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Within-country poverty convergence: evidence from Mexico

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Abstract

Trends in aggregate growth and poverty reduction hide a multiplicity of development processes at the local level. The analysis reported in this paper exploits a unique panel dataset of poverty maps covering almost 2400 municipalities in Mexico and spanning 22 years, first, to test hypothesis that there is within-country income convergence. Second, through a decomposition of the poverty convergence elasticity, the analysis investigates whether this convergence, if it exists, has translated into poverty convergence. In a context of overall stagnant economic growth and poverty reduction since 1990, the analysis finds evidence of both income and poverty convergence among municipalities. As a cause of these, the results point to a combination of positive performance among the poorest municipalities and stagnant or deteriorating performance among more well-off municipalities. Redistributive programs such as cash transfers to poor households have played an important role in driving these results by bolstering income growth among the poorest municipalities, while also inducing progressive changes in the distribution of income.

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Notes

  1. International Monetary Fund, World Economic Outlook Database, October 2020. Extreme and total poverty headcount rates stand at about 21% and 53%, respectively, in both 1992 and 2014 according to official statistics from the National Council for the Evaluation of Social Development Policy (CONEVAL).

  2. A third form, related to conditional \(\beta \)-convergence, is club convergence, whereby conditional convergence may cluster in countries around different steady-state equilibriums (Durlauf and Johnson 1995; Quah 1996, 1997; Su 2003).

  3. Two papers have used a preliminary version of this dataset to examine changes across municipalities. Villalobos Barría et al. (2016) have analyzed, through Gaussian mixture modeling, the 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.

  4. For years ending in zero, the census data correspond to the general census of the population and of housing; for 2005, the data are taken from the population and housing count; and, for 2015, they are taken from the intercensal survey, which is based on a sample of 5.9 million households that is representative at the municipal level. Unless otherwise stated, from here onward, the term census refers indistinguishably to these three data sources.

  5. In 2014–15, both the Household Income and Expenditure Survey and the intercensal survey represented random samples of the 2010 general census sample frame.

  6. Even in survey-census pairings where gaps exist, for instance 1990–1992 and 2014–2015, it is possible to identify common covariates \(X_{hm}\) that satisfy the equality criterion both because the survey is a random sample of the census sample frame and because such covariates capture virtually the same context given that some characteristics of households and individuals change only slowly over time.

  7. The income concept used corresponds to 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.

  8. The implication of these varying rates of convergence can be illustrated, for instance, by simulating the amount of time to close the gap in half between the municipality in the 90th percentile and the one in the 10th percentile of municipalities’ mean per capita income. At the speed of convergence of 1992–2014 (\(-0.007\)), it would take 99 years; at the speed observed over 2000–2014 (\(-0.019\)), it would take 54 years; and, by using the speed of convergence of 2000–2005 (\(-0.043\)) it would take 24 years. By comparison, one of the most famous findings in the convergence literature suggests it would take 35 years for East Germany’s income to converge half way to West Germany’s at an estimated convergence rate of 2% in 1990 (Barro and Sala-i Martin 1991).

  9. The focus is on total public spending only because no sizable differences in the rates of convergence 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 techniques. The results are consistent, that is, poor municipalities converge at a more rapid rate relative to rich municipalities.

  10. Indeed after 2010, the growth rate per year was almost zero, thus a negative but significant sign of a zero value should be nuanced.

  11. 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 (Aroca et al. 2005; Chiquiar 2005; Esquivel 1999; García-Verdú 2005; Rodríguez-Oreggia 2007; Rodríguez-Pose and Sánchez-Reaza 2005). There are at least two explanations for this coexistence. The first source of the discrepancy is that state-level analyses typically use the state gross domestic product, 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 the results of state-level studies tend to be biased by the weight exerted by large urban agglomerations concentrating a number of municipalities, this issue can be naturally avoided in municipality-level analyses.

  12. As a 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%, whereas it was negative among non-US border municipalities: \(-0.9\)%.

  13. Indeed, growth rates in mean per capita income in municipalities located in states along the US border averaged \(-0.1\)% annually, whereas the non-US border counterparts recorded an annual average rate of 0.8%.

  14. Similarly, \(g_i\left( P_{it}\right) =\delta +\eta g_i\left( G_{it}\right) +\nu _{it}\) can represent the partial inequality elasticity of poverty or the percent change in the poverty headcount ratio as a result of a 1% increase in inequality, holding per capita income constant, with the expectation that \(\eta >0\), and with \(g_i\left( G_{it}\right) \) as the annualized rate of change in inequality. The growth and inequality elasticity parameters can be denoted as \(\eta ^y\) and \(\eta ^G\), respectively, and hence, under log normality, changes in poverty rates can be expressed as \(g_i\left( P_{it}\right) \approx \eta ^y g_i\left( y_{it}\right) +\eta ^G g_i\left( G_{it}\right) \).

  15. According to the dataset, extreme poverty rates are around 30% higher in rural municipalities than in urban municipalities, and twice the size in non-US border municipalities than in municipalities in border states.

  16. These elasticities, in general, are more responsive to growth the lower the value of the poverty line. For instance, relative to the extreme (food) poverty line, the elasticity almost invariably contracts by half in absolute value in the case of a higher (assets) poverty line (see table 33 in the ancillary file).

  17. The specifications of this augmented model are shown in tables 37–44 in the ancillary file.

  18. The computation of these elasticities through ordinary least squares yields: \(-1.505\) in 1992 and \(-1.662\) in 2000.

  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 coefficient, as in Bénabou (1996).

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Acknowledgements

The authors are grateful to Ted Enamorado for his comments and research assistance. The authors would also like to thank Maria E. Dávalos and Gerardo Esquivel for significant contributions to this work, as well as Oscar Calvo-González, Paloma Anos-Casero, Louise Cord, Jozef Draaisma, Norbert Fiess, Thania de la Garza Navarrete, Rodrigo García-Verdú, Gonzalo Hernández-Licona, Fernando Blanco, Sandra Martínez-Aguilar, Edgar Medina, Pablo Saavedra, Kinnon Scott, Paul Segal, Andy Sumner, Miguel Székely, Gaston Yalonetzky, Robert Zimmerman, officials at CONEVAL and Mexico’s Ministry of Social Development, and participants at the 24th Annual LACEA Meeting 2019 held in Puebla, Mexico, and the EADI Nordic Conference 2017, held in Bergen, Norway, for helpful comments and suggestions. We also thank the editor and two anonymous reviewers for their constructive comments and suggestions, which greatly improved this paper.

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Authors and Affiliations

  1. Regional Bureau for Latin America and the Caribbean, United Nations Development Programme, New York, USA

    Luis F. López-Calva

  2. Department of International Development, King’s College London, London, UK

    Eduardo Ortiz-Juarez

  3. Poverty and Equity Global Practice, The World Bank, 1818 H St NW., Washington, DC, 20433, USA

    Carlos Rodríguez-Castelán

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  1. Luis F. López-Calva
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Correspondence to Carlos Rodríguez-Castelán.

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Table 11 Annex: Summary statistics of the income, poverty, and inequality dataset
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Fig. 11
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Extreme poverty headcount ratios (% of municipalities’ population)

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López-Calva, L.F., Ortiz-Juarez, E. & Rodríguez-Castelán, C. Within-country poverty convergence: evidence from Mexico. Empir Econ 62, 2547–2586 (2022). https://doi.org/10.1007/s00181-021-02109-0

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