## _wp13135 - References

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### I. INTRODUCTION
- Inclusive growth defined as both the pace and distribution of economic growth; necessary for sustainable poverty reduction (Berg and others 2011a; Kraay, 2004).
- Inclusiveness encompasses equity, equality of opportunity, and protection in market and employment transitions (Commission on Growth and Development 2008).
- Traditional separation of growth and inequality analyses is problematic; recent work suggests no necessary trade-off between equity and efficiency (Okun 1975 challenged; Berg and Ostry, 2011b).
- Paper develops a unified measure of inclusive growth to integrate growth and income distribution.
- Distinction between absolute and relative pro-poor growth:
  - Absolute definition: growth is pro-poor if poor people benefit in absolute terms (Ravallion and Chen, 2003).
  - Relative definition: growth is pro-poor only if incomes of poor grow faster than population as a whole (Dollar and Kraay, 2002; IMF 2011).
  - Example: society might prefer average growth 6 percent with poor growing 4 percent over average 2 percent with poor growing 3 percent.
- Empirical focus: ex post inequality (inequality of outcome).
- Motivation: rising global inequality linked to technological change, financial deepening, aspects of globalization (Acemoglu and Autor, 2011; Aizeman, Lee, and Park 2012; IMF 2007); concerns about historical high levels of global inequality (Milanovic 2010).
- Paper objectives:
  - Section II: develop a measure of inclusive growth using a macro social mobility function.
  - Section III: document evolution of inclusive growth focusing on emerging markets and low income countries.
  - Section IV: examine sources of inclusive growth in emerging markets and low income countries, with some evidence from advanced economies.
- Three contributions:
  1. A unified macro measure of inclusive growth integrating growth and income distribution — first applied in a cross-country context.
  2. Use of the measure to study dynamics and determinants in low income countries and emerging markets.
  3. Investigation of relations between inclusive growth and macro fundamentals (macro stability, competitiveness) and structural change (globalization, trade and financial integration).

### II. MEASUREMENT OF INCLUSIVE GROWTH
- Framework: utilitarian social welfare function; inclusive growth depends on (i) income growth and (ii) income distribution.
- Required properties of the underlying social welfare function:
  - Increasing in its argument (captures growth).
  - Satisfies transfer property: any transfer from poor to rich reduces function (captures distribution).
- Introduce generalized concentration curve called social mobility curve, S(y), following Ali and Son (2007):
  - S(y) = cumulative distribution of a social mobility vector with underlying function Φ(y1, y2, …, yn) satisfying the two properties above.
  - n = number of persons with incomes y1, y2, …, yn where y1 is poorest and yn is richest.
- Continuous-time presentation:
  - Let ȳ(i) be average income of the bottom i percent, where i varies from 0 to 100, and ȳ is mean income.
  - Plot ȳ(i) for i to create social mobility curve (curve AB in Figure 1).
  - Higher S(y) implies greater social mobility; growth is inclusive if social mobility curve moves upward at all points.
- Social mobility index computed from area under social mobility curve:
  - ȳ_k = ∫_0^100 ȳ(i) di / 100
  - If all incomes equal, ȳ_k = ȳ; deviation of ȳ_k from ȳ indicates inequality.
- Income equity index (IEI) following Ali and Son (2007):
  - ρ = ȳ_k / ȳ
  - For complete equality, ρ = 1; higher ρ (closer to 1) = higher income equality.
  - Rearranged: ȳ_k = ρ ȳ    (Equation (1))
- Differentiation gives decomposition of change in social mobility index:
  - d ȳ_k = ρ d ȳ + ȳ d ρ
  - Inclusive growth defined as change in social mobility index d ȳ_k; growth is more inclusive if d ȳ_k > 0.
  - Decomposes inclusive growth into:
    - Contribution of increase in average income (keeping distribution constant).
    - Contribution of change in income distribution (keeping average income constant).
- Fundamental relation (percent-change form):
  - (d ȳ_k) / ȳ_k = (d ȳ) / ȳ + (d ρ) / ρ
  - Integrates growth and equity into one measure of inclusive growth (percent change in ȳ_k).
- Interpretation of sign combinations:
  - d ȳ > 0 and d ρ > 0: Unambiguously inclusive (AB → A1B1).
  - d ȳ > 0 and d ρ < 0: Higher per capita income at expense of equity (AB → A2B2).
  - d ȳ < 0 and d ρ > 0: Equity objective achieved at cost of average income contraction (AB → A3B3).
  - d ȳ < 0 and d ρ < 0: Unambiguously non-inclusive (AB → A4B4).
- Weighting note:
  - Measure can assign different weights to growth and equity dimensions; moderate changes do not significantly alter country rankings or dynamics.
  - Measure already assigns decreasing weight to higher income groups (a transfer from 10th decile to 1st decile increases ȳ_k and ρ more than a transfer from 10th to 2nd decile).

### III. EVOLUTION OF INCLUSIVENESS
- Data and measurement:
  - Cross-country analysis uses World Bank Povcal database (Chen and Ravallion, 2004); data cover 143 countries during 1980–2010 with varying observations by country and variable.
  - Average income for deciles calculated using quintile income-share data and real GDP per capita (2000 international dollars chain-series).
  - Focus on percentage changes in ȳ_k for cross-country comparisons.
- Main findings:
  - Relatively few countries have achieved strong inclusive growth.
  - Limited gains in inclusiveness explained by low growth in some countries and widening inequality in others.
  - Decomposition per Equation (2) shows wide dispersion of outcomes.
  - Most countries fall in quadrants showing higher per capita income with either lower or higher inequity — no simple trade-off between growth and equality.
  - China: rapid inclusive growth—rapid per capita income growth benefitted all quintiles; equity declined but growth effect dominated, yielding large upward shift in indifference curve and greater ȳ_k.
  - India: similar to China—high growth benefitted everyone but equity decreased.
  - Brazil, Mexico, Malaysia, Thailand: increases in inclusiveness came from both growth and improvement in equity (positive d ρ), but growth not as fast as in China.
- Analytical tools described:
  - Inclusiveness Matrix with four scenario descriptions mapped to sign combinations of d ȳ and d ρ.
  - Distribution of Emerging Markets on Inclusiveness Matrix — chart measures proportionate average annual change over periods from early 1990s to latest available data; size represents initial economy size (PPP GDP per capita categories: below 420, 5000, 10,000 and 14,621).
  - Indifference curves highlight inclusiveness dynamics for selected emerging economies (Brazil, Malaysia, Mexico, Thailand, India, China).
- Data and methods caveats:
  - Cross-country comparisons of inequality suffer from data reliability, coverage, and inconsistent methodology; authors use Povcal database and methods to mitigate these issues.
  - Appendix presents evolution of inclusive growth for a few advanced economies for validation against established views on OECD income dynamics.

### Appendix Figure A3 and related empirical patterns
- Appendix Figure A3 documents similar shifts in social mobility in OECD countries for illustration.
- Continuum of inclusive growth from “pure income growth” (example: China) to “zero income growth but a better distribution” (example: Kenya), with India between these extremes.
- Regional heterogeneity:
  - Conventional wisdom: growth often comes at the price of rising inequality; regions differ in the growth-equity tradeoff and there are instances of high growth without compromising equity.
  - Appendix Figure A4: orthogonal correlations with fitted trend lines by region show:
    - Middle East and Central Asia and Western Hemisphere regions have a positive slope.
    - Other regions display a negative slope (with exceptions/outliers such as Bhutan or Mali).
  - Appendix Figure A5: decomposes inclusive growth into growth and equity components for selected Emerging Markets, illustrating that both magnitude and shift of incomes across the population matter for inclusive growth.

### Panel regression specification and data
- Dependent variable:
  - Inclusive growth (y*) is the log-difference of y* in country i at time t over a non-overlapping unbalanced 5-year panel of 143 countries from 1970-2010.
- Regression controls:
  - Initial level of per capita PPP-adjusted income (conditional convergence), averages of standard growth and inequality determinants over each 5-year period, country fixed effects (ηi), period effects (γt), and an idiosyncratic error term (εi,t).
  - Controls include education, trade openness, investment, inflation, output volatility, financial variables, structural transformation measures, REER deviations, and infrastructure quality.

### Key regression findings for Emerging Markets (selected coefficients from Table 2)
- Conditional convergence (lag GDP per capita (logs)):
  - Coefficients: -0.211**, -0.203*, -0.300***, -0.468***, -0.605***, -0.528**, -0.558***.
- Education:
  - Coefficients: 0.397***, 0.309*, 0.120, 0.261*, 0.783***, 0.173, 0.560**.
- Trade Openness:
  - Coefficients: 0.246**, 0.194*, -0.0442, 0.418***, 0.223, 0.0130, -0.00118.
- Investment (Fixed Investment % of GDP):
  - Coefficients: 0.949**, 1.030, 0.945, 0.786, -0.141, 0.439, 1.018**.
- Inflation:
  - Coefficients: -0.0275*, -0.0280*, -0.0227*, -0.0830***, -0.0524***, -0.00349, -0.00129.
- GDP Volatility:
  - Coefficients include 2.126**, -2.175**, -0.991, 0.223, -1.604, -0.788, -1.235.
- Financial openness and FDI:
  - Financial Openness: coefficient 0.000547** (in one specification).
  - FDI: coefficient 0.0101*** (in one specification).
- Financial deepening (credit-to-GDP):
  - Coefficients: -0.160, -0.164, -0.0390, -0.0176, -0.0822, 0.112, -0.137 (generally negative and not consistently significant).
- Structural and competitiveness indicators:
  - REER Deviations: -0.00245*** (negative association with inclusive growth).
  - Infrastructure Quality: 0.131*** (positive association).
  - Service export sophistication (logs): 0.500***.
  - Goods export sophistication (logs): 0.390*.
- Model fit and sample:
  - Observations across specifications: 261, 234, 234, 111, 98, 139, 146.
  - R-squared values: 0.263, 0.284, 0.376, 0.285, 0.514, 0.150, 0.288.
  - Number of countries: 99, 98, 93, 63, 49, 58.

### Robustness and advanced-economy results (Table A2)
- Advanced economies (pre-GFC) show similar proximate determinants:
  - lag GDP per capita (logs): -0.112***, -0.104***, -0.0937***, -0.128***, -0.142***.
  - Education: 0.0153***, 0.0151***, 0.0149***, 0.0151***, 0.0187***.
  - Trade Openness: 0.00122***, 0.00134***, 0.00141***, 0.00107***, 0.00114**.
  - Investment: 0.00358***, 0.00361***, 0.00361***, 0.00350***, 0.00466**.
  - Inflation: -0.00839***, -0.00816***, -0.00848***, -0.00794***, -0.0106***.
  - ICT: 0.289*** (included in one specification).
  - Financial openness and FDI: insignificant in advanced economy specifications reported.
- Observations and fit:
  - Observations: 254 for most specifications, one with 181 observations.
  - R-squared: 0.418, 0.420, 0.430, 0.464, 0.436.
  - Number of countries: 18 (or 17 in one specification).

### Interpretation of proximate determinants
- Factors positively associated with inclusive growth in Emerging Markets:
  - Lower initial incomes (conditional convergence), trade openness, fixed investment, better education levels (tertiary attainment particularly strong in robustness checks), FDI, financial openness (in some specifications), service export sophistication, goods export sophistication, and infrastructure quality.
- Factors negatively associated (or ambiguous) with inclusive growth:
  - Inflation and REER deviations from PPP (competitiveness concerns) are negatively associated.
  - Financial deepening (credit-to-GDP) shows negative coefficients and is not statistically significant overall — consistent with literature linking financial development to higher inequality.
  - ICT stock does not display a discernible impact in Emerging Markets (potentially due to limited data), while ICT is significant in advanced economies.
- Structural transformation:
  - Upgrading manufacturing or service sophistication is associated with higher inclusive growth, with service sophistication driven by globalization showing a greater impact.

### Policy implications and areas for future research
- Policy levers for fostering inclusive growth in Emerging Markets:
  - Promote macroeconomic stability (control inflation and volatility).
  - Invest in human capital (education attainment, especially tertiary education).
  - Encourage trade openness, FDI, and moving up the value chain in goods and services.
  - Support infrastructure quality and competitiveness (monitor REER deviations).
  - Be cautious about relying solely on financial deepening; monitor distributional consequences of financial sector development.
- Open questions for future research:
  - Relation between fiscal consolidation (post-GFC withdrawal of stimulus/bailouts) and inclusive growth.
  - Need for more granular national and sub-national data to analyze inclusive growth evolution.
  - Role, speed, reach, and channels of technological advancement in fostering or hindering inclusive growth.
  - Links between unemployment, labor market institutions, and inclusive growth; lack of consistent unemployment data precluded strong empirical analysis in this paper.

### Appendix materials referenced (selected)
- Appendix Figure A1: Evolution of Inclusiveness across the Globe (percentage change in y*, intensity indicated).
- Appendix Figure A2: Inclusive Growth vs. absolute pro-poor growth measures; regression linking Inclusive Growth (-0.11, St. Error 0.03, t-value -3.42, p-value < 0.0001, N 85) and GDP/cap Growth (-0.06, 0.03, -1.68, 0.0961, N 85) and Change in Gini (0.05, St. Error 0.02, t-value 2.45, p-value 0.016, N 85).
- Appendix Figures A3–A5 and Tables A1–A2 provide country-level decompositions and regional illustrations referenced above (e.g., Table A1 lists Growth in GDP per capita, inclusive growth (y*), equity growth (ω), and initial/latest years for many Emerging Markets).

*Source: _wp13135 - References.*

### References .............................................................................................................

### _wp13135 - References

### I. INTRODUCTION
- Inclusive growth defined as both the pace and distribution of economic growth; necessary for sustainable poverty reduction (Berg and others 2011a; Kraay, 2004).
- Inclusiveness encompasses equity, equality of opportunity, and protection in market and employment transitions (Commission on Growth and Development 2008).
- Traditional separation of growth and inequality analyses is problematic; recent work suggests no necessary trade-off between equity and efficiency (Okun 1975 challenged; Berg and Ostry, 2011b).
- The paper develops a unified measure of inclusive growth to integrate growth and income distribution.
- Distinction between absolute and relative pro-poor growth:
  - Absolute definition: growth is pro-poor if poor people benefit in absolute terms (Ravallion and Chen, 2003).
  - Relative definition: growth is pro-poor only if incomes of poor grow faster than population as a whole (Dollar and Kraay, 2002; IMF 2011).
  - Example illustrating potential suboptimality of the relative definition: society might prefer average growth 6 percent with poor growing 4 percent over average 2 percent with poor growing 3 percent.
- Focus of empirical study: ex post inequality (inequality of outcome).
- Motivation: rising global inequality, linked in literature to technological change, financial deepening, aspects of globalization (Acemoglu and Autor, 2011; Aizeman, Lee, and Park 2012; IMF 2007); concerns about historical high levels of global inequality (Milanovic 2010).
- Paper objectives:
  - Section II: develop a measure of inclusive growth using a macro social mobility function.
  - Section III: document evolution of inclusive growth focusing on emerging markets and low income countries.
  - Section IV: examine sources of inclusive growth in emerging markets and low income countries, with some evidence from advanced economies.
- Three contributions:
  1. A unified macro measure of inclusive growth integrating growth and income distribution — first applied in a cross-country context.
  2. Use of the measure to study dynamics and determinants in low income countries and emerging markets.
  3. Investigation of relations between inclusive growth and macro fundamentals (macro stability, competitiveness) and structural change (globalization, trade and financial integration).

### II. MEASUREMENT OF INCLUSIVE GROWTH
- Framework: utilitarian social welfare function from consumer choice literature; inclusive growth depends on (i) income growth and (ii) income distribution.
- Required properties of underlying social welfare function:
  - Increasing in its argument (captures growth).
  - Satisfies transfer property: any transfer from poor to rich reduces function (captures distribution).
- Introduce generalized concentration curve called social mobility curve, S(y), following Ali and Son (2007):
  - S(y) = cumulative distribution of a social mobility vector with underlying function Φ(y1, y2, …, yn) satisfying the two properties above.
  - n = number of persons with incomes y1, y2, …, yn where y1 is poorest and yn is richest.
- Continuous-time presentation:
  - Let ȳ(i) be average income of the bottom i percent, where i varies from 0 to 100, and ȳ is mean income.
  - Plot ȳ(i) for i to create social mobility curve (curve AB in Figure 1).
  - Higher S(y) implies greater social mobility; growth is inclusive if social mobility curve moves upward at all points.
- Social mobility index computed from area under social mobility curve:
  - ȳ_k = ∫_0^100 ȳ(i) di / 100  (presented in text as integral form; in PDF: ݕതכ ൌන ݕത௜ ೫)
  - If all incomes equal, ȳ_k = ȳ; deviation of ȳ_k from ȳ indicates inequality.
- Income equity index (IEI) following Ali and Son (2007):
  - ρ = ȳ_k / ȳ
  - For complete equality, ρ = 1; higher ρ (closer to 1) = higher income equality.
  - Rearranged: ȳ_k = ρ ȳ    (Equation (1))
- Differentiation gives decomposition of change in social mobility index:
  - d ȳ_k = ρ d ȳ + ȳ d ρ    (presented in text as ݕ݀തכ ݕ݀തݕכ߱ൌݕത൅ത)
  - Inclusive growth defined as change in social mobility index d ȳ_k; growth is more inclusive if d ȳ_k > 0.
  - Decomposes inclusive growth into:
    - Contribution of increase in average income (keeping distribution constant).
    - Contribution of change in income distribution (keeping average income constant).
- Fundamental relation (percent-change form):
  - (d ȳ_k) / ȳ_k = (d ȳ) / ȳ + (d ρ) / ρ
  - This integrates growth and equity into one measure of inclusive growth (percent change in ȳ_k).
- Interpretation of sign combinations (visualized in Figure 2):
  - d ȳ > 0 and d ρ > 0: Unambiguously inclusive (AB → A1B1).
  - d ȳ > 0 and d ρ < 0: Higher per capita income at expense of equity (AB → A2B2).
  - d ȳ < 0 and d ρ > 0: Equity objective achieved at cost of average income contraction (AB → A3B3).
  - d ȳ < 0 and d ρ < 0: Unambiguously non-inclusive (AB → A4B4).
- Weighting note:
  - The measure can assign different weights to growth and equity dimensions; moderate changes do not significantly alter country rankings or dynamics.
  - The measure already assigns decreasing weight to higher income groups (a transfer from 10th decile to 1st decile increases ȳ_k and ρ more than a transfer from 10th to 2nd decile).

### III. EVOLUTION OF INCLUSIVENESS
- Cross-country analysis uses World Bank Povcal database (Chen and Ravallion, 2004); data cover 143 countries during 1980–2010 with varying observations by country and variable.
- Average income for deciles calculated using quintile income-share data and real GDP per capita (2000 international dollars chain-series).
- Focus on percentage changes in ȳ_k for cross-country comparisons.
- Main findings:
  - Relatively few countries have achieved strong inclusive growth.
  - Limited gains in inclusiveness explained by low growth in some countries and widening inequality in others.
  - Decomposition per Equation (2) shows wide dispersion of outcomes.
  - Most countries fall in quadrants showing higher per capita income with either lower or higher inequity — no simple trade-off between growth and equality.
  - China identified as a country with rapid inclusive growth: rapid per capita income growth benefitted all quintiles; equity declined but growth effect dominated, yielding large upward shift in indifference curve and greater ȳ_k.
  - India similar to China: high growth benefitted everyone but equity decreased.
  - Brazil, Mexico, Malaysia, Thailand: increases in inclusiveness came from both growth and improvement in equity (positive d ρ), but growth not as fast as in China.
- Tools presented:
  - Inclusiveness Matrix (Table 1) with four scenario descriptions mapped to sign combinations of d ȳ and d ρ.
  - Distribution of Emerging Markets on Inclusiveness Matrix (Figure 3) — chart measures proportionate average annual change over periods from early 1990s to latest available data; size represents initial economy size (PPP GDP per capita categories: below 420, 5000, 10,000 and 14,621).
  - Indifference curves (Figure 4) highlight inclusiveness dynamics for selected emerging economies (Brazil, Malaysia, Mexico, Thailand, India, China).
- Data and methods caveats:
  - Cross-country comparisons of inequality suffer from data reliability, coverage, and inconsistent methodology; authors use Povcal database and methods to mitigate these issues.
  - Appendix presents evolution of inclusive growth for a few advanced economies for validation against established views on OECD income dynamics.

*Source: _wp13135 - References.*

### Appendix Figure A3 documents similar shifts in social

### _wp13135 - Appendix Figure A3 documents similar shifts in social

### Overview
- Appendix Figure A3 documents similar shifts in social mobility in OECD countries for illustration.
- The paper defines inclusive growth as integrating both the pace of income growth and its distribution, linking micro and macro dimensions of inequality and growth.
- There is a continuum of inclusive growth from “pure income growth” (example: China) to “zero income growth but a better distribution” (example: Kenya), with India positioned between these extremes.

### Major empirical patterns and regional heterogeneity
- Conventional wisdom: growth often comes at the price of rising inequality; however, regions differ in the growth-equity tradeoff and there are instances of high growth without compromising equity.
- Appendix Figure A4: orthogonal correlations with fitted trend lines by region show:
  - Middle East and Central Asia and Western Hemisphere regions have a positive slope.
  - Other regions display a negative slope (with exceptions/outliers such as Bhutan or Mali).
- Appendix Figure A5: decomposes inclusive growth into growth and equity components for selected Emerging Markets, illustrating that both magnitude and shift of incomes across the population matter for inclusive growth.

### Panel regression specification and data
- Inclusive growth (y*) is the log-difference of y* (inclusive growth) in country i at time t over a non-overlapping unbalanced 5-year panel of 143 countries from 1970-2010.
- Regression includes initial level of per capita PPP-adjusted income (conditional convergence), averages of standard growth and inequality determinants over each 5-year period, country fixed effects (ηi), period effects (γt), and an idiosyncratic error term (εi,t).
- The specification follows standard cross-country growth regressions with controls for education, trade openness, investment, inflation, output volatility, financial variables, structural transformation measures, REER deviations, and infrastructure quality.

### Key regression findings for Emerging Markets (Table 2)
- Conditional convergence:
  - lag GDP per capita (logs) coefficients: -0.211**, -0.203*, -0.300***, -0.468***, -0.605***, -0.528**, -0.558*** (robust t-statistics in parentheses in table).
- Education:
  - coefficients: 0.397***, 0.309*, 0.120, 0.261*, 0.783***, 0.173, 0.560**.
- Trade Openness:
  - coefficients: 0.246**, 0.194*, -0.0442, 0.418***, 0.223, 0.0130, -0.00118.
- Investment (Fixed Investment % of GDP):
  - coefficients: 0.949**, 1.030, 0.945, 0.786, -0.141, 0.439, 1.018**.
- Inflation:
  - coefficients: -0.0275*, -0.0280*, -0.0227*, -0.0830***, -0.0524***, -0.00349, -0.00129.
- GDP Volatility:
  - coefficients include 2.126**, -2.175**, -0.991, 0.223, -1.604, -0.788, -1.235.
- Financial openness and FDI:
  - Financial Openness: coefficient 0.000547** (in one specification).
  - FDI: coefficient 0.0101*** (in one specification).
- Financial deepening (credit-to-GDP) generally has negative coefficients (e.g., -0.160, -0.164, -0.0390, -0.0176, -0.0822, 0.112, -0.137) but is not statistically significant overall.
- Structural and competitiveness indicators:
  - REER Deviations: -0.00245*** (negative association with inclusive growth).
  - Infrastructure Quality: 0.131*** (positive association).
  - Service export sophistication (logs): 0.500***.
  - Goods export sophistication (logs): 0.390*.
- Model fit and sample:
  - Observations across specifications: 261, 234, 234, 111, 98, 139, 146.
  - R-squared values: 0.263, 0.284, 0.376, 0.285, 0.514, 0.150, 0.288.
  - Number of countries: 99, 98, 93, 63, 49, 58.

### Robustness and advanced-economy results (Table A2)
- Advanced economies (pre-GFC) show similar proximate determinants:
  - lag GDP per capita (logs): coefficients -0.112***, -0.104***, -0.0937***, -0.128***, -0.142***.
  - Education: 0.0153***, 0.0151***, 0.0149***, 0.0151***, 0.0187***.
  - Trade Openness: 0.00122***, 0.00134***, 0.00141***, 0.00107***, 0.00114**.
  - Investment: 0.00358***, 0.00361***, 0.00361***, 0.00350***, 0.00466**.
  - Inflation: -0.00839***, -0.00816***, -0.00848***, -0.00794***, -0.0106***.
  - ICT: 0.289*** (in one specification where ICT is included).
  - Financial openness and FDI are insignificant in advanced economy specifications reported.
- Observations and fit:
  - Observations: 254 for most specifications, one with 181 observations.
  - R-squared: 0.418, 0.420, 0.430, 0.464, 0.436.
  - Number of countries: 18 (or 17 in one specification).

### Interpretation of proximate determinants
- Factors positively associated with inclusive growth in Emerging Markets:
  - Lower initial incomes (conditional convergence), trade openness, fixed investment, better education levels (tertiary attainment particularly strong in robustness checks), FDI, financial openness (in some specifications), service export sophistication, goods export sophistication, and infrastructure quality.
- Factors negatively associated (or ambiguous) with inclusive growth:
  - Inflation and REER deviations from PPP (competitiveness concerns) are negatively associated.
  - Financial deepening (credit-to-GDP) shows negative coefficients and is not statistically significant overall — consistent with earlier literature linking financial development to higher inequality.
  - ICT stock does not display a discernible impact in Emerging Markets (potentially due to limited data), while ICT is significant in advanced economies.
- Structural transformation:
  - Upgrading manufacturing or service sophistication is associated with higher inclusive growth, with service sophistication driven by globalization showing a greater impact.

### Policy implications and areas for future research
- Key policy levers for fostering inclusive growth in Emerging Markets:
  - Promote macroeconomic stability (control inflation and volatility).
  - Invest in human capital (education attainment, especially tertiary education).
  - Encourage trade openness, FDI, and moving up the value chain in goods and services.
  - Support infrastructure quality and competitiveness (monitor REER deviations).
  - Be cautious about relying solely on financial deepening; monitor distributional consequences of financial sector development.
- Open questions for future research:
  - The relation between fiscal consolidation (post-GFC withdrawal of stimulus/bailouts) and inclusive growth.
  - Need for more granular national and sub-national data to analyze inclusive growth evolution.
  - The role, speed, reach, and channels of technological advancement in fostering or hindering inclusive growth.
  - Links between unemployment, labor market institutions, and inclusive growth; lack of consistent unemployment data precluded strong empirical analysis in this paper.

### Appendix materials referenced
- Appendix Figure A1: Evolution of Inclusiveness across the Globe (percentage change in y*, intensity indicated).
- Appendix Figure A2: Inclusive Growth vs. absolute pro-poor growth measures; includes regression linking Inclusive Growth (-0.11, St. Error 0.03, t-value -3.42, p-value < 0.0001, N 85) and GDP/cap Growth (-0.06, 0.03, -1.68, 0.0961, N 85) and Change in Gini (0.05, St. Error 0.02, t-value 2.45, p-value 0.016, N 85).
- Appendix Figures A3–A5 and Tables A1–A2 provide country-level decompositions and regional illustrations referenced above (e.g., Table A1 lists Growth in GDP per capita, inclusive growth (y*), equity growth (ω), and initial/latest years for many Emerging Markets).

*Source: _wp13135 - Appendix Figure A3 documents similar shifts in social (IMF working paper content provided).*

### REFERENCES

### _wp13135 - REFERENCES

### References

- Acemoglu, Daron and David Autor, 2011, “Skills, Tasks and Technologies: Implications for Employment and Earnings,” NBER Working Paper No. 16082 (Cambridge, Massachusetts: National Bureau of Economic Research).
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*References list as provided in the source PDF*

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