World Income Inequality

Database (WIID) Carlos Gradín (UNU-WIDER)

UNU-WIDER — UN DESA workshop on inequality

UN Headquarters, New York, 6 May 2019

WIID History

• A database with cross-country information on income inequality.

– Initially compiled in 1997–99, published in Sept. 2000.– Subsequently updated and expanded

• WIID 2 (May 2008)• WIID 3a,b (June 2014)• WIID 3c (September 2015)• WIID 3.4 (January 2017).

• Most recent version (December 2018)

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https://www.wider.unu.edu/database/world-income-inequality-database-wiid4

https://www.wider.unu.edu/database/world-income-inequality-database-wiid4

WIID: Research• Global inequality and poverty:

– Sala i Martin (Quarterly J. Economics, 2010); Ackland et al. (R. Economics and Statistics, 2013); Niño-Zarazúa and Tarp (R. Income and Wealth, 2017); Roope et al. (Economics Letters, 2018).

• Relationship of inequality with issues such as:

– economic growth: Jovanovic (Economic Systems, 2018),

– foreign direct investment: Huang et al. (R. World Economics, 2010)– real exchange rate: Min et al. (Annals of Economics and Finance, 2015).– institutional development: Amendola et al. (Public Chice, 2013)– labor regulations: Alesina et al. (J. Economic Growth, 2018) – economic sanctions: Afesorgbor and Mahadevan (World Development, 2018)– public sector expansion: Lee et al. (American Sociological R., 2011)– political conflict: Cramer & Kaufman (Comparative Political Studies, 2011)– religiosity: Immerzeel and Tubergen (European Sociological R., 2013)– skilled immigration: Kahanec and Zimmermann (IZA J. Migration, 2015)

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Study (187) %Atkinson & Micklewright 1992 1.3Brandolini 1998 1.1Leigh 2005 1.1Szekely & Hilgert 2002 1.1Fields 1989 1.0

WB source %World Bank 2018 14.8Jain 1975 3.0Deininger & Squire 2004 1.7

NSA %Statistics Canada 1.4Taiwan DGB 1.1US Bureau 0.9Statistics Finland 0.6

UN source %ECLAC 5.6UNICEF 4.4UN 0.7

Secondary data

WIID 4

• Simplifying contents and structure of variables

– Grouping similar categories, while providing more details in a different variable

– String variables changed to numeric– Labels corrected

• Updating (2017) and cleaning (duplicates, errors)

• Additional information

– Variables identifying the source– Complementary variables: Per capita GDP PPP (WB), Population

(UN), Region (UN and WB)

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Distributional statistics

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Total PartialQuintiles 6,933 241Deciles 6,239 99Bottom 5% 1,595Top 5% 2,179

TotalAll 11,101Gini 11,054

TotalMean 5,786Median 4,520

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57 obs. 1867-1949

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3,446 unique country-year observations189 countries/entities Exceptions: Libya, North Korea, some Gulf states, some microstates.

> 1 obs. per country-year if relevant information addedEx. consistency among time series, different resource concepts, area, eq. scales.

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Variety of concepts

Resource Scale Coverage

Geo Population

IncomeConsumptionEarnings

Per capitaEquivalizedPer household(No adjustment)

AllRuralUrbanPart

AllEc. ActiveSpecific

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All countries

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LACN America

Europe & CA

E Asia

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S Asia MENA

SSA

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Income or expenditureExpenditure Income Ratio I/E

India 0.394 0.543 1.38China 0.431 0.439 1.02

Gini index of income and expenditure

Source: Gradín and Wu (2019), using IHDS and CHIP.

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Region Ratio% cases

with ratio>1East Asia & Pacific 1.07 60.4

China 1.02 40.0Europe & Central Asia 1.07 68.1Latin America & Caribbean 1.20 97.1Middle East & North Africa

(Egypt & Jordan)1.23 100

North America (US) 1.30 100South Asia 1.28 100

India 1.45 100Sub-Saharan Africa 1.20 87.8Total 1.12 75.2

Income Gini / Consumption GiniCases with both income and consumption

Source: Gradín and Wu (2019), based on WIID 4.

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Region Country (years)

E Asia & P

Rural/urban China (1990-2010), China (2002),

Indonesia (2012), Philippines (1985), Singapore

(1993)

Europe & CA

Belarus (1995-2006), Bulgaria (1989), Croatia (2009),

Estonia (1997, 2004), Hungary (1993, 1997-2007),

Latvia (1997), Lithuania (1997, 2003, 2004, 2008),

Macedonia (1997, 2002-06, 2008), Poland (1990,

1992, 2008-09, 2015), Portugal (1980, 1990), Russia

(1993, 2004, 2007, 2010, 2013), Slovakia (2004-08),

Slovenia (1998, 2002-03), Spain (2002, 2003)LAC Mexico (1984)

SSABotswana (1994, 2003), Ethiopia (2011), Nigeria

(1980, 1986)

Countries with higher Gini in consumption than in income

Source: Gradín and Wu (2019) based on WIID 4.

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Especial cases,e.g. Argentina

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WIID (pc)(LIS)

Germany (income)

India (consumption)

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WIID (pc)(World Bank)

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Sri Lanka (income)

WIID (per capita)(WB)

D&S, 2004

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Sri Lanka (consumption)

WIID (pc)(WB)

WIID (no adjustment)(NSA)

D&S, 2004

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Sri Lanka (consumption)

WIID (pc)(NSA; WB)

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US (income)WIID (no adjustment)(Brandolini 1998)

WIID (pc)(Census Bureau)

WIID (pc)(LIS)

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WIID (pc)(SEDLAC)

WIID (no adjustment)(Fields, 1989)

WIID (pc)(ECLAC)

Brazil (income)

China (income)

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WIID (pc)(NBS)

WIID (no adjustment)(Dowling & Soo, 1983)

WIID (pc)(Ravallion & Chen, 2007)

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South Africa (income)

Gini by region (weighted average) in WIID

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Sub-Saharan Africa

Latin America and the Caribbean

Middle East and North Africa

Europe and Central AsiaEast Asia and the Pacific

North America

South Asia

World

Source: WIID 4, using a standardization like in Niño-Zarazúa and Tarp, ROIW 2017

SWIID: Standardized World Income Inequality (Solt)GCIP: Global Consumption and Income Project (Jayadev, Lahoti, and Reddi)EHII: Estimated Household Income Inequality (UT, Galbraith)ATG: All The Ginis (Milanovic)

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SWIID GCIP EHII ATG WIIDCountries/entities 192 161 151 193 189

Years1960-2017 1960-2015 1963-2015 1948-2017

(1867…)

1960-2017N Country/years 5,226 8,839 4,882 2,341 3,446N Observations 10,452 17,678 4,882 5,121 11,101Original Gini X XEstimated Gini X X XImputed (missing) X XScale Sq. root Per capita Several Several

Concepts Market income Consumption Several Several

Net income Net income Gross income Several Several

Datasets

India (consumption)

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WIID(World Bank)

GCIP

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Sri Lanka (consumption)

GCIP

WIID(NSA ; WB)

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Sri Lanka (income)

WIID (NSA ; WB)

GCIP

SWIID (re-scaled)

EHII

China (income)

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WIID(Dowling and Soo, 1983; Ravallion and Chen, 2007; NBS)

GCIP

China (income)

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WIID(Dowling and Soo, 1983; Ravallion and Chen, 2007; NBS) SWIID

(re-scaled)

China (income)

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WIID(Dowling and Soo, 1983; Ravallion and Chen, 2007; NBS)

EHII

China (income)

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WIID(Dowling and Soo, 1983; Ravallion and Chen, 2007; NBS)

GCIP

SWIID(re-scaled)

EHII

Brazil (income)

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WIID(Fields, 1989 and SEDLAC)

GCIP

Brazil (income)

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SWIID(re-scaled)

WIID(Fields, 1989 and SEDLAC)

Brazil (income)

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EHII

WIID(Fields, 1989 and SEDLAC)

Brazil (income)

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GCIPSWIID(re-scaled)

WIID (Fields, 1989 and SEDLAC)

EHII

Final remarks

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• Secondary datasets

– Pros: coverage– Cons: lack of harmonization, inflexibility (no microdata),

poor documentation …

• Do we still need these compilations?

– Increasing availability of microdata and harmonization• Limited coverage

– Standardization• Handy, but comes at a price• Not unique, depends on sources and methods

– Imputation of missing observations

www.wider.unu.eduHelsinki, Finland

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THANKS!