Appendix A. Inclusive Growth Indicator

Our inclusive growth indicator is in line with Anand et al. (2013) which is built on Ali and Son (2007). The latter propose a methodology to measure inclusive growth in terms of increasing a social opportunity function, which depends on two factors: i) average opportunities available to the population, and ii) how opportunities are shared among the population. Following Ali and Son (2007) and considering income as an opportunity, we can derive a social welfare function considering a distribution of income xt for population n, where all individuals are indexed by ascending order of income levels i = 1,2,..., n, where x1 is the poorest person and xn is the richest person. The social welfare function will be:

W=W(x1, x2, , xn)(1)

which is an increasing function of its argument. We then can define a social opportunity function where the ith person with income xi enjoys the opportunity yi:

0=0(y1,y2, , yn)(2)

which is increasing in its argument, meaning that the opportunity function increases when the income (opportunity) of any individual increases. More equally distributed income opportunities (pro-poor redistribution) will indicate more inclusiveness. To address that we can consider a measure of inclusiveness based on the concept of generalized concentration curve.30 Ali and Son (2007) define a generalized concentration curve, or a social mobility curve (SMC) Sc:

Sc(y1,y1+y22y1+y2+y33,,Σjiyji,,Σjnyjn)(3)

where the last term is equal to the population mean of the available opportunities, i is the percentile of income. Hence, SMC represents the opportunities available for the bottom i-th percentiles of income or the ability for the bottom percentiles in the income distribution to escape into the higher income groups.

To capture the magnitude of the change in income distribution, Anand et al. (2013) use a simple form of the social mobility curve to calculate a so-called Social Mobility Index (SMI) by integrating the area under the social mobility curve:

y¯*=0100y¯id(4)

The greater y¯* the greater the income, in the case in which all individuals in the population have the same income, in other words if the income distribution is completely equal, then y¯* will be equal to mean income y¯. In the case in which y¯* is lower than y¯, the distribution of income is inequitable.

Ali and Son (2007) propose the income equity index (IEI):

ω=y¯*y¯(5)

which is scaled between 0 and 1, with 1 indicating a completely equal income distribution and 0 indicating a totally unequal one. Rearranging we obtain:

y¯*=ω*y¯(6)

differentiating (6) we obtain the inclusive growth equation:

dy¯*=y¯dω+ωdy¯(7)

which integrates equity and income growth into single measure. We define the change in social mobility index dy¯* as the change of the degree of growth inclusiveness. If dy¯*>0, growth is considered as inclusive. This measure allows us to decompose inclusive growth into income growth and change in equity.

dy*¯y*¯=dy¯y¯+dωω(8)

Equation (8) is the fundamental equation integrating GDP per capita growth and equity index growth into a single measure of inclusive growth comparable over time. Obviously, inclusive growth can be achieved through i) increasing average income growth, ii) increasing income equity index growth; iii) a combination of i) and ii).

B. Variables and Descriptive Statistics

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Source: Authors’ calculation.

C. Alternative Specifications and Scenarios.

Table 1.

Arellano-Bond dynamic panel-data estimation.

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Source: Authors’ calculation. * denotes statistical significance at 10 percent level, ** denotes statistical significance at 5 percent level, *** denotes statistical significance at 1 percent level. Robust standard errors are in parentheses.

Arellano-Bond test for autocorrelation

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Source: Authors’ calculation.Note: Null hypothesis: no autocorrelation.
Table 2.

Determinants of Inclusive Growth, excluding CPI inflation rate.

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Source: Authors’ calculation. * denotes statistical significance at 10 percent level, ** denotes statistical significance at 5 percent level, *** denotes statistical significance at 1 percent level. Robust standard errors are in parentheses.
Table 3.

Determinants of Inclusive Growth, Alternative Specifications.

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Source: Authors’ calculation. * denotes statistical significance at 10 percent level, ** denotes statistical significance at 5 percent level, *** denotes statistical significance at 1 percent level. Standard errors are in parentheses.
Table 4.

Determinants of Inclusive Growth, excluding China and India.

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Source: Authors’ calculation. * denotes statistical significance at 10 percent level, ** denotes statistical significance at 5 percent level, *** denotes statistical significance at 1 percent level. Standard errors are in parentheses.
Table 5.

Inclusive Growth, GDP per capita Growth, and Equity Growth, 1990–2017.

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Source: Authors’ calculations. Note: Initial years are 1993 for Indonesia, 1992 for Malaysia, 1991 for Philippines, 1990 for Thailand, 1992 for Vietnam. Latest years are 2017 for Indonesia, 2015 for Malaysia, 2015 for Philippines, 2017 for Thailand, 2016 for Vietnam.
Table 6.

ASEAN, Scenario Analysis Results.

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Note: Redistribution data is for 2015–2017 based on data availability. FLFP is as of 2017 is 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.

References

  • Acemoglu, D. and D. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” NBER Working Paper No. 16082 (Cambridge, Massachusetts: National Bureau of Economic Research).

    • Search Google Scholar
    • Export Citation
  • Aizenman, J., M. Lee, and D. Park, 2012. “The Relationship between Structural Change and Inequality: A Conceptual Overview with Special Reference to Developing Asia.” ADBI Working Paper No. 2012.

    • Search Google Scholar
    • Export Citation
  • Albanesi, S. 2007. “Inflation and inequality.” Journal of Monetary Economics, Elsevier, Vol. 54(4), pages 10881114, May.

  • Aghion, P., E. Caroli, and C. Garcia-Penalosa. 1999. “Inequality and Economic Growth: The Perspective of the New Growth Theories.” Journal of Economic Literature, 37 (4):16151660.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asian Development Bank (ADB). 2016. Asian Development Outlook (ADO) “Asia’s Potential Growth”.

  • Asian Development Bank (ADB). 2019. Asian Development Outlook (ADO) “Strengthening Disaster Resilience”.

  • Asian Development Bank Institute. 2019. “Demystifying Rising Inequality in Asia.” Edited by B. Huang, P. J. Morgan, and N. Yoshino.

  • Ali, I., and Son, H. H. 2007. Measuring Inclusive Growth. Asian Development Review, 24 (1), 1131.

  • Anand, R., Mishra, S., and Peiris, S. J. 2013. Inclusive Growth: Measurement and Determinants. IMF Working Paper, 13/135.

  • Aoyagi, C., Ganelli, G., and Murayama, K. 2016. “How Inclusive Is Abenomics?Journal of International Commerce, Economics and Policy, Vol. 07, Issue 01.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aoyagi, C. and Ganelli, G. 2015. “Asia’s Quest for Inclusive Growth Revisited.” Journal of Asian Economics, Vol. 40(C), pp. 2946.

  • Balakrishman, R., S., Chad and S., Murtaza. 2013. “ The Elusive Quest for Inclusive Growth: Growth, Poverty, and Inequality in Asia.” IMF Working Paper 13/152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benabou, R., 2000. “Unequal Societies: Income Distribution and the Social Contract.” American Economic Review, Vol. 90(1), pp. 96129.

  • Berg, A. and Ostry, J. 2011. “Inequality and Unsustainable Growth: Two Sides of the Same Coin?IMF Staff Discussion Note, 11/08.

  • Berg, A., Ostry, J., and Zettelmeyer, J. 2012. “What makes growth sustained?Journal of Development Economics, 98 (2), 149166.

  • Calderon, C. and L. Serven 2004, The Effects of Infrastructure Development on Growth and Income Distribution, Policy Research Working Papers.

    • Search Google Scholar
    • Export Citation
  • Carvalho, L. and A. Rezai. 2015. “Personal income inequality and aggregate demand.” Cambridge Journal of Economics, Volume 40, Issue 2, March 2016, Pages 491505,

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaudhuri, S. and Ravallion, M. 2006. “Partially Awakened Giants: Uneven Growth in China and India.” Policy Research Working Paper; No. 4069. World Bank, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Claus, I., J. Martinez-Vazquez, and V. Vulovic. 2012. Government Fiscal Policies and Redistribution in Asian Countries. ADB Economics Working Paper Series No. 310. Asian Development Bank.

    • Search Google Scholar
    • Export Citation
  • Corak, M. 2013. “Income Inequality, Equality of Opportunity, and Intergenerational Mobility.” Journal of Economic Perspectives, 27 (3):79102.

  • Clements, B., R. Mooij, S. Gupta, and M. Keen. 2015. Inequality and Fiscal Policy. International Monetary Fund.

  • Cuberes and Teignier. 2016. “Aggregate Effects of Gender Gaps in the Labor Market: A Quantitative Estimate,” Journal of Human Capital 101.

    • Search Google Scholar
    • Export Citation
  • Demirgüç-Kunt, A., L. Klapper, D. Singer, S. Ansar, and J. Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution, World Bank. International Telecommunication Union.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dollar, D. and A. Kraay. 2002. “Growth is Good for the Poor”. Journal of Economic Growth 7:195.

  • Doumbia, D. and T. Kinda. 2019. Reallocating Public Spending to Reduce Income Inequality. IMF Working Paper, 19/188.

  • Elborgh-Woytek, K., M. Newiak, K. Kochhar, S. Fabrizio, K. Kpodar, P. Wingender, B. Clements, and G. Schwartz. 2013. IMF Staff Discussion Note 13/10. “Women, Work, and the Economy: Macroeconomic Gains from Gender Equity.”

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furceri, D, P Loungani, J D Ostry, and P Pizzuto, 2020, “Will Covid-19 affect inequality? Evidence from past pandemics,” Covid Economics 12: 13857.

    • Search Google Scholar
    • Export Citation
  • Garcia-Penalosa, C. and S. Turnovsky. 2007. “Growth, Income Inequality, and Fiscal Policy: What Are the Relevant Trade‐offs?Journal of Money, Credit and Banking, 39: 369394.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonzales, Jain-Chandra, Kochhar, Newiak, and Zeinullayev (2015). IMF Staff Discussion Note 15/20. “Catalyst for Change: Empowering Women and Tackling Income Inequality”.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B. and G. Wan (2019), Overview of Income Inequality in Asia: Profile, Drivers and Consequences; in Demystifying Rising Inequality in Asia. Asian Development Bank Institute, Tokyo.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund 2014. “Fiscal Policy and Income Inequality.” Board Paper. International Monetary Fund, Washington, D.C.

  • International Monetary Fund 2015. Causes and Consequences of Income Inequality: A Global Perspective. Staff Discussion Note 15/13.

  • International Monetary Fund 2018a. Regional economic outlook. Asia and Pacific: Asia at the forefront: growth challenges for the next decade and beyond. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund 2018b. World Economic Outlook: Growth Slowdown, Precarious Recovery. Washington, DC, April.

  • International Monetary Fund 2019. Finance and Development, March 2019.

  • International Monetary Fund Country Report No. 18/62 Malaysia. International Monetary Fund, Washington, D.C.

  • International Monetary Fund Country Report No. 19/71 Malaysia. International Monetary Fund, Washington, D.C.

  • International Monetary Fund Country Report No. 18/287 Philippines. International Monetary Fund, Washington, D.C.

  • International Monetary Fund Country Report No. 18/143 Thailand. International Monetary Fund, Washington, D.C.

  • International Monetary Fund Country Report No. 18/32 Indonesia. International Monetary Fund, Washington, D.C.

  • International Monetary Fund Country Report No. 18/215 Vietnam. International Monetary Fund, Washington, D.C.

  • Jain-Chandra, S., Kinda, T., Kochhar, K., Piao, S., and Schauer, J. 2016. “Sharing the Growth Dividend: Analysis of Inequality in Asia.” IMF Working Paper No. 16/48.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kakwani, N. 1980. “On a Class of Poverty Measures.” Econometrica. 48–2, 437446.

  • Keeley, B. 2015. “What’s happening to income inequality?”, in Income Inequality: The Gap between Rich and Poor, OECD Publishing, Paris.

  • Keynes, John Maynard 1936. The General Theory of Employment, Interest and Money. London, Macmillan.

  • Kuznets, S. 1955. Economic Growth and Income Inequality. American Economic Review 45 (1), 128.

  • OECD/ERIA 2018, SME Policy Index: ASEAN 2018: Boosting Competitiveness and Inclusive Growth, SME Policy Index, OECD Publishing, Paris/ERIA, Jakarta, https://doi.org/10.1787/9789264305328-en. Chapter 13. Cambodia.

    • Search Google Scholar
    • Export Citation
  • Okun, A. M. 1975, Equality and Efficiency: the Big Trade-Off (Washington: Brooking Institution Press).

  • Ostry, J. D., Berg, A., and Tsangarides, C.G. 2014. Redistribution, Inequality, and Growth. IMF Staff Discussion Notes 14/02.

  • Ravallion, M., and S. Chen, 2003, “Measuring Pro-Poor Growth,” Economics Letters, 78, 9399.

  • Romer, C. and D. Romer 1998. “Monetary Policy and the Well-Being of the Poor.” NBER Working Paper No. 6793.

  • Schmillen, Achim Daniel; Tan, Mei Ling; Abdur Rahman, Amanina Binti; Lnu, Shahrul Natasha Binti Halid; Weimann Sandig, Nina. 2019. Breaking Barriers: Toward Better Economic Opportunities for Women in Malaysia (English). The Malaysia Development Experience Series. Washington, D.C.: World Bank Group.

    • Search Google Scholar
    • Export Citation
  • Solow, R. M. 1956. A contribution to the theory of economic growth. The quarterly Journal of economics, 6594.

  • Solt, Frederick. 2019. “Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database.” SWIID Version 8.0, February 2019.

    • Search Google Scholar
    • Export Citation
  • United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). 2017. Fostering Women’s Entrepreneurship in ASEAN.

  • World Bank. 2016. Poverty and Shared Prosperity 2016: Taking on Inequality. Washington, DC: World Bank.

  • World Income Inequality Report. 2018. World Inequality Lab. https://wir2018.wid.world/

  • Zhuang, J., Kanbur, R., and Rhee, C. 2014. “Rising Inequality in Asia and Policy Implications.” ADBI Working Paper 463.

1

*We thank Nada Choueiri and numerous IMF colleagues for useful comments. Any errors are solely ours.

3

While our regression analysis is based on a wider sample of Asian countries, the descriptive analysis of ASEAN trends presented in next section focuses on Indonesia, Malaysia, Philippines, Thailand, and Vietnam. The choice of both country groups is dictated by data availability.

4

The World Bank defines poverty headcount ratio at $1.90 per day (2011 PPP) as extreme poverty, while poverty headcount ratios at $3.20 and $5.50 (2011 PPP) represent World Bank’s lower middle-income and upper middle-income country poverty lines respectively. Depending on the level of economic development, some countries have low extreme poverty rates, hence, all three ratios are presented on this chart. For more details see https://www.worldbank.org/en/topic/poverty/lac-equity-lab1/poverty .

5

Based on available data for Indonesia, Lao, Malaysia, Myanmar, Philippines, Thailand, Vietnam. Data source: PovcalNet.

6

A Gini index of 0 represents perfect equality, while an index of 100 represents perfect inequality.

7

See Doumbia and Kinda (2019) for the case study on tackling inequality in Indonesia.

8

See inclusiveness matrix in Anand et al. (2013) for more details.

9

The ratio of female to male labor force participation rate is calculated by dividing the female labor force participation rate (percentage share of female who participate in the labor force in total working-age female population aged 15 and older) by the male labor force participation rate and multiplying by 100. International Labour Organization, ILOSTAT database.

10

Global Findex Database by World Bank.

11

In the Philippines, the authorities tried to tap available facilities to pursue financial inclusion using digitalized solutions by easing know-your-customer requirements to be able to distribute assistance such as payment of benefits targeted to the vulnerable. The Bangko Sentral ng Pilipinas continues to leverage on emerging digital innovations (e.g., use of blockchain technology, fintech, and mobile payments) to promote a more efficient and inclusive financial system. Also, various digital platforms have empowered more Filipinos, including women, to become self-earning entrepreneurs.

12

The country choice was dictated by data availability.

13

Since country data for income distribution are not available for each year, to construct the equity index we calculated missing values using the linear interpolation method.

14

The countries in the PovcalNet database that use incomes as a measure of welfare are mostly high-income countries and Latin America and Caribbean countries. 75 percent of the countries in the Bank PovcalNet database use per capita consumption as a welfare aggregate (World Bank 2016).

15

The equity index ω is the integral of the area under the Social Mobility Curve (Figure 5) and is scaled between 0 and 1 (1 is perfectly equitable income distribution) and ω=Δy¯*/Δy¯. See the technical note in Appendix A.

16

See technical details in Appendix A.

17

Based on the method used by Ostry et al. (2014), Aoyagi and Ganelli (2015).

18

More details on variables, data sources, definitions are provided in Appendix B.

20

See more about income and consumption welfare aggregates in World Bank (2016), Box 1.1.

21

Consumption-based inequality measure is used for all countries in our data sample except for Malaysia, where income-based inequality measure is used.

22

Granger causality test is another useful technic to identifying causality relationship, however, it was not possible to apply to our data set due to missing observations (unbalanced panel).

24

See more details in Aoyagi et al. (2015).

25

IMF Country Report No. 19/71 Malaysia. International Monetary Fund Washington, D.C.

27

See more on the role of women in the workforce in Malaysia in Schmillen et al. (2019).

30

See Kakwani (1980) for more details.

Determinants of Inclusive Growth in ASEAN
Author: Victoriia Alekhina and Mr. Giovanni Ganelli