. - The extent to which growth expands pro-poor public services depends on society’s preferences for private versus public goods and the composition of public goods. - Empirical regularity cited as Wagner’s Law (Wagner 1893): as countries become richer, government can raise more fiscal revenue and increase capacity to provide public goods. - Conditional effect: increased government size can reduce poverty and improve income distribution provided spending is efficient and composition benefits the poor. - Reference to Figure 6: Tax Revenues and Spending on Health and Education, by Country Group (percent of GDP, 2010-19 average) supporting the stylized fact that richer countries raise more fiscal revenue and spend more on public goods. - Redistribution - Impact of growth on poverty and inequality through redistribution depends on social preferences; poverty and inequality reduction may be treated as “normal goods.” - Cross-country evidence (Figure 7) shows higher-income countries engage in more redistribution (measured as difference between Gini before and after taxes and transfers). - Composition and incidence of taxes and transfers matter: e.g., energy subsidies in developing countries may be intended to support the poor but largely benefit the rich who spend more on energy products. - Reference to Cerra et al. 2021, Chapter 12 and 13 for elaboration on taxation and spending policies. --- ### 3. Factors and Markets - Employment of factors - In short and medium term, factors like labor and capital may not be fully employed. - Recessions (financial distress, pandemics) can reduce long-term output and generate spikes in unemployment and inequality and declines in capacity utilization. - Unemployment creates income losses in the short term, especially for lower-income groups (lower educational attainment, ethnic minorities, women). - Scarring effects: von Wachter, Song, and Manchester (2009) find that 15 to 20 years after a layoff, earnings can be depressed by as much as 20 percent. - Okun’s law: unemployment varies inversely with cyclical growth (Ball, Leigh, and Loungani 2017); higher growth generates employment and improves inclusion. - Economic volatility is associated with both lower growth and higher inequality (Cerra et al. 2021, Chapter 11). - Poverty traps can prevent low-income individuals from taking up more remunerative activities without government intervention (e.g., microcredit). - Labor supply response - Growth that generates higher returns to labor induces more work effort, but if leisure is a normal good, higher-income people increase work less than low-income people. - Empirical finding: Bick, Fuchs-Schündein, and Lagakos (2018) show the average adult worker in a low-income country works 50 percent more hours than adult workers in high-income countries. - Within countries, average hours worked decrease with level of wages, except in very high-income countries (including the United States) where hours increase with wage rate. - Growth leads to demographic changes: decline in number of children and increased parental investment in upbringing/education; may induce women to enter the labor force, raising family incomes and reducing poverty in some cases. - Becker (1992) framework: economic growth can lower fertility, which reduces labor supply and thus increases the return to labor. - Differentiated labor and skill-biased technical change - Labor is heterogeneous: educational attainment and skills vary. - Technological progress has generally complemented skilled and educated workers more than unskilled workers, increasing demand for the former and reducing it for the latter. - Result: skilled-biased technological change contributes to rising wage skill premium and increased inequality of labor income (Krusell et al. 2000). - United States evidence: college wage premium roughly doubled between 1980 and 2012 for both male and female workers (Autor 2014; Autor, Katz, and Kearney 2008), partly due to skill-biased technological change. - Since late 1980s, skill-biased technological change has led to job market polarization: increased demand for skilled and unskilled workers at expense of middle-class jobs (Goldin and Katz 2007). - Cross-country evidence (Brueckner, Dabla Norris, and Gradstein 2015): for 80 countries, instrumental-variable regressions show a 1 percent increase in real GDP per capita reduces the Gini coefficient by around 0.08 percentage points on average; importance of national income in explaining inequality is significantly reduced when education proxies are introduced, making education a probable channel. - Market structure - Many industries deviate from perfect competition: natural monopolies, policy-induced monopolies, or rent-supported industries (natural resources) generate high returns to owners without commensurate rises in payments to labor. - Returns to entrepreneurship, capital, land, and resource ownership can rise faster than returns to labor (especially unskilled labor). - Larger markets can provide higher returns to owners if competition is avoidable; network effects and tournament effects can amplify disparities. - Evidence of rising market concentration and markups: - Diez, Leigh, and Tambunlertchai (2018) document a generalized increase in market concentration across advanced economies and industries. - De Loecker and Eeckhout (2018) analyze global evolution of market power from 1980 to 2016 using Worldscope data for more than 60,000 firms in 134 countries; rising markups and market power concentrated in advanced economies. - United States: De Loecker, Eeckhout, and Unger (2020) show markups nearly tripled between 1980 and 2016, increasing from 21 percent above marginal cost to 61 percent. - Rise in markups concentrated in firms already in the upper tail; these firms expanded at the expense of low-markup firms. - Macroeconomic and distributional consequences of higher markups and market power: - High markups → lower investment and lower wages at high levels of markups and profitability. - For a cost-minimizing firm, labor share is inversely related to the markup. - Greater market power implies fewer firms, lower output, reduced aggregate demand for labor, negatively affecting real wages and income inequality. - Autor et al. (2020): industries with largest increases in market concentration have also experienced larger declines in the labor market share. - Cerra et al. (2021, Chapter 6) discusses market structure’s role in shaping inclusive growth in more detail. *Source: wpiea2021068-print-pdf - 1. The Neoclassical Growth Model* ### 4. Unbalanced Growth ### 4. Unbalanced Growth ### Overview - Different sectors, industries, regions, and firms may grow at different rates, producing uneven returns and unequal income effects. - “Creative destruction” (Schumpeter 1942) can displace workers or cause stagnant wages for some groups. - Pecuniary externalities (e.g., higher housing rents) can reduce real incomes of the poor (Matlack and Vigdor 2008). ### Sectoral Composition - Empirical evidence shows the sectoral composition of growth matters for poverty reduction (Loayza and Raddatz 2010). - Findings from cross-country and country-specific studies: - Agriculture is identified as the most effective poverty-reducing sector, followed by construction and manufacturing (Loayza and Raddatz 2010). - Mining, utilities, and services do not have a statistically significant impact on poverty alleviation (Loayza and Raddatz 2010). - India (Ravallion and Datt 1996): agriculture and services correlated with declines in poverty in rural and urban areas; industrial growth did not have a systematic impact on poverty. - China (Ravallion and Chen 2007): agriculture growth was the most important driver for poverty alleviation. - Indonesia (Suryahadi, Suryadarma, and Sumarto 2009): service-sector growth strongly correlated with poverty reduction in rural and urban areas; agriculture growth correlated with rural poverty declines. - Ivanic and Martin (2018): in poor countries, productivity gains in agriculture are generally—although not always—more effective in reducing global poverty than equivalent productivity gains in industry or services; the effectiveness of agriculture fades as average income rises. - Implication: growth concentrated in sectors not intensive in unskilled labor (e.g., oil and mining) may be insufficient to reduce poverty. ### Capital intensity - Growth concentrated in capital- or innovation-intensive sectors can yield higher returns to capital and entrepreneurs than to labor. - Labor share of output has fallen across advanced and emerging market economies due to capital deepening and technological progress (Dao, Das, and Koczan 2019). - Piketty (2015) documents that when the return on capital exceeds the growth rate of GDP, inequality can increase because capital owners are concentrated at the top of the income distribution. The difference between return to capital (r) and the growth rate of GDP (g) amplifies wealth inequality over time. - Countervailing effects: - Capital-intensive growth could spill over and stimulate labor-intensive sectors, benefiting the poor. - Strong productivity growth in labor-intensive agriculture could reduce demand for rural labor, potentially increasing poverty and urban unemployment. ### Technology and innovation - Innovation driven by potential rents contributes to growth and a rising share for the top 1 percent; innovations have limited impact on inequality in the bottom 99 percent and may be positively correlated with social mobility (Aghion et al. 2019). - Conceptual distinction (Galor and Tsiddon 1997): - “Invention”: draws on ability, leads to higher inequality and higher intergenerational mobility. - “Innovation”: depends on human capital correlated with parental human capital, leads to lower inequality but lower intergenerational mobility. - ICT adoption (Michaels, Natraj, and Van Reenen 2014, sample of 11 OECD countries, 1980–2004): - Industries with higher ICT growth increased demand for highly educated workers at the expense of middle-educated workers. - Demand for low-skilled workers was not affected; result is labor-market polarization across OECD economies. - ICT can increase bargaining power of large entities able to collect and analyze large amounts of individual data, raising inequality. - Robot adoption (Graetz and Michaels 2018, 17 OECD countries, 1993–2007): - Increased robot use contributed to higher labor productivity and average wages, lower output prices, but reduced employment shares of low-skilled workers. - Acemoglu and Restrepo (2020) for US: adopting robots led to higher productivity gains but lower aggregate employment and wages; estimate that, on average, one robot displaces three workers, even after accounting for productivity and price effects. - Aghion et al. (2020) for French manufacturing: net positive effects from automation on employment, including unskilled workers, and no discernible impact on wages. - Cerra et al. (2021, Chapters 3 and 5) analyze links between technology, labor markets, and inequality in more detail. ### Trade - Stolper-Samuelson theorem (Hecksher-Ohlin context) implications: - In a developing country abundant in unskilled labor, tariff reduction increases exports of labor-intensive goods and raises compensation of unskilled workers, reducing inequality. - In developed countries abundant in skilled labor, trade liberalization reduces wages of unskilled workers relative to skilled ones, raising inequality. - Empirical observation: the skill premium has increased in both advanced and developing countries, mainly due to skill-biased technological change; thus, factors beyond trade (notably technology) are important drivers of inequality (Cerra et al. 2021, Chapter 7). ### Financial liberalization - Financial globalization affects income distribution through multiple channels (Cerra et al. 2021, Chapter 8): - Foreign direct investment (FDI) typically flows to high-skilled sectors, potentially raising the skill premium and increasing inequality (Cragg and Epelbaum 1996). - Other capital flows (portfolio, debt, equity) have ambiguous impacts on inequality. - Higher global financial integration can improve financial intermediation and help the poor accumulate human and physical capital, but can also increase frequency of financial crises (Kaminsky and Reinhart 1999). - Capital account liberalization may raise government debt (Azzimonti, de Francisco, and Quadrini 2014), increasing likelihood of a debt crisis; financial and debt crises often lead to severe recessions that disproportionately affect the poor and raise inequality (Cerra et al. 2021, Chapter 11). - Institutional quality matters: strong institutions can channel financial flows to productive uses and enable consumption smoothing for the poor; weak institutions can give disproportionate access to well-connected actors and exacerbate inequality. *Source: 4. Unbalanced Growth, wpiea2021068-print-pdf* ### 2. From Inequality to Growth ### 2. From Inequality to Growth ### Empirical illustration: initial inequality and subsequent growth - Bénabou (1996) compares growth outcomes of East Asian and Latin America economies conditional on initial levels of income inequality; conventional wisdom is that relatively equal distribution of income and land in East Asian economies contributed to their observed high economic growth rates, while high concentration of wealth and income in Latin America is attributed to weaker dynamism. - Left panel of Figure 9: correlation between income inequality in 1980 and average GDP per capita growth in the subsequent 30 years for selected Latin American and Asian economies; on average, countries with lower initial inequality exhibited higher rates of economic growth. - Right panel of Figure 9: extended sample of advanced and developing countries confirms the relationship between initial income inequality and subsequent growth. - Data sources cited: Standardized World Income Inequality Database (SWIID); World Bank; and authors’ calculations. - Note: Market Gini is before taxes and transfers. ### Cross-country growth studies: consistent negative association - Many cross-country growth studies following Barro and Sala-i-Martin (1995) find inequality (typically measured by a Gini coefficient) enters with a negative and statistically significant sign in growth regressions. - Bénabou (1996) surveys 23 empirical studies and finds consistent negative association between initial inequality and growth despite differences in data sets, sample periods, and measures. - Quantitative effect reported across studies: "a one-standard-deviation decrease inequality raises the annual growth of GDP in the range of 0.5 percentage points to 0.8 percentage points." - Alesina and Rodrik (1994): income and land inequality are statistically significant variables that decrease long-term growth in a sample of 70 advanced and developing countries. - Perotti (1996): negative and robust association between inequality (inversely related to the share of the middle class, third and fourth quantiles) and growth; social political instability and fertility rates could drive the relationship. ### Heterogeneities: development level, sectors, and time horizon - Barro (2000): splitting 100 countries into high- and low-income samples yields a negative relationship between inequality and growth for poor countries and a positive relationship for richer countries; interpretation: in presence of credit constraints, inequality prevents low-income households from accumulating human and physical capital, lowering growth in poor countries; positive relationship in richer economies consistent with Kaldor (1957). - Erman and te Kaat (2019): using 22 industries in 86 countries (1980–2012), higher income inequality increases growth rates of industries that use physical capital intensively and decreases growth rates of industries that use skilled labor intensively; lower human capital associated with inequality drives its negative effect on growth; consistent with Galor and Moav (2004). - Panel data studies yield mixed results: - Forbes (2000): panel of 45 advanced economies and emerging markets (1966–1995) finds higher inequality leads to higher growth in short and medium term. - Cingano (2014): fixed effects panel for 30 OECD countries (1970–2010) finds negative effect of inequality on growth. - Berg et al. (2018): net inequality has a negative effect on growth in a sample of advanced and developing countries; moderate redistribution through taxes and transfers does not have statistically significant effects on growth. - Time-horizon effects: - Brueckner and Lederman (2018): income inequality may be beneficial for transitional growth in poor countries but harmful in high-income economies. - Halter, Oechslin, and Zweimüller (2014): higher inequality beneficial in short term but net effect tends to be negative in long term. - Berg, Ostry, and Zettelmeyer (2012); Berg and Ostry (2017): inequality reduces duration of growth spells, with most results coming from cross-country differences rather than changes over time. ### Nonlinearities and role of mobility - Banerjee and Duflo (2003): growth is an inverted U-shaped function of changes in inequality; a change in the Gini coefficient in either direction is correlated with lower future growth, rejecting standard linear specifications. - Aiyar and Ebeke (2020): negative effect of inequality on growth largely depends on degree of intergenerational mobility; in countries with higher intergenerational mobility, negative impact of inequality can be more easily reversed; in their specification, the nonlinear term proposed by Banerjee and Duflo (2003) is not statistically significant, suggesting intergenerational mobility could capture the nonlinear relationship. ### Synthesis on mixed evidence - Mixed evidence largely arises from methodological differences: cross-country approaches include between-country inequality (capturing persistent channels like institutional quality, credit constraints, and redistribution policies) while panel data approaches include only within-country variation over time. - Because channels such as political economy and credit constraints do not change much over time, their influence is stronger in cross-country than time-series dimensions, explaining stronger negative results in cross-country regressions versus mixed panel results. - Inconsistent findings are expected given differences in country coverage, sample period, time horizon, model specification, and econometric method. ### B. Channels from Poverty and Inequality to Growth 1. Channels by which Inequality Can Boost Growth - Incentives - Inequality provides incentives to work, save, and invest—those who do receive higher returns; differential returns incentivize behaviors that promote growth. - Milton Friedman (Friedman 1962; Friedman and Friedman 1980) argued redistributive policies that reduce inequality could distort incentives and induce inefficient allocation; in a free market, people should be rewarded according to marginal productivity. - Compensation schemes that reward relative performance and generate inequality can incentivize workers to invest in skills and exert effort (Lazear and Rosen 1981). - Savings - Kaldor (1957) hypothesized richer individuals save a larger share of income, so higher income inequality can lead to a higher national savings rate, higher investment rate, greater capital accumulation, and higher growth. - Evidence for the United States (Dynan, Skinner, and Zeldes 2004) supports that saving rates and marginal propensity to save are positively correlated with income level, consistent with Kaldor’s hypothesis. *Italic: Source: wpiea2021068-print-pdf - 2. From Inequality to Growth* ### 2. Channels by which Inequality and Poverty Can Depress Growth ### 2. Channels by which Inequality and Poverty Can Depress Growth ### Poverty Traps and Human Capital - Poverty undermines growth by hindering accumulation of human capital through both health and education. - Poverty is associated with high rates of malnutrition, especially in developing countries (Cerra et al. 2021, Chapter 14). Stunting (a low height-to-age ratio) and child survival rates are correlated with income across and within countries. - Poor nutrition impairs children’s capacity to learn; poor children may be kept out of school to support family incomes or because families cannot afford school fees. - Students from poor households have higher learning gaps even when attending school (World Bank 2018). - Inequality of wealth, not just inequality of income, reduces the effectiveness of educational interventions (Deininger and Olinto 2000). - Lower-income countries experience higher poverty rates and have weaker capacity to supply public goods such as health and education. - Empirical associations (Figure 10): - Public spending per capita on health and education is lower for countries with high poverty rate. - Higher poverty is associated with lower access to doctors and higher illiteracy rates. - Data are for latest observation available including 142 countries from the World Bank’s POVCALNET database. - Poverty headcount ratio uses the poverty measure of $3.20 per day in 2011 purchasing power parity (PPP) terms. - Development-stage effect (Galor and Moav 2004): - In early development, when physical capital drives growth, inequality can raise growth by channeling resources to savers. - Later, when human capital is the main engine, more equality supports growth by alleviating credit-constraint adverse effects on human capital accumulation. ### Credit Market Imperfections - Weak credit markets impede the poor from borrowing to invest in physical or human capital, reducing growth. - Galor and Zeira (1993): financial frictions raise interest costs; below a threshold of initial wealth, borrowing cost exceeds return to human capital and the poor do not invest; higher inequality reduces growth; redistribution can enable poor to invest and stimulate growth. - López and Servén (2015): endogenous growth model with learning-by-doing externalities and subsistence consumption; poor consumers with no access to capital markets lead to lower growth when the share of poor is high; empirical results support this prediction. - Banerjee and Newman (1993): wealth inequality affects occupational choice (workers, entrepreneurs, self-employed); highly unequal societies can stagnate because wages remain too low; intermediate inequality can allow a “take off” to a developed economy. - Aghion and Bolton (1997): accumulation of capital by rich can benefit poor via increased funds available for investment; laissez-faire is not efficient; permanent redistribution can achieve optimal allocation. ### Demand and Structural Transformation - Inequality shapes the composition of demand and can impact growth and structural transformation. - For goods with economies of scale, sales must be large enough to cover fixed costs; if only high-income individuals can afford goods, some inequality may be required to make technology adoption feasible (Murphy, Shleifer, and Vishny 1989). - Income generated by such sectors can spill over into demand for other goods and spur industrialization only if income is distributed broadly enough. - Productivity improvements (learning by doing) can reduce costs and prices, making goods affordable to more people; this can trigger mass production and industrialization if inequality is not too severe (Matsuyama 2002). ### Risk Aversion and Decision-Making Capabilities - Poverty affects long-term growth through impacts on individual decision-making. - Risk and uncertainty: the poor may be more risk averse because they have more to lose (Banerjee 2000); in absence of financial and insurance markets, the poor avoid risky but profitable investments. - Dercon (2005) surveys evidence; if the poor could insure against risks as the rich do, their income could be higher by at least 25 percent. - Temptation and self-control: Banerjee and Mullainathan (2010) model “temptation” goods that provide present utility but reduce future utility; if share of expenditures on temptation goods declines with income, poverty traps can emerge where the poor overvalue present consumption and underinvest in future income opportunities. - Empirical evidence: the poor spend a large fraction of income on non-survival necessities such as alcohol, tobacco, and festivities (Banerjee and Duflo 2007). - Attention scarcity: Shah, Mullainathan, and Shafir (2012) show through experiments that the poor devote significant attention to satisfying basic needs, leaving less cognitive bandwidth for other problems (e.g., investment decisions to grow businesses). - Lower aspirations: La Ferrara (2019) reviews theory and empirical evidence that aspirations are correlated with poverty rates and income inequality; PISA-based measures of student expectations show poorer students have lower aspirations, reducing incentives to invest in future opportunities and perpetuating poverty. - Behavioral mechanisms jointly lead to self-perpetuating poverty via reduced savings, repeated borrowing at very high rates instead of self-financing (Banerjee and Duflo 2005), and failure to invest in profitable small-scale investments (e.g., fertilizer) (Duflo, Kremer, and Robinson 2011). ### Political Economy - Two political-economy channels by which inequality can depress long-term growth: - Redistribution channel: inequality generates political pressure for redistribution, potentially increasing distortionary taxation, lowering investment and growth. - Alesina and Rodrik (1994), building on Barro (1990): government spending is productive but financed by distortionary capital taxation; taxation and growth exhibit an inverted “U” relationship. The median voter may prefer a tax higher than the growth-maximizing rate, producing lower growth when wealth is more unequally distributed. - Persson and Tabellini (1994) obtain similar theoretical results. - Milanovic (2000, 2010) finds empirical support that more unequal countries redistribute more to the poor. - Saint-Paul and Verdier (1993) challenge the view that redistribution harms growth: when tax revenues fund education, growth can be higher; growth effects depend on tax distortions and expenditure benefits. Redistribution demand also depends on the decisive voter’s income relative to the mean (Meltzer and Richard 1981); skewness of income distribution matters (Saint-Paul and Verdier 1996). - Institutional channel: inequality enables the rich to shape institutions to their benefit, damaging growth. - Glaeser, Scheinkman, and Shleifer (2003): elites can subvert legal, political, and regulatory institutions via: - Weakening protection of property rights for the broad population, discouraging non-elite capital accumulation. - Influencing regulations to protect incumbents against entrants, harming innovation, capital accumulation, and growth. - High initial inequality facilitates elite capture of institutions; weak institutions can in turn lead to higher inequality. Inequality reduces growth primarily in countries with poor rule of law. - Findings align with the concept of “extractive political institutions” where power concentration benefits the elite at the expense of broader growth (Acemoglu and Robinson 2019). *Source: wpiea2021068-print-pdf - 2. Channels by which Inequality and Poverty Can Depress Growth* ### Chapter 15) discusses the political economy factors that influence the supply and demand for reform and ### Chapter 15) discusses the political economy factors that influence the supply and demand for reform and ### Sociopolitical Unrest - Inequality can lead to polarization, social unrest, and violence when voter demands cannot be met through traditional political systems. - Alesina and Perotti (1996): - Find an increase in inequality (inversely related to the income of the middle class in their estimation) has a statistically negative effect on political stability. - Construct an index of political stability based on a dummy for democratic regimes, number of assassinations and deaths, and number of coups. - Find political instability negatively affects investment, a key determinant of long-term growth. - Identify three mechanisms linking instability to lower investment: - Shorter government horizons → higher taxation and lower investment due to lower reputational costs of taxation for short-duration regimes. - Social unrest → disruption of productive activities → reduction in productivity. - Political instability → increased uncertainty → investors postpone projects or invest abroad. - Rodrik (1999): - Studies interaction between social conflict (measured by inequality or ethnic and linguistic fragmentation) and quality of government institutions in response to external shocks (specifically, terms of trade shocks). - Argues that social conflict exacerbates negative shocks through macroeconomic mismanagement, particularly under weak institutions. - As societies polarize, populist policies with palliative short-term effects create uncertainty, lower investment, and poor long-term growth. ### Gender Inequality - Galor and Weil (1996) theoretical model: - Measure gender inequality as the wage gap between male and female workers. - As capital per capita increases, productivity rises more for female than male workers (rewards to “brain relative to brawn” increase). - Decline in the wage gap increases the opportunity cost of raising children → fertility falls → female labor force participation rises. - Lower fertility → lower population growth → higher capital per capita → positive feedback loop boosting growth and relative female wages. - The model implies possible development traps: low capital per capita → low female wages → high fertility → high population growth → depressed capital per capita → self-perpetuating stagnation. - Empirical corroboration: - Kremer and Chen (2002) and de la Croix and Doepke (2003) find inequality is associated with higher fertility differentials within countries, with the poor having more children and achieving less education, leading to lower growth. - Bertay, Dordevic, and Sever (2020), using a difference-in-difference approach for advanced economies and emerging markets, find that gender inequality reduces real economic growth at the industry level for the manufacturing sector. - Cuberes, Newiak, and Teignier (2017) find gender inequality in labor markets leads to income losses of 15.5 percent in OECD countries and 17.5 percent in non-OECD countries. - Stotsky (2006) discusses macroeconomic impacts of gender inequality. - Cerra et al. (2021, Chapter 16) examines gender and inclusive growth more extensively. ### Conclusion and Policy Implications - The paper traces factors and policies affecting the nexus of growth, inequality, and poverty and presents main channels (Figure 11) and empirical evidence summaries (Figure 12). - Two main conclusions on the impact of growth on inclusion: - Nearly universal consensus: growth reduces poverty. Growth in emerging and low-income economies has had a first-order effect on poverty reduction by increasing education, health, job opportunities, and access to public goods and services. - Impact of growth on inequality is ambiguous and depends on growth sources. Example: skill-biased technological change can benefit capital owners and skilled workers disproportionately, potentially increasing inequality. Identifying sectors driving growth is crucial for understanding inclusiveness. - Two main conclusions on the impact of inclusion on growth: - Poverty impedes growth by reducing ability and incentives of the poor to accumulate physical and human capital and assets; curtails access to markets and public services; and distorts incentives for entrepreneurship and forward-looking behavior. Empirical evidence supports a negative effect of poverty on growth. - Impact of inequality on growth is less straightforward: inequality can incentivize effort and investment, but can also negatively affect growth via distributional pressures, lower institutional quality, greater social conflict, and higher fertility rates. - Policy recommendations: - Prioritize policies to promote growth, because growth helps reduce poverty. Growth is a necessary condition for lifting incomes and expanding access to nutrition, health, education, and high-quality jobs. - The Growth Report (Commission on Growth and Development 2008) describes policies adopted successfully in countries that achieved an average rate of 7 percent per year or more for 25 years or longer, across five broad categories: - Accumulation of human and physical capital. - Innovation and technology adoption. - Efficient allocation of resources. - Macroeconomic stabilization. - Social inclusion. - Evaluate distributional consequences as well as growth consequences of policy interventions; market forces alone will not guarantee balanced growth. - Implement public measures to ensure losers of economic transformation have opportunities to move to better jobs and provide social protection during transitions. - Differentiate between inequality of outcomes and inequality of opportunities: - Some inequality of outcomes provides incentives for saving, investing, skill acquisition, innovation, and risk-taking, which can enhance growth. - Inequality of opportunities (barriers based on initial income/wealth/place of birth/race/ethnicity/sexual orientation/disabilities) prevents many poor and marginalized people from contributing to growth and can fuel discontent, political instability, and social unrest if unaddressed. - Policy message: removing barriers to markets and public goods and services can improve growth and equity simultaneously. Examples of policy areas to expand access: health care, education, safety, justice, social protection, and finance. *Source: IMF working paper chapter content provided in the input.* ### REFERENCES ### REFERENCES ### Major topics covered in the bibliography - Inequality and economic growth - Banerjee, A., and E. Duflo. 2003. "Inequality And Growth: What Can The Data Say?" Journal of Economic Growth 8: 267–99. - Barro, R. 2000. “Inequality and Growth in a Panel of Countries.” Journal of Economic Growth 5: 5–32. - Berg, A., and J. D. Ostry. 2017. “Inequality and Unsustainable Growth: Two Sides of the Same Coin?” IMF Economic Review 65 (4): 792–815. - Deininger, K., and L. Squire. 1998. “New Ways of Looking at Old Issues: Inequality and Growth.” Journal of Development Economics 57 (2): 259–87. - Voitchovsky, S. 2005. “Does the Profile of Income Inequality Matter for Economic Growth? Distinguishing between the Ef f ects of Inequality in Dif ferent Parts of the Income Distribution.” Journal of Economic Growth 10 (3): 273–96. - Poverty, pro-poor growth, and poverty reduction strategies - Dollar, D., and A. Kraay. 2002. "Growth Is Good for the Poor." Journal of Economic Growth 7 (3): 195–225. - Ravallion, M., and S. Chen. 2007. "China's (Uneven) Progress against Poverty." Journal of Development Economics 82 (1): 1–42. - Datt, G., M. Ravallion, and R. Murgai. 2019. “Poverty and Growth in India over Six Decades.” American Journal of Agricultural Economics 102 (1, January): 4–27. - Fan, S., P. Hazell, and S. Thorat. 2000. “Government Spending, Growth, and Poverty in Rural India.” American Journal of Agricultural Economics 82 (4): 1038–51. - Bluhm, R., D. de Crombrugghe, and A. Szirmai. 2018. "Poverty Accounting." European Economic Review 104 (C): 237–55. - Distribution, redistribution, and political economy - Alesina, A., and D. Rodrik. 1994. "Distributive Politics and Economic Growth." The Quarterly Journal of Economics 109 (2): 465–90. - Milanovic, B. 2000. “The Median Voter Hypothesis, Income Inequality, and Income Redistribution: An Empirical Test with the Required Data.” European Journal of Political Economy 16 (3): 367–410. - Persson, T. and G. Tabellini. 1994. “Is Inequality Harmful for Growth?” American Economic Review 84 (3): 600-621. - Meltzer, A., and S. Richard. 1981. “A Rational Theory of the Size of Government.” Journal of Political Economy 89 (5): 914–27. - Labor markets, technology, automation, and market power - Acemoglu, D., and P. Restrepo. 2020. "Robots and Jobs: Evidence from US Labor Markets." Journal of Political Economy 128 (6): 2188–44. - Autor, D., D. Dorn, L. F. Katz, C. Patterson, and J. Van Reenen. 2020. “The Fall of the Labor Share and the Rise of Superstar Firms.” The Quarterly Journal of Economics 135 (2): 645–709. - Graetz, G., and G. Michaels. 2018. "Robots at Work." The Review of Economics and Statistics 100 (5): 753–68. - De Loecker, J., and J. Eeckhout. 2018. "Global Market Power." NBER Working Paper 24768, National Bureau of Economic Research, Cambridge, MA. - Diez, F. J., D. Leigh, and S. Tambunlertchai. 2018. "Global Market Power and its Macroeconomic Implications." IMF Working Paper 18/137, International Monetary Fund, Washington, DC. - Human capital, education, fertility, demographics, and growth - Becker, G. S. 1992. "Fertility and the Economy." Journal of Population Economics 5 (3): 185–201. - Galor, O., and O. Moav. 2004. “From Physical to Human Capital Accumulation: Inequality and the Process of Development.” The Review of Economic Studies 71 (4): 1001–1026. - Bloom, D. E., D. Canning, and G. Fink. 2010. "Implications of Population Ageing for Economic Growth." Oxford Review of Economic Policy 26 (4): 583–61. - Saint-Paul, G., and T. Verdier. 1993. "Education, Democracy and Growth." Journal of Development Economics 42 (2, December): 399–407. - Finance, fiscal policy, and macro-structural determinants - Azzimonti, M., E. de Francisco, and V. Quadrini. 2014. "Financial Globalization, Inequality, and the Rising Public Debt." American Economic Review 104 (8): 2267-2302. - Beck, T., A. Demirgüç-Kunt, and R. Levine. 2007. “Finance, Inequality, and the Poor.” Journal of Economic Growth 12 (1): 27–49. - Woo, J. 2011. “Growth, Income Distribution, and Fiscal Policy Volatility.” Journal of Development Economics 96 (2): 289–313. - Fabrizio, S., D. Furceri, R. Garcia-Verdu, B. G. Li, S. V. Lizarazo, M. Mendes Tavares, F. Narita, and A. Peralta-Alva. 2017. “Macro-structural Policies and Income Inequality in Low-income Developing Countries.” IMF Staff Discussion Notes No. 17/01, International Monetary Fund, Washington, DC. ### Methodological and theoretical contributions reflected in the references - Foundational growth and distribution theories - Solow, R. M. 1956. "A Contribution to the Theory of Economic Growth." The Quarterly Journal of Economics 70 (1): 65–94. - Kaldor, N.1957. “A Model of Economic Growth.” The Economic Journal 67 (268): 591–624. - Kuznets, S. 1955. “Economic Growth and Income Inequality.” American Economic Review 45 (1): 1–28. - Schumpeter, J.1942. Capitalism, Socialism and Democracy. - Empirical approaches to inequality, poverty, and growth - Ravallion, M. 2012. "Why Don't We See Poverty Convergence?" American Economic Review 102 (1): 504–23. - Kraay, A. 2006. "When Is Growth Pro-Poor? Evidence from a Panel of Countries." Journal of Development Economics 80 (1): 198–227. - Ferreira, F. H. G., C. Lakner, M. A. Lugo, and B. Özler. 2018. “Inequality of Opportunity and Economic Growth: How Much Can Cross‐Country Regressions Really Tell Us?” Review of Income and Wealth 64 (4): 800–27. ### Recurring policy-relevant themes - Redistribution and growth trade-offs and complementarities - Berg, A., J. D. Ostry, C. G. Tsangarides, and Y. Yakhshilikov. 2018. "Redistribution, Inequality, and Growth: New Evidence." Journal of Economic Growth 23 (September): 259–305. - Ostry, J., P. Loungani, and A. Berg. 2019. Confronting Inequality. Foreword by Joseph E. Stiglitz. New York: Columbia University Press. - Sectoral and composition effects on poverty reduction - Loayza, N. V., and C. Raddatz. 2010. "The Composition of Growth Matters for Poverty Alleviation." Journal of Development Economics 93 (1, September): 137–51. - Ivanic, M., and W. Martin. 2018. "Sectoral Productivity Growth and Poverty Reduction: National and Global Impacts." World Development 109 (C, September): 429–39. - Gender, opportunity, and distributional dimensions - Bertay, A. C., L. Dordevic, and C. Sever. 2020. “Gender Inequality and Economic Growth: Evidence from Industry-Level Data.” IMF WP 20/119, International Monetary Fund, Washington, DC. - Stotsky, J. 2006. “Gender and Its Relevance to Macroeconomic Policy: A Survey.” IMF Working Paper 06/233, International Monetary Fund, Washington, DC. *Content derived from the REFERENCES section of the source PDF wpiea2021068-print-pdf* --- _Source: https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021068-print-pdf.pdf_