Abstract
Measurement of the middle class has recently come to the center of policy debate in middle-income countries as they search for the potential engines of growth and good governance. This debate assumes, first, that there is a meaningful definition of class, and second, that the thresholds which define relatively homogeneous groups in terms of pre-determined sociological characteristics can be found empirically. This chapter aims at proposing a view of the middle class based on vulnerability to poverty. Following this approach, this chapter exploits panel data to determine the amount of comparable income—associated with a low probability of falling into poverty—which could define the lower bound of the middle class. It looks at absolute thresholds, challenging the view that people just above the poverty line are actually part of the middle class. In an analogy with poverty measurement, there is a degree of arbitrariness in the definition of specific thresholds, but the concept behind them is clear and economically meaningful. The estimated lower threshold is used in cross-section surveys to quantify the size and the evolution of middle classes in Chile, Mexico, and Peru over the past two decades. The evidence also shows that the middle class has increased significantly in all three countries. There is an important group of people, however, who cannot be defined as middle class from this perspective but remain vulnerable to fall back into poverty.
We would like to thank Paloma Anos-Casero, Nancy Birdsall, François Bourguignon, Francisco Ferreira, Peter Lanjouw, Santiago Levy, Maria Ana Lugo, Nora Lustig, Julian Messina, Jamele Rigolini, Carlos Silva-Jauregui, and the team of the Mobility and Middle Class Regional Flagship in the office of the Chief Economist for Latin America and the Caribbean at The World Bank, as well as participants at the 15th LACEA Meeting held in Medellin, for helpful comments. The initial discussions that led to the implementation of the vulnerability approach to middle-class analysis took place while the authors were working at UNDP in New York. We are indebted to Rebeca Grynspan for her intellectual input into the shaping of this concept. Recommendations by two anonymous referees substantially improved this chapter. Megan Rounseville provided invaluable research and editing support.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Recently, Foster and Wolfson (2010) proposed a threshold-free approach to measure income polarization, which can be applied to middle-class analysis. Although this approach presents a less arbitrary definition of the middle class, it is a relative measure that can tell whether the middle class is (or not) greater in two time periods, but there is no information on the magnitude of the change. A discussion and application of relative measures, related to polarization, for Latin American countries are in Cruces et al. (2011b).
- 2.
- 3.
The literature has proposed a number of measures designed to capture the welfare consequences of vulnerability for poor households. See for example the discussion by Dercon (2002).
- 4.
Longitudinal data for Chile, Mexico, and Peru—described in detail in Sect. 4—allow us to observe the incidence of events that could potentially affect welfare. In Mexico, the data show that while the incidence of loss of crops and livestock affects in higher magnitude the poorest households (1st quintile)—probably due to these activities are mostly rural, where poor population could be more exposed to shocks—an important result is that the occurrence of deaths of any household member (8.8%), economic shocks—defined as business bankruptcy or unemployment—(6.6%), and health shocks for which an individual was hospitalized (12%) is somewhat stable through the income distribution suggesting thus that the entire population is prone to negative shocks. While the occurrence of health shocks is stable for all five quintiles in Mexico, in Chile and Peru it shows a growing pattern through the income distribution. Although this pattern suggests a higher use of hospitalization for upper quintiles according to the capacity to pay, these results evidence a generalized exposure to risks in all three countries.
- 5.
Unless otherwise indicated, all income or expenditure figures are expressed in PPP terms (2005 international $).
- 6.
Atkinson and Brandolini (2011) have a consistent view in the sense that they suggest that income distribution and income measures alone can no longer represent accurate definitions of the middle class. They argue for the inclusion of wealth holdings and position in the labor market into income definitions of class. The latter has been the longest tradition in sociological work.
- 7.
According to the World Bank, when comparing poverty rates across countries within the Latin American and the Caribbean region, the $4 a day poverty line provides a more meaningful standard compared to the $1.25 and $2 a day poverty lines which are typically used to measure poverty in less developed regions (the poverty line is a conditional mean of the national poverty lines—conditional on GDP per capita).
- 8.
In the estimation shown below, the dependent variable includes those households in the categories always poor (in t0 and t1) and entered poverty (in t1), as we are interested also in those who “remain poor.” Sensitivity analysis was carried out using both definitions and the results are consistent, though the quality of the estimation is weakened due to the number of actual transitions.
- 9.
The lower threshold lies between the 40th (Chile) and the 60th (Mexico and Peru) percentiles of the income distribution, and it is sensitive to different probabilities of falling into poverty. In Chile, it lies in the 22th, 27th, 38th, and 52th percentiles for a probability of 0.20, 0.15, 0.10, and 0.05, respectively. The figures in Mexico are the 39th, 49th, 58th, and 74th percentiles, respectively; while in Peru are the 40th, 52th, 58th, and 72th percentiles, respectively. The idea that looking at actual movements into poverty in the region—which are on average 10% of households every year in a 15-year period using synthetic panels (Cruces et al., 2011a)—becomes relevant. A probability to fall into poverty of, say, 30% or 50% would be inconsistent with evidence. Actually, the finding that lower levels of vulnerability are reached at relatively high percentile levels in the current distributions is a very important finding in itself for policy purposes.
- 10.
Estimated as the average of the independent variables for an estimated probability range between 0.09 and 0.11. Tables 6, 7, and 8 in the Appendix show the coefficients and standard errors from Eqns. (1) and (2) for Chile, Mexico, and Peru, respectively, while Figure 7 shows the correlation between the estimated probabilities from Eqn. (1) and predicted income from Eqn. (2).
- 11.
Ferreira et al. (2012) carry out a “validation” of the threshold by looking at income levels that are consistent with self-reported middle-class status, showing that $10 a day corresponds to the lower envelope of such income levels.
- 12.
In Chile, it lies in the 92th percentile in 2009, in the 97th percentile in Mexico (2010), and in the 98th percentile in Peru (2010).
- 13.
References
Atkinson, A. B., & Brandolini, A. (2011). On the Identification of the ‘Middle Class’. In J. C. Gornick & M. Jaentti (Eds.), Inequality and the Status of the Middle Class. Stanford University Press.
Banerjee, A., & Duflo, E. (2008). What is Middle Class About the Middle Classes Around the World? Journal of Economic Perspectives, 22(2), 3–28.
Birdsall, N. (2010). The (indispensable) Middle Class in Developing Countries. In R. Kanbur & M. Spence (Eds.), Equity and Growth in a Globalizing World. The World Bank.
Bourguignon, F. (2002). The Growth Elasticity of Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods. DELTA Working Paper 2002–03. DELTA, Ecole normale supérieure.
Cafiero, C., & Vakis, R. (2006). Risk and Vulnerability Considerations in Poverty Analysis: Recent Advances and Future Directions. SP Discussion Paper 0610. The World Bank.
Calvo, C., & Dercon, S. (2007). Vulnerability to poverty. CSAE Working Paper 2007–03. University of Oxford.
Cruces, G., Lanjouw, P., Lucchetti, L., Perova, E., Vakis, R., & Viollaz, M. (2011a). Intra-generational Mobility and Repeated Cross-sections. A Three-country Validation Exercise. Policy Research Working Paper 5916. The World Bank.
Cruces, G., Lopez-Calva, L. F., & Battiston, D. (2011b). Down and Out or up and in? In Search of Latin America’s Elusive Middle Class. CEDLAS Working Paper 113. Universidad Nacional de La Plata.
Dercon, S. (2002). Income Risk, Coping Strategies and Safety Nets. World Bank Research Observer, 17(2), 141–166.
Easterly, W. (2001). Middle Class Consensus and Economic Development. Journal of Economic Growth, 6(4), 317–336.
Elbers, C., & Gunning, J. (2003). Vulnerability in a Stochastic Dynamic Model. Discussion Paper 2003–070/2. Tinbergen Institute.
Erikson, R., Goldthorpe, J., & Portocarero, L. (1979). Intergenerational Class Mobility in Three Western European Societies. British Journal of Sociology, 30(4), 415–441.
Ferreira, F., Messina, J., Rigolini, J., Lopez-Calva, L. F., Lugo, M. A., & Vakis, R. (2012). Economic Mobility and the Latin American Middle Class. The World Bank.
Foster, J., & Wolfson, M. (2010). Polarization and the Decline of the Middle Class: Canada and the U.S. Journal of Economic Inequality, 8(2), 247–273.
Goldthorpe, J. H. (1987). Social Mobility and Class Structure in Modern Britain. Clarendon Press.
Goldthorpe, J. H., & McKnight, A. (2004). The Economic Basis of Social Class. CASE Paper 80. London School of Economics.
Hoddinott, J., & Quisumbing, A. (2008). Methods for Microeconometric Risk and Vulnerability Assessments. Available at SSRN: https://doi.org/10.2139/ssrn.1281055
Kharas, H., & Gertz, G. (2010). The New Global Middle Class: A Cross-over from West to East. In C. Li (Ed.), China’s Emerging Middle Class: beyond Economic Transformation. Brookings Institution Press.
Ligon, E., & L. Schechter (2004). Evaluating Different Approaches to Estimating Vulnerability. SP Discussion Paper 0210. The World Bank.
Lopez-Calva, L. F., & Lustig, N. (Eds.). (2010). Declining Inequality in Latin America: A Decade of Progress? Brookings Institution Press.
Lustig, N., Lopez-Calva, L. F., & Ortiz-Juarez, E. (2012). Declining Inequality in Latin America in the 2000s: The Cases of Argentina, Brazil, and Mexico. World Development. https://doi.org/10.1016/j.worlddev.2012.09.013
Milanovic, B., & Yitzhaki, S. (2002). Does Decomposing World Income Distribution: Does the World Have a Middle Class? Review of Income and Wealth, 48(2), 155–178.
Ravallion, M. (2010). The Developing World’s Bulging (but Vulnerable) Middle Class. World Development, 38(4), 445–454.
Sen, A. (1983). Poor, Relatively Speaking. Oxford Economic Papers, 35(2), 153–169.
Wright, E. O. (1979). Class Structure and Income Determination. Academic Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Correlation between the estimated probabilities of fall into poverty and—predicted—income; Chile, Mexico, and Peru. (Source: Author’s calculations based on longitudinal data from CASEN, MxFLS, and ENAHO)
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
López-Calva, L.F., Ortiz-Juarez, E. (2022). A Vulnerability Approach to the Definition of the Middle Class. In: Goulart, P., Ramos, R., Ferrittu, G. (eds) Global Labour in Distress, Volume I. Palgrave Readers in Economics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-89258-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-89258-6_26
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-89257-9
Online ISBN: 978-3-030-89258-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)