a decade of falling inequality in mexico

8/3/2019 A Decade of Falling Inequality in Mexico

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United Nations Development Programme

POVERTY REDUCTION

January 2010

A Decade of Falling Inequality in

Mexico: Market Forces or State Action?

Discussion Paper


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A Decade of FallingInequality in Mexico:

Market Forces or State

Action?

Gerardo Esquivel, Nora Lustig and John Scott

January 2010


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Copyright January 2010

United Nations Development Programme

Bureau or Development Policy

Poverty Group

304 East 45th Street

New York, NY, 11375U.S.A.E-mail:[email protected]: www.undp.org/poverty

Abstract

Mexicos Gini coef cient ell rom 0.543 in 1996 to 0.498 in 2006. Standard decompositionanalysis suggests that improvement in incomes at the bottom o the distribution vis--vis theupper deciles is linked to higher relative wages o low-skilled workers, a rise in the share oremittances and an expansion o government monetary transers targeted to the poor. The allin the skill premium appears to be associated with a reduction in the share o unskilled workersin Mexicos labour orce. This change in the composition o the labour orce coincided with, andwas probably caused by, a signicant expansion o government spending on basic education.The equalizing role o transers, mainly driven by the conditional cash transer programmeProgresa/Oportunidades, rose over time. Benets incidence analysis, in addition, demonstratesthat government redistributive spending on education, health and nutrition became moreprogressive over the last decade. In 2006, taken together, taxes and transers (including in-kindtransers) reduced the Gini by 12.6 percent. However, 58 percent o government redistributivespending was still regressive (o which 11 percent increased income inequality). In order orinequality to continue to all in the uture, Mexico needs to phase out regressive transers and

improve the quality o education, particularly or the poor.Gerardo Esquivel is a proessor o economics at El Colegio de Mexico. He can be

reached at [email protected]. Nora Lustig is Stone Proessor o Latin American Economicsat Tulane University and nonresident ellow at the Center or Global Development and theInter-American Dialogue; she can be reached at [email protected]. John Scott is a proessoro economics at Centro de Investigacion y Docencia Economicas (CIDE) and can be reached [email protected].

Acknowledgements

This paper was prepared or the joint PG/BDP and RBLAC project Markets, the State and theDynamics o Inequality in Latin America coordinated by Luis Felipe Lopez-Calva (Director othe Poverty Cluster o the Regional Bureau o Latin America and the Caribbean) and Nora Lustig(Samuel Z. Stone Proessor o Latin American Economics, Tulane University and non-residentFellow o the Center or Global Development and the Inter-American Dialogue). Projectmanagers in PG/BDP included, at various times, Selim Jahan (Director, Poverty Practice), RathinRoy (Acting Cluster Leader, Inclusive Development) and Shantanu Mukherjee (Policy Advisor,Microeconomics).


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The coordinators and project managers are greatly indebted to Fedora Carbajal or

her excellent research assistance; Mariellen Jewers, Michael Lisman, Shivani Nayyar and Anita

Palathingal or their very valuable editorial recommendations; and Marina Blinova, Elia Carrasco,

Patrice Chiwota, Queenee Choudhury, Jacqueline Estevez, Maria Fernanda Lopez-Portillo and

Alexandra Solano or their very helpul support in the administration o this project.

Disclaimer

The views expressed in this publication are those o the authors and do not necessarily represent

those o the institutions to which they are af liated or the United Nations, including UNDP, or

their Member States.


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1. Introduction

Mexico is among the most unequal countries in the world. However, it is making progress inbecoming more equal rom 1996 to 2006, Mexicos Gini coef cient ell rom 0.543 to 0.498or by 0.8 percent per year, and rom 2000 to 2006 it ell by 1 percent per year.1 The decline

in inequality coincided with important changes in Mexicos economic and social policy. In1994, the North American Free Trade Agreement (NAFTA) with the United States and Canadawent into eect, thereby establishing the largest ree trade area in the world, and the mostasymmetrical in terms o countries relative GDP. 2 Mexico also implemented two importantgovernment transers programs: Procampo in 1994 and Progresa (later called Oportunidades)in 1997. Procampo is an income support programme or agricultural producers designed tohelp them ace the transition costs resulting rom the opening o agriculture under NAFTA.The second programme, Progresa/Oportunidades, is a targeted, conditional cash transerprogramme. It is considered Mexicos most important anti-poverty programme.

In terms o growth, barely a year ater NAFTA came into eect, Mexico aced a severemacroeconomic crisis.3 The peso crisis that began in December 1994 led to a sharp decline ineconomic activity during 1995 when per capita GDP ell to the tune o 8 percent. The economyrecovered rather quickly rom the crisis. Between 1996 and 2000, Mexicos per capita GDPgrew at a rate o 4 percent per year. However, between 2000 and 2006, growth slowed downsignicantly; per capita GDP grew at only 1 percent per year. This low-growth period is preciselywhen income inequality started to decline more rapidly.

Using standard non-parametric decomposition methods, this article will analyze theproximate determinants behind the reduction in income inequality between the mid-1990sand 2006. In particular, it will look at the roles played by the reduction in both labour incomeinequality and non-labour income inequality. It will also analyze the impact o changes indemographics, such as the ratio o adults and o working adults per household. The paper will

examine to what extent the reduction in labour income inequality was due to a decline in thewage skill premium. It will explore the inuence o changes in the labour orce composition, interms o education and experience, on the decline in the wage skill premium, as well as examinethe relationship between the labour orce composition and changes in public spending oneducation. Then, the paper will analyze the contribution o changes in government transers,with particular emphasis on Progresa/Oportunidades, on the reduction in non-labour incomeinequality. Finally, using standard incidence analysis, the paper will look at the distributiveimpact o government redistributive spending and taxes.

1 The Gini reported in this paragraph is calculated using total income (it includes monetary income and imputedvalue o owners occupied housing). The decomposition o income inequality by the source presented below usescurrent monetary income (excludes capital gains and non-monetary income). The change in the Gini coef cientbetween 19962006, 19962000 and 20002006 was ound to be statistically signicant at the 95 percent level.The condence intervals were constructed applying the bootstrap method with 150 replications. There is Lorenzdominance or the comparisons o 2006 and 1996, 2006 and 2000 and 2000 and 1996.

2 See Tornell and Esquivel (1998) or more details on these issues.

3 For an analysis o the peso crisis, see Lustig (1998).


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Poverty Reduction Discussion Paper

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The paper is organized into seven sections. Section 1 presents the evolution o Mexicospost-NAFTA distribution o income, using dierent measures o inequality and denitionso income at the national as well as rural and urban levels. Section 2 analyzes the proximatedeterminants o inequality using standard non-parametric decomposition methods. Section 3explores the causes o the reduction in labour income inequality. Section 4 discusses the changes

in government spending on education and its impact on making access to education moreequitable. Section 5 analyzes the impact oProgresa/Oportunidades on income inequality inMexico. Section 6 examines the incidence o government redistributive spending and the netimpact o transers and taxes on inequality. Finally, section 7 presents the main conclusions.

2. Income inequality ater NAFTA: 19942006

We begin our analysis by speciying what measure o inequality and what denition o incomewill be used in this paper, since dierent measures and denitions could not only lead to dierentestimates o inequality but also to slightly dierent conclusions about its determinants.4 In this

paper, we use the Gini coef cient as our preerred measure o inequality.5 This measure satisesall the desirable properties o an inequality indicator, and is decomposable by proximatedeterminants as well as income sources.6 The paper uses total income per capita unless speciedotherwise. 7 All o our estimates use inormation rom the National Survey o Household Incomeand Expenditures (ENIGH, or its Spanish acronym).8 There are comparable surveys availableor the years 1994, 1996, 1998, 2000, 2002, 2004, 2005 and 2006. 9 The surveys capture incomenet o taxes and contributions to social security; income includes transers rom Procampo andProgresa/Oportunidades.

4 Our results are or the most part robust to dierent income concepts as well as inequality measures. Corbachoand Schwartz (2002) have a survey o Gini coef cient estimates in Mexico or dierent periods and dierentincome denitions. CONAPO (2007) and Corts (2008) show recent estimates o inequality using monetaryincome.

5 Other measures o inequality such as the Theil index show similar trends as those described in the text. Theyare available rom the authors upon request.

6 These principles are: adherence to the Pigou-Dalton transer principle, symmetry, independence o scale,homogeneity and decomposability. The Gini, however, is not additively decomposable as the Theil index.

7 Income includes labour income and non-labour income. The ormer includes all the income that is reportedas labour income in ENIGH, including labour income rom the sel-employed. Non-labour income includesincomes rom own businesses, assets (including capital gains), pensions (public and private) and public transers

(Oportunidades and Procampo), and private transers (e.g., remittances), as well as non-monetary income(imputed rent on owner occupied housing and consumption o own production, common in poor rural areas).Of cial poverty measures in Mexico use net current income: that is, capital gains and gits and in-kind transers toother households are subtracted rom current total income. Current monetary income, the concept used in thedecomposition o inequality by the National Survey o Household Income and Expenditures , does not includenon-monetary income and consumption o own production (common in poor rural areas) and excludes capitalgains.

8 In Spanish, Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH).

9 Surveys or 1984 and 1989 are not as comparable but they are still used or lack o a better alternative.


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Figure 1, Gini coef cients or alternative income denitions, 19842006

Source: Esquivel (2008).

Note: Current income excludes income rom sales o durables and capital gains.

Using alternative denitions o income, Figure 1 shows the evolution o the Gini coef cientin Mexico or 1984-2006. The gure clearly shows the existence o an inverted-U that peaks in 1994

and steadily declines thereater.10 Table 1 shows the distribution o current monetary income bydeciles, the Gini coef cient and other inequality measures rom 1984 to 2006. Ater rising by severalpercentage points between the mid-1980s and mid-1990s, the Gini coef cient or monetaryhousehold per capita income declined rom 0.539 to 0.506 between 1996 and 2006.11 The paceo decline accelerated between 2000 and 2006 when the Gini ell at 1 percent a year. In general,the other measures o inequality shown in table 1 ollow the same trend as the Gini coef cient.But, since the Gini is more sensitive to what happens to the middle o the distribution, its patternis slightly dierent rom inequality measures based on top/bottom ratios.12 One dierence seenin table 1, or example, is that all the other measures tend to peak around 1998, whereas the Ginipeaks in 1994. The dierences between the Gini and the other inequality measures are due toimportant changes in the tails o Mexicos income distribution: in 1994 and 1998 both the top andthe bottom deciles suered losses in their income shares, but although the top deciles loss wasgreater in percentage points, the bottom decile lost more in relative terms.

10 The rapid increase in inequality that took place between 1984 and 1994 has been studied by, among others,Bouilln, Legovini and Lustig (2003) and Legovini, Bouilln and Lustig (2005).

11 The dierence between the Gini coef cients shown here and those mentioned in the introduction is due tothe act that the latter are Ginis or total income whereas the ormer are or monetary income.

12 Sen and Foster (1973).


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TABLE 1, Distribution o monetary household per capita income, 19842006 (percent)

YearDeciles 1984 1989 1992 1994 1996 1998 2000 2002 2004 2005 2006

I 1.2 1.0 0.9 0.9 1.1 0.8 1.0 1.2 1.1 1.1 1.2II 2.4 2.2 2.0 2.0 2.2 1.9 2.1 2.3 2.4 2.3 2.5III 3.3 3.2 3.0 2.8 3.1 2.9 3.0 3.3 3.4 3.2 3.3IV 4.3 4.2 3.8 3.7 4.0 3.8 3.9 4.2 4.2 4.1 4.2V 5.6 5.2 4.9 4.8 5.1 4.9 5.0 5.3 5.3 5.2 5.3VI 7.1 6.5 6.1 6.1 6.4 6.2 6.3 6.5 6.6 6.5 6.7VII 9.0 8.3 7.9 7.8 8.2 7.9 7.9 8.3 8.3 8.2 8.0VIII 11.8 10.9 10.8 10.5 10.8 10.7 10.5 11.1 10.9 10.6 10.8IX 17.0 15.9 16.1 16.2 16.1 15.9 16.0 16.3 16.0 15.4 16.0X 38.3 42.6 44.6 45.3 43.1 45.0 44.4 41.7 41.8 43.7 42.2

Top 10 / Bottom 10 31.9 41.7 51.3 48.7 40.2 54.8 44.9 36.3 36.7 40.8 34.3Top 20 / Bottom 20 15.4 18.0 21.3 21.3 18.2 22.4 19.9 16.9 16.2 17.8 15.7Top 10 / Bottom 40 3.4 4.0 4.6 4.8 4.2 4.8 4.5 3.8 3.8 4.1 3.8Gini Coef cient 48.9 53.4 55.1 56.4 53.9 55.2 54.1 51.3 50.8 51.8 50.6


Note: The concept o current monetary income does not include the imputed value o owners occupied housingand income rom sales o durables and capital gains. The income concept used in the decomposition byproximate actors uses total household income (monetary and non-monetary) household per capita income.Since it is diferent rom current monetary income, the Gini coe cients need not coincide.

The evolution o Mexicos distribution o income can also be analyzed using the GrowthIncidence Curves (GICs) suggested by Ravallion and Chen (2003). These curves show thepercentage change in per capita income along the entire income distribution between twopoints in time. Figure 2 shows the GIC or 19962006 as well as or 19962000 and 20002006.13

Looking rst at the negative slope in the rst graph shows why Mexicos income inequalitydiminished during 19962006: the income o the lower deciles grew aster than the income othe upper deciles. For example, the bottom percentiles income grew at more than our timesthat o the top percentile.

Esquivel (2008) also presents GICs or urban and rural areas or 19942006. 14 Theyeature the dierent patterns ollowed by the urban and rural income distributions during thisperiod. In the urban areas, income growth was pretty at across the entire distribution exceptor the top three deciles, which experienced smaller, and in some cases even negative, incomegrowth rates. In rural areas, the GIC had a negative slope, indicating that the bottom hal othe rural income distribution had higher income growth rates than the top segment o the

distribution. Finally, comparing the growth rates between rural and urban areas showed thatthe average income growth in rural areas was greater than in urban areas a pattern that,given the relatively large ruralurban gap, reduces inequality.

13 The GICs are constructed using total per capita income, which includes monetary and non-monetary andcapital gains.

14 Rural areas are dened as townships with less than 15,000 inhabitants.


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Figure 2, Growth Incidence Curves, national, urban and rural, 1996-2006 (percent)

Growth Incidence Curve, 19962006



Source: Authors elaboration based on ENIGHs 1996, 2000 and 2006.

Note: GICs are based on total household per capita income; rural reers to households living in townships opopulation size less than 15,000.


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Breaking out GICs or 19962000 and 20002006 also provides important details aboutMexicos overall decline in inequality rom 19962006. The GICs in 19962000 and 2000-2006both have negative slopes. However, in 20002006 the negative slope was more pronounced,showing a larger increase o bottom incomes. The GICs in 19962000 and 20002006 share theollowing common aspects: (1) the poorest two deciles o the income distribution experienced

an above-average increase in their monetary income, and (2) the income o the top decile grewat below-average rates. The increase or the lowest two deciles seems to be associated with theincome growth in the rural sector. The lackluster growth o the top deciles income is associatedwith the dynamics o the urban sector o Mexicos economy. In act, the pattern in the urbanareas in 19962000 and 20002006 is almost identical: a very at GIC throughout most o theincome distribution, with the income o the top two deciles growing at lower rates than thato the rest o the distribution (Esquivel, 2008). Changes in rural areas account or the dierencebetween the two GICs. In 20002006, the rural GIC curve clearly showed a pro-poor pattern, withthe bottom hal o the distribution enjoying substantial income growth rates (over 5 percentper year); whereas the upper hal o the distribution had much smaller income gains. Theseresults suggest that during 20002006 there must have been some actors that beneted the

bottom part o the rural income distribution, as well as other actors that hurt in relativeterms the upper part o the urban income distribution. The next section investigates whichactors may explain this behaviour.

3. The proximate determinants o the decline in income

inequality: Labour and non-labour income and

demographic actors

The purpose o this section is to identiy the proximate determinants o Mexicos decline in

inequality between 1996 and 2006, and quantiy each proximate determinants contributionto the total decline. The proximate determinants considered in our analysis are: (1) ratio oadults to total number o members in the household, (2) proportion o adults working to totalnumber o adults in the household, (3) labour earnings per working adult, and (4) householdnon-labour income (which includes government transers and remittances) per adult. It is worthemphasizing that our analysis in this section is limited to these our proximate determinantso inequality. However, each proximate determinant, in turn, is the result o behavioural andexternal processes, which are not modelled here. For example, the rst proximate determinantcaptures the impact o changes in ertility and lie expectancy. The second is inuenced bydecisions to participate in the labour orce and the demand or labour. The third and ourth

labour earnings per working adult and household non-labour income are determined bymany actors: or example, market orces and state action aecting the demand or labour bycharacteristics (education, experience, gender, sector, ormal/inormal); decisions by individuals(and their parents) to invest in education and other orms o capital, to participate in the labourmarket and how (wage earner vs. sel-employed, ull-time vs. part-time), to migrate and sendremittances; and government transers. A ormal analysis o these actors goes beyond the scopeo this paper. However, we will explore some plausible explanations or the observed trends.


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Each proximate determinants contribution to the total decline in inequality wasquantied by applying the method proposed by Barros et al. (2006). In essence, the methodconsists o decomposing the change in an inequality measure into the contributions romchanges in the distribution o the proximate determinants, taken one at a time, plus thecontributions rom changes in the interaction (correlation) o proximate determinants with each

other. The contributions are estimated through a series o sequential counteractual simulationswhich assume that the distribution o the proximate determinant o interest remains the sameas in the base year.15 The method is based on the ollowing sequence o identities:

y = a r (1)

r = o + t (2)

and

t = u w (3)

Hence,

y = a (o + u w) (4)Identity (1) expresses household per capita income, with y as a product o the proportion

o adults in the household, a, and household income per adult, r. Identity (2) expresseshousehold income per adult, r, as the sum o household non-labour income per adult, o, andhousehold labour income per adult, t. For Identity (3), household labour income per adult, t,is expressed as the product o the proportion o working adults, u, and the labour income perworking adult in the household, w. Identity (4) relates per capita household income,y, to its ourproximate determinants: (1) the proportion o adults in the household, a, (2) household non-labour income per adult, o, (3) proportion o working adults, u, and (4) labour income perworking adult in the household, w. Visually, these identities are presented in gure 3. For adetailed description o the methodology see Barros et al. (2006).

Using this method, Alejo et al. (2009) estimated that the contribution o changes in theour proximate determinants mentioned above accounted or 1.40 percentage points o thedecline in the Gini coef cient rom 1996 to 2000, and 3.07 percentage points o the declinein the Gini coef cient rom 2000 to 2006. 16 The results based on Alejo et al. are summarized intable 2. As one can see, the changes in all o the our proximate determinants were equalizingand the changes in all the interactions between the proximate determinants combined wereunequalizing in both 19962000 and 20002006.17 The reduction in the inequality o labourincome (labour earnings per worker rom wages and rom sel-employment) was the mostimportant contribution to the reduction in inequality in both 19962000 and 20002006.

15 Note that although you can apply this method using any inequality indicator, the results will vary dependingon the indicator. Also, the results will be sensitive to which year is chosen as the base year and the sequenceselected to construct the counteractual simulations.

16 This decomposition was based on inequality measures calculated using total (monetary plus non-monetary)income while the next decomposition exercise was based on inequality measures estimated using monetaryincome only. The results, however, should not be very sensitive to the use o dierent concepts because bothmonetary and non-monetary income ollowed the same pattern o change.

17 The individual interaction terms between pair o variables were all unequalizing too. See Alejo et al. (2009).


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The reduction in labour income inequality (leaving out the interaction terms) accounted or87.1 percent o the decline in inequality in 19962000 and or 65.5 percent o the decline in20002006. Given its relative importance, we analyze below which actors explain the reductionin inequality in the distribution o labour income per worker, ocusing on the skilled/unskilledwage gap and the latters relationship to trade liberalization and the educational upgrading o

the labour orce.18

Figure 3, Household per capita income and its determinants

Source: Barros et al. (2009a).

According to table 2, the equalizing contribution o the proportion o adults in thehousehold (which measures the dependency ratio) rose rom 7.7 percent in 19962000 to10.3 percent in 20002006. Also, the equalizing contribution o the proportion o workingadults in total adults (which measures both the supply-side decisions to participate in thelabour market and the demand-side conditions o nding employment) rose by severalpercentage points rom 19962000 to 20002006. Between 1996 and 2000 and between 2000and 2006, the average household size ell rom 5.68 to 5.16 and rom 5.16 to 4.97, respectively,and the proportion o working adults in the household rose rom 58 percent to 59 percent and

rom 59 to 62 percent, respectively.19 These trends may be reecting two important changesin demographic patterns: (1) the reduction in ertility rates overall, with more pronounceddeclines among the poorer sectors o the population; (2) the increase in emale labour orceparticipation, particularly among the poorer sectors. Between 1996 and 2006, the average

18 Labour income includes all the income that individuals reported as labour income in the ENIGHs, including allthe wages and salaries as well as the income reported by sel-employed individuals.

19 See Esquivel (2008).


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number o children per household in the lowest quintile ell rom 2.3 to 1.7, whereas in the topquintile, it ell rom 1.5 to 1.3. 20 The participation o adult women in the labour orce during thisperiod rose rom 45.3 to 57 percent. 21

Table 2, Contribution o the proximate determinants to changes in income inequality,19962000 and 20002006

Marginal

contribution o the

proximate actor

Years 19962000 Years 20002006Contribution to

the change (*)In percentage

Contribution to

the change (*)In percentage

Proportion oadults

-0.19 7.7 -0.50 10.3

Non labor incomeper adult

-0.01 0.4 -0.73 15.1

Proportion oworking adults

-0.12 4.9 -0.44 9.1

Labor income per

working adult

-2.18 87.1 -3.19 65.5

SUBTOTAL -2.50 100.0 178.7 -4.87 100.0 158.3Interactions (all othem)

1.10 -78.7 1.79 -58.3

TOTAL (change inGini coef cient)

-1.40 100.0 -3.07 100.0

Source: Authors calculations based on Alejo et al. (2009)

Notes: 1. The change in Gini coe cient is in percentage points.2. * reers to the contribution o the actor to the change in the Gini coe cient measured in percentage points.3. The change in the Gini coe cient between 1996 and 2000 was ound to be statistically signicant at the 95 percentlevel. The condence intervals were constructed applying the bootstrap method with 150 replications.4. A negative (positive) sign means that a marginal increase in the source is equalizing (unequalizing).

The equalizing contribution o changes in the distribution o non-labour income to thedecline in overall inequality rose sharply rom 19962000 to 20002006 (table 2). In 19962000,non-labour income contributed a meager 0.4 percent to the reduction in inequality. In contrast,in 2000-2006 it accounted or 15.1 percent o the total decline in inequality, making non-labourincome the second most important contributor to the decline in inequality in this period, othe our proximate determinants considered in this decomposition. Non-labour income is avery heterogeneous concept. It includes incomes stemming rom the ownership o capital(such as prots, interests and rents), which tend to be concentrated at the top o the incomedistribution, but it also includes private transers (such as remittances), which tend to be more

concentrated in the middle and lower-middle ranges o the distribution. Finally, non-labourincome also includes government transers (such as pensions), which are concentrated in themiddle and upper-middle ranges o the income distribution, as well as targeted government

20 Number o children under 12 per household classied by parental income quintile. Source: SEDLAC. Availableat www.depeco.econo.unlp.edu.ar/sedlac/.

21 Share o individuals between 25 and 64 years old in the labour orce. Source: SEDLAC. Available atwww.depeco.econo.unlp.edu.ar/sedlac/.


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transers (such as the conditional cash transer programme Progresa/Oportunidades), which areconcentrated in the bottom o the distribution.22

In what ollows, we conduct a decomposition o the Gini coef cient to investigate thecontribution o dierent income sources to the observed inequality o monetary income inMexico in 1994, 2000 and 2006. 23 Lerman and Yitzhaki (1985) showed that the Gini coef cient

or total income inequality (G) withKincome sources can be expressed as:

where Sk

is the share o source kin total income, Gk

is the Gini coef cient o the income sourcek, andR

kis the Gini correlation between the income source kand total income.24

This decomposition o the Gini coef cient shows that the contribution o incomesource kto inequality depends on the interaction o three elements: (1) the relative importanceo the particular income source in total income (S

k), (2) the level o inequality o that income

source (Gk), and (3) the correlation between the distribution o that income source and that o

total income (Rk). Thereore, an income source (k) that represents a relative large share o totalincome (high S

k), could have a large eect on inequality as long as it is unequally distributed

(i.e. i it has a relatively high Gk). However, i G

kis low, it will dwar the contribution o that

income source. On the other hand, i an income source is very unequally distributed (high Gk),

but it is not highly correlated with total income (meaning it has a lowRk, such as in the case o

well-targeted transer programs), then the contribution o such a source could in act becomenegative, that is, equalizing.

Stark, Taylor and Yitzhaki (1986) showed that with this type o decomposition one canestimate the eect o a small percentage change () in a given income source on total inequality(holding all other income sources constant) through the ollowing expression:

or, alternatively

This expression means that the percentage change in inequality resulting rom a marginalpercentage change in income source kis equal to the relative contribution o component ktooverall inequality minus the initial share in total income o income source k.

22 For a description o this programme, see section 6.

23 The base year used in this decomposition is 1994, and the income concept is current monetary income. In theprevious decomposition, the base year used is 1996 and the income concept is total income. The dierence in theincome concept used in the two decompositions does not aect the main conclusions obtained rom the results.

24 In comparison with the previous method, Lerman and Yitzhakis only allows you to see by how much inequalitywould change i the share o a particular income source increases but its distribution remains unchanged. It is,thereore, a static decomposition and applies to very small changes. In contrast, the previous method is designedto analyze the impact o a change in the distribution o a particular income source. It is thereore a dynamicdecomposition.


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We proceed to decompose the Gini coef cients or monetary income ollowing theapproach just described or the years 1994, 2000 and 2006.25 The results o our decompositionexercise are shown in table 3 and the marginal eects are summarized in gure 4. At thenational level, there are three inequality-increasing and three equalizing sources o income.The inequality-increasing sources o income include pensions, income rom own businesses

(prots) and income rom property (rents).26

The equalizing sources o labour income (since2000) include remittances and transers.27 Their marginal negative (equalizing) eects on theGini coef cient have increased over the years considered here. In the case o urban and ruralareas, the signs o the marginal eects o the dierent income sources are similar to those atthe national level but there are important dierences (table 3). For example, labour income isa very important inequality-reducing orce in urban areas, but not in rural areas. In act, labourincome in rural areas is inequality-increasing in 2006. Transers are a very important equalizingincome source in rural areas but less so in urban areas.

Table 3, Decomposition o Gini or monetary income and marginal eects by income source

Sk Gk Rk Share MarginalEect Sk Gk Rk Share MarginalEect Sk Gk Rk Share MarginalEect

National Level 1994 2000 2006

Labor income 0.62 0.70 0.83 0.63 0.01 0.58 0.67 0.79 0.57 -0.02 0.60 0.66 0.80 0.58 -0.01

Own businesses 0.25 0.87 0.66 0.25 0.00 0.25 0.88 0.68 0.28 0.02 0.22 0.88 0.67 0.24 0.02

Property rents 0.02 0.99 0.80 0.03 0.01 0.02 0.99 0.80 0.03 0.01 0.03 0.99 0.82 0.04 0.01

Pensions 0.05 0.96 0.64 0.05 0.00 0.06 0.96 0.66 0.07 0.01 0.07 0.95 0.70 0.09 0.02

Transers 0.05 0.95 0.47 0.04 -0.01 0.06 0.93 0.38 0.04 -0.02 0.06 0.89 0.32 0.03 -0.03

Remittances 0.01 0.98 0.25 0.01 -0.01 0.02 0.97 0.39 0.02 -0.01 0.02 0.97 0.31 0.01 -0.01

Urban Level 1994 2000 2006

Labor income 0.66 0.63 0.81 0.64 -0.02 0.62 0.62 0.77 0.57 -0.04 0.62 0.61 0.77 0.57 -0.04

Own businesses 0.22 0.89 0.66 0.24 0.02 0.23 0.89 0.67 0.27 0.04 0.21 0.88 0.65 0.24 0.03

Property rents 0.03 0.99 0.76 0.04 0.01 0.02 0.99 0.80 0.04 0.01 0.03 0.98 0.80 0.05 0.02

Pensions 0.05 0.95 0.52 0.04 0.00 0.07 0.95 0.58 0.08 0.01 0.08 0.94 0.63 0.09 0.01Transers 0.04 0.96 0.48 0.03 -0.01 0.04 0.95 0.38 0.03 -0.01 0.05 0.93 0.37 0.03 -0.02

Remittances 0.01 0.99 0.29 0.00 0.00 0.01 0.99 0.49 0.01 0.00 0.01 0.98 0.32 0.01 -0.01

Rural Level 1994 2000 2006

Labor income 0.51 0.70 0.73 0.50 -0.01 0.50 0.69 0.72 0.48 -0.02 0.53 0.72 0.79 0.55 0.02

Own businesses 0.34 0.81 0.67 0.35 0.01 0.30 0.84 0.70 0.34 0.04 0.24 0.86 0.69 0.25 0.02

Property rents 0.02 0.99 0.74 0.02 0.01 0.01 0.99 0.71 0.02 0.00 0.02 0.99 0.78 0.02 0.01

Pensions 0.04 0.98 0.71 0.05 0.01 0.04 0.97 0.69 0.06 0.01 0.06 0.98 0.79 0.08 0.02

Transers 0.07 0.93 0.42 0.05 -0.02 0.10 0.89 0.42 0.07 -0.03 0.10 0.78 0.31 0.04 -0.06

Remittances 0.03 0.97 0.46 0.03 0.00 0.05 0.96 0.48 0.04 -0.01 0.06 0.95 0.52 0.05 -0.01


Note: A negative (positive) sign means that a marginal increase in the source is equalizing (unequalizing). For more details see

text and Lerman and Yitzhaki (1985).

25 In the decomposition exercise Esquivel (2009) made use o the descogini Stata programme written byLpez-Feldman (2006).

26 Pensions include both private and public pensions and the pensions that are part o government welaretransers. Pensions are gross, that is, contributions to social security are not subtracted.

27 Transers include public and private (including gits and donations) except or remittances and the pensionsthat are part o government welare transers (the latter are included under pensions).


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Figure 4, Decomposition o the Gini coef cient by source: National, urban and rural

National Mexico: Marginal Eect on Gini Coef cient by Income Source

Urban Mexico: Marginal Eect on Gini Coef cient by Income Source

Rural Mexico: Marginal Eect on Gini Coef cient by Income Source



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In 1994, remittances did not seem to have a large negative marginal (equalizing) eecton inequality in either sector, although they were relevant at the national level. This apparentlyparadoxical result is explained by the act that while the Gini correlation between remittancesand rural monetary income is close to 50 percent, the Gini correlation between remittances andmonetary income at the national level is much lower (see table 3). In that sense, remittances

used to have an eect at the national level because they were heavily concentrated on thebottom hal o the national income distribution.28 Thereore, remittances worked as anequalizing source o income by reducing the rural-urban income gap and not through thesector specic income distribution. This, however, changed in 2000 and more decisively in2006, when remittances were also equalizing or rural and or urban areas taken separately.

Concentrating on the components o non-labour income, in table 3 we can observethat income derived rom capital ownership (owning businesses and property rents) increasedoverall income inequality. Further, the inequality-increasing contribution o income rom ownbusinesses grew rom 1994 to 2006. Remittances are equalizing. The equalizing contribution oremittances grew in both urban and rural areas separately, but not at the national level (whichhas been discussed above). Pensions (which include both private and public pensions) areinequality-increasing; their contribution to overall inequality grew over time, and at all levels,national, urban and rural. Closer scrutiny reveals that the increase in pensions contribution tooverall inequality is due to an increase in pensions share o total income and, above all, the Ginicorrelation o pensions with total income. The (positive) Gini correlation o pensions rose rom0.64 in 1994 to 0.66 in 2000 to 0.70 in 2006. It is not clear what caused this.

In the case o transers, in contrast, their contribution is equalizing and rises over time bothat the national level as well as or urban and rural areas (table 3).29 In act, by 2006, transers became,over time, the income source with the largest equalizing eect o all the sources considered inthis exercise: that is, a marginal increase in transers would reduce inequality by more than amarginal increase in labour income or remittances. This was or three reasons: their share in totalincome rose, their own inequality ell and their Gini correlation with total monetary income ell.These changes were particularly pronounced in the rural areas where the share o transers intotal income rose rom 7 percent in 1994 to 10 percent in 2000 and 2006; the Gini coef cient otransers ell rom 0.93 in 1994 to 0.89 in 2000 and 0.78 in 2006; and, the Gini correlation betweentransers and total monetary income ell rom 0.42 in 1994 and 2000 to 0.31 in 2006.30

The share o transers in total income rose because there was a signicant expansionin coverage o public transers. The percentage o households that received some source onon-labour income rose rom 1994 to 2006: 23.8 percent o households received a public or aprivate monetary transer in 1994; in 1996, it was 29 percent; in 2000, it was 34 percent, and in2006, 45.5 percent o all households reported receiving part o their monetary income through

a private or public transer.31 Although Procampo had been expanded since its creation in

28 See, or example, Esquivel and Huerta-Pineda (2007).

29 Transers here include government transers and private transers excluding pensions and remittances.Private transers (excluding remittances) are relatively small on average.

30 The Gini coef cient or transers is very high because it is calculated or the entire population, including thosewho do not receive any transers.

31 Esquivel (2008).


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1994, the lions share o the expansion in households receiving non-labour income was due toimplementation o the conditional cash transer programme Progresa/Oportunidades in 1997.32

Progresa/Oportunidades did not exist in 1996, so the percentage o households receivingtransers rom this source was equal to zero in 1996; in 2006, however, Progresa/Oportunidadesreached 14.8 percent o households in Mexico.33

Alejo et al. (2009) estimate the contribution o the combined marginal eect o thechanges in public transers that occurred in terms o coverage, average benet and distributionamong recipients oallpublic transers (pensions,Progresa/Oportunidades,Procampo, etc.). Giventhat these authors included pensions in public transers, their results are not strictly comparablewith the previous decomposition.34 Nevertheless, some o the ndings are insightul. While thecombined marginal eect o what they call public transers increased inequality or 19962000,they were equalizing or 20002006. In the latter period, the equalizing eect o the increasein coverage (percentage o households who receive public transers) and in the magnitude othe average benet more than compensated or the unequalizing eect caused by a rise ininequality in the distribution o public transers among recipients. 35 Also, during 2000-2006,the equalizing marginal contribution o the changes in public transers were large enough tocompensate or the unequalizing eect stemming rom changes in the interaction term thatmeasures the association (correlation) between public transers and total income. In contrast,during 19962000, the unequalizing eect o the interaction term dominated.

In sum, starting in the late 1990s, the size o monetary government transers becamemore generous, transers became more equally distributed among recipients, and the recipientso transers increasingly belonged to the relatively poorer segments o the population. This mustundoubtedly reect the implementation o Progresa/Oportunidades, which will be analyzedbelow. However, the act that the Gini correlation between transers and total monetary incomeis positive (table 3) reects that government transers are not as progressive as one would likethem to be. I they were, the correlation would have been negative indicating that transersare inversely allocated with respect to income. Nonetheless, an encouraging sign is that theGini correlation between transers and total monetary income ell quite signicantly between1994 and 2006.

Given that the all in labour income inequality (especially in urban areas) and the increasein pro-poor transers in rural areas appear to be the two most important orces in the decline ininequality, sections 4 and 6 will analyze them more closely.

32 Based on what we know about the distributive eects o the Procampo (relatively regressive) and Progresa/Oportunidades (very progressive) discussed below, it is quite likely that they can actually account or a large deal

o the dynamics o income inequality in rural areas. For general equilibrium eects, see Taylor, Dyer and Yunez-Naude (2005).

33 For more details about Progresa/Oportunidades see, or example, Levy (2006).

34 In this decomposition, pensions were treated as i the ull amount corresponds to a public transer. Strictlyspeaking, this is not the case, since pensions include private pensions, and also because part o public pensions ispersonal savings (contributions by employees) and not transers rom the government. Since the public transero pensions tends to be regressive (see later in text), the equalizing eect o government transers in 20022006was probably quite strong to make the total (including pensions) to be equalizing too.

35 Alejo et al. (2009), table 16.


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4. Labour income inequality and the skilled/unskilled wage gap

The results o the decomposition exercises o the previous section suggest that one o themost important equalizing orces between 1996 and 2006 has been the evolution o labourincome inequality. Note that labour income is basically the result o multiplying hours worked

by hourly wages (here dened as including remunerations to the sel-employed). I we assumethat hours worked did not change much rom 1996 to 2006, the change in labour incomeinequality must have been caused by changes in hourly wage inequality. 36 We shall ocus onone o the dimensions o wage inequality: the skilled/unskilled wage gap. As shown in Bouillnet al. (2004), changes in the returns to education (the returns to years o schooling becamemore convex) accounted or a signicant share o the rise in household per capita incomeinequality between 1984 and 1994. During the 1994/96-2004 period the opposite appears tohave occurred.

Figure 5 shows the evolution o the ratio o non-production workers wages toproduction workers wages 37 rom 1984 to 2007. 38 The pattern o wage inequality is remarkablysimilar to the evolution o inequality in the various denitions o income that were shown ingure 1. Similar to the pattern o income inequality in gure 1, gure 5 shows an increase inwage inequality between 1984 and the mid-1990s ollowed by a steady decline since then.The rapid increase in wage inequality that occurred in Mexico between 1984 and the mid-1990s has been the subject o a airly large body o research. 39 The increase in the skilled/unskilled gap coincided with the unilateral trade liberalization o the Mexican economythat started in the mid-1980s. In that sense, the evolution o Mexicos wage inequality wassomehow unexpected; Mexico is a relatively unskilled-labour abundant country (at least romthe perspective o its main trade partner, the United States), and standard theories o tradewould have predicted exactly the opposite pattern (i.e., a reduction in the skilled/unskilledwage ratio). 40

The explanations that have been proposed to explain this apparent paradox can bedivided roughly into two groups: the rst groups explanations emphasize actors aecting thebottom part o the income distribution (that is, the segment mostly comprised by less skilled

36 Actually, between 1996 and 2006, weekly hours in all jobs ell very slightly rom 45.6 to 45.1, and the declinewas concentrated in low education (poorer) workers which would be an unequalizing change. This means thatthe equalizing changes in the distribution o hourly earnings must have been large enough to compensate orthe unequalizing eect o the changes in the distribution o hours worked. Data on weekly hours and hourlywages can be ound in www.depeco.econo.unlp.edu.ar/sedlac/.

37 This ratio is also usually identied as the skilled/unskilled wage ratio, where non-production workers are usedas a proxy or skilled labour and production workers are a proxy or unskilled labour. This is, o course, an over

simplication, since there are production workers that are highly skilled and non-production workers that arerelatively unskilled.

38 The data or this graph came rom the Industrial Survey in Mexico, which has monthly and annual data ontotal wages paid and total hours worked in the industry by both production and non-production workers. Thisgure is an updated version o similar gures published in Esquivel and Rodrguez-Lpez (2003) and Chiquiar(2008).

39 See, or example, Esquivel and Rodrguez Lpez(2003), Airola and Juhn (2005), Robertson (2007), Acosta andMontes-Rojas (2008), Chiquiar (2008), Verhoogen (2008), and the reerences cited therein.

40 For a review o the literature or Mexico and Latin America more broadly, see de Hoyos and Lustig (2009).


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and less experienced workers); the second groups explanations emphasize actors aecting theupper part o the distribution. In the rst group, or example, we have theories emphasizing thereduction in real minimum wages (Fairris, Popli and Zepeda, 2008), as well as theories suggestingthat the mid-1980s reduction in taris disproportionally aected industries intensive in low-skilled workers (Hanson and Harrison, 1999). In the second group, or example, some theories

have emphasized the role o an increase in the demand or skilled workers associated with oneor more o the ollowing: the presence o oreign investment (Feenstra and Hanson, 1997), askill-biased technological change (Cragg and Eppelbaum, 1996 and Esquivel and Rodrguez-Lpez, 2003), or a process o quality-upgrading due to an increase in exports (Verhoogen,2008). Other explanations have suggested that education inequality could have also playeda role (Lpez-Acevedo, 2006) or that these trends could be indicating only short-run eects(Canonero and Werner, 2002). O course, many o the proposed explanations rom both groupsor the pre-NAFTA increase o wage inequality in Mexico are not mutually exclusive, and couldin act be (at least partially) correct.

Figure 5, Skilled / unskilled industrial wages, 19842007


In comparison to the 1984 and the mid-1990s inequality increase, the post-1996reduction in wage inequality in Mexico has been much less studied. Among existing research,Robertson (2007) suggests that Mexicos manuacturing workers are now complements to,rather than substitutes or, U.S. workers. He also posits that there has been an importantexpansion o assembly line activities in Mexico (the so-called maquiladoras), which has


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increased the demand or less skilled workers. 41 Campos (2008) emphasizes the supply-side explanations based on changes in the composition o the labour orce. Esquivel (2008)investigates the role o demand-side and supply-side actors by looking at the patterns inmale workers mean-log wages in Mexico or selected years and or dierent combinations oeducation and years o experience. Workers are classied according to the level o education

achieved (less than lower secondary, lower secondary, upper secondary, and collegeeducation) and to the number o years o work experience (less or more than 20 years oexperience). Between 1989 and 1994, most o the changes in the wage distribution occurredin the upper tail o the distribution (workers with high wages and high levels o education andexperience).42 The increase in wage inequality in those years was not caused by a (relative)decline in the wages o the low-skilled or low-experienced workers; rather it was the resulto a rise in the wages o the high-skilled or highly experienced workers. This basically makesexplanations based on changes in the lower tail o the wage distribution such as thosebased on a alling real minimum wage or a bias against unskilled-labour intensive industriescaused by trade liberalization not convincing.

In contrast, between 1996 and 2006, the reduction in wage inequality was caused bythe changes in the wage distribution that took place in the lower tail o the income distribution;workers with lower levels o education and/or less years o experience had the largest increasesin their average wages. This suggests that any convincing story o the post-NAFTA reductionin wage inequality has to explain the relative increase in the wages o the low-skilled/lessexperienced workers (as opposed to the reduction o the wages o the high-skilled/moreexperienced workers).43

The discussion o the literature above conrms the impression that there is no singleexplanation or the evolution o wage inequality in Mexico since 1984. Indeed, the act thatthe 19841994 increase in wage inequality is associated with increases in the upper tailo the income distribution, whereas the post-NAFTA reduction in wage inequality is mostlyassociated with increases in the bottom tail, suggest that there at least two leading orces atplay. During 19841994, the only explanations that seem to be compatible with the observedtrend in inequality are those suggesting the presence o skill-biased technological change,either exogenously (Cragg and Eppelbaum, 1996 and Esquivel and Rodrguez-Lpez, 2003) orendogenously via the presence o multinational rms (Feenstra and Hanson, 1997) and/or bythe quality upgrading o exporting rms (Verhoogen, 2008).

41 Robertson (2007) noticed that the pattern o wage inequality in Mexico is puzzling because no singletheory could explain the evolution o wage inequality beore and ater NAFTA. There are, however, some

tentative theoretical explanations or the pattern. For example, Atolia (2007) has suggested that, under certaincircumstances, even i the standard prediction rom a Hecksher-Ohlin-Samuelson model works as predicted inthe long run, there may be some short-run (or transitory) eects o trade liberalization that may lead to a dierentoutcome rom those o the long run. The dierence between short-run and long-run eects on inequality resultrom two actors: rst, an asymmetry in the contraction and expansion o some sectors, and second, because othe capital-skill complementarity in production.

42 The data was collected and organized by Campos (2008).

43 It should be noted that this occurred within a context in which average real and legislated minimum wageswere practically at since the mid-1990s.


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For the post-NAFTA period, there are at least three possible explanations. Two, which arenot mutually exclusive (and have already been mentioned), are: an increase in the relative supplyo skilled workers (Campos, 2008) and an increase in the demand or unskilled labour resultingrom an expansion in assembly line activities (maquiladoras) in Mexicos manuacturing sector(Robertson, 2007). Any o these two by itsel could be suf cient to explain the reduction in the

skilled wage premium that is observed in the data.However, a third explanation o the observed pattern could be consistent with a lagged

eect o the standard Hecksher-Ohlin eect in an unskilled-labour abundant country such asMexico (Chiquiar, 2008). The predicted pattern o a lower skill premium may have maniesteditsel with a lag either because the impact o trade liberalization on wages could have taken aew years, as suggested by Canonero and Werner (2002), or, alternatively, because an underlyingeect had not showed up in the data beore due to the presence o a stronger orce, such as askill-biased technological change as argued by Esquivel and Rodrguez-Lpez (2003).

Figure 6, Workorce composition by education and experience levels, 19892006


To test these alternative hypotheses one would need a much more detailed and rigorous

analysis, which goes beyond the scope o this paper. However, based on the patterns o wageinequality reviewed here, we might be able to identiy which hypothesis is more plausible.Figure 6 shows the composition o Mexicos workorce between 1989 and 2006, according tothe levels o education and experience. The observed pattern reects the interaction o bothsupply and demand actors. In general, the gure shows that rom 1989 to 2006 there was botha reduction in the share o the least skilled (those with less than lower secondary education)and less experienced (those with less than 20 years o experience) workers, and an increasein the share o the most skilled (those with college education) and more experienced (those


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with more than 20 years o experience) workers. The most dramatic changes, however, tookplace in the share o those workers with less than lower secondary education (but more than20 years o experience). In act, this group, which accounted or almost 55 percent o workorcein 1989, only represented about one third o the workorce by 2006. The share o workers withless than lower secondary education but more than 20 years o experience declined by about

20 percentage points in a 17-year span. This reduction was compensated by an increase inthe shares o all the other groups o workers. These trends, which had already been presentbetween 1989 and 1994, accelerated in the post-NAFTA period.

Thereore, these results suggest that most o the relative increase in the wages olow-skilled/low-experience workers is associated with the change in the composition o theworkorce in Mexico. In particular, the increase is associated with a reduction in the relativenumber o unskilled workers. This result is not incompatible with the hypothesis o an increasein the demand or unskilled workers as suggested by Robertson (2007). But Robertsons story,by itsel, cannot explain the simultaneous increase in the relative wages and the reduction inthe share o these workers in Mexicos labour orce.

Figure 7 , Change in share o total workers by education and

experience vs. change in log wage, by gender, 19962006


Figure 7 shows some results that reinorce our view. The x-axis shows the change inthe share o the eight dierent groups o workers according to their levels o education andexperience between 1996 and 2006. These three categories that include the less educated/less experienced workers have seen a decline in their participation in Mexicos workorce. They-axis indicates the average change in the log wage o male and emale workers that belong toeach one o these groups. As expected, the groups whose shares have diminished in the pastdecade are those that have had the largest increase in their wages. Notice that the increase inthe wages o these workers is close to 20 percent, and in some cases even close to 30 percent,


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or this 10-year period. On the contrary, those categories o workers that have increased theirshare in Mexicos workorce (the more educated/more experienced workers) tend to have hadeither stagnant or decreasing wages since 1996. Figure 7 supports the hypothesis that thechange in Mexicos workorce composition appears to be one o the leading orces explainingthe reduction in wage and labour income inequality in Mexico in the post-NAFTA period.

Table 4, Schooling inequality among adults, 19842006

Population

Quintile

Average Schooling (in years) Change (in percent)

1984 1994 2006 19841994 19942006

1 2.2 2.8 4.8 27 73

2 3.0 3.9 6.4 30 65

3 3.9 5.1 7.3 32 43

4 5.6 6.7 8.9 19 34

5 7.9 9.9 12.1 25 22

Mean 4.9 6.1 8.3 27 36

ConcentrationCoef cient

0.345 0.347 0.276 1 -21

Gap decile 10-1 6.5 8.8 9.4 36 6

Source: Scott (2009a).

Note: Adults are dened as people between 25 and 65 years old.

The reduction in the relative supply o workers with low levels o skills (education)might be the result o the signicant increase in average years o schooling or the bottomtwo quintiles, which reduced educational attainment inequality considerably between 1994and 2006 (table 4). Public spending on education changed substantially in the 1990s, which,

combined with the eects o the conditional cash transer programme Progresa/Oportunidades(discussed in section 6), signicantly increased access to secondary education or the poor. Thenext section will analyze the changes in public spending on education and their impact onequality o access to education in more detail.

5. The rising progressivity in government spending on

education

Public spending on education in the 1970s and 1980s was heavily biased towards higher

education.44

In the 1970s, the share o educational spending allocated to upper secondaryand tertiary education grew rom 20 percent to 42 percent while the share o spending onbasic education declined by an equivalent amount, despite an expansion in enrollment inpublic basic education rom 9.7 to 16.5 million students. The impact on spending per studentin basic education was aggravated in the 19831988 adjustment period, when basic educationabsorbed a disproportionate share o budgetary cuts. This bias was reversed ater 1988, with an

44 For details on methodology and sources o inormation or this section, see Scott (2009a).


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increasing reallocation o educational spending towards basic education. 45 Between 1992 and2002, spending per student in tertiary education expanded in real terms by only 7.5 percent. Incontrast, spending per student on primary education increased by 63 percent. The relative ratioo spending per student in tertiary versus primary education thus declined rom a historicalmaximum o 12 in 19831988, to less than 6 in 19942000 (by comparison, the average ratio or

high-income OECD countries is close to 2).46

In addition to the budgetary allocation between educational levels, progressivity ineducational spending was constrained by the limited use o post-primary public educationservices by the poor, even when these were ully subsidized. This is explained by supply (limitedavailability o secondary schools in rural areas) as well as demand constraints (high opportunitycost o attending school or basic education in poor rural households). Both o these actorswere addressed in the 1990s through the expansion o basic education acilities and, mostnotably, the conditional cash transer programme Progresa (called Oportunidades since 2000)launched in 1997. Progresa/Oportunidades tied monetary transers to keeping children o poorhouseholds in school and to receiving basic health services.47 Numerous impact evaluationstudies have shown the programmes positive and statistically signicant impact on poorchildrens education and health. Progresa/Oportunidades and its impact are discussed in detailin section 6 below.48

The eect o these reorms may be observed in gure 8, which presents the distributiono benets rom dierent levels o public education received by population deciles (ranked byper capita household income). Figure 8 also shows the distribution o total education spending,comparing the distributions or 1992 and 2006. Combining the budgetary and participationeects that is, combining the act that coverage became more targeted to the poor andthat more o the budget was allocated to these categories and programs the distribution ototal public spending on education has changed qualitatively over the decade, rom (mildly)regressive to progressive in absolute terms,49 with the poorest decile obtaining a share oeducational spending (12 percent) twice as large as that o the richest decile (6 percent).50

45 Aspe and Beristin (1984) ound that public spending in education was quite inequitable beore the changesthat started in 1988 (p. 323).

46 See OECD (2008).

47 For a description o Progresa/Oportunidades see, or example, Levy (2006).

48 See, or example, Parker (2005) and Schultz (2000) and reerences cited in section 6.

49 Progressivity in absolute terms means that the poor receive a disproportional share o transers, that is, the xpercent poorest population receives more than x percent o transers, while progressivity in relative terms meansthat the transers received by the poor are higher as a share o their pre-transer income than those received bythe rich. We ollow a common, i somewhat conusing, practice below in using the term progressive/regressivewithout qualication, to mean progressive in absolute terms in the case o spending, and in relative terms in thecase o taxes.

50 In order to estimate the eect o transers in kind such as public spending on education, Scott (2009b) appliesa benet incidence analysis based on the use o public services reported in ENIGH, valued at cost o provision.This imputed distribution o transers received, valued in monetary terms, is then used to obtain an estimate othe (monetary and in kind) post-transer Gini coef cient, and thus by comparing it to the pre-transer Gini o the total distributional impact o all transers. These imputations augment the concept o non-monetaryincome reported in ENIGH, and dier rom the non-monetary concepts already included in the latter (notablyimputed owners occupied housing rent) because o the method o valuation used to obtain the relevant


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Figure 8, Distribution o benets o public spending on education by income decile,

1992 and 2006 (percent)

Source: Scott (2009b).

Note: Population deciles ordered by pretranser household income per capita.

monetary values: cost o provision (in kind public services) versus sel-reported valuation (imputed rent). It wouldo course be possible to apply a standard decomposition analysis on the (post-transer) Gini coef cient obtainedrom this augmented concept o total income, thus allowing in principle a direct and ull comparison o theeects on inequality and its evolution o private vs. public income sources. Given the noted measurement issues,however, such a direct comparison would have to be interpreted with care. This would also imply a substantialrevision o the (total income) Gini coef cients reported or countries. The present analysis reports the estimatedeect o these transers on the Gini coef cient in purely accounting terms, ollowing common practice in benetincidence analysis.


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6. Pro-poor government spending: The distributional impact o

Progresa/Oportunidades

As discussed above, in addition to changes in the distribution o labour income, transers inparticular, government transers became an important equalizing orce rom 2000 to 2006,

compared to the earlier years. Transers equalizing contribution has been increasing over timeboth at the national level as well as or the urban and rural areas (table 3). 53 According to thedecomposition results presented in table 3, transers became the income source with thelargest equalizing eect o all the sources considered in this exercise: that is, a marginal increasein transers would reduce inequality by more than a marginal increase in labour income orremittances. Transers became more equalizing over time because their share in total incomerose (mostly because o a signicant increase in coverage) and their own inequality and Ginicorrelation with total monetary income ell. These changes were particularly pronounced in therural areas.

The sharp rise in the role and equalizing impact o government transers was a

consequence o a signicant policy shit in 1997, when the government launched the conditionalcash transer programme Progresa (whose name was changed to Oportunidades in 2000).54Progresa/Oportunidades is an innovative ederal government programme that targets rural andurban households in Mexico that all within the extreme poverty category.55 The programmetransers cash to beneciary households provided they take their children or periodic visits tohealth centres and send them to school. The logic behind the cash transer is to compensateor credit market ailures and/or parenting ailures.56

Progresa/Oportunidades has three components: education, nutrition and health. Theeducation component grants cash transers based on school attendance and high schoolcompletion, and or school supplies. The nutrition component oers cash or in-kind transers(nutritional supplements, vaccinations, preventative treatments, etc.), based on regular visitsto a health clinic. The average monthly transer is about $35, and estimated total transers areequivalent to, on average, 25 percent o eligible rural households average monthly income.57By the end o 2005, Progresa /Oportundiades granted benets to 5 million amilies (about24 percent o the Mexican population). The budget or Progresa/Oportunidades in 2005 equaled

53 Transers here include government transers and private transers excluding pensions and remittances. Thus,it is not equivalent to government transers only.

54 For a detailed analysis oPrograma de Educacin, Salud y Alimentacin (Progresa) see, or example, Levy (2006).

55 This section is based on Lustig (2007).

56 Making the transers conditional to a certain behaviour has the purpose o ensuring that additional resourcesare really used by beneciary households to improve childrens levels o health, nutrition and education, therebybreaking intergenerational poverty traps. The conditionality logic resides in the preoccupation that parents,because o ignorance or indierence, are not good agents or the principal (their children), and (withoutconditionality) may devote the transers to expenditures that do not have an impact in improving their childrenshuman capital.

57 This gure o $35 can increase in amilies with school-age children. In 2005, Progresa/Oportunidades grantedmoney and species equivalent to direct monetary monthly assistance o $44.30 per amily. Source: Levy (2006).


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0.36 percent o GDP (compared with 0.02 percent in 1997), and it commanded 2.29 percent othe programmable public expenditure budget.58

Numerous studies have estimated that, among other dimensions, the programmehas had signicant impacts on health and education. In oering beneciaries scholarships toattend post-primary school, Progresa/Oportunidades addresses a principal restriction to access

to post-primary education among the rural poor. Comparing enrollment beore the programme(1996-97) and ater the programme (2002-03), an average increase o 24 percent was oundin rural areas.59 O note was enrollment in secondary education in rural areas, which rose by11 percent or girls and 7.5 percent or boys ater two years o launching the programme. 60 Byproviding demand-side subsidies and empowering public service users, rather than providers,it complemented traditional supply-side spending in health and education with demand-sidesubsidies. In act, demand or health services among beneciaries was 67 percent higher thandemand in communities not participating in the programme.61 Inant mortality was ound toall at a rate 11 percent higher among beneciaries compared to non-beneciaries. 62 Anotherstudy estimated a reduction o 11 percent in maternal mortality and a 2 percent reduction ininant mortality in rural communities compared to those not in the programme.63

Likewise, many studies show that Progresa/Oportunidades has had a positive impacton household consumption, savings and investments, which helped reduce poverty andinequality in Mexico. For 2004, the poverty incidence o programme participants (percentageo population associated with the programme that is below the poverty line) ell by 9.7 percentin rural areas and 2.6 percent in urban areas. In the same year, there was a reduction in thepoverty gap (18.7 percent) and a reduction in the severity o poverty (28.7 percent) in ruralareas,64 as well as reductions in the urban poverty gap (4.9 percent) and urban poverty severity(1.7 percent).65 In terms o its impact on the distribution o income, table 5 shows that the directeect o Progresa/Oportunidades transers reduced the Gini coef cient rom 0.502 to 0.494,which is the equivalent o close to one th o the decline experienced in the Gini coef cientbetween 1996 and 2006 (table 8).66

58 Although the Social Development Secretariat is in charge o the programme, most o the health budget,mainly associated to the provision o health services, is part o the conventional denition used in health publicexpenditure.

59 Parker (2005).

60 Schultz (2000).

61 Bautista et al., (2004).

62 Barham, (2005).63 Hernndezet al., (2003).

64 Poverty gap measures the mean distance o the poor with respect to the programmes poverty line, andreects a worsening or improvement in the conditions o the poor. On the other hand, povertys severity measuresthe average o the squared distances o the poor with respect to the poverty line and reects inequality amongthe poor population.

65 Cortes and Banegas 2006.

66 The impact on the Gini coef cient takes account only the direct eect. The eects on inequality o changes inbehavior or through higher human capital among the poor are not contemplated in this calculation.


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Politically, Progresa/Oportunidades set new standards or social policy in Mexico. 67 It isthe rst social programme in Mexico to apply transparent targeting mechanisms; it eectivelyidenties the poorest rural localities and households, using, at the latter level, proxy-meanstests based on a ull census o socioeconomic characteristics and economic assets within theselocalities. Progresa/Oportunidades is also the rst social programme in Mexico to be subject to

rigorous impact evaluations which were planned rom the beginning o the programme.68

The programme is also notable in having survived not only a change o administration (noother major anti-poverty initiative over the past two decades has done this), but also inhaving survived the rst change in 70 years o the political party in power. In act, rather thandiscard the programme, the new partys administration changed its name rom Progresa toOportundiades, and starting in 2001 the new government increased coverage rom 2.3 to 4.2million households (mainly in rural areas), and added semi-urban and urban localities to thealready established rural ones.

Table 5, Direct distributive impact o targeted monetary transers:

Total and Progresa/Oportunidades, 2006 (percent)

DecilesAll Monetary

Targeted

Progresa /

Oportunidades

Primary or

Market Income

1 1.8 1.7 1.5

2 2.7 2.7 2.5

3 3.6 3.5 3.4

4 4.4 4.4 4.3

5 5.3 5.3 5.3

6 6.7 6.7 6.7

7 8.2 8.1 8.2

8 10.7 10.7 10.8

9 15.7 15.7 15.810 41.0 41.1 41.4

Gini 49.2 49.4 50.2

Change in Gini -2.1 -1.7

Source: Authors elaboration based on ENIGH (2006).

Note: 1. Distribution o total primary current income per capita beore and atertargeted monetary transers. For more details, see Scott (2005).2. The programmes in this table reer to monetary transers only, corresponding tothe rst item under D in table 6.

Progresa/Oportunidades also marked a signicant shit in the distribution o oodsubsidies in Mexico. As shown in gure 9, the reallocation o ood subsidies through Progresa/Oportunidades transormed the broadly neutral distribution o government spending in oodsubsidies in Mexico into a highly progressive one: with the share beneting the poorestdecile increasing rom 8 to 33 percent between 1994 and 2000. Several actors may explain

67 This section is based on Scott 2006.

68 See, or example, www.ipri.prg or the principal evaluation results. Also, see Parker (2005) and Schultz (2000).


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this shit. First, the discovery o a redistribution technology: the piloting oProgresa in 1997showed that distributing ood subsidies (and cash transers) to poor amilies in remoterural localities was operationally easible. Second, the empowerment o rural voters as aconsequence o Mexicos democratization process gave them potentially more voice in theallocation o government resources. Third, unlike health and education, ood subsidies are

not under the purview o Mexicos powerul public sector unions, which have successullyblocked pending reorms in the case o education and health services. Finally, in contrast toeducation and health service provision, the reallocation o ood subsidies is not constrainedby labour shortages (lack o qualied doctors and teachers in poor rural areas) or physicalinrastructure.69

Figure 9, Distribution o public spending on ood subsidies, Procampo and health services or the

uninsured by income decile, 1990s2000s (percent)

Source: Scott (2003).Note: 1. Food subsidies: population deciles ordered by pretranser household income per capita.2. Users o health services: population deciles ordered by pretranser household income per capita. The use o healthservices is measured in millions o users as a percentage o the corresponding decile.

The political economy o Progresa/Oportunidades survival through variousadministrations is also a result o a perect combination o actors that merit note. In contrastto the intensive government media campaigns accompanying most agship anti-povertyprograms in Mexico, the government rerained rom such a campaign in the case oProgresa,which acilitated its political survival beyond the administration and the PRI regime. 70 Second,the rapid expansion o the programme (with 2.5 million direct beneciary households by theend o the 2000) ensured support or it rom a large constituency. Third, just as importantly,the decision to make the programme transparent and invest in ambitious and highly credibleexternal impact evaluations also contributed signicantly to the political survival oProgresa/

69 For a discussion o the political economy o this reorm see Scott (2009b).

70 The PRI (Partido Revolucionario Institucional, Revolutionary Institutional Party) regime reers to the (almost)one-party regime that characterized Mexico or 70 years.


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Oportunidades. However, it was not completely immune to damaging political economy issues.In 2001, when it was rebranded as Oportunidades, the programme was expanded to urban areasand upper secondary education, inevitably reducing its targeting ef ciency rom 71 percent oits resources beneting the poorest quintile in 2000, to 55 percent in 2006.

In sum, Progresa/Oportunidades is an example o redistributive ef ciency. With as little

as 0.36 percent o GDP and 4 percent o redistributive spending (table 7), the programmeaccounts or 18 percent o the change in the post-transers Gini, and 81 percent o the changein the Gini ater including programmes targeted to the poor (table 8). 71 I one comparesthe redistributive impact o this programme with the redistributive impact o all monetarytransers and subsidies, the impacts are equal. This implies that the progressive incidence othe remaining transers targeted to the poor and other spending categories is wiped out by theregressive eect o all other untargeted monetary transers and subsidies.

Thus, unortunately, the redistributive ef ciency o Progresa/Oportunidades is anisolated case among redistributive instruments currently operating in Mexico. Even thoughgovernment spending became undoubtedly more pro-poor during the last decade, a lot

still needs to be changed to make redistributive spending a powerul instrument to reduceinequality in Mexico. This will be the topic o the next section. In what ollows, we will analyzethe incidence o all redistributive spending and identiy which spending categories must bechanged i scal resources are to be used more progressively.

7. The distributional impact o state action: Government

spending and taxes

Since the early to mid-1990s, social spending, and particularly spending on programs targeted

to the poor, has expanded considerably (gures 10 and 11). As discussed above, and shownin gures 8 and 9, spending on education, health and nutrition became more progressive. Inaddition, with the introduction oProgresa/Oportunidades, the Mexican government oundways to redistribute income through transers in an ef cient and cost-eective way. Despitethis progress, 58 percent o government redistributive spending in 2006 was regressive (owhich 11 percent increased income inequality). In advanced countries, government transersare able to reduce primary (i.e., market) income inequality by 30 to 50 percent. In contrast,in Mexico, the post-transers Gini coef cient in 2006 was only 9.3 percent and a meager1.7 percent i we exclude transers in-kind lower than the primary income pre-transersGini.

71 The gure o 18 percent is obtained by dividing the percentage change in the Gini due to Progresa/Oportunidades in table 5 (-1.7 percent) by the percentage change in the Gini due to transers shown in table 8(-9.3 percent).


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Figure 10, Evolution o social spending, 19252006 (percent)

Source: Scott (2009b).

Note: Programmable spending excludes debt servicing.

Figure 11, Evolution o extreme poverty, anti-poverty spending,

GDP and Gini (average annual change in percent)

Note: 1. Poverty is measured using the o cial extreme (pobreza alimentaria orood poverty) poverty line.2. Poverty programmes or 20002006 are those listed under D in table 6. For19891994, the list was diferent; in particular, it included Pronasol and excluded

Progresa/Oportunidades (launched in 1997).

Estimating the redistributive impact o government spending aces considerablemethodological challenges. Most income and expenditure surveys report government monetarytransers as a component o household income. The contribution o these transers to overallincome inequality can then be estimated through the application o standard decompositiontechniques on inequality measures by income source. This is the path ollowed above. However,


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while in mature welare states, monetary transers represent between a third and a hal o totalsocial spending, and account or reductions in inequality on the order o 20 to 50 percent indeveloping countries monetary transers are a small part o total government transers, and inthe case o Latin America account or reductions in inequality o around 2 percent. 72 The largestshare o redistributive spending in Latin America occurs through government transers in-kind,

which are not included, in general, in the income concept measured in household surveys.In Mexico, the two cash transers programmes reported in the ENIGH survey

(Oportunidades and Procampo) represent a mere 5 percent o the portion o public spendingdevoted to redistributive objectives (table 6).73 The remaining 95 percent o governmenttransers is not covered by the Gini decomposition analysis presented above. This means thatstandard analysis o inequality dynamics excludes a signicant portion o a true measureo income, which, i measured correctly, would have to include all government transers. Toillustrate this, in 2006, average household income increased by 4.1 percent when all monetarytransers and subsidies were included, and increased by 12.8 percent when in-kind transers(education and health) were added to the latter.

Table 6 presents the 2006 totals o government transers in cash and in kind as wellas in the orm o subsidies (including value added tax [VAT] exemptions or scal spending)or the main redistributive categories and programmes.74 The programmes are described intable 7. The categories and programmes covered here include all public education and healthspending (at the ederal and state levels), as well as all ederal public spending on pensions (thesubsidized component), energy and agricultural subsidies, and the main programmes targetedto the poor. Altogether, these items comprise a total o 25 programmes or specic spendingcategories which, in 2006, amounted to close to $100 billion, or 10 percent o Mexicos GDP,12.8 percent o primary (beore taxes and transers, also called pre-scal) household income,60 percent o total public spending (the remainder includes spending on administration,deence, etc.) and 97 percent o total social spending. Using ENIGH 2006, we analyze theirdistributive impact using standard benet incidence analysis.75

72 These reductions in mature welare states are measured in purely accounting terms: i.e., as the dierencebetween the pre-transer Gini and post-transer Gini without taking into account any behavioural responses. SeeErvik (1998), Smeeding and Ross (1999).

73 We put redistributive in quotation marks because not all these transers are progressive.

74 In-kind transers, which include mainly education and health services, may be highly valued by some (thoughnot all) beneciaries. They are non-tradable, highly labour intensive, depend on locally available inrastructureor access, and are thus highly variable in quality. Thus, one relevant implication or the present analysis is thatthe gap between the public cost o the transer, and the monetary benets to recipients, is likely to be larger orin-kind transers than or monetary transers or direct subsidies. Another is that the low quality o services mayact as an implicit, but eective, sel-selection targeting mechanism.

75 Public spending data on all programs reported in table 6 are obtained rom the Public Accounts o theFederation or the relevant years. For health spending at the state level, we use the National and State HealthAccounts, published by the Health Ministry. Education spending at the state level is estimated rom ederal perstudent spending rates and the total o students in schools nanced by states as reported by the EducationMinistry. Spending on education and health at the state levels includes spending on education and healthnanced through earmarked ederal transers, revenues rom ederal revenue sharing programs and scalrevenues collected by the states themselves.


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Table 6, Government redistributive spending by category and programme, 2006

ConceptMillions Current

Mexican Pesos

Total

Transers (%)

Household inc. beore

taxes and trans. (%)

Household income beore taxes and transers 7,521,608 100

Taxes (including social security contributions) 1,038,283 13.8Social Spending (of cial classication) 988,369 13.1

Total Transers Analized, o which 964,567 100 12.8

Transers in kind (lines A+B) 654,675 68 8.7

Other Transers* (lines C+D) 309,892 32 4.1

Memo item: Total untargeted to the poor 916,696 95 12.1

Total targeted to the poor 47,871 5 0.6

A. Education 402,385 42 5.3

Preschool 44,583 5

Primary 135,352 14

Lower Secondary 86,817 9

Upper Secondary 52,932 5

Tertiary 82,701 9

B. Health 252,290 26 3.4

Services or Insured 159,986 16

Services or Uninsured (SSA), o which 92,304 10

Seguro Popular 11,700 1

IMSS-Oportunidades 5,716 1

C. Subsidies* 262,021 27 3.5

Pensions** , o which 85,230 9 1.1

IMSS 50,004 5

ISSSTE 35,226 4

Consumption Subsidies, o which 107,153 11 1.4

Residential Areas Electricity Subsidy 64,935 7

Gasoline and other uels subsidy 42,218 4

Memo item: Consumption Subsidies ***, o which 270,102 3.6VAT exemptions on ood and prescription drugs 162,949

Agricultural Subsidies, o which 69,638 7 0.9

Procampo 15,025 2

D. Programs Targeted to the Poor, o which 47,871 5 0.6

Targeted monetary (mainly), o which 45,179 4

Oportunidades 33,526 3

Vivienda (Tu Casa) 4,234 0.4

DIF/ Feeding Programs 2,806 0.3

Liconsa 1,300 0.1

Pr

a decade of falling inequality in mexico

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