Nora Lustig Samuel Z. Stone Professor of Latin American Economics Dept. of Economics Tulane...

Nora Lustig Samuel Z. Stone Professor of Latin American

EconomicsDept. of Economics

Tulane UniversityNonresident Fellow, Center for Global

Development

China Development Research Foundation, BeijingFebruary 23, 2011

1

Using Gini coefficient,

19 percent more unequal than Sub-Saharan Africa

37 percent more unequal than East Asia

65 percent more unequal than developed countries

2

Gini Coefficient by Region (in %), 2004

32.233.6

38.9 38.9 39.1

44.7

53.2

20.0

25.0

30.0

35.0

40.0

45.0

50.0

55.0

60.0

High Income Europe andCentral Asia

South Asia North Africaand the

Middle East

East Asia andthe Pacific

Sub-SaharanAfrica

Latin Americaand the

Caribbean

Gin

i co

eff

icie

nt

3

35

40

45

50

55

60

Early 90s Mid. 90s Early 2000s Mid. 2000s

Gin

i co

effi

cien

t

0=

Gini Coefficient for Latin America: early 1990s-mid 2000s

(Gasparini et al., 2009)

5

Inequality in most Latin American countries (12 out of 17) has declined (roughly 1.1% a year) between (circa) 2000 and (circa) 2009.

Decline is robust to period selected.

Decline has continued despite global financial crisis.

6

The decline took place in: Persistently high inequality countries

(Brazil) and normally low inequality countries (Argentina)

Fast growing countries (Chile and Peru), slow growing countries (Brazil and Mexico) and countries recovering from crisis (Argentina and Venezuela)

Countries with left populist governments (Argentina), left social-democratic governments (e.g., Brazil, Chile) and center-right governments (e.g., Mexico and Peru)

14

In-depth analysis in four countries:Argentina (Gasparini and Cruces) (urban; 2/3 of pop)

Brazil (Barros, Carvalho, Mendoca & Franco)

Mexico (Esquivel, Lustig and Scott)Peru (Jaramillo & Saavedra)

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0.582

0.589

0.594

0.588

0.596

0.587

0.599

0.615

0.634

0.612

0.580

0.602

0.599

0.600

0.600

0.598 0.592 0.594

0.587

0.581

0.5690.566

0.560

0.552

0.544

0.5390.53

0.54

0.55

0.56

0.57

0.58

0.59

0.60

0.61

0.62

0.63

0.64

0.65

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Gin

i c

oe

ffic

ien

t

Evolution of the degree of inequality in per capita income: Brazil, 1995-2009

Inequality has continued to decline in Brazil The Gini Coefficient reached its lowest level in the last three decades.

1981-2009

0.42

0.44

0.46

0.48

0.50

0.52

0.54

19

92

19

93

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94

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95

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96

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92

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09

Decline in inequality is statistically significant and significant in terms of order of magnitude

There is Lorenz dominance (unambiguous decline independently of choice of inequality measure)

Robust to income concept (e.g., monetary vs. total)

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Representative sample of Latin American diversity:high/medium/low ineq high/low growth Populist/social democratic/center-right governments

21

Sample Representative of High and Low Inequality Countries

(Latin America: Gini Coefficient by Country; circa 2007; in percent)

43.5

46.8

48.2 48.348.9

49.7 49.7 49.9

51.8 52.353.2

54.3 54.4 54.8 54.9

57.2

59.3

51.6

40

45

50

55

60

65

Ven

ezu

ela

Uru

gu

ay

Arg

en

tin

a

Do

min

ican

Rep

.

Co

sta

Ric

a

Peru

El

Salv

ad

or

Mexic

o

Ch

ile

Nic

ara

gu

a

Para

gu

ay

Ecu

ad

or

Gu

ate

mala

Bra

zil

Pan

am

a

Bo

livia

Ho

nd

ura

s

TO

TA

L C

OU

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RIE

S

Gin

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All four countries experienced substantial market-oriented reforms in the 1990s (and since the 1980s in the case of Mexico). In particular, trade and foreign investment regimes were liberalized, many state-owned enterprises were privatized and, more generally, markets were deregulated. These four countries also faced significant macroeconomic crises between 1995 and 2006 and, except for Argentina, have pursued broadly prudent fiscal and monetary policies in particular since 2000. Starting in 2003, Argentina and Peru benefited from very favorable terms of trade as a result of the boom in commodity prices. As a result, Argentina and Peru have enjoyed high per capita growth rates between 2003 and 2006: 7.8 and 5.2 percent a year, respectively. In contrast, in Brazil and Mexico, GDP per capita growth was modest: 2.7 and 2.8 percent a year, respectively

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-3

-1

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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Av

era

ge

an

nu

al g

row

th r

ate

(%

)

Distribution of countries (%)

Distribution of countries according to the average per capita GDP growth rate between 1990 and 2005

Brazilian top 10%

Brazilian bottom 10%

China

Germany

Haiti

Over the last years, the income of the Brazilian poor has been growing as faster as per capita GDP in China. By the other hand, the income of the

richest has been growing as faster as per capita GDP in Germany.

Are the results of declining or slowly rising incomes at the top credible? A well-known fact is that household surveys do not capture well incomes at the top. That can be due to limitations of the representativeness of the sample at the top and/or non-sampling errors such as under-reporting of incomes at the top, particularly non-wage income. In order to check the extent to which surveys may be underestimating incomes at the top, we calculated the average income of the two “richest” households. In 2006, the monthly total household income in current dollars was equal to 14,779 for Argentina, 70,357 for Brazil, 43,148 for Mexico and 17, 563 for Peru. Do these numbers “make sense”? If we had access to tabulations based on income tax returns as they exist for all of the advanced countries, we would be able to estimate the average income of the top .01 and .001 percent of the distribution. Regretfully, governments in Latin America do not make this information available (we can guess why).

The Rich in Latin America: Estimates of Monthly Income (circa 2007-2009)

Merrill Lynch High Net Worth Individuals (HNWI) and Ultra-High Net Worth Individuals (UHNWI): 2007(in millions of dollars otherwise specified)

LATAM WORLDWEALTH** POP AVE. WEALTH*** Monthy Inc*** WEALTH POP AVE. WEALTH*** Monthy Inc***

HNWI (US$1 m or more in assets)* $6,200,000 400000 $15,500,000 $64,583 $40,700,000 $10,100,000 $4,029,703 $16,790

UHNWI (US$30 m or more in assets)* $2,200,000 4400 $500,000,000 $2,083,333 $14,300,000 $103,300 $138,431,752 $576,799____________________________________________________________________________________________________________________________FORBES Billionaires in Latin America(in millions of dollars otherwise specified)30 billionaires (US$1 b or more in assets) $115,000 30 $3,833,333,333 $15,972,222Carlos Slim $35,000,000,000 $145,833,333____________________________________________________________________________________________________________________________Source: top panel Merrill Lynch and Capgemini World Wealth Report (2009); bottom panel Forbes, April 2009Note: Population figures in units.* Investible assets exclude primary residence, collectibles, consumables and consumer durables.** total wealth for UHNWI in Latin America was assumed to be the same proportion of HNWI' wealth as for the world which equalled 35 percent.*** in dollars per month. Author's estimates based on Merril Lynch and assumption **; monthly returns were calculated assuming a yearly 5 percent return on investible assets.

top 1% income sharetop 0.1% income share Gini Coeff. G* Gini Coeff. G1 Gini Coeff. G2

Argentina (%) Argentina (%)(1) (2) (3) (4) (5)

1997 12.39 4.27 0.469 0.492 0.5351998 12.57 4.37 0.485 0.508 0.5501999 13.53 5.22 0.470 0.497 0.5412000 14.34 5.68 0.486 0.516 0.5602001 12.91 5.22 0.511 0.536 0.5742002 15.53 6.92 0.519 0.552 0.5942003 16.85 7.40 0.509 0.546 0.5922004 16.75 7.02 0.488 0.524 0.573

Source: Alvaredo and Piketty, Ch 4 in Lopez-Calva and Lustig. 2010.Notes : Top shares in columns (1) and (2) are taken from Alvaredo, 2010. G* denotes the Gini coefficient of individual income based on the Greater Buenos Aires

household survey, own calculations. All results correspond toOctober surveys, except for 2003 (May). Only income earners with positive income were considered and nofurther adjustments were applied. G1≈S+(1-S)G*, where S is the estimate of the top 0.1% income share.

G2≈S+(1-S)G*, where S is the estimate of the top 1% income share.The Greater Buenos Aires households’ survey is taken as representative of Argentina as a whole (see chapter CRUCES-GASPARINI in this volume).

Greater Buenos Aires

Top income shares in Argentina and Gini Coefficient in Greater Buenos Aires, 1997-2004

FIGURE 6Gini coefficient in the Greater Buenos Aires 1980-2004

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.651

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G2

There are many different factors that affect the distribution of income over time: “… the evolution of the distribution of income is the result of many different effects—some of them quite large—which may offset one another in whole or in part.” (Bourguignon et al., 2005)

Useful framework: to consider the ‘proximate’ factors that affect the distribution of income at the individual and household level:1. Socio-demographic factors 2. Distribution of assets and personal characteristics3. Return to assets and characteristics4. Utilization of assets and characteristics5. Transfers (private and public)

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Proportion of working adults as a share of the total number of adults (and total household members) rose; partly linked to the sharp increase in female labor force participation:1990-2006 by 18.1 p.pts in Mexico, 14.2 in Argentina, 12.0 in Brazil and 5.8 in Peru.Dependency ratios improved

proportionately more for low incomes.Working adults (except for Peru) became

more equally distributed (female adults participated proportionately more for low incomes)

Average years of schooling rose faster for the

bottom quintile than for the top quintile. => Distribution of education (human

capital) became more equal in all four countries 34

Household per capita income and its determinants

Per capita household income

Per capita household income

Proportion of adults in the household

•FERTILITY

Proportion of adults in the household

•FERTILITY

Household income per

adult

Household income per

adult

Household non-labor income per adult

•RENTS & PROFITS•REMITTANCES•GOV. TRANSFFERS

Household non-labor income per adult

•RENTS & PROFITS•REMITTANCES•GOV. TRANSFFERS

Household labor income

per adult

Household labor income

per adult

Proportion of working adults

•PARTICIPATION IN LABOR FORCE•EMPLOYMENT OPPORT

Proportion of working adults

•PARTICIPATION IN LABOR FORCE•EMPLOYMENT OPPORT

Labor income per working adult in the household

•WAGES BY SKILL/OTHER•HOURS WORKED

Labor income per working adult in the household

•WAGES BY SKILL/OTHER•HOURS WORKED

DEMOGRAPHIC

•MARKET•POLITICS/INST.•STATE

•DEMOGRAPHIC

•MARKET

•DEMOGRAPHIC•MARKET•POLITICS/INST./SOC. NORMS•STATE(EDUCATION)

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Per capita household income can be written as:

y = a ( u w + o)

This identity relates changes in per capita household income, y, to its four proximate determinants: (i) changes in the proportion of adults in the household,

a;(ii) changes in the proportion of working adults, u; (iii) changes in labor income per working adult in the

household, w; and (iv) changes in household non-labor income per adult, o.

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ARGENTINA (urban areas): 2000-2006

Marginal Contribution of Source:

In Percentage Points

Demographic Factors (adults per household)

-0.20 8

Non-labor Income -0.68 26

Part. in Labor Market -0.43 17

Earnings per Worker -1.30 50

SUBTOTAL -2.61 100 91

Interactive Term (all) -0.26 9

TOTAL -2.87 100

BRAZIL: 2001-2006




-0.23 6.6

Non-labor Income -1.61 45.2

Part. in Labor Market -0.15 4.1

Earnings per Worker -1.57 44.1

SUBTOTAL -3.56 100.0 120.8

Interactive Term (all) 0.61 -20.8

TOTAL -2.94 100.0

In Percent

In Percent

MEXICO: 2000-2006




-0.50 10.3


Part. in Labor Market -0.44 9.1

Earnings per Worker -3.19 65.5

SUBTOTAL -4.87 100.0 158.3

Interactive Term (all) 1.79 -58.3

TOTAL -3.07 100.0

PERU: 1997-2006




-1.43 59.2


Part. in Labor Market 0.08 -3.4

Earnings per Worker 1.21 -50.1

SUBTOTAL -2.42 100.0 59.5

Interactive Term (all) -1.65 40.5

TOTAL -4.07 100.0

In Percent

In Percent

Demographics: Changes in the ratio of adults per household were equalizing, albeit the orders of magnitude were generally smaller except for Peru.

Labor force participation: With the exception of Peru, changes in labor force participation (the proportion of working adults) were equalizing. This effect was stronger in Argentina.

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Labor income (Earnings): In Argentina, Brazil, and Mexico between 44% and 65% of the decline in overall inequality is due to a reduction in earnings per working adult inequality. In Peru, however, changes in earnings inequality were unequalizing at the household level but not so at the individual workers’ level.

Non-labor income: Changes in the distribution of non-labor income were equalizing; the contribution of this factor was quite high in Brazil and Peru (45% and 90%, respectively).

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=> Decline in labor inc0me (except for Peru at the household level) and non-labor income inequality important determinants of the decline in overall income inequality (in per capita household income)

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Decline in labor income inequality: employment generated by recovery: open

unemployment fell from 14.8% in 2000 to 9.6% in 2006

shift in favor of more low-skilled, labor-intensive sectors as a result of the devaluation

rise in the influence of labor unions which compresses wages

fading of the one-time effect of skill-biased technical change that occurred in the 1990s

Decline in non-labor income inequality: more progressive government transfers: Jefes

y Jefas de Hogar program launched in 200243

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Argentina: Returns to education

Ratio predicted wages

19801986

19921998

2006

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Secondary/primary College/primary

Decline in labor income inequality: About 50% accounted for by decline in

attainment inequality (quantity effect) and less steep returns --wage gap by skill narrows—(price effect). Latter dominant. (See Gini for years of schooling and returns by skill in next two slides)

About 25% accounted for by decline in spatial segmentation; especially, reduction in wage differentials between metropolitan areas and medium/small municipalities. Also, decline in sectoral segmentation.

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Figure 7.2: Evolution of the wage differential among metropolitan regions and different sized municipalities: 1995-2006

6,1

18,4

10,410,1

10,7

12,9

11,0

9,4 9,3

6,2 6,4

4,55,6

30,229,5

32,0

28,0

26,0 25,7

23,0

20,7

17,1

19,315,4

14,5 14,5

13,3 13,2 13,1 13,6

11,810,7 11,4

0

5

10

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25

30

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1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Years

Diff

eren

tial (

%)

Differential between metropolitan regions and medium country

municipalities

Differential between medium and small country municipalities

Differential between metropolitan regions and small country

municipalities

Contribution of changes in the distribution of income from assets (rents, interest and dividends) and private transfers was unequalizing but limited.

Most of the impact of non-labor income on the reduction of overall income inequality was due to changes in the distribution of public transfers: changes in size, coverage and distribution of public transfers. Bolsa Familia accounts for close to 10 percent of the decline in household per capita income inequality.

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Decline in labor income inequality: Educational attainment became more equal and returns less

steep. The latter seems to be associated with the decline in relative

supply of workers with low educational levels. Between 1989 and 2006, the share of workers with less than lower-secondary education fell from 55% to around 33%.

It coincides with the period in which government gave a big push to basic education. ▪ Between 1992 and 2002 spending per student in tertiary education

expanded in real terms by 7.5 percent while it rose by 63 percent for primary education.

▪ The relative ratio of spending per student in tertiary vs. primary education thus declined from a historical maximum of 12 in 1983-1988, to less than 6 in 1994-2000 (by comparison, the average ratio for high-income OECD countries is close to 2).

Next two slides show: Gini for yrs. of schooling and returns to schooling 51

The equalizing contribution of government transfers increased over time (both at the national level as well as for urban and, especially, rural households). By 2006 transfers became the income source with the largest equalizing effect of all the income sources considered.

Remittances became more equalizing too but with a smaller effect than government transfers.

Both more than offset the increasingly unequalizing impact of pensions.

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The sharp rise in the role and equalizing impact of public transfers was a consequence of a significant policy shift in 1997, when the government launched the conditional cash transfer program Progresa/Oportunidades.

During 1996-2006 the size of public transfers increased; they became more equally distributed among recipients, and the recipients of transfers increasingly belonged to relatively poorer segments of the population.

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Labor income inequality: Changes in educational structure were equalizing

at the household and individual workers levels. Changes in returns to education, however, were

equalizing at the individual workers level but not at the household level. Changes in assortative matching might have been a factor.

Earnings gap by skill narrowed at the individual workers level as in the other countries. Fading out of skill-biased technical change and a more equal distribution of education/educational upgrading.

Next two slides show the Gini for years of schooling and the returns to schooling.

57

Educational upgrading and a more equal distribution of educational attainment have been equalizing (quantity effect). No “paradox of progress” this time.

Changes in the steepness of the returns to education curve have been equalizing at the individual workers level (price effect). Except for Peru, they have been equalizing at the household level too.

Changes in government transfers were equalizing: more progressive government transfers (monetary and in-kind transfers); expansion of coverage, increase in the amount of transfers per capita, better targeting.

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Increase in relative demand for skilled labor petered out: Fading of the unequalizing effect of skill-biased technical change in the 1990s: Argentina, Mexico & Peru.

Decline in relative supply of low-skilled workers: Expansion of basic education since the 1990s: Brazil, Mexico and Peru .

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Other effects:Decline in spatial labor market

segmentation in Brazil. In the case of Argentina, the

decline also driven by a pro-union government stance and by the impetus to low-skill intensive sectors from devaluation. In Brazil, increase in minimum wages.

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In the four countries government transfers to the poor rose and public spending became more progressive

▪ In Argentina, the safety net program Jefes y Jefas de Hogar.

▪ In Brazil and Mexico, large-scale conditional cash transfers => can account for between 10 and 20 percent of reduction in overall inequality. An effective redistributive machine because they cost around .5% of GDP.

▪ In Peru, in-kind transfers for food programs and health. Also access to basic infrastructure for the poor rose.

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In the race between skill-biased technological change and educational upgrading, in the last ten years the latter has taken the lead (Tinbergen’s hypothesis)

Perhaps as a consequence of democratization and political competition, government (cash and in-kind) transfers have become more generous and targeted to the poor

Will the momentum towards a less unequal Latin America continue?

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Despite the observed progress, inequality continues to be very high and the bulk of government spending is not progressive.

The decline in inequality resulting from the educational upgrade of the population will eventually hit the ‘access to tertiary education barrier’ which is much more difficult to overcome: inequality in quality and ‘opportunity cost’ are high and costly to address.

Making public spending more progressive in the future is likely to face more political resistance (entitlements of some powerful groups).

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Argentina: current monetary (no imputations for owner’s occupied housing); assumes it’s after taxes for wage earners & before for other (assumption); after monetary government transfers (in survey).

Brazil: current monetary plus imputed value of income in kind (no imputations for owner’s occupied housing); before taxes (assumption) and after monetary government transfers (in survey and assumption).

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From 1930s to 2000s: inverted U to a U

Alvaredo and Piketty, chapter 4, in Lopez-Calva and Lustig (2010)

Nora Lustig Samuel Z. Stone Professor of Latin American Economics Dept. of Economics Tulane...

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