## Inclusive Growth: Definitions and Concepts

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### Poverty and Poverty Lines
- Poverty headcount ratios fell substantially in several ASEAN countries:
  - Philippines: from 13.9 percent in 2000 to 6.1 percent in 2015.
  - Contextual reference: 6.5 percent in 2016.
- Less extreme poverty remains high at $3.2 and $5.5 lines in some countries:
  - Indonesia (2015): nearly 33 percent living below $3.20 per day; more than 60 percent living below $5.50 per day.
  - Philippines (2015): nearly 33 percent living below $3.20 per day; more than 60 percent living below $5.50 per day.
- Extreme poverty (PovcalNet coverage for available countries, 2015):
  - Around 33 million people living below the $1.90 per day line in ASEAN (based on available data for Indonesia, Lao, Malaysia, Myanmar, Philippines, Thailand, Vietnam).

### Income Inequality: Trends, Measures, and Empirical Associations
- Regional and temporal trends:
  - GDP per capita growth in ASEAN was sustained and positive over the last two decades except during aftermath of the Asian crisis (early 2000s) and the global financial crisis in 2009.
  - Population-weighted average Gini in developing East Asia and Pacific: 33.5 percent in the 1990s → 38.4 percent in the 2010s.
- Country-specific Gini changes (1990s to 2010s):
  - Notable progress in income equality: Malaysia, Philippines, Thailand.
  - Worsened equality: China, Indonesia, Lao PDR.
  - Slight improvement: Mongolia, Vietnam.
  - Indonesia: Gini increased between early 1990s and 2013 but declined through 2017; trend expected to continue (Doumbia and Kinda 2019).
- Income shares by quintile in ASEAN:
  - Top 20 percent income share exceeds 40 percent in Indonesia, Malaysia, Philippines, Thailand, and Vietnam.
  - Trends since 1990s:
    - Top 20 percent share declined in Malaysia, Philippines, Thailand.
    - Top 20 percent share increased in Indonesia.
    - Bottom 20 percent income share increased in Malaysia, Philippines, Thailand; declined in Indonesia and Vietnam.
- Empirical associations (IMF 2015):
  - A 1 percentage point increase in the income share of the top 20 percent is associated with a 0.08 percentage point decline in GDP growth in the following 5 years.
  - A 1 percentage point increase in the income share of the bottom 20 percent is associated with a 0.38 percentage point increase in GDP growth in the following 5 years.
- Social spending and policy bottlenecks:
  - Social spending is relatively low in Asia; lower revenue collection and insufficient and poorly targeted social policies are highlighted as bottlenecks to reducing inequality.

### Definitions and Measurement of Inclusive Growth
- Variants of pro-poor growth concepts:
  - Absolute pro-poor growth (Ravallion and Chen 2003): growth inclusive if poor individuals benefit in absolute terms.
  - Relative pro-poor growth (Dollar and Kraay 2002): growth inclusive if income of poor grows faster than average income.
  - Ali and Son (2007): growth inclusive if it increases a social opportunity function (average opportunities and their distribution).
- Adopted operational definition (based on Anand et al. 2013):
  - Inclusive growth integrates income distribution and income per capita growth into a single indicator.
  - Requires i) an increase in average income through growth or ii) an increase in income equality, or a combination.
  - Equity index ω: integral of the area under the Social Mobility Curve, scaled between 0 and 1 (1 = perfectly equitable distribution).
  - Inclusive growth ∆y* interpreted as real income per capita growth ∆y adjusted for change in the equity index ∆ω; ∆y* = ∆y if income distribution is unchanged.
- Visualization and empirical observation:
  - Figure 5 (cumulative average GDP per capita per population quintiles for ASEAN, 1990-1993 vs. 2015-2017): growth broadly inclusive in five countries shown because indifference curves shifted upwards, but gains were uneven:
    - Larger gains for top quintiles than bottom quintiles.
    - Indonesia and Vietnam show largest unequal gains.
    - Philippines and Thailand show relatively more equitable growth (less steep indifference curves).
- Pathways to inclusive growth:
  - Higher GDP per capita growth, higher equity, or a combination. Mixed cases can yield inclusiveness if one component offsets a decline in the other.

### Empirical Analysis: Scope, Variables, and Model
- Sample and period:
  - Panel of eleven Asian countries, 1992–2017, including six ASEAN members: Indonesia, Malaysia, Philippines, Lao PDR, Thailand, Vietnam; plus Bangladesh, China, India, Mongolia, Sri Lanka.
- Dependent variable:
  - Inclusive growth measure ∆y* from Anand et al. (2013).
  - Equity index constructed from PovcalNet income distribution data; missing values linearly interpolated where necessary.
  - Income measure: net disposable income for Malaysia; total per capita consumption expenditure for other countries (PovcalNet); both include public and private transfers.
- Regressors (selected):
  - CPI inflation rate.
  - Fiscal redistribution: difference between market Gini and net (after tax) Gini.
  - Female labor force participation (FLFP): percentage share of females age 15+ in labor force.
    - 2017 female-to-male labor force participation ratios: 63.5 percent in Indonesia, 65.5 percent in Malaysia, 61.5 percent in the Philippines, 78 percent in Thailand, 88 percent in Vietnam; East Asia and Pacific average: 77.7 percent in 2017 (ILO estimates).
  - Productivity growth: GDP per person employed.
  - Net FDI inflows as percent of GDP.
  - Credit to private sector by banks (% of GDP).
  - Savings ratio: net national savings as percent of GNI.
  - Digitalization/financial inclusion proxy: growth rate of mobile cellular subscriptions per 100 people.
    - Figure 6: mobile cellular subscriptions per 100 positively related to percentage reporting digital payments (age 15+).
- Empirical specification:
  - ∆y*_{i,t} = β0 + β1 y̅_{i,t} + β2 x_{i,t} + u_i + γ_t + v_{i,t}
    - y̅_{i,t}: initial level of per capita PPP-adjusted income (log).
    - x_{i,t}: vector of determinants listed above.
    - u_i: time-invariant country-specific effect; γ_t: unobserved period effect.
- Novel contributions:
  - Inclusion of FLFP and digitalization (mobile subscriptions per 100) as structural drivers.
  - Decomposition of inclusive growth into income growth and equity growth components.
  - Application of empirical results to scenario analysis of ASEAN reform plans.

### Methods, Decomposition, and Data Limitations (Appendix A)
- Decomposition:
  - Model assesses impact of regressors on ∆y̅∗ and decomposes into ∆y̅ (income per capita component) and ∆ω (equity index component).
- Data limitations and measurement issues:
  - Income distribution data limited for developing Asia; consumption expenditure used for most countries except Malaysia (income-based).
  - Income-based vs consumption-based welfare:
    - Income-based measures indicate potential purchasing capacity.
    - Consumption-based measures indicate realized purchases.
  - Consumption-based measures may understate top incomes and tend to be smoother than income; consumption-based inequality likely underestimates true inequality.
  - Several important drivers excluded due to limited time-series data: accessibility/quality of education, quality of overall infrastructure, accessibility to healthcare, financial inclusion, fintech, and other indicators.
- Endogeneity and identification:
  - Potential reverse causality and omitted-variable endogeneity acknowledged (example: inflation rate).
  - Approaches to detect/address endogeneity:
    - Arellano–Bond estimator with instrumental variables (results consistent with main results).
    - Panel regression excluding inflation rate (results consistent with benchmark).
    - Inclusion of lagged dependent variable as robustness (results consistent).
    - Granger causality test not applied due to missing observations (unbalanced panel).

### Empirical Results — High-level Findings
- Fiscal redistribution:
  - Significant positive impact on inclusive growth through both per capita income and equity growth.
  - A 1 point increase in the difference between market and net Gini can boost per capita growth by about 0.4 percent and improve equity by almost 0.3 percent.
- Female labor force participation (FLFP):
  - Positive and statistically significant contributor to inclusive growth through both channels.
  - A one percentage point increase in FLFP raises GDP per capita growth by approximately 0.2 percentage point and equity index growth by 0.07 percentage point.
- Productivity growth:
  - Affects inclusive growth primarily via per capita income increases; does not improve equity.
- Net FDI inflows:
  - Positively and significantly impact GDP per capita growth.
- Financial deepening (credit-to-GDP):
  - Not significant for inclusive growth in this analysis.
- Net savings ratio:
  - Significantly and positively affects inclusive growth through both channels.
- Digitalization (mobile subscriptions proxy):
  - Significantly positive impact on inclusive growth through both channels (weak significance in some specifications).
- ASEAN context:
  - Gini-based redistribution remains low in ASEAN; redistributive policies historically less emphasized compared with growth policies.

### Key Regression Estimates (Table 2: Panel Regression Results)
- Dependent variables: ∆y̅∗ (inclusive growth); ∆y̅ (income per capita growth); ∆ω (equity index growth).
- Selected coefficient estimates (standard errors in parentheses):
  - Lagged GDP per capita:
    - ∆y̅∗: 1.925** (0.92)
    - ∆y̅: -0.899 (0.70)
    - ∆ω: 2.824*** (0.64)
  - CPI inflation rate:
    - ∆y̅∗: -0.030 (0.03)
    - ∆y̅: -0.016 (0.03)
    - ∆ω: -0.013 (0.02)
  - Redistribution:
    - ∆y̅∗: 0.687*** (0.19)
    - ∆y̅: 0.402* (0.15)
    - ∆ω: 0.285** (0.13)
  - Female labor force participation:
    - ∆y̅∗: 0.268*** (0.06)
    - ∆y̅: 0.203*** (0.044)
    - ∆ω: 0.066* (0.04)
  - Productivity growth:
    - ∆y̅∗: 0.850*** (0.04)
    - ∆y̅: 0.886*** (0.03)
    - ∆ω: -0.036 (0.01)
  - FDI:
    - ∆y̅∗: 0.073*** (0.02)
    - ∆y̅: 0.086*** (0.02)
    - ∆ω: -0.013 (0.01)
  - Credit-to-GDP ratio:
    - ∆y̅∗: 0.003 (0.01)
    - ∆y̅: 0.009 (0.01)
    - ∆ω: -0.006 (0.00)
  - Savings ratio:
    - ∆y̅∗: 0.063*** (0.02)
    - ∆y̅: 0.034*** (0.01)
    - ∆ω: 0.029** (0.01)
  - Digitalization:
    - ∆y̅∗: 0.480* (0.27)
    - ∆y̅: 0.295 (0.21)
    - ∆ω: 0.185 (0.18)
- Sample statistics:
  - Number of observations: 215 (for each dependent variable).
  - Number of countries: 11.
  - R-squared: 0.81 (inclusive growth), 0.89 (income per capita growth), 0.20 (equity index growth).
  - Prob > F: 0.000 (for all regressions).
- Significance notation:
  - * at 10 percent, ** at 5 percent, *** at 1 percent.

### Robustness Checks and Dynamic Panel Evidence
- Robustness:
  - Alternative specifications (Appendix Table 3): inclusion of lagged dependent variable, total labor force participation instead of FLFP, and trade openness — results broadly consistent.
  - Excluding China and India — signs and significance broadly consistent (Table 4).
- Arellano-Bond dynamic panel estimates (dependent variable ∆y̅∗):
  - Redistribution: 0.773*** (0.31)
  - Female labor force participation: 0.274*** (0.09)
  - Productivity growth: 0.852*** (0.04)
  - FDI: 0.067*** (0.02)
  - Savings ratio: 0.054*** (0.02)
  - Digitalization: 0.441* (0.25)
  - CPI inflation rate: -0.022 (0.02)
  - Credit-to-GDP ratio: -0.009 (0.02)
  - Number of observations: 196; Number of countries: 11; Prob > F: 0.000; Sargan p-value: 0.070
- Alternative specification excluding CPI inflation rate (benchmark consistency):
  - Redistribution: 0.684*** (0.19)
  - Female labor force participation: 0.266*** (0.06)
  - Productivity growth: 0.827*** (0.04)
  - FDI: 0.076*** (0.02)
  - Savings ratio: 0.065*** (0.02)
  - Digitalization: 0.429* (0.26)
  - Number of observations: 215; Number of countries: 11; R-squared: 0.81; Prob > F: 0.000

### Descriptive Statistics (selected)
- Inclusive growth:
  - Obs 220; Mean 4.42; Std. Dev. 3.19; Min -12.38; Max 14.37.
- Income per capita growth:
  - Obs 220; Mean 4.44; Std. Dev. 3.23; Min -14.35; Max 15.15.
- Equity index growth:
  - Obs 220; Mean -0.02; Std. Dev. 1.01; Min -4.06; Max 3.27.
- FLFP:
  - Obs 286; Mean 52.03; Std. Dev. 16.48; Min 23.02; Max 80.02.
- FDI:
  - Obs 286; Mean 3.10; Std. Dev. 4.85; Min -37.16; Max 43.91.
- Productivity growth:
  - Obs 275; Mean 3.94; Std. Dev. 3.28; Min -16.94; Max 12.66.
- Savings ratio:
  - Obs 274; Mean 19.16; Std. Dev. 8.71; Min -9.69; Max 1.97.

### Scenario Analysis: Malaysia (selected results)
- Policy targets and modeled impacts (∆y̅∗, ∆y̅, ∆ω):
  - FLFP increase from 54.7 percent to 56.5 percent:
    - ∆y̅∗: 0.49; ∆y̅: 0.36; ∆ω: 0.13.
  - Labor productivity increase from 3.7 percent to 4.0 percent:
    - ∆y̅∗: 0.26; ∆y̅: 0.27; ∆ω: -0.01.
  - Redistribution increase from 1.3 to East Asia & Pacific level of 4.7:
    - ∆y̅∗: 2.35; ∆y̅: 1.36; ∆ω: 0.99.
  - Total impact of all three targets:
    - ∆y̅∗: 3.10; ∆y̅: 1.99; ∆ω: 1.11.
- Simulation source: Authors’ calculations (based on benchmark model).

### ASEAN Scenario Analysis (selected country-level impacts)
- Indonesia:
  - Redistribution (-5.5 to 4.7):
    - ∆y̅∗: 7.7; ∆y̅: 4.5; ∆ω: 3.3.
  - FLFP (52.17 to 59.3 percent):
    - ∆y̅∗: 1.8; ∆y̅: 1.3; ∆ω: 0.5.
  - Labor productivity (1.3 to 4.7 percent):
    - ∆y̅∗: 2.9; ∆y̅: 3.0; ∆ω: –0.1.
- Philippines:
  - Redistribution (6.0 to 9.1):
    - ∆y̅∗: 2.1; ∆y̅: 1.2; ∆ω: 0.9.
  - FLFP (44.9 to 59.3 percent):
    - ∆y̅∗: 3.9; ∆y̅: 2.9; ∆ω: 1.0.
- Thailand:
  - Redistribution (1.6 to 4.7):
    - ∆y̅∗: 2.1; ∆y̅: 1.2; ∆ω: 0.9.
  - Labor productivity (2.1 to 4.7 percent):
    - ∆y̅∗: 2.2; ∆y̅: 2.3; ∆ω: –0.1.
- Vietnam:
  - Redistribution (2.2 to 4.7):
    - ∆y̅∗: 1.7; ∆y̅: 1; ∆ω: 0.7.
  - Labor productivity (4.6 to 4.7 percent):
    - ∆y̅∗: 0.1; ∆y̅: 0.1; ∆ω: 0.
- Notes:
  - Redistribution data for 2015-2017 based on availability.
  - FLFP as of 2017 (national statistics); East Asia & Pacific FLFP (2017) is a World Bank/ILO regional aggregate.
  - Labor productivity data are 2017 regional aggregates by World Bank.

### Policy-Relevant Conclusions and Recommendations
- Decomposition insight:
  - Inclusive growth ∆y̅∗ increases when GDP per capita growth and/or equity index growth increase.
- Empirical policy-relevant findings:
  - Fiscal redistribution and FLFP are strongly associated with higher inclusive growth through both income and equity channels.
  - Labor productivity growth and FDI inflows contribute primarily through the income per capita channel.
  - Savings contribute positively to inclusiveness.
  - Digitalization (mobile subscriptions proxy) shows a weakly significant positive association; deeper financial inclusion and digitalization can support tax collection, public service delivery, and data accuracy.
- Suggested policy directions:
  - Pursue well-balanced structural reforms addressing both aggregate inclusive growth and its components.
  - Use fiscal redistribution to boost inclusive growth while minimizing negative efficiency impacts; choose redistributive instruments carefully (composition of taxes and spending matters).
  - Increase female labor force participation through labor market reforms to raise both income growth and equity.
  - Deepen financial inclusion: promote account ownership, improve access to financial institutions, and increase affordability of financial services across income groups.
  - Consider expanding conditional cash transfer programs, noncontributory means-tested social pensions, and improving access of low-income families to education and health services as redistributive instruments (subject to administrative capacity).

*Source: wpiea2020118-print-pdf*

### 6.5 percent in 2016. On the other hand, the Philippines saw a less marked poverty declining

### Inclusive Growth: Definitions and Concepts

### Poverty and Poverty Lines
- Poverty headcount ratios fell substantially in several ASEAN countries: from 13.9 percent in 2000 to 6.1 percent in 2015 in the Philippines; 6.5 percent in 2016 (contextual reference point present in text).
- Despite low shares below the extreme poverty line of $1.90 a day, less extreme poverty remains high at $3.2 and $5.5 lines in some countries:
  - Nearly 33 percent of the population were living below $3.20 per day in Indonesia in 2015.
  - More than 60 percent were living below $5.50 per day in Indonesia in 2015.
  - Nearly 33 percent of the population were living below $3.20 per day in the Philippines in 2015.
  - More than 60 percent were living below $5.50 per day in the Philippines in 2015.
- Around 33 million people were still living below the $1.90 per day extreme poverty line in ASEAN in 2015 (based on available data for Indonesia, Lao, Malaysia, Myanmar, Philippines, Thailand, Vietnam; data source: PovcalNet).

### Income Inequality: Trends and Measures
- GDP per capita growth in ASEAN was sustained and positive over the last two decades except during two episodes: aftermath of the Asian crisis (early 2000s) and the global financial crisis in 2009.
- Population-weighted average of the Gini index in developing East Asia and Pacific increased from 33.5 percent in the 1990s to 38.4 percent in the 2010s.
- Country-specific Gini changes (1990s to 2010s) highlighted:
  - Notable progress in income equality: Malaysia, Philippines, Thailand.
  - Worsening equality: China, Indonesia, Lao PDR.
  - Slight improvement: Mongolia, Vietnam.
  - In Indonesia, Gini increased between early 1990s and 2013 but declined through 2017; trend expected to continue (Doumbia and Kinda 2019).
- Income shares by quintile in ASEAN:
  - Income share of the top 20 percent exceeds 40 percent in Indonesia, Malaysia, Philippines, Thailand, and Vietnam.
  - Trends since the 1990s:
    - Decline in top 20 percent share in Malaysia, Philippines, Thailand.
    - Increase in top 20 percent share in Indonesia.
    - Bottom 20 percent income share increased in Malaysia, Philippines, Thailand; declined in Indonesia and Vietnam.
- Empirical associations cited:
  - IMF (2015): An increase in the income share of the top 20 percent by 1 percentage point is associated with a 0.08 percentage point decline in GDP growth in the following 5 years.
  - IMF (2015): An increase in the income share of the bottom 20 percent by 1 percentage point is associated with a 0.38 percentage point increase in GDP growth in the following 5 years.
- Social spending is relatively low in Asia; lower revenue collection and insufficient and poorly targeted social policies are highlighted as bottlenecks to reducing inequality.

### Definitions and Measurement of Inclusive Growth
- Inclusive growth definitions vary: monetary (poverty, income inequality) vs. non-monetary (inequality of opportunities).
  - Absolute pro-poor growth (Ravallion and Chen 2003): growth is inclusive if poor individuals benefit in absolute terms.
  - Relative pro-poor growth (Dollar and Kraay 2002): growth is inclusive if income of poor grows faster than average income.
  - Ali and Son (2007): growth inclusive if it increases a social opportunity function (average opportunities and their distribution).
- Adopted definition (based on Anand et al. 2013):
  - Inclusive growth integrates income distribution and income per capita growth into a single indicator.
  - Requires i) an increase in average income through growth or ii) an increase in income equality, or a combination of both.
  - The equity index ω is the integral of the area under the Social Mobility Curve, scaled between 0 and 1 (1 is perfectly equitable income distribution).
  - Inclusive growth ∆y* is interpreted as real income per capita growth ∆y adjusted for change in the equity index ∆ω; ∆y* = ∆y if income distribution is unchanged.
- Visualization and empirical finding:
  - Figure 5 (cumulative average GDP per capita per population quintiles for ASEAN, 1990-1993 vs. 2015-2017) suggests growth was broadly inclusive in the five countries shown because indifference curves shifted upwards, but income growth was uneven across quintiles:
    - Larger gains for top quintiles than bottom quintiles.
    - Indonesia and Vietnam show the largest unequal gains.
    - Philippines and Thailand show relatively more equitable growth (less steep indifference curves).
- Inclusive growth can be achieved through:
  - Higher GDP per capita growth,
  - Higher equity,
  - Or a combination. Mixed cases exist where inclusiveness arises despite reductions in equality if per capita income growth outpaces equity decline, and vice versa.

### Empirical Analysis: Determinants, Variables, and Model
- Scope and sample:
  - Panel of eleven Asian countries covering 1992 to 2017, including six ASEAN members: Indonesia, Malaysia, Philippines, Lao PDR, Thailand, Vietnam; plus Bangladesh, China, India, Mongolia, Sri Lanka.
- Dependent variable:
  - Inclusive growth measure from Anand et al. (2013): ∆y* (income growth adjusted for equity growth).
  - Equity index constructed from PovcalNet income distribution data; missing values linearly interpolated where necessary.
  - Income measure: net disposable income for Malaysia; total per capita consumption expenditure for other countries (PovcalNet); both include public and private transfers.
- Regressors (monetary, fiscal, macro-structural drivers):
  - CPI inflation rate.
  - Fiscal redistribution: difference between market Gini and net (after tax) Gini.
  - Female labor force participation (FLFP): percentage share of females participating in the labor force (age 15+).
    - 2017 ratio of female to male labor force participation rate: 63.5 percent in Indonesia, 65.5 percent in Malaysia, 61.5 percent in the Philippines, 78 percent in Thailand, 88 percent in Vietnam; East Asia and Pacific average: 77.7 percent in 2017 (ILO estimates).
  - Productivity growth: GDP per person employed.
  - Net FDI inflows as percent of GDP.
  - Credit to private sector by banks (% of GDP): indicator of financial deepening.
  - Savings ratio: net national savings as percent of GNI.
  - Digitalization / financial inclusion proxy: growth rate of mobile cellular subscriptions per 100 people (mobile devices as proxy for financial inclusion and digital payments).
    - Figure 6 relates mobile cellular subscriptions (per 100 people) to percentage reporting having made or received digital payments in the past year (age 15+); trend positive.
- Empirical specification:
  - ∆y*_{i,t} = β0 + β1 y̅_{i,t} + β2 x_{i,t} + u_i + γ_t + v_{i,t}
    - Dependent variable: ∆y*_{i,t} (proxy of inclusive growth).
    - y̅_{i,t}: initial level of per capita PPP-adjusted income (log).
    - x_{i,t}: vector of determinants listed above.
    - u_i: time-invariant country-specific effect.
    - γ_t: unobserved period effect.
- Novel contributions of the analysis:
  - Inclusion of two structural drivers: female labor force participation and financial inclusion proxied by mobile cellular subscriptions per 100 people.
  - Decomposition of inclusive growth into income growth and equity growth components to analyze transmission channels.
  - Application of empirical results to scenario analysis of ASEAN countries’ reform plans.

*Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020118-print-pdf.pdf*

### Appendix A.

### Appendix A.

### Methods and decomposition
- Model assesses impact of regressors on inclusive growth measure ∆y̅∗ and decomposes dependent variable into:
  - income per capita component ∆y̅
  - equity index component ∆ω
- Decomposition clarifies whether regressors affect inclusive growth through per capita income growth or equality improvements.
- Technical details and variable definitions are referenced to Appendices A and B and to methods used by Ostry et al. (2014), Aoyagi and Ganelli (2015).

### Data limitations and measurement issues
- Income distribution data availability for developing Asia is limited; for most sample countries, distribution reflects consumption expenditure rather than income.
- Distinction between income-based and consumption-based welfare:
  - Income-based measures indicate potential goods/services purchasable.
  - Consumption-based measures indicate realized outcomes (purchased goods/services).
- Consumption-based measures may understate top incomes due to incomplete survey coverage of top earners (underreported entrepreneurial and capital incomes).
- Consumption tends to be smoother than income (income more volatile); consumption-based inequality likely underestimates true inequality in many cases.
- In the sample, consumption-based inequality measure is used for all countries except Malaysia, where income-based inequality measure is used.
- Several potentially significant inclusive growth drivers could not be included due to limited time-series data: accessibility/quality of education, quality of overall infrastructure, accessibility to healthcare, financial inclusion, fintech, and other indicators.

### Endogeneity concerns and identification
- Potential reverse causality between inclusive growth and regressors (example: inflation rate) and omitted-variable endogeneity noted.
- Approaches used to detect/address endogeneity:
  - Arellano–Bond estimator regression with instrumental variables; results consistent with main empirical results (presented in Appendix, Table 1).
  - Panel regression excluding the inflation rate; results consistent with benchmark model (Appendix, Table 2).
  - Excluding suspect variables as robustness check; excluding inflation rate does not change main conclusions.
  - Inclusion of lagged dependent variable to capture unobserved effects (Appendix, Table 3); results consistent with benchmark model.
- Granger causality test could not be applied due to missing observations (unbalanced panel).

### Empirical results (high-level)
- Fiscal redistribution has a significant positive impact on inclusive growth through both per capita income growth and equity growth.
  - A 1 point increase in the difference between market and net Gini (redistributive policies) can boost per capita growth by about 0.4 percent and improve equity by almost 0.3 percent.
- Female labor force participation (FLFP) is a positive and statistically significant contributor to inclusive growth through both channels:
  - A one percentage point increase in FLFP raises GDP per capita growth by approximately 0.2 percentage point and equity index growth by 0.07 percentage point.
- Labor productivity growth likely affects inclusive growth through per capita income increases but does not improve equity.
- Net FDI inflows positively and significantly impact GDP per capita growth.
- Financial deepening (credit-to-GDP ratio) is not significant for inclusive growth in this analysis.
- Net savings ratio significantly and positively affects inclusive growth through both channels.
- A proxy for digitalization has a significantly positive impact on inclusive growth through both channels.
- ASEAN evidence: Gini-based redistribution remains low in ASEAN; redistributive policies historically less emphasized relative to growth-supporting macro policy.

### Key regression estimates (Table 2: Determinants of Inclusive Growth, Panel Regression Results)
- Dependent variables: ∆y̅∗ - inclusive growth; ∆y̅ – income per capita growth; ∆ω – equity index growth.
- Lagged GDP per capita:
  - ∆y̅∗: 1.925** (0.92)
  - ∆y̅: -0.899 (0.70)
  - ∆ω: 2.824*** (0.64)
- CPI inflation rate:
  - ∆y̅∗: -0.030 (0.03)
  - ∆y̅: -0.016 (0.03)
  - ∆ω: -0.013 (0.02)
- Redistribution:
  - ∆y̅∗: 0.687*** (0.19)
  - ∆y̅: 0.402* (0.15)
  - ∆ω: 0.285** (0.13)
- Female labor force participation:
  - ∆y̅∗: 0.268*** (0.06)
  - ∆y̅: 0.203*** (0.044)
  - ∆ω: 0.066* (0.04)
- Productivity growth:
  - ∆y̅∗: 0.850*** (0.04)
  - ∆y̅: 0.886*** (0.03)
  - ∆ω: -0.036 (0.01)
- FDI:
  - ∆y̅∗: 0.073*** (0.02)
  - ∆y̅: 0.086*** (0.02)
  - ∆ω: -0.013 (0.01)
- Credit-to-GDP ratio:
  - ∆y̅∗: 0.003 (0.01)
  - ∆y̅: 0.009 (0.01)
  - ∆ω: -0.006 (0.00)
- Savings ratio:
  - ∆y̅∗: 0.063*** (0.02)
  - ∆y̅: 0.034*** (0.01)
  - ∆ω: 0.029** (0.01)
- Digitalization:
  - ∆y̅∗: 0.480* (0.27)
  - ∆y̅: 0.295 (0.21)
  - ∆ω: 0.185 (0.18)
- Sample statistics:
  - Number of observations: 215 (for each dependent variable)
  - Number of countries: 11
  - R-squared: 0.81 (inclusive growth), 0.89 (income per capita growth), 0.20 (equity index growth)
  - Prob > F: 0.000 (for all regressions)
- Note: * denotes statistical significance at 10 percent level, ** at 5 percent level, *** at 1 percent level. Standard errors in parentheses.

### Robustness checks
- Alternative specifications estimated (Appendix Table 3): inclusion of lagged dependent variable, total labor force participation instead of FLFP, and trade openness. Results broadly consistent with benchmark estimation.
- Excluding China and India from benchmark model produces broadly consistent signs and significance (Table 4).

### Policy implications and scenario analysis (Malaysia 11MP example)
- Structural reform evaluation maps changes in policy variables into effects on inclusive growth using marginal effects from benchmark regressions.
- 11MP targets and simulation assumptions:
  - FLFP increase from 54.7 percent to 56.5 percent.
  - Labor productivity growth assumed to increase to 4 percent (Malaysia has already achieved 3.7 percent target; simulation assumes increase to 4 percent).
  - Fiscal redistribution assumed to increase from current level of 1.3 to East Asia and Pacific level of 4.7 (targeting a Gini of 0.385 or 38.5).
- Simulation results reported in Table 3 (based on benchmark model):
  - Total impact on GDP per capita growth of achieving the three targets: 2 percent.
  - Total impact on equity index growth of achieving the three targets: approximately 1.1 percent.
- Policy notes:
  - Redistributive policies should be chosen to minimize negative efficiency impacts; composition of fiscal policy (taxes vs. spending) determines distributional outcomes.
  - Suggested fiscal instruments (per IMF (2014) and literature): expand conditional cash transfer programs as administrative capacity improves; expand noncontributory means-tested social pensions; improve access of low-income families to education and health services.
  - Future research could analyze alternative fiscal indicators (e.g., ratio of direct to indirect taxes, spending components) and include additional structural drivers when longer time series are available.

*Source: Appendix A, wpiea2020118-print-pdf*

### 3.1 percent on inclusive growth.

### 3.1 percent on inclusive growth.

### Scenario analysis — Malaysia (selected results)
- Policy changes and modeled impacts on inclusive growth (∆y̅∗), GDP per capita growth (∆y̅), and equity index growth (∆ω):
  - Female labor force participation (from 54.7 percent to 56.5 percent)
    - ∆y̅∗: 0.49
    - ∆y̅: 0.36
    - ∆ω: 0.13
  - Labor productivity (from 3.7 percent to 4.0 percent)
    - ∆y̅∗: 0.26
    - ∆y̅: 0.27
    - ∆ω: -0.01
  - Redistribution increases to East Asia & Pacific average (from 1.3 to 4.7)
    - ∆y̅∗: 2.35
    - ∆y̅: 1.36
    - ∆ω: 0.99
  - Total impact
    - ∆y̅∗: 3.10
    - ∆y̅: 1.99
    - ∆ω: 1.11
- Source: Authors’ calculations.

### ASEAN scenario analysis (selected country-level impacts)
- Indonesia
  - Redistribution increases to East Asia & Pacific average (from -5.5 to 4.7)
    - ∆y̅∗: 7.7
    - ∆y̅: 4.5
    - ∆ω: 3.3
  - FLFP increases to East Asia & Pacific average (from 52.17 to 59.3 percent)
    - ∆y̅∗: 1.8
    - ∆y̅: 1.3
    - ∆ω: 0.5
  - Labor productivity growth increases to East Asia & Pacific average (from 1.3 to 4.7 percent)
    - ∆y̅∗: 2.9
    - ∆y̅: 3.0
    - ∆ω: –0.1
- Philippines
  - Redistribution increases to East Asia & Pacific average (high income) (from 6.0 to 9.1)
    - ∆y̅∗: 2.1
    - ∆y̅: 1.2
    - ∆ω: 0.9
  - FLFP increases to East Asia & Pacific average (from 44.9 to 59.3 percent)
    - ∆y̅∗: 3.9
    - ∆y̅: 2.9
    - ∆ω: 1.0
- Thailand
  - Redistribution increases to East Asia & Pacific average (from 1.6 to 4.7)
    - ∆y̅∗: 2.1
    - ∆y̅: 1.2
    - ∆ω: 0.9
  - Labor productivity growth increases to East Asia & Pacific average (from 2.1 to 4.7 percent)
    - ∆y̅∗: 2.2
    - ∆y̅: 2.3
    - ∆ω: –0.1
- Vietnam
  - Redistribution increases to East Asia & Pacific average (from 2.2 to 4.7)
    - ∆y̅∗: 1.7
    - ∆y̅: 1
    - ∆ω: 0.7
  - Labor productivity growth increases to East Asia & Pacific average (from 4.6 to 4.7 percent)
    - ∆y̅∗: 0.1
    - ∆y̅: 0.1
    - ∆ω: 0
- Notes:
  - Redistribution data is for 2015-2017 based on data availability.
  - FLFP is as of 2017 based on national statistics; East Asia & Pacific average for FLFP (2017) is a regional aggregate by World Bank (based on ILO estimate and includes all income levels).
  - Labor productivity data is of 2017, East Asia & Pacific average (regional aggregate by World Bank, includes all income levels).
  - Source: Authors’ calculations.

### Key empirical findings (determinants of inclusive growth)
- Arellano-Bond dynamic panel estimates (dependent variable: ∆y̅∗ — inclusive growth)
  - Redistribution: 0.773*** (robust standard error 0.31)
  - Female labor force participation: 0.274*** (0.09)
  - Productivity growth: 0.852*** (0.04)
  - FDI: 0.067*** (0.02)
  - Savings ratio: 0.054*** (0.02)
  - Digitalization: 0.441* (0.25)
  - CPI inflation rate: -0.022 (0.02)
  - Credit-to-GDP ratio: -0.009 (0.02)
  - Number of observations: 196
  - Number of countries: 11
  - Prob > F: 0.000
  - Sargan p-value: 0.070
- Alternative specification excluding CPI inflation rate
  - Redistribution: 0.684*** (0.19)
  - Female labor force participation: 0.266*** (0.06)
  - Productivity growth: 0.827*** (0.04)
  - FDI: 0.076*** (0.02)
  - Savings ratio: 0.065*** (0.02)
  - Digitalization: 0.429* (0.26)
  - Number of observations: 215
  - Number of countries: 11
  - R-squared: 0.81
  - Prob > F: 0.000

### Descriptive statistics (selected)
- Sample size and central tendencies (variable: Obs, Mean, Std. Dev., Min, Max)
  - Inclusive growth: 220; Mean 4.42; Std. Dev. 3.19; Min -12.38; Max 14.37
  - Income per capita growth: 220; Mean 4.44; Std. Dev. 3.23; Min -14.35; Max 15.15
  - Equity index growth: 220; Mean -0.02; Std. Dev. 1.01; Min -4.06; Max 3.27
  - FLFP: 286; Mean 52.03; Std. Dev. 16.48; Min 23.02; Max 80.02
  - FDI: 286; Mean 3.10; Std. Dev. 4.85; Min -37.16; Max 43.91
  - Productivity growth: 275; Mean 3.94; Std. Dev. 3.28; Min -16.94; Max 12.66
  - Savings ratio: 274; Mean 19.16; Std. Dev. 8.71; Min -9.69; Max 1.97

### Policy-relevant conclusions and recommendations
- Inclusive growth is decomposed into GDP per capita growth and equity index growth; inclusive growth (∆y̅∗) increases when average income growth and/or equity index growth increase.
- Empirical results indicate:
  - Fiscal redistribution and female labor force participation (FLFP) are strongly associated with higher inclusive growth, contributing through both equity and income channels.
  - Labor productivity growth and FDI inflows contribute to inclusive growth primarily through the income per capita channel.
  - Savings also contribute to inclusiveness.
  - Digitalization (proxied by mobile cellular subscriptions) shows a weakly significant positive association with inclusive growth; deeper financial inclusion and broader digitalization can support tax collection, public service delivery, and data accuracy.
- Policy implications emphasized:
  - ASEAN countries should pursue well-balanced structural reform approaches that consider both aggregate inclusive growth and its components (income growth and equity growth).
  - Fiscal redistribution offers a potential to boost inclusive growth without a trade-off between efficiency and equity.
  - Labor market reforms to increase female labor force participation can simultaneously boost income per capita growth and equity.
  - Governments should further deepen financial inclusion by promoting account ownership, improving accessibility to financial institutions, and increasing affordability of financial services across income groups.

*Source: Authors’ calculations and analysis from the provided content.*

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*Source: wpiea2020118-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020118-print-pdf.pdf_