Policy Research Working Paper 9038
Poverty Convergence in a Time of Stagnation
A Municipal-Level Perspective from Mexico
(1992–2014)
Luis F. Lopez-Calva
Eduardo Ortiz-Juarez
Carlos Rodríguez-Castelán
Poverty and Equity Global Practice
October 2019
Policy Research Working Paper 9038
Abstract
This paper exploits a novel municipal-level data set to municipalities and stagnant and deteriorating performance
explore patterns of convergence in income and poverty in among richer municipalities. Re distributive programs, such
Mexico during 1992–2014. The paper finds that, despite as federal transfers to poor municipalities and cash transfers
a context of overall stagnant economic growth and pov- to poor households, seem to have played an important role
erty reduction, there is evidence of income and poverty in driving these results by bolstering income growth among
convergence at the municipal level. The findings suggest the poorest municipalities, while also inducing progressive
that these convergence processes stem from a combination changes in the distribution of income.
of considerable positive performance among the poorest
This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to
provide open access to its research and make a contribution to development policy discussions around the world. Policy
Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted
at crodriguezc@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Poverty Convergence in a Time of Stagnation: A
Municipal-Level Perspective from Mexico (1992–2014)∗
Luis F. Lopez-Calva† Eduardo Ortiz-Juarez‡ Carlos Rodr´ an§
ıguez-Castel´
Keywords: Income, poverty, inequality, convergence, growth elasticity, small area
estimation
JEL codes: I32, O47, O54, R11
∗
The authors are grateful to Ted Enamorado for his comments and research assistance. The au-
thors would also like to thank Maria E. D´ avalos and Gerardo Esquivel for signiﬁcant contributions
ekely (peer reviewer), Oscar Calvo-
to this work, as well as Jozef Draaisma (peer reviewer), Miguel Sz´
Gonz´ alez, Paloma Anos-Casero, Louise Cord, Norbert Fiess, Thania de la Garza Navarrete, Rodrigo
Garc´ ıa-Verd´u, Gonzalo Hern´ andez-Licona, Fernando Blanco, Sandra Mart´ ınez-Aguilar, Edgar Med-
ina, Pablo Saavedra, Kinnon Scoot, Gaston Yalonetzky, Robert Zimmerman, oﬃcials at CONEVAL
and Mexico’s Ministry of Social Development, and participants at the EADI Nordic Conference
2017, held in Bergen, Norway, for helpful comments and suggestions. The datasets and codes nec-
essary to replicate the exercises in this paper are available from the authors upon request. The
ﬁndings, interpretations, and conclusions in this paper are entirely those of the authors. They do
not necessarily represent the views of the UNDP, the World Bank, its Executive Directors, or the
countries they represent.
†
United Nations Development Programme; e-mail: luis.lopez-calva@undp.org
‡
King’s College London; e-mail: eduardo.ortiz@kcl.ac.uk
§
World Bank; e-mail: crodriguezc@worldbank.org
1 Introduction
Despite the government’s implementation of an ambitious agenda of economic and
social reform, the performance of Mexico has been mediocre in economic growth and
poverty reduction since the early 1990s. In 1992–2014, the country’s gross domestic
product (GDP) expanded at an annual average rate of 2.5 percent. Per capita, this
translates into an annual growth rate of only 0.9 percent, making Mexico the second-
worst performer in continental Latin America during the period. This mediocre
performance was mirrored in income poverty reduction. In 1992, the oﬃcial poverty
headcount ratios for extreme and total poverty were, respectively, 21.4 percent and
53.1 percent. More than 20 years later, in 2014, these ratios remained virtually
unchanged, at 20.6 percent and 53.2 percent, respectively.1
This poor performance in income growth and the long-run stagnation in poverty rates
leave the impression that little has changed in the living standards of the population,
especially among the poorest. This is particularly surprising, given that Mexico
experienced many changes during this period, including multiple economic crises,
the implementation of an ambitious set of structural reforms, and innovations in
redistributive social policy. It may be, however, that these aggregate ﬁgures are
masking trends at the subnational level. To understand clearly how income and
poverty are changing, one needs to zoom in to a higher level of spatial disaggregation
and unpack how these patterns vary within Mexico.
Speciﬁcally, this paper zooms in to explore whether some municipalities have been
persistently lagging behind in pockets of poverty (income convergence) and whether
poorer, converging municipalities have been able to translate their relative income
gains into poverty reduction (poverty convergence). The analysis of convergence in
the mean per capita incomes of municipalities follows the framework proposed by
Barro and Sala-i Martin (1991). It aims at an understanding of whether poorer mu-
nicipalities have been capturing the income gains resulting from modest growth and
social spending and whether there has been a reduction in regional disparities. The
analysis of convergence in poverty headcount ratios applies the poverty convergence
decomposition by Ravallion (2012) to assess the eﬀects of initial poverty on both the
income growth process and the sensitivity of poverty reduction to income growth.
While several studies have looked at short-term (growth) convergence in income
among states in Mexico, this paper contributes by leveraging a unique data set on
1
All ﬁgures on GDP growth rates come from the World Bank’s World Development Indicators
database. Oﬃcial income-based poverty rates are from Mexico’s National Council for the Evaluation
of Social Development Policy (CONEVAL).
2
municipalities to provide a far more disaggregated look at convergence over a longer
period, while also addressing distributional concerns. The data set includes variables
on income, poverty, and inequality, spans 22 years (ﬁve waves of data over 1992–2014),
and covers 2,361 municipalities. To the best of our knowledge, this is the ﬁrst study
that implements Ravallion’s (2012) method using municipal-level data.
The rest of the paper is organized as follows. Section 2 reviews the relevant em-
pirical and theoretical literature on convergence, highlighting the scarcity of work
exploring within-country poverty convergence at a high level of geographical disag-
gregation. Section 3 presents the methodology used to construct the dataset, which is
a novel exercise applying small area estimation techniques to household surveys and
population censuses to compute comparable measures of mean household per capita
income, poverty headcount ratios, and the extent of inequality at the municipality
level. Sections 4 and 5, respectively, test for convergence in mean per capita incomes
and poverty headcount ratios across municipalities. The analysis emphasizes compar-
isons among subgroups of municipalities that exhibit sizable disparities (for example,
urban versus rural, and municipalities located in states along the U.S. border versus
the rest) as well as across subperiods to contextualize the various changes Mexico has
experienced over the years (that is, economic crises, ups and downs in overall poverty
rates, and the expansion of public expenditure). Both analyses explore the role of
redistributive programs, such as targeted cash transfers to the poor and ﬁscal redis-
tribution to subnational governments, in driving these changes. Section 6 digs deeper
to explore the role of the initial distribution of poverty and inequality in determining
the speed of convergence and decomposes the estimated magnitude of poverty con-
vergence. Section 7 brings together the main messages of each section to conclude.
Taken together, the ﬁndings suggest that income and poverty convergence have taken
place at the municipality level in Mexico during 1992–2014 and that redistribution
has played an important role.
2 Literature review and past evidence in Mexico
The theoretical and empirical literature on economic growth oﬀers stylized facts on
which the analysis of economic development paths at the municipality level can be an-
chored. A ﬁrst widely studied stylized fact stems from the inﬂuential works of Baumol
(1986) and Barro and Sala-i Martin (1991, 1992, 1995) on the convergence hypothesis,
often labeled the catch-up eﬀect or the advantage of backwardness, whereby poorer
countries tend to experience more rapid economic growth rates than richer countries,
in eﬀect, catching up to the latter.
3
Two well-known concepts of convergence are used in this paper: sigma convergence
(σ -convergence) and beta convergence (β -convergence) (Quah 1993). σ -convergence
focuses on the reduction of income dispersion across units of analysis (for example,
see Sala-i Martin 1996), and it is commonly estimated using a standard measure of
statistical dispersion. β -convergence focuses on the negative relationship between ini-
tial levels of income and subsequent growth rates and is commonly estimated using
parametric approaches, such as log-linear and nonlinear growth regressions (though
non-parametric methods, such as discrete Markov chains, are also common practice).
The latter concept distinguishes at least two forms of convergence in the long-run:
absolute β -convergence, whereby the incomes of poorer countries converge toward a
common steady state, and conditional β -convergence, whereby income convergence,
not necessarily toward a common steady state, is conditional on the structural char-
acteristics of economies. A third form, related to conditional β -convergence, is club
convergence, whereby conditional convergence may cluster countries around diﬀerent
steady-state equilibriums (Durlauf and Johnson 1995; Quah 1996, 1997; Su 2003). A
summary of the theoretical implications and empirical support (or lack thereof) for
each of these concepts is presented in Galor (1996).
While most of this literature has focused on the convergence of average income levels,
an emerging strand of research has opened the debate on whether country income
distributions also converge toward a common invariant state. For example, this re-
enabou 1996; Raval-
search looks at issues such as income inequality convergence (B´
lion 2003; Lin and Huang 2011), and whether income convergence is also accompanied
by poverty convergence (Cuaresma et al. 2017; Sala-i Martin 2006; Ravallion 2012).
Ravallion (2012) demonstrates that, in standard log-linear growth models with pa-
rameters independent of the initial distribution, the existence of income convergence
should also reveal the existence of poverty convergence.
This latter implication, that income growth is a necessary condition for poverty re-
duction, has been widely studied in the literature. Lustig et al. 2016, for example,
show how income growth is a main driver of poverty reduction in Latin America. In
general, the consensus is that higher growth rates tend to reduce poverty headcounts
at a faster pace, particularly if absolute poverty measures are used (Dollar et al. 2016;
ekely 2008; Fosu
Dollar and Kraay 2002; Ferreira and Ravallion 2011; Foster and Sz´
2017; Grimm 2007; Kraay 2006; Ravallion 1995, 2001). This advantage of economic
growth and how quickly it reduces poverty, however, usually depend on both the
initial income distribution and the changes in distribution experienced because of
economic expansion.
4
This conditionality leads to a second stylized fact: the initial parameters of the
income distribution matter both for growth and the eﬃciency with which growth is
able to reduce poverty. According to a well-established theoretical argument, initial
conditions dull economic growth and its impact when market failures translate into
credit constraints that trigger diminished investments in physical and human capital
or, worse, leave investment opportunities entirely unexploited. In particular, if credit
rationing combines with investment indivisibilities, this is especially harmful for the
enabou 1996; Durlauf
poor (Aghion and Bolton 1997; Banerjee and Duﬂo 2003; B´
1996; Galor and Zeira 1993; Hoﬀ 1996; Ljungqvist 1993; Piketty 1997).
Built on similar arguments, an array of empirical studies on the constraints and deter-
minants of growth has thus tested the role of the initial parameters of the distribution
in growth models and conﬁrmed that either higher initial poverty (Ravallion 2012) or
higher initial inequality (Alesina and Rodrik 1994; Clarke 1995; Deininger and Squire
1998; Knowles 2005; Persson and Tabellini 1994; Ravallion 1998) are signiﬁcant con-
straints to future growth rates. Moreover, some studies have also demonstrated that
such unfavorable initial parameters tend to curb the impact that a given growth
rate can exert on the proportionate rate of poverty reduction, as revealed by dimin-
en 2006;
ished elasticities of poverty to growth (Bourguignon 2003; Lopez and Serv´
Ravallion 1997, 2004, 2007, 2012).
Most of the empirical literature on (income) convergence does not explicitly address
the inﬂuence of the initial distribution of income on subsequent poverty reduction
and growth. In Ravallion’s (2012) sample of almost 90 countries that have recorded
noticeable rates of growth and poverty reduction and in which there are unambiguous
signs of income convergence, there is no signiﬁcant evidence that countries starting
out poorer experienced higher relative rates of poverty reduction thereafter. This
counterintuitive result is attributed to initial poverty, which, as revealed by a decom-
position of the speed of poverty convergence, oﬀsets the advantage of higher growth
rates among poorer countries, that is, income convergence and the growth elasticity
of poverty reduction.
Taking advantage of a unique panel dataset (1992–2014) on income, poverty, and
inequality across municipalities in Mexico, this paper tests most of the above con-
clusions to provide a more disaggregated, longer-term perspective on the conver-
gence paths and changes in well-being. Previous studies of convergence in Mexico
have mainly focused on income growth paths among states, and, while some have
found evidence of convergence in the years before the end of the import substitu-
tion model, most have consistently reported evidence of divergence after that. For
5
instance, Esquivel (1999) shows that, while the pace of convergence across states
was relatively fast over 1940–60, it halted and started to reverse over the next 35
years. This divergence was conﬁrmed by subsequent studies focused on 1985–2000
ıa-Verd´
(Chiquiar 2005; Garc´ ıguez-Oreggia 2007; Rodr´
u 2005; Rodr´ ıguez-Pose and
anchez-Reaza 2005). In general, regional divergence during these years was linked
S´
to trade liberalization and the entry into force of the North American Free Trade
Agreement, which bolstered the emergence of club convergence in the states that
had beneﬁted the most from these reforms given their initial endowment of relatively
high-skilled labor and better public infrastructure.
Empirical evidence on convergence at a higher level of geographical disaggregation,
namely, municipalities, has been scarce in Mexico. This is primarily because of the
lack of a sample with robust income information and statistical power at that level.
A couple of studies have reported dramatic disparities among municipalities in in-
opez-Calva et al. 2008; Sz´
come and poverty in 2000 (L´ ekely et al. 2007) by applying
small area estimation techniques to impute incomes from the main household income
survey to the population census. Using this technique and logistic regressions, Mex-
ico’s National Council for the Evaluation of Social Development Policy (CONEVAL)
has computed rates of and changes in income poverty between 2000 and 2005 and
multidimensional poverty between 2010 and 2015 across municipalities. This paper
provides the ﬁrst long-run assessment of regional disparities and paths in income,
poverty, and inequality, based on comparable data on municipalities.2
3 Mapping income, poverty, and inequality in mu-
nicipalities
Capturing long-run trends in income, poverty, and inequality among municipalities
requires a dataset of intertemporally comparable indicators of well-being that are
statistically representative of the population in each municipality. The availability of
such a dataset, however, may entail a trade-oﬀ between relatively high precision in
the measurement of, say, household income and signiﬁcant geographical detail. One
might exploit household surveys designed to capture all sources of income and thus
2
Two papers have used a preliminary version of this dataset to examine changes across munic-
ıa et al. (2016) have analyzed, through Gaussian mixture modeling, the
ipalities. Villalobos Barr´
univariate and joint distribution of human development indicators —income, infant mortality, and
years of schooling— and the conformation of development clusters in 1990, 2000, and 2010. Using
the same preliminary dataset, Enamorado et al. (2016) study the causal eﬀect of inequality on drug-
related homicides and report a sizable decline in inequality in the majority of municipalities over
1990–2010.
6
retrieve household income with a high degree of precision. However, as with any
restricted sample, these surveys are usually representative only nationwide or across
provinces or states.
The universal coverage of the population can be approximated through population
censuses, which typically provide relevant inputs to measure several dimensions of
well-being at high levels of disaggregation (for example, in Mexico, CONEVAL’s
social backwardness index or the marginalization index of the National Population
Council).3 This greater geographical detail, however, comes at the cost of a lack
of robustness in the information on household incomes. Censuses are not designed
to collect comprehensive data on income. They provide an incomplete picture of
household monetary circumstances, which, at least for the purposes of this analysis,
represents a main weakness.
To address the dilemma between precision and geographical detail and, hence, to
make the relevant dataset available for empirical analysis, the Elbers et al. (2003)
small area estimation technique is used to impute household per capita income from
surveys to corresponding households in censuses. This is accomplished by predicting,
from an income model in the survey, the parameters and distribution of errors, which
are then used to simulate the income distribution in the census dataset from which
to compute poverty and inequality indicators.
Two critical steps are necessary to make this model work properly. The ﬁrst step con-
sists of considering the household survey as a random sample of the total population
found in the sample frame of the census. The second step is to identify a set of poten-
tial explanatory variables that are common between the survey and the census and
that satisfy a conceptual and statistical equality criterion. This means, respectively,
that these variables should measure the same phenomenon in both datasets and that
the respective distributions of the variables are statistically indistinguishable, that
is, the sample mean is statistically equal to the population mean. The variables that
satisfy this criterion are then candidate regressors in the modeling of household per
capita income in the survey dataset.
3
These indexes, computed across villages, municipalities, or states, summarize the degree of
deprivation in 12 and 9 indicators, respectively, and focus on the dimensions of education, access
to basic services in the dwelling, the quality and size of dwellings, health (the social backwardness
index), and labor income (the marginalization index). The two indexes are computed through the
principal component analysis technique and are then stratiﬁed into ﬁve groups according to the
degree of backwardness or marginalization: very low, low, medium, high, and very high.
7
Formally, the model takes a generalized least squares form
ln (yhm ) = α + βXhm + γZm + µhm (1)
to estimate the joint distribution of per capita income y in the household h located
in municipality m, conditional on two sets of covariates: Xhm , which includes house-
hold and individual characteristics, and Zm , which includes ﬁxed characteristics of
the municipality of residence. The parameter α is a household-speciﬁc eﬀect; β and
γ are the correlation parameters between the corresponding sets of covariates and
ln (yhm ); and, µhm = ηm + hm represents an error term, where ηm is the component
that is common to all households located in the same municipality (assumed to be
homoscedastic and i.i.d.), and hm is the component that is speciﬁc to each house-
hold (assumed to be heteroscedastic because it depends on the characteristics of the
household and the municipality). The estimates of β , γ , and µhm are then applied
to the corresponding sets of covariates Xhm and Zm in the census to simulate, using
the bootstrap method, the distribution of household per capita income.
The empirical support of extensive applications has shown this methodology to be
robust (Alderman et al. 2002; Bedi et al. 2007). It has become common practice to
use the methodology to improve targeting in developing countries facing the dilemma
between precision and geographical detail. In Mexico, the small area estimation
technique has already been applied to map income-based poverty oﬃcially across
municipalities, but only in 2000 and 2005.4 This paper relies on data on ﬁve points
in time over a 22-year period by pairing available rounds of the Household Income
and Expenditure Survey (ENIGH) and censuses collected in or around the same
years (1990–92, 2000, 2005, 2010, and 2014–15).5 At each point in time, the survey
is a random sample of the corresponding census sample frame.6 This allows strict
comparability of the distributions of a given variable between both data sources. Even
in pairings where gaps exist, that is, 1990–92 and 2014–15, it is possible to identify
common sets of covariates Xhm that satisfy the equality criterion, mainly because
4
These exercises were conducted by CONEVAL by simulating income in the population censuses
of 2000 and 2005. Though new rounds of census data were available in 2010 and 2015, the estimates
of poverty followed a multidimensional approach based on a diﬀerent methodology. Thus, a longer
series of comparable income-based poverty data across municipalities is not oﬃcially available.
5
The census data correspond to the general census of population and housing for the years
ending in zero; for 2005, the data are taken from the population and housing count; and, for 2015,
they are taken from the intercensal survey. Unless otherwise stated, from here onward, the term
census refers indistinguishably to these three data sources.
6
In 2014–15, both the ENIGH and the intercensal survey represented random samples of the
2010 general census sample frame.
8
they capture virtually the same context as characteristics of some households change
slowly over time.
These Xhm sets include characteristics of individuals, households, and dwellings. The
set Zm considers some of these variables to be aggregated at the municipality-level
along with data on the coverage and availability of public services and infrastructure.7
This helps raise the precision of the estimates by minimizing the ratio of the variance
of the error ηm relative to the variance of the total error µhm , that is, the share of
the variance of errors that results from unexplained diﬀerences across municipalities.
The parameters of equation (1) are estimated using the whole sample in each round
of the ENIGH. The income distribution is then simulated based on 200 repetitions in
the corresponding census dataset, each covering Mexico’s total population. The only
exception is the census data in 2015, which represent a sample of 5.9 million house-
holds; yet, this dataset has suﬃcient statistical power to provide reliable statistics at
the municipality level.
Based on the simulated income distribution, poverty and inequality indicators were
computed across municipalities and validated through several tests.8 The income
concept used is household net per capita income, which includes labor income, income
from businesses owned by the household, nonlabor income, such as public and private
transfers, and an estimate of the imputed rent of owner-occupied dwellings, self-
consumption, and in-kind transfers and gifts received. The measurement of poverty
was based on the Foster et al. (1984) family of indexes by comparing this income
concept with three poverty lines: food poverty, deﬁned as the inability to acquire
a basic food basket; capabilities poverty, deﬁned as the inability to cover the value
of the food basket, plus expenditures on health and education; and assets poverty,
deﬁned as the inability to acquire the latter plus expenditures on clothing, housing,
and transportation. The municipalities’ inequality levels were computed through an
array of well-known indicators such as the Gini coeﬃcient.
This exercise yielded a robust, novel municipality-level dataset with income-based in-
dicators that are comparable both over time and across 2,361 municipalities on which
it was possible to compute reliable estimates on each data point over time. These
municipalities represent 96 percent of Mexico’s current municipalities and cover ap-
proximately 98 percent of the country’s population. Summary statistics derived from
7
Some of these variables are derived from the census datasets, while others are taken from
administrative records collected in the National System of Municipal Information (SNIM for its
acronym in Spanish) and the National Institute of Statistics and Geography (INEGI, for its acronym
in Spanish).
8
A detailed description of the small area estimation methodology used on each data point is
available from the authors upon request.
9
this dataset suggest that mean per capita income in Mexico has virtually stagnated
during most of the period under study and exhibited a slight increase only after 2010.
Indeed, the annualized growth rate reveals that per capita income expanded by only
0.8 percent in real terms between 1992 and 2014, consistent with the GDP per capita
performance described in the introduction. Accordingly, the poverty headcount ra-
tios have not experienced a signiﬁcant improvement between the initial and ﬁnal year,
though there were important changes in the ﬁrst ﬁve years of the 2000s (see annex,
panel a).
In this context of relative stagnation in income growth and overall poverty rates in the
long run, the next section focuses on the growth trajectories of mean per capita income
of municipalities (constant Mexican pesos at August 2014 prices) with the aim of
answering two key initial questions: (1) Have poorer municipalities been persistently
lagging in pockets of poverty, or have they captured income gains, thereby catching
up to richer municipalities? and (2) What are the trends in income disparities across
municipalities?
4 Convergence in the mean per capita income of
municipalities
A well-established hypothesis in the economic growth literature is income conver-
gence, whereby incomes tend to grow more quickly in poorer areas than in richer
areas. To examine income growth paths across Mexican municipalities, the anal-
ysis applies Barro and Sala-i-Martin’s (1991) framework on β -convergence and σ -
convergence over 1992-2014, with a particular focus on the 2000s. Starting with
β -convergence, for each time-span of length τ , the annualized growth rate in mean
per capita income (y ) in municipality i between the most recent time (t) and the
initial year (t − τ ) is given by
gi (yit ) = ln (yit /yit−τ ) /τ (2)
Hence, the empirical speciﬁcation to analyze the growth process in mean per capita
income of municipalities can be written as
gi (yit ) = α + βln yit−τ + µit (3)
10
where ln yit−τ is the log initial per capita income; the parameter α is a municipality-
speciﬁc eﬀect; β is a parameter indicative of the speed of absolute income convergence;
and, µit is a stochastic term.
Estimates of this model, summarized in table 1, panel a, reveal signs of absolute β -
convergence in incomes across municipalities in 1992–2014, as shown by a signiﬁcant
coeﬃcient of –0.007, indicating that per capita income grew more quickly in poorer
municipalities than in their richer counterparts, at an annual convergence rate of 0.7
percent. A closer look at subperiods, however, shows that the catch-up eﬀect took
place during 2000–14 only, with a coeﬃcient of –0.019, whereas, in the 1990s, no
evidence of income convergence was found. These opposed results are also illustrated
in ﬁgure 1. Further exploring the 2000s, the speed of income convergence was greater
in the ﬁrst ﬁve years, at an annual rate of 4.3 percent, consistent with the marked
reduction in overall poverty headcount ratios from the high levels they had reached
after the Tequila Crisis. Income convergence was still evident after 2005, though it
occurred at a slower pace, potentially slowed by the various economic shocks that led
to recession and nontrivial contractions in the economy.
Figure 1: The mean per capita income of municipalities converged after 2000
Source : World Bank calculations.
Note : The area of symbols is proportional to municipalities’ population. The regression line has a
slope of 0.001 in panel a, and −0.019 in panel b (signiﬁcant at the 1 percent level). Mean per capita
incomes are in real terms at August 2014 prices.
A breakdown by municipality population size also yields remarkable results. In
1992–2014, the catch-up eﬀect in rural municipalities (deﬁned as those with fewer
than 15,000 inhabitants) was at least twice as large as that observed across urban
counterparts. Indeed, relative to the latter, the speed of income convergence across
11
Table 1: Absolute income β -convergence across municipalities, 1992-2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−0.007*** 0.001 −0.019*** −0.043*** −0.020*** −0.013***
ln yit−τ
(0.001) (0.003) (0.001) (0.003) (0.003) (0.003)
Obs. 2,361 2,361 2,361 2,361 2,361 2,361
R2 0.102 0.000 0.342 0.313 0.076 0.022
b. Urban municipalities
−0.008*** −0.003 −0.019*** −0.045*** −0.020*** −0.009***
ln yit−τ
(0.001) (0.004) (0.001) (0.003) (0.004) (0.004)
Obs. 944 944 1,017 1,017 1,022 1,022
R2 0.138 0.002 0.334 0.323 0.076 0.012
c. Rural municipalities
−0.018*** −0.027*** −0.031*** −0.077*** −0.035*** −0.068***
ln yit−τ
(0.001) (0.004) (0.002) (0.003) (0.004) (0.008)
Obs. 1,417 1,417 1,344 1,344 1,339 1,339
R2 0.235 0.050 0.415 0.395 0.062 0.188
Source : World Bank calculations.
Note : The table presents estimates of the parameter β in equation (3), weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth rate in the mean per capita income of municipalities. ln yit−τ are municipalities’ initial per
capita income. All variables are in log-scale and in real per capita terms at August 2014 prices.
Urban (rural) municipalities are deﬁned as those with more (fewer) than 15 thousand inhabitants.
The intercepts are shown in table 1 in the ancillary ﬁle. Robust standard errors are in parentheses
*** p < .01, ** p < .05, * p < .1
rural municipalities was consistently faster and statistically signiﬁcant in each sub-
period (see table 1, panels b and c). While no evidence of convergence across urban
municipalities was found in the 1990s, convergence occurred in rural ones at an an-
nual rate of 2.7 percent. Moreover, although the speed of convergence halved in both
groups during 2005–10 relative to the previous ﬁve years, the pace had recovered
across rural municipalities by 2010–14, whereas it slowed even further in urban ones
(table 1, panels b and c, columns 4–6).
To examine the conditional income β -convergence hypothesis, whereby paths of mean
per capita income growth are conditional on factors such as the initial conditions and
structural characteristics of municipalities, the speciﬁcation in (3) is rewritten as
gi (yit ) = α + βln yit−τ + γXit−τ + µit (4)
12
to allow for the inclusion of a set of municipality-level characteristics Xit−τ that are
presumed to exert an inﬂuence on mean per capita income growth.
This Xit−τ set includes components of public spending and revenue across munici-
palities at the initial year of each period under study, which is relevant in light of
the reforms in the federal transfer system undertaken in the 1990s. In particular, the
1998 reform that introduced Ramo 33, which aimed at redistributing additional ﬁscal
revenues to subnational governments for social development, has allowed municipal-
ities to beneﬁt from larger volumes of federal transfers. For example, average per
capita unconditional (participaciones federales) and conditional (Ramo 33) federal
transfers, respectively, increased twofold and threefold in real terms in 2000–14 (see
annex, panel b).
Making equation (4) conditional on, for instance, total per capita public expenditure
in the initial year reveals that the speed of convergence over 1992–2014 jumped from
the 0.7 percent found in the absolute setting to 1.2 percent and that the pace of
conditional income convergence was, again, particularly rapid in the ﬁrst ﬁve years
of the 2000s. Although there was no evidence of absolute income convergence in
the 1990s, conditional convergence did record a rate of 1.6 percent in these years,
and it was signiﬁcant at the 1 percent level (table 2, panel a).9 Table 2, panels b
and c show, respectively, the estimates in urban and rural municipalities, with two
particular results. First, income convergence occurred, again, at a more rapid pace
in rural municipalities than in urban ones in all periods under study. Second, and
consistent with the whole sample, there are signs of conditional convergence in urban
municipalities in the 1990s, at an annual rate of 2 percent.
The conditional model shifts the convergence rates upward in most cases relative to
the absolute model. The only exception is 2005–10 when the magnitude of income
convergence remained virtually unchanged. A plausible explanation is that the co-
eﬃcient of initial per capita public spending was negative during those years when
the economy was hit by various adverse shocks. The expectation, conﬁrmed in the
remaining cases, is that the point estimate of the variable is positive and signiﬁcant,
9
The focus is on total public spending only because no sizable diﬀerences in the rates of con-
vergence appear if particular components of public spending or revenues are used instead, and this
reduces the sample signiﬁcantly because no disaggregated public ﬁnance data are available for all
municipalities (see tables 2–11 and 17–26 in the ancillary ﬁle). Moreover, to exploit the panel
dataset of municipalities and control for time-invariant factors, conditional convergence is estimated
using ﬁxed eﬀects models, which consistently conﬁrm convergence, as in the standard ordinary least
squares model. Random eﬀects speciﬁcations also produce coeﬃcients with the same signs. As
extra robustness checks, 5-year and 10-year averages are used for the public spending variables and
generalized method of moments (GMM) techniques. The results are again consistent; that is, poor
municipalities converge at a faster rate when compared to rich municipalities.
13
Table 2: Tests of β -convergence conditional on total public spending, 1992-2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−0.012*** −0.016*** −0.020*** −0.047*** −0.020*** −0.015***
ln yit−τ
(0.001) (0.005) (0.001) (0.003) (0.004) (0.003)
0.003*** 0.012*** 0.004*** 0.008** −0.008* 0.024***
Public spending
(0.001) (0.004) (0.001) (0.003) (0.004) (0.005)
Obs. 2,234 2,234 2,193 2,193 2,116 2,045
R2 0.166 0.056 0.342 0.318 0.089 0.061
b. Urban municipalities
−0.013*** −0.020*** −0.021*** −0.049*** −0.020*** −0.014***
ln yit−τ
(0.002) (0.006) (0.002) (0.003) (0.004) (0.004)
0.003** 0.012*** 0.006*** 0.011*** −0.006 0.028***
Public spending
(0.001) (0.005) (0.002) (0.004) (0.005) (0.006)
Obs. 923 923 971 971 985 937
R2 0.216 0.067 0.345 0.333 0.086 0.066
c. Rural municipalities
−0.020*** −0.044*** −0.031*** −0.083*** −0.035*** −0.077***
ln yit−τ
(0.001) (0.005) (0.002) (0.003) (0.005) (0.009)
0.002*** 0.016*** 0.001 0.009*** −0.013*** 0.012**
Public spending
(0.001) (0.002) (0.001) (0.003) (0.004) (0.005)
Obs. 1,311 1,311 1,222 1,222 1,131 1,108
R2 0.253 0.112 0.417 0.405 0.095 0.220
Source : World Bank calculations.
Note : The table presents estimates of parameters β and γ in equation (4), weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth rate in the mean per capita income of municipalities over the period. ln yit−τ and public
spending are for the initial year and are in log-scale and in real per capita terms at August 2014
prices. Urban (rural) municipalities are deﬁned as those with more (fewer) than 15,000 inhabitants.
The intercepts are shown in tables 2–11 in the ancillary ﬁle. Robust standard errors are in paren-
theses.
*** p < .01, ** p < .05, * p < .1
meaning that the initial level of public spending exerts a positive inﬂuence on in-
come growth through, for instance, the allocation of resources to public investment
or transfers and subsidies. If the model in (4) controls for the latter components
instead of total public spending, it can be veriﬁed that both public investment and
transfers and subsidies exhibit a negative and signiﬁcant sign during 2005–10 (see
tables 2–11 in the ancillary ﬁle). Hence, it seems that the initial level of per capita
public spending in 2005 was not suﬃcient to promote income growth through those
channels in an environment of economic and ﬁscal contraction toward the end of the
2000s and thereby accelerate the pace of convergence.
14
In a variation of model (4), a control was also run for the annualized growth rate
in the number of beneﬁciary families in Prospera, Mexico’s ﬂagship conditional cash
transfer (CCT) program, to capture the inﬂuence of the program’s expansion on the
speed of convergence since its launch as Progresa in 1997. By 2000, the program
was beneﬁting around 2.4 million families living in extreme poverty; ﬁve years later,
the number reached 4.9 million, which is equivalent to an annual growth rate of 20
percent. While the expansion continued after 2005, this was at a signiﬁcantly lower
rate, 2.4 percent annually, reaching 5.7 million and 6.0 million families in 2010 and
2014, respectively.
Table 3 summarizes the estimates of this conditional model. It suggests that, in
general, the speed of income convergence rose relative to the corresponding coeﬃcients
shown in table 2. The point estimate for the CCT variable exhibits a positive and
signiﬁcant eﬀect in both 2000–14 and 2000–05, but it is particularly high in the latter
period, coinciding with the dramatic expansion in CCT coverage. This expansion
seems to have boosted the rate of convergence in the ﬁrst years of the decade through
the rise in per capita income in municipalities with the poorest populations (column
2). After 2005, the sign of the variable became negative, and the variable had no
apparent inﬂuence on the pace of income convergence, suggesting that the subsequent
growth in CCT coverage was too small to exert a substantial eﬀect on the mean per
capita income of municipalities.
A noticeable ﬁnding throughout all previous speciﬁcations is that the income conver-
gence process continued after 2010. Though it occurred at a slower pace than in the
previous two ﬁve-year periods in terms of the whole sample, the pace was particularly
high across rural, poorer municipalities in 2010–14. What explains this result given
that, as suggested before, the expansion in CCT coverage should not have had much
eﬀect in the last part of the period under study? More and better federal transfers
allocated to municipalities may hold the answer. A recent redistributive assessment
of the Social Infrastructure Contributions Fund (FAIS, for its acronym in Spanish),
which is a crucial component of Ramo 33, suggests that the identiﬁcation of prior-
ity attention zones within the country improved the targeting and implementation
of federal transfers for municipal social infrastructure and that this had a positive,
though modest eﬀect both on the level and growth of household incomes across all
ıguez-Castel´
municipalities in 2000–14 (Rodr´ an et al., 2017). The study highlights
that such transfers were crucial to improving a number of socioeconomic indicators
within municipalities, in particular in 2010–14, which may reﬂect better targeting on
less well advantaged groups.
15
Table 3: Tests of β -convergence conditional on public spending and CCT data,
2000–14
(1) (2) (3) (4)
2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−0.025*** −0.059*** −0.026*** −0.015***
ln yit−τ
(0.002) (0.004) (0.003) (0.004)
0.003** 0.006** −0.010*** 0.025***
Public spending
(0.001) (0.003) (0.003) (0.005)
0.035*** 0.067*** −0.023 −0.055**
Annual growth in CCT coverage
(0.010) (0.014) (0.026) (0.024)
Obs. 1,957 1,957 2,106 2,035
R2 0.367 0.348 0.182 0.065
b. Urban municipalities
−0.025*** −0.060*** −0.027*** −0.014***
ln yit−τ
(0.002) (0.005) (0.003) (0.004)
0.004*** 0.008** −0.009** 0.029***
Public spending
(0.001) (0.003) (0.004) (0.006)
0.033*** 0.066*** −0.023 −0.055**
Annual growth in CCT coverage
(0.010) (0.015) (0.027) (0.025)
Obs. 878 878 975 927
R2 0.369 0.364 0.197 0.072
c. Rural municipalities
−0.033*** −0.095*** −0.035*** −0.076***
ln yit−τ
(0.002) (0.004) (0.005) (0.009)
0.000 0.010*** −0.013*** 0.008
Public spending
(0.001) (0.003) (0.004) (0.006)
0.011 0.058*** −0.072 −0.110*
Annual growth in CCT coverage
(0.013) (0.011) (0.044) (0.057)
Obs. 1,079 1,079 1,131 1,108
R2 0.434 0.426 0.098 0.230
Source : World Bank calculations.
Note : The table presents estimates of parameters β and γ in equation (4), weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth rate in the mean per capita income of municipalities over the period. ln yit−τ and public
spending are for the initial year and are in log-scale and in real per capita terms in August 2014
prices. The growth rate in CCT coverage is the annualized growth rate in the number of beneﬁciary
families in each municipality over the period. Urban (rural) municipalities are deﬁned as those
with more (fewer) than 15,000 inhabitants. The intercepts are shown in tables 3–6 and 8–11 in the
ancillary ﬁle. Robust standard errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
16
A critical aspect of all previous results is that the income convergence process took
place in a context of overall low growth in mean per capita income, which averaged
0.8 percent over 1992–2014.10 . A closer look at the data reveals a relatively higher
growth rate in this period among the poorest municipalities (for instance, 2.5 per-
cent annually among the poorest 10 percent), whereas it was negative among the
richest ones (for example, –0.6 percent annually among the top 10 percent). Indeed,
nonanonymous growth incidence curves for some revealing periods (ﬁgure 2) show
that, over 1992–2000, the bottom 10 percent of municipalities experienced positive
income growth, averaging 2 percent annually, while the rest observed negative rates,
–1.1 percent among the remaining 90 percent and –1.9 percent among the top 10
percent.
Figure 2: Poorer municipalities experienced higher growth in income than richer ones
Source : World Bank calculations.
The story in 2000–14 was, in general, more optimistic. During these years, the
vast majority of municipalities experienced positive growth, though there were again
10
The documented process of income convergence across municipalities over 1992-2014 can coexist
with patterns of regional divergence after the entry into force of the North American Free Trade
Agreement, as reported by the literature focusing on growth at the level of states (World-Bank
2018). There are at least two explanations for this coexistence. The ﬁrst source of the discrepancy
is that state-level analyses typically use state’s GDP, a metric that, while measuring the value of
production, often fails to reﬂect average living standards as measured by microdata, as in this paper.
A second source is the unit of analysis. While results from state-level studies tend to be biased by
the weight exerted by large urban agglomerations concentrating a number of municipalities, in
municipality-level analysis that issue can be naturally avoided.
17
those at the bottom who exhibited relatively higher rates. This performance was
mainly driven by the high rates achieved during the ﬁrst ﬁve years of the decade,
which beneﬁted a larger share of municipalities at the bottom. Mean per capita
income among the poorest half expanded by 6.8 percent annually, while, among the
upper half, it increased only by an annual rate of 0.4 percent and reduced by 1.3
percent among the top 10 percent. In 2005–10, the economic slowdown took a toll
on the income performance of municipalities, with growth rates averaging 0.6 percent
annually and, with the exception of the poorest 10 percent of municipalities, the rest
experienced an average rate of –0.8 percent.
Thus, the observed process of income convergence stems from a combination of pos-
itive and relatively high growth in mean per capita income among the ﬁrst decile of
municipalities and stagnant and negative growth among those located at the mid-
dle and top of the distribution, respectively. To explore this process, the analysis
focused on two additional groups of municipalities characterized by dissimilar levels
of development and exposure to economic shocks: those located in states along the
U.S. border, which are more economically well integrated with the United States and
exhibit higher levels of mean per capita income, and the rest, hereafter referred to as
non–US border municipalities.
The estimates of model (4) across both groups, conditional on per capita public
spending, show that the speed of income convergence was evident throughout all pe-
riods and consistently higher in municipalities of the former group (table 4, panels b
and c). A careful look at how income growth performed in each group reveals some
clues to understanding the result. For instance, over 1992–2000, income convergence
in non–U.S. border municipalities resulted, again, from relatively high growth rates
among the poorest municipalities and negative rates among the rest. By contrast, the
speed of convergence across those in border states stems from an inverted-U-shaped
growth pattern. That is, while mean per capita income among both the poorest and
richest 20 percent contracted, the contraction occurred at a lower annual rate in the
former, –0.2 and –0.7 percent, respectively. Remarkably, the bulk of municipalities
in the middle of the distribution experienced positive growth rates. It seems, then,
that, while the Tequila Crisis had adverse nationwide eﬀects, some relatively poorer
municipalities in states along the U.S. border may have slightly beneﬁted from the
devaluation of the currency and the entry into force of the North American Free
Trade Agreement, thus catching up with their richer counterparts, and relatively
18
more quickly than in the rest of the country.11
Table 4: Tests of β -convergence conditional on total public spending, 1992–2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−0.012*** −0.016*** −0.020*** −0.047*** −0.020*** −0.015***
ln yit−τ
(0.001) (0.005) (0.001) (0.003) (0.004) (0.003)
Obs. 2,234 2,234 2,193 2,193 2,116 2,045
R2 0.166 0.056 0.342 0.318 0.089 0.061
b. Municipalities in states along the U.S. border
−0.017*** −0.028*** −0.022*** −0.051*** −0.060*** −0.044**
ln yit−τ
(0.004) (0.010) (0.004) (0.012) (0.009) (0.017)
Obs. 267 267 262 262 267 266
R2 0.250 0.113 0.226 0.256 0.198 0.055
c. Municipalities in non-U.S. border states
−0.011*** −0.020*** −0.019*** −0.044*** −0.014*** −0.017***
ln yit−τ
(0.001) (0.006) (0.001) (0.003) (0.004) (0.003)
Obs. 1,967 1,967 1,931 1,931 1,849 1,779
R2 0.154 0.052 0.307 0.274 0.056 0.089
Source : World Bank calculations.
Note : The table presents estimates of the parameter β in equation (4), weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth rate in the mean per capita income of municipalities over the period. ln yit−τ and public
spending are for the initial year and are in log-scale and in real per capita terms at August 2014
prices. The coeﬃcients for per capita public spending and the intercept are shown in tables 2–6 and
12–16 in the ancillary ﬁle. Robust standard errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
The diﬀerence in the speed of convergence between non–U.S. border municipalities
(1.4 percent) and municipalities in border states (6.0 percent) over 2005–10 may
also be explained by the following growth patterns. Income growth averaged almost
8 percent annually among the poorest 10 percent of municipalities in both groups,
while it decreased among the top 10 percent. The diﬀerence lies in the magnitude of
this loss: it averaged –1.6 percent annually in non–U.S. border municipalities, whereas
it was –5.4 percent annually in border states. In this case, then, it seems that the
United States–originated housing bubble, which unleashed the global ﬁnancial crisis,
had a strong regional bias, with disproportionate eﬀects on those municipalities most
11
As reference, growth in mean per capita income over 1992–2000 was positive in municipalities
located in border states, with an annual rate of 0.3 percent, whereas it was negative among non–U.S.
border municipalities: –0.9 percent.
19
integrated with the United States.12 Similar growth patterns may also explain the
diﬀerence in the speed of income convergence between the groups over 2010–14.
A salient outcome of the documented process of income β -convergence within the
country is that it was quite eﬀective in reducing regional disparities, in particular
after 2000, which is consistent with empirical evidence of an overall decline of income
inequality in the following years (Esquivel et al. 2010), and also conﬁrmed by the
analysis with our data, as shown later. Figure 3 shows the evolution of the standard
deviation of logged mean per capita income across municipalities, or σ -convergence.
Starting with the whole sample, after regional disparities increased sharply in the
1990s, they experienced a steep decline during the ﬁrst ﬁve years of the 2000s and
continued declining moderately up to 2010. Regional disparities remained relatively
unchanged after that; yet, it is signiﬁcant that, relative to 1992, income dispersion
was almost 8 percent lower by 2014.
Figure 3: Regional disparities narrowed sharply over the 2000s
Source : World Bank calculations.
Similar results in terms of trends and orders of magnitude are evident across both
urban and non–U.S. border municipalities, with declines in income dispersion of 8.6
percent and 6.1 percent, respectively, in 1992–2014. Two additional results are worth
noting. First, income disparities in rural municipalities deteriorated slightly after the
12
Indeed, growth rates in mean per capita income in municipalities located in states along the
U.S. border averaged –0.1 percent annually, whereas their non–U.S. border counterparts recorded
an annual average rate of 0.8 percent.
20
sharp decline in the ﬁrst half of the 2000s, and, although the diﬀerences narrowed
again after 2010, the level recorded in 2014 was virtually the same as the one in 1992.
Second, the relatively high β -convergence coeﬃcients across municipalities in border
states seem to have reduced income dispersion along the U.S. border at a rate of 22
percent in 1992–2014.
5 Testing for poverty convergence
Have poorer, converging municipalities been able to translate their relative income
gains into poverty reduction? If per capita income follows a log-normal distribu-
tion, then any change in the poverty headcount ratio is determined, in a magnitude
η , by two components: one that is attributable to changes in income and one that
is attributable to changes in the distribution of income. The relationship between
each component and changes in poverty is illustrated in ﬁgure 4 over 1992–2014.
As expected, those municipalities that experienced relatively higher rates of poverty
reduction, according to the food poverty line, were those that experienced higher
growth rates in mean per capita income (panel a), but also experienced progres-
sive changes in the distribution of income (panel b), because such changes imply
transferring resources from richer to poorer populations, thus stimulating poverty
reduction.13
Focusing on the ﬁrst component, for now, let
gi (Pit ) = δ + ηgi (yit ) + νit (5)
be the partial elasticity of poverty to growth in the mean per capita income of munic-
ipalities, representing the percent change in the poverty headcount ratio as a result
of a 1 percent increase in income, holding the income distribution constant. gi (Pit )
is the annualized change in poverty rates, calculated as in (2); η is the elasticity
parameter, with the expectation that η < 0; δ is a municipality-speciﬁc eﬀect; and,
νit is a stochastic term.14
13
The bulk of the following analysis focuses on extreme poverty as measured by the food poverty
line. This is deliberate because the narrative is consistent, and conclusions hold when using higher
thresholds such as the capabilities and assets poverty lines.
14
Similarly, gi (Pit ) = δ + ηgi (Git ) + νit can represent the partial inequality elasticity of poverty
or the percent change in the poverty headcount ratio as a result of a 1 percent increase in inequality,
holding per capita income constant, with the expectation that η > 0, and with gi (Git ) as the
annualized rate of change in inequality. The growth and inequality elasticity parameters can be
denoted as η y and η G , respectively, and hence, under log normality, changes in poverty rates can be
expressed as gi (Pit ) ≈ η y gi (yit ) + η G gi (Git ).
21
Figure 4: Changes in food poverty rates, inequality, and per capita income, 1992–2014
Source : World Bank calculations.
Note : The area of the symbols is proportional to the total population of the municipalities. The
regression line has a slope of −1.42 in panel a and 0.63 in panel b (both signiﬁcant at the 1 percent
level).
Estimates of (5) conﬁrm that higher growth rates in income tend to reduce poverty.
In 1992–2014, for instance, a 1 percent growth rate in the mean per capita income of
municipalities would lead to a 1.4 percent decline in the food poverty headcount ratio
(table 5, panel a). The results also suggest that food poverty is more responsive to
growth among both urban municipalities and those located in states along the U.S.
border relative to their corresponding counterparts (panels b–e).
According to the data, such counterparts consistently exhibit higher food poverty
rates over time: around 30 percent higher in rural municipalities than in urban ones,
and twice the size in non–U.S. border municipalities than in those located in border
states. Thus, food poverty tends to be more responsive to growth in municipalities
where poverty rates are relatively lower, which ﬁts well-known evidence that, under
log normality, holding the income distribution constant, the growth elasticity will
decrease in absolute value as the poverty rate rises (Bourguignon, 2003). In other
words, poverty itself seems to act as a barrier to poverty reduction.15
Regardless of the context-speciﬁc magnitude of the growth elasticity parameter, the
fact that growth in the mean per capita income of municipalities tends to reduce
food poverty rates, plus the previous evidence of income convergence, imply that
those municipalities with relatively high initial poverty headcount rates (lnPit−τ )
15
These elasticities, in general, are also more responsive to growth, the lower the value of the
poverty line. For instance, relative to the food poverty line, the elasticity almost invariably contracts
by half in absolute value in the case of the assets poverty line (see table 33 in the ancillary ﬁle).
22
Table 5: Growth elasticities of food poverty reduction across municipalities,
1992–2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−1.425*** −1.291*** −1.671*** −1.504*** −1.472*** −1.736***
gi (yit )
(0.089) (0.072) (0.083) (0.114) (0.082) (0.077)
Obs. 2,361 2,361 2,361 2,361 2,361 2,361
R2 0.535 0.411 0.517 0.432 0.427 0.549
b. Urban municipalities
−1.513*** −1.348*** −1.736*** −1.620*** −1.457*** −1.751***
gi (yit )
(0.109) (0.091) (0.092) (0.133) (0.102) (0.094)
Obs. 944 944 1,017 1,017 1,022 1,022
R2 0.540 0.391 0.511 0.456 0.400 0.521
c. Rural municipalities
−1.142*** −1.018*** −1.210*** −0.942*** −1.482*** −1.680***
gi (yit )
(0.041) (0.052) (0.150) (0.060) (0.067) (0.111)
Obs. 1,417 1,417 1,344 1,344 1,339 1,339
R2 0.599 0.488 0.580 0.312 0.540 0.686
d. Municipalities in states along the U.S. border
−1.878*** −1.112** −1.837*** −1.276*** −1.551*** −1.904***
gi (yit )
(0.308) (0.531) (0.166) (0.371) (0.255) (0.175)
Obs. 267 267 267 267 267 267
R2 0.501 0.127 0.600 0.201 0.339 0.660
e. Municipalities in non-U.S. border states
−1.298*** −1.263*** −1.434*** −1.436*** −1.324*** −1.698***
gi (yit )
(0.065) (0.077) (0.095) (0.143) (0.074) (0.092)
Obs. 2,094 2,094 2,094 2,094 2,094 2,094
R2 0.587 0.464 0.453 0.435 0.450 0.516
Source : World Bank calculations.
Note : The table presents estimates of the parameter η in equation (5), weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth in the food poverty headcount ratios of municipalities over the period. gi (yit ) are the
annualized changes in mean per capita income at the municipal level over the period at August 2014
prices. Urban (rural) municipalities are deﬁned as those with more (fewer) than 15,000 inhabitants.
The intercepts are shown in table 32 in the ancillary ﬁle. Robust standard errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
should have experienced higher subsequent rates of poverty reduction over the period
under study. To test this, let
gi (Pit ) = α + βln Pit−τ + µit (6)
23
be the empirical speciﬁcation for the annualized proportionate change in poverty
rates, or poverty convergence, where β is the speed of poverty convergence parameter.
Indeed, estimates of (6) suggest that poorer municipalities reduced their headcount
ratios at a faster pace than richer and poverty increasing counterparts over 1992–2014.
In fact, food poverty rates among the 20 percent of municipalities with the lowest
incidence in 1992 recorded nontrivial increases by 2014 (ﬁgure 5). A closer look at
subperiods reveals a positive sign of the poverty convergence parameter in the 1990s,
indicating that poorer municipalities became poorer after the Tequila Crisis or, at
least, that their poverty rates stagnated. Conversely, sizable signs of poverty con-
vergence are found after 2000, in particular during 2000–05 (table 6, panel a). The
breakdown by population size in panels b and c reveals that both urban and rural
municipalities experienced poverty convergence, though poverty convergence in the
latter occurred even in the 1990s and, in general, at a faster pace than in the former.
Figure 5: Convergence in food poverty rates, 1992–2014
Source : World Bank calculations.
Note : The area of the symbols in panel a is proportional to the total population of the municipalities.
The regression line has a slope of –0.012 in panel a (signiﬁcant at the 1 percent level).
Sizable poverty convergence in the 1990s also occurred across municipalities located
in states along the U.S. border, whereas the opposite sign was found across non–U.S.
border municipalities. After 2000, though convergence unambiguously occurred in
both groups, those in border states exhibited a considerably higher coeﬃcient dur-
ing 2005–10 (table 6, panels d and e). The evidence presented in Section 4 above
helps explain these results: poorer municipalities in border states were able to con-
verge relatively more quickly in the 1990s and late-2000s because mean per capita
24
Table 6: Tests of food poverty convergence across municipalities, 1992–2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−0.012*** 0.014** −0.031*** −0.055*** −0.038*** −0.048***
lnPit−τ
(0.002) (0.006) (0.002) (0.006) (0.005) (0.006)
Obs. 2,361 2,361 2,361 2,361 2,361 2,361
R2 0.206 0.025 0.565 0.333 0.175 0.164
b. Urban municipalities
−0.013*** 0.014** −0.031*** −0.057*** −0.038*** −0.044***
lnPit−τ
(0.002) (0.007) (0.002) (0.007) (0.006) (0.006)
Obs. 944 944 1,017 1,017 1,022 1,022
R2 0.229 0.024 0.575 0.353 0.193 0.142
c. Rural municipalities
−0.017*** −0.032*** −0.033*** −0.077*** −0.031*** −0.114***
lnPit−τ
(0.001) (0.007) (0.006) (0.015) (0.007) (0.009)
Obs. 1,417 1,417 1,344 1,344 1,339 1,339
R2 0.192 0.064 0.375 0.276 0.031 0.426
d. Municipalities in states along the U.S. border
−0.023*** −0.044*** −0.027*** −0.058*** −0.097*** −0.038*
lnPit−τ
(0.004) (0.008) (0.004) (0.011) (0.013) (0.023)
Obs. 267 267 267 267 267 267
R2 0.375 0.185 0.281 0.194 0.392 0.031
e. Municipalities in non-U.S. border states
−0.009*** 0.019** −0.028*** −0.053*** −0.020*** −0.055***
lnPit−τ
(0.002) (0.008) (0.003) (0.010) (0.003) (0.006)
Obs. 2,094 2,094 2,094 2,094 2,094 2,094
R2 0.114 0.041 0.507 0.282 0.067 0.243
Source : World Bank calculations.
Note : The table presents estimates of the parameter β in equation (6), weighted by municipal pop-
ulation at the initial year of each period under study. The dependent variable is the annualized
growth in the food poverty headcount ratios of municipalities over the period. lnPit−τ are munici-
palities’ initial poverty headcount ratio. All variables are in log-scale. Urban (rural) municipalities
are deﬁned as those with more (fewer) than 15,000 inhabitants. The intercepts are shown in table
34 in the ancillary ﬁle. Robust standard errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
incomes in their richer counterparts were disproportionately aﬀected by the economic
contractions that characterized these years.
25
6 Initial distribution and the speed of poverty con-
vergence
While poorer municipalities experienced poverty convergence for most of the period
1992-2014, little is known about the inﬂuence of the parameters of the initial distri-
bution of income in shaping the speed of poverty convergence. Focusing on initial
poverty, the analysis builds on Ravallion’s (2012) decomposition of poverty conver-
gence elasticity to explore how the initial poverty headcount ratios of municipalities
might aﬀect their advantage, given their poorer start, through two channels: the
growth rates in mean per capita income and the impact of that growth on poverty
reduction, as revealed by the partial elasticity of poverty to mean per capita income.
Starting with the ﬁrst channel, the analysis estimates three augmented versions of
the income β -convergence model in (4). In the ﬁrst version, the annualized growth
rates in mean per capita income depend on the initial per capita income of the
municipalities, plus their initial food poverty headcount ratios:
gi (yit ) = α + βln yit−τ + γlnPit−τ + µit (7)
Estimates of the parameter γ reveal some adverse eﬀects of initial poverty on income
growth at any given initial mean, although the coeﬃcient is sizable (–0.022) and
signiﬁcant at the 1 percent level only in the 1990s (table 7, panel a). An opposing
result is shown in column 4, where the food poverty headcount ratio in 2000 exerted
a positive eﬀect (0.007) on growth in the subsequent ﬁve years. While this eﬀect is
small and signiﬁcant only at the 10 percent level, it coincided with the more rapid
expansion of CCTs across the poorest households located in the most marginalized
municipalities.16
As initial poverty rates are not independent from other parameters of the distribution,
the analysis added, as a third regressor in the second version of the model, the initial
inequality in municipalities, measured by the Gini coeﬃcient (lnGit−τ ). The results
now reveal a positive and signiﬁcant, yet moderate eﬀect of initial poverty rates on
income growth during both 1992–2014 and 1992–2000 and a more sizable eﬀect during
2000–05 (see table 7, panel b), which supports the plausible argument that initially
poorer municipalities experienced higher subsequent growth in the mean per capita
16
The coeﬃcient over 2000–05 even increases for higher values of the poverty line: 0.014 and
0.041 in the case of, respectively, the capabilities and assets poverty lines. In both cases, the eﬀects
are statistically signiﬁcant at the 1 percent level (see table 37 in the ancillary ﬁle).
26
Table 7: Growth in mean per capita income conditional on initial parameters,
1992–2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. Conditional on initial poverty
−0.009*** −0.033*** −0.022*** −0.032*** −0.036*** −0.003
ln yit−τ
(0.003) (0.008) (0.003) (0.007) (0.008) (0.010)
−0.001 −0.022*** −0.002 0.007* −0.010* 0.006
lnPit−τ
(0.002) (0.005) (0.002) (0.004) (0.005) (0.006)
Obs. 2,361 2,361 2,361 2,361 2,361 2,361
R2 0.103 0.039 0.344 0.318 0.084 0.024
b. Conditional on initial poverty and inequality
0.005 0.014* −0.014*** −0.000 −0.022** 0.003
ln yit−τ
(0.003) (0.008) (0.004) (0.008) (0.010) (0.013)
0.008*** 0.009** 0.002 0.021*** −0.002 0.009
lnPit−τ
(0.002) (0.005) (0.002) (0.004) (0.007) (0.008)
−0.045*** −0.155*** −0.031*** −0.130*** −0.058*** −0.016
lnGit−τ
(0.005) (0.015) (0.011) (0.026) (0.017) (0.024)
Obs. 2,361 2,361 2,361 2,361 2,361 2,361
R2 0.222 0.170 0.363 0.379 0.098 0.025
c. Conditional on initial poverty and inequality and extra controls
− − −0.003 −0.004 −0.021** 0.003
ln yit−τ
− − (0.004) (0.010) (0.009) (0.014)
− − 0.016*** 0.035*** 0.007 0.015
lnPit−τ
− − (0.003) (0.007) (0.006) (0.010)
− − −0.038*** −0.112*** −0.054*** −0.031
lnGit−τ
− − (0.006) (0.016) (0.014) (0.025)
− − 0.007*** 0.011*** 0.007*** 0.012***
Public sector payroll
− − (0.001) (0.003) (0.003) (0.004)
− − −0.000 0.000 −0.003* −0.000
Public investment
− − (0.000) (0.001) (0.002) (0.003)
− − −0.001 0.000 −0.008*** 0.007**
Public transfers/subsidies
− − (0.001) (0.002) (0.001) (0.003)
− − 0.033*** 0.059*** −0.019 −0.062**
Growth in CCT coverage
− − (0.011) (0.015) (0.025) (0.025)
Obs. − − 1,793 1,793 1,910 2000
R2 − − 0.440 0.403 0.234 0.075
Source : World Bank calculations.
Note : The table presents the estimates of equation (7) and extensions, weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth rate in the mean per capita income of municipalities over the period. ln yit−τ , lnPit−τ ,
lnGit−τ , and all public expenditure variables are for the initial year and are in log-scale. All
monetary variables are in real per capita terms at August 2014 prices. The growth rate in CCT
coverage is the annualized growth rate in the number of beneﬁciary families in each municipality
over the period. The empty cells in panel c indicate that models conditional on CCT data were not
estimated because the data are available only from 2000 onward. The intercepts are shown in tables
37–40 in the ancillary ﬁle. Robust standard errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
27
income as a result of the expansion in CCTs among the poorest. In the rest of the
subperiods, the coeﬃcients are statistically indistinguishable from zero.
To investigate these results further, the analysis tested the previous augmented model
by adding extra controls for concepts of either public spending or revenues and with
and without CCT data. Invariably, the story holds under diﬀerent speciﬁcations:
the positive and signiﬁcant eﬀects of initial food poverty headcount ratios on income
growth are found over 2000–14, in particular during the expansion of CCT coverage
in 2000–05.17 One such speciﬁcation is shown in table 7, panel c, in which the point
estimates for the annualized growth rate in the number of beneﬁciary families exhibit
positive and signiﬁcant eﬀects in the ﬁrst years of the program’s expansion, consistent
with the ﬁndings in the conditional income β -convergence model above.
The second channel, that is, the growth elasticity of poverty reduction, can be an-
alyzed through a variation of equation (5) by regressing gi (Pit ) on the growth rate
in mean per capita income interacted with the initial poverty headcount ratios. This
adjusted rate is given by the growth rate in municipality’s mean per capita income,
multiplied by 1 minus the municipality’s initial poverty headcount ratio (Pit−τ ), which
penalizes more the sensitivity of food poverty to subsequent growth rates in munic-
ipalities starting out relatively poorer. The poverty-adjusted growth elasticity of
poverty reduction is then deﬁned as follows:
gi (Pit ) = η (1 − Pit−τ ) gi (yit ) + νit (8)
The estimates for the whole sample of municipalities are shown in table 8 (panel a).
Notice that they increased in absolute value in all periods relative to the ordinary
elasticities in table 5. To illustrate the implications of the poverty-adjusted elasticity,
consider, for instance, the value of –1.983 in 1992–2014. If a municipality’s initial
food poverty rate is 10 percent and the municipality experiences a 4 percent annual
growth rate in mean per capita income, then that municipality would expect an
annual poverty reduction of 7.1 percent. If, instead, initial poverty stands at 70.0
percent, and the annual income growth rate is again 4 percent, then the municipality
would expect a poverty reduction of only 2.4 percent annually. Then, as in the
previous section, poverty tends to be less responsive to growth, or the elasticity
declines in absolute value, the higher the initial poverty rate.
However, the estimates reveal that poverty-adjusted elasticities are consistently higher
in absolute value in poorer municipalities than in richer counterparts. For instance, at
17
The various speciﬁcations of this augmented model are shown in tables 37–44 of the ancillary
ﬁle.
28
an initial food poverty rate of 63 percent or more, at or above one standard deviation
above the mean, a 1 percent increase in growth during 1992–2014 would lead to an
annual decline in the poverty rate of almost 3.4 percent, whereas, in municipalities
with initial food poverty at 20.0 percent or less, at or below one standard deviation
below the mean, the elasticity is roughly −2 (table 8, panels b and c, column 1).
Table 8: Poverty-adjusted growth elasticities, 1992–2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. All municipalities
−1.983*** −1.885*** −2.288*** −2.280*** −1.874*** −1.990***
(1 − Pit−τ ) gi (yit )
(0.151) (0.135) (0.163) (0.195) (0.118) (0.116)
Obs. 2,361 2,361 2,361 2,361 2,361 2,361
R2 0.499 0.421 0.453 0.489 0.432 0.451
b. Municipalities with relatively low initial food poverty rates
−1.984*** −1.756*** −1.870*** −2.247*** −1.337*** −2.182***
(1 − Pit−τ ) gi (yit )
(0.233) (0.233) (0.167) (0.249) (0.170) (0.155)
Obs. 426 426 436 436 383 440
R2 0.486 0.293 0.365 0.423 0.277 0.601
c. Municipalities with relatively high initial food poverty rates
−3.387*** −2.863*** −2.872*** −3.911*** −2.444*** −3.082***
(1 − Pit−τ ) gi (yit )
(0.124) (0.242) (0.142) (0.264) (0.106) (0.095)
Obs. 433 433 458 458 425 457
R2 0.882 0.785 0.596 0.621 0.828 0.881
Source : World Bank calculations.
Note : The table presents estimates of the parameter η in equation (8), weighted by municipal
population at the initial year of each period under study. The dependent variable is the annualized
growth in the food poverty headcount ratios of municipalities over the period. (1 − Pit−τ ) gi (yit )
are the annualized changes in mean per capita income at the municipal level over the period at
August 2014 prices, adjusted by municipalities’ initial food poverty headcount ratio. Municipalities
with low (high) initial food poverty rates are those with headcount ratios one standard deviation
below (above) the mean headcount ratios for the whole sample. The intercepts are shown in table
45 in the ancillary ﬁle. Robust standard errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
Hence, contrary to the linear relationship by which the ordinary growth elasticity of
poverty reduction falls in absolute value as poverty rates rise, it can be readily ver-
iﬁed that the poverty-adjusted growth elasticity follows a concave relationship with
poverty (ﬁgure 6). In other words, those municipalities with very high levels of food
poverty in 1992 experienced suﬃciently higher subsequent growth in mean per capita
income to achieve substantial rates of poverty reduction by 2014, which unambigu-
ously occurred (see ﬁgure 5, panel b), at least as substantial as in contexts of low
poverty and relatively high income growth. A salient result is observed during the
29
ﬁrst ﬁve years of the 2000s. Coinciding with the expansion of the CCT program, a
1 percent increase in the poverty-adjusted growth rate would lead to a 3.9 percent
reduction in food poverty headcount ratios among the poorest municipalities, while
the corresponding poverty reduction among less poor counterparts would be only 2.2
percent (see table 8, panels b and c, column 4).
Figure 6: Eﬃciency of growth in reducing food poverty, by initial poverty rates,
1992–2014
Source : World Bank calculations.
Note : The area of the symbols is proportional to the total population of municipalities. For visibility
purposes, the elasticities in both panels are capped at −10.
To understand how the extent of poverty convergence was shaped by the initial food
poverty rates of municipalities, the analysis exploited all previous evidence computed
for each channel to apply Ravallion’s (2012) decomposition of poverty convergence
elasticity. This decomposition results from the derivative of equations (7) and (8) as
−1
∂gi (Pit ) ∂lnPit−τ
= ηβ (1 − Pit−τ ) + ηγ (1 − Pit−τ ) − ηgi (yit ) Pit−τ (9)
∂lnPit−τ ∂lnyit−τ
∂gi (Pit )
where ∂lnPit−τ
is the speed of food poverty convergence, equivalent to the parameter β
in (6); the ﬁrst element at the right-hand side of the equation is the mean convergence
eﬀect; the second element, ηγ (1 − Pit−τ ), is the eﬀect of initial poverty; and the
third element, ηgi (yit ) Pit−τ , represents the poverty elasticity eﬀect. Based on the
estimates of η in table 8; the parameters β and γ in table 7; the ordinary elasticities
of municipalities’ initial food poverty with respect to their initial mean per capita
30
income ( ∂lnPit−τ 18
∂lnyit−τ
) ; and, the sample means of Pit−τ and gi (yit ), the computation of
(9) yields virtually the same food poverty convergence rates calculated above (see
table 6, panel a).
For instance, the poverty convergence rate calculated based on (9) is −0.011 dur-
ing 1992–2014, which is very close to the coeﬃcient of −0.012 computed based on
equation (6) for the same period. The decomposition of that rate reveals that the
convergence eﬀect accounted for −0.007 and that poverty was actually responsive to
growth, with a poverty elasticity eﬀect of −0.005. By contrast, the initial poverty
rates of municipalities exerted an adverse, yet moderate eﬀect of 0.001. In the 1990s
only, unsurprisingly, a convergence eﬀect of −0.024 was more than oﬀset by both the
initial poverty eﬀect (0.024) and the poverty elasticity (0.015) eﬀect, thus conﬁrm-
ing the signiﬁcant poverty divergence of 0.014 found in those years. Meanwhile, in
2000–14, both convergence and poverty elasticity eﬀects explain in similar magnitudes
(−0.016 and −0.020, respectively) the totality of the speed of poverty convergence
(−0.034), with only a slightly adverse eﬀect of initial poverty, at 0.002. These re-
sults conﬁrm that the process of income convergence and the eﬃciency of growth in
reducing poverty eﬀectively translated into poverty convergence during 1992–2014 in
general, but particularly after 2000.
Focusing on the ﬁrst ﬁve years of the 2000s, probably the most revealing period un-
der study, the decomposition oﬀers a remarkable result: the three eﬀects moved in
the same favorable direction. The convergence rate of −0.055 was mostly explained,
again in similar magnitudes, by the convergence eﬀect (−0.024) and the poverty elas-
ticity eﬀect (−0.022). But, saliently, the initial poverty rates of municipalities also
contributed an eﬀect of −0.009, equivalent to 16 percent of the speed of poverty
convergence. This result supports the evidence in tables 7 and 8 for this period,
which suggest plausibly that starting out (very) poor in 2000 was associated with
high growth rates in mean per capita income in the next ﬁve years. In a context of
disappointing economic growth, such high rates could have been the result of the ex-
plosive expansion of cash transfers among the extreme poor and of social spending in
general, potentially having the double eﬀect of bolstering per capita incomes enough
to have reduced food poverty, while fostering progressive changes in the distribution,
which, in turn, may promote poverty reduction (see ﬁgure 4, panel b).
To shed some light on the latter issue, the analysis also explored the role of inequality.
Initial inequality in municipalities tends to exert sizable and signiﬁcant adverse eﬀects
18
The computation of these elasticities through ordinary least squares yields −1.505 in 1992,
−1.662 in 2000, −1.664 in 2005, and −1.553 in 2010.
31
on subsequent growth rates in mean per capita income (see table 7). This is consistent
with a large body of empirical literature on growth. Moreover, the data also reveal
that initial inequality tends to curb the impact that growth in mean per capita income
has on food poverty reduction, thus aligning with cross-country empirical evidence
that wide inequality causes the poor to accrue a smaller share of the gains from
growth in income. For instance, in those municipalities with a Gini coeﬃcient at or
below one standard deviation below the mean in 1992 (equivalent to 0.37 or less), a 1
percent growth in mean per capita income over 1992–2014 would lead to a decline in
food poverty of roughly 2 percent annually. In contrast, in those municipalities with
an initial Gini of 0.48 or more, at or above one standard deviation above the mean,
the poverty reduction would occur at 1.07 percent a year (table 9, panel a, column
1).
This tendency of food poverty to be less responsive to growth in more unequal mu-
nicipalities is conﬁrmed in all subperiods, and it generally holds when growth rates
in mean per capita income are adjusted by initial poverty (table 9, panel b) or even
by initial inequality (table 9, panel c) as
gi (Pit ) = η (1 − Git−τ ) gi (yit ) + νit (10)
which yields a distribution-corrected growth elasticity of poverty, as proposed by
Ravallion (1997), where Git−τ is the initial Gini coeﬃcient.
A closer examination of the data, however, suggests that the relationship between
initial inequality and the eﬃciency of growth in reducing poverty in a country with
dramatic regional disparities is far from linear. The nonlinearity is conﬁrmed in ﬁgure
7. Even when growth elasticities are computed using the ordinary growth rate, there
is a dim indication that food poverty rates over 1992–2014 were more responsive
to growth in some highly unequal municipalities than in low-inequality counterparts
(ﬁgure 7, panel a). This indication becomes clearer after penalizing more the income
growth rates in municipalities with relatively higher Gini coeﬃcients in 1992 (ﬁgure
7, panel b). Sizable changes in mean per capita income and (hence) in food poverty
rates thus occurred not only among the poorest municipalities, as documented above,
but also among municipalities with relatively high initial inequality.
Indeed, the distribution of municipalities according to their Gini coeﬃcient in 1992
reveals that poverty reduction tended to be slightly higher among the top 40 percent
more unequal municipalities (ﬁgure 8, panel a). Inequality among the latter also de-
clined markedly over 1992–2014, which suggests that the magnitude of food poverty
32
Table 9: Growth elasticities of poverty in low and high inequality contexts, 1992–2014
(1) (2) (3) (4) (5) (6)
1992-2014 1992-2000 2000-2014 2000-2005 2005-2010 2010-2014
a. Ordinary growth elasticities
Municipalities with relatively low initial inequality
−2.015*** −1.569*** −1.779*** −1.738*** −1.673*** −2.261***
gi (yit )
(0.143) (0.158) (0.273) (0.417) (0.096) (0.292)
Obs. 370 370 313 313 371 336
R2 0.734 0.414 0.693 0.400 0.541 0.659
Municipalities with relatively high initial inequality
−1.069*** −0.924*** −1.241*** −1.184*** −1.662*** −1.613***
gi (yit )
(0.050) (0.073) (0.239) (0.273) (0.214) (0.141)
Obs. 364 364 344 344 342 364
R2 0.769 0.508 0.359 0.329 0.496 0.584
b. Poverty-adjusted growth elasticities
Municipalities with relatively low initial inequality
−2.588*** −2.357*** −1.561*** −3.904*** −3.019*** −2.561***
(1 − Pit−τ ) gi (yit )
(0.191) (0.335) (0.879) (1.265) (0.190) (0.397)
Obs. 370 370 313 313 371 336
R2 0.636 0.434 0.305 0.264 0.584 0.533
Municipalities with relatively high initial inequality
−1.676*** −1.468*** −1.829*** −1.875*** −2.611*** −2.045***
(1 − Pit−τ ) gi (yit )
(0.062) (0.104) (0.420) (0.352) (0.339) (0.253)
Obs. 364 364 344 344 342 364
R2 0.785 0.497 0.400 0.396 0.525 0.519
c. Distribution-corrected growth elasticities
Municipalities with relatively low initial inequality
−3.042*** −2.336*** −2.383*** −2.418*** −2.403*** −3.149***
(1 − Git−τ ) gi (yit )
(0.221) (0.249) (0.375) (0.590) (0.137) (0.415)
Obs. 370 370 313 313 371 336
R2 0.717 0.401 0.678 0.395 0.542 0.656
Municipalities with relatively high initial inequality
−2.191*** −1.905*** −2.362*** −2.301*** −3.051*** −2.709***
(1 − Git−τ ) gi (yit )
(0.106) (0.154) (0.452) (0.518) (0.385) (0.236)
Obs. 364 364 344 344 342 364
R2 0.769 0.515 0.367 0.339 0.479 0.579
Source : World Bank calculations.
Note : The table presents estimates of the parameter η in equations (5), (8) and (10), weighted
by municipal population at the initial year of each period under study. The dependent variable is
the annualized growth in the food poverty headcount ratios of municipalities over the period. The
growth rates in mean per capita income are the annualized changes at the municipal level over the
period at August 2014 prices. Municipalities with low (high) initial inequality are those with Gini
coeﬃcients at or below (at or above) one standard deviation below (above) the mean Gini coeﬃcient
for the whole sample. The intercepts are shown in table 48 in the ancillary ﬁle. Robust standard
errors are in parentheses.
*** p < .01, ** p < .05, * p < .1
33
Figure 7: Eﬃciency of growth in reducing food poverty, by initial inequality levels;
1992–2014
Source : World Bank calculations.
Note : The areas of the symbols are proportional to the total population of the municipalities. For
visibility purposes, the elasticities in both panels are capped at −10.
reduction observed among poorer municipalities was not the result of income gains
only, but also of progressive changes. It also suggests a plausible process of inequality
convergence across municipalities, which is conﬁrmed by a coeﬃcient of −0.04 during
1992–2014 (signiﬁcant at the 1 percent level) that results from the standard model
for the annualized proportionate change in inequality, as in equations (3) or (6).19 In
addition, it can be conﬁrmed that, in the majority of the initially poorest municipali-
ties where subsequent food poverty reduction took place, the latter was accompanied
by a decline in the Gini coeﬃcient (ﬁgure 8, panel b).
These results seem to suggest that, in general, inequality in the country declined
over the period under study, which is conﬁrmed by a population-weighted average
reduction of −0.8 Gini points during 1992–2014. This reduction, however, was far
from generalized across municipalities. About 71 percent of all municipalities, which
account for almost half of the country’s population, experienced a decline in inequal-
ity above the national average, reaching −5.3 Gini points, and slightly more than 4
percent of municipalities also improved their inequality level, though at a lower rate
than the national ﬁgure, reaching only −0.4 Gini points. The remaining 25 percent of
municipalities, which are home to the other half of the country’s population, experi-
enced a deterioration in inequality of around 3.4 Gini points, on average. Despite the
19
The magnitude and signiﬁcance of the inequality convergence parameter are robust to the
speciﬁcation that regress the annualized absolute diﬀerence in inequality levels on the initial Gini
enabou (1996).
coeﬃcient, as in B´
34
Figure 8: Annualized rates of change in food poverty and inequality, 1992–2014
Source : World Bank calculations.
last result, which is basically a reﬂection of the rebound of inequality in the country
after 2010, this highlights that the vast majority of municipalities experienced, in
general, progressive changes in their income distribution and that this occurred over
most of the last quarter century: the population-weighted national average shows a
decline of −1.2 and −4.1 Gini points in the 1990s and in the ﬁrst decade of the 2000s,
respectively.
7 Summing up
Between 1992 and 2014, Mexico experienced relative stagnation in both economic
growth and poverty reduction. These aggregate numbers leave the impression that
little has changed in the living standards of the population. This paper explores how
taking a more disaggregated approach to measuring changes in living standards can
help to better unpack this picture. By analyzing income per capita convergence and
poverty convergence at the municipality-level over diﬀerent subperiods, this paper
ﬁnds that key changes in living standards have indeed taken place. In particular,
the analysis reveals the following three main ﬁndings related to income convergence,
poverty convergence, and the role of the initial distribution of income.
First, in terms of income convergence, this paper ﬁnds that mean per capita income
grew consistently more quickly in the poorest municipalities than in richer counter-
parts. This conﬁrms that, in general, convergence occurred in a sizable and signiﬁcant
magnitude; however, the speed of income convergence was more rapid after 2000 and
heterogeneous between urban and rural municipalities and between those located in
35
the north of the country and the rest. Second, in terms of poverty convergence, this
paper ﬁnds that growth in mean per capita income among poorer, converging munic-
ipalities was relatively eﬃcient in reducing poverty headcount ratios. This suggests
that the process of income convergence eﬀectively translated into an unambiguous
process of poverty convergence. Third, in terms of the role of the initial distribution
of income in determining convergence processes, this paper ﬁnds that the growth of
income among the poorest in a context of stagnant or disappointing overall economic
growth promoted sizable reductions in food poverty rates, whereas declining inequal-
ity —and inequality convergence— eventually made growth rates more eﬃcient in
reducing subsequent poverty rates in the less advantaged municipalities.
From a policy perspective, redistributive programs such as the accelerated expansion
of cash transfers and the improved federal allocations to municipalities, in particular,
had a positive impact on both income convergence and poverty convergence. Appar-
ently, increasing transfers had the double eﬀect of bolstering suﬃciently high growth
rates in income among the poorest, while fostering progressive changes in the distri-
bution of income. While these results are good news from an egalitarian perspective,
it is noticeable that the convergence processes partially took place because richer
municipalities were losing ground or standing still at best. While this gives less cause
for celebration, all subnational changes analyzed in this paper highlight, in general,
that the poorest regions in Mexico have been able to achieve development gains even
in the face of nontrivial economic crises that could have seriously undermined equity
within the country.
36
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Annex: Summary statistics of the income, poverty and inequality dataset
1992 2000 2005 2010 2014
Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.
a. Descriptive statistics of the income, poverty, and inequality dataset
Simple averages across municipalities
Real per capita income (MXN$ August 2014) 1,738.0 842.7 1,578.3 932.2 1,699.3 902.4 1,663.5 881.0 1,966.9 967.4
Gini coeﬃcient 0.426 0.055 0.385 0.062 0.379 0.053 0.341 0.045 0.384 0.039
Poverty headcount (% of population)
Food poverty 41.6 21.4 44.5 25.4 37.7 22.0 38.7 23.7 35.6 19.5
Capabilities poverty 49.6 21.4 52.3 24.9 46.0 22.4 47.5 24.0 44.0 20.1
Assets poverty 68.6 17.9 70.7 20.2 67.0 19.3 69.7 20.1 65.4 18.1
Population-weighted averages of municipality ﬁgures
Real per capita income (MXN$ August 2014) 2,765.2 1,424.6 2,870.8 1,492.9 2,966.1 1,295.8 2,755.6 1,215.5 3,245.6 1,452.1
Poverty headcount (% of population)
Food poverty 25.9 19.3 24.1 21.7 19.7 17.4 20.8 17.7 20.9 15.6
Capabilities poverty 33.6 20.4 31.5 23.0 26.7 18.8 28.9 18.9 28.3 17.0
Assets poverty 55.7 19.4 53.3 21.7 49.1 18.6 54.1 18.3 50.6 17.7
Average population by municipality 34,016 100,736 40,525 120,304 42,839 127,807 45,817 131,249 49,742 141,243
b. Summary statistics on municipalities’ public spending and revenues
42
Simple averages across municipalities
Public spending (per capita, MXN$ August 2014) 80.3 103.2 158.3 124.3 254.1 160.0 343.6 199.7 418.8 308.6
Public sector payroll 25.0 50.3 41.6 41.4 76.4 69.0 91.4 80.4 105.1 92.0
Transfers and subsidies 7.2 14.1 21.9 23.8 25.2 22.5 34.5 45.1 25.5 32.9
Public investment 21.4 30.6 40.2 48.2 76.0 52.4 123.8 93.3 162.8 185.0
Public debt 5.8 10.6 7.0 18.3 11.5 15.3 15.5 18.6 14.0 19.0
Public revenues (per capita, MXN$ August 2014) 80.3 103.2 157.9 124.5 254.0 160.4 344.1 200.3 419.3 309.7
Taxes 8.1 16.9 5.4 11.5 9.4 17.8 10.8 19.4 12.8 24.1
Unconditional federal transfers (participaciones ) 53.8 76.4 95.8 86.5 126.1 122.1 145.5 131.2 163.5 164.1
Conditional federal transfers (Ramo 33 ) − 17.5 59.2 50.0 88.4 44.5 140.7 85.2 202.1 175.2
Average CCT beneﬁciary families by municipality − − 1,156 1,607 2,077 2,815 2,413 3,439 2,527 3,915
Number of municipalities covered in the dataset 2,361 2,361 2,361 2,361 2,361
Total population covered in the analysis 80,310,818 95,678,853 101,144,021 108,174,343 117,439,680
Total population in the country 81,249,645 97,483,412 103,263,388 112,336,538 119,530,753
Total CCT beneﬁciary families in the country − 2,437,297 4,892,284 5,682,617 5,965,275
Source : World Bank calculations based on ENIGH and census datasets; World Bank calculations based on the public ﬁnance dataset of the National
Institute of Statistics and Geography (INEGI) and on administrative records of the ﬂagship CCT program —introduced as Progresa in 1997 and rebranded
as Oportunidades in 2002 and more recently as Prospera.