Financial Crises, Poverty, and Income Distribution
Author: International Monetary Fund

Contributor Notes

Author’s E-Mail Address: ebaldacci@imf.org, ldemello@imf.org, minchauste@imf.org
Publication Date:
01 Jan 2002
eISBN:
9781451842050
Language:
English
Keywords:
WP; income; financial crisis; financial crisis episode; distribution data; inequality indicator; poverty incidence data; financial crisis literature
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Developing and transition economies are prone to financial crises, including balance of payments and banking crises. These crises affect poverty and the distribution of income through a variety of channels: slowdowns in economic activity, relative price changes, and fiscal retrenchment, among others. This paper deals with the impact of financial crises on the incidence of poverty and income distribution, and discusses policy options that can be considered by governments in the aftermath of crises. Empirical evidence, based on both macro- and microlevel data, shows that financial crises are associated with an increase in poverty and, in some cases, income inequality. The provison of targeted safety nets and the protection of specific social programs from fiscal retrenchment remain the main short-term propoor policy responses to financial crises.

Abstract

Developing and transition economies are prone to financial crises, including balance of payments and banking crises. These crises affect poverty and the distribution of income through a variety of channels: slowdowns in economic activity, relative price changes, and fiscal retrenchment, among others. This paper deals with the impact of financial crises on the incidence of poverty and income distribution, and discusses policy options that can be considered by governments in the aftermath of crises. Empirical evidence, based on both macro- and microlevel data, shows that financial crises are associated with an increase in poverty and, in some cases, income inequality. The provison of targeted safety nets and the protection of specific social programs from fiscal retrenchment remain the main short-term propoor policy responses to financial crises.

I. Introduction

Developing and transition economies are prone to financial crises, including balance of payments and banking crises. These crises affect poverty and income distribution through a variety of channels (Box 1). Financial crises typically lead to slowdowns in economic activity and, consequently, rises in formal unemployment and/or falls in real wages. A contractionary policy mix is conventionally implemented in response to a financial crisis, including fiscal retrenchment and a tightening of the monetary stance. Fiscal retrenchment, in turn, often leads to cuts in public outlays on social programs, transfers to households, and wages and salaries, among others (World Bank, 2000). Exchange rate realignments result in changes in relative prices, likely to affect some social groups more adversely than others, and consequently changes in poverty and income distribution indicators. Conventional wisdom is that the poor suffer disproportionately to the nonpoor in periods of crisis.

The question this paper addresses is how the poor are affected by financial crises.2 Important policy questions are whether income distribution, not only the incidence of poverty, is affected by financial crises and whether the impact of crises on poverty and on income distribution is stronger in countries where the distribution of income is more skewed. Easterly (2001) shows that the poor are hurt less by falling standards of living in countries where the distribution of income is more unequal because the poor have a lower share of income to begin with. In the wake of financial crises, emphasis on poverty headcounts, without reference to changes in income distribution, may lead to inadequate policy recommendations. This is because the impact of financial crises on the incidence of poverty is often estimated under the assumption that the distribution of income remains unchanged in the short term.

The objectives of this paper are (1) to estimate the impact of financial crises on the incidence of poverty and on the distribution of income; and (2) to evaluate the policy options considered by governments in the aftermath of crises to mitigate their adverse impact on the poor.3 The postcrisis impact on the poor is yet to be assessed through a systematic analysis, both from the cross-country perspective and at the microlevel. Macrolevel data allow for the estimation of the empirical relationship between financial crisis and poverty from a cross-country perspective. Microlevel data allow for a more in-depth analysis of the individual and household characteristics that are correlated with poverty, including demographics and earnings by occupation. We also assess whether the cross-country evidence presented here is consistent with that based on microlevel data. In this study, we use microlevel data for Mexico.

Financial Crises, Poverty, and Income Distribution

The main channels through which financial crises affect poverty and income distribution are

  • A slowdown in economic activity. A financial crisis may lead to a fall in earnings of both formal and informal-sector workers due to job losses in the formal sector and reduced demand for services in the informal sector. Reduced working hours and real wage cuts also adversely affect the earnings of the poor. Entry of unemployed formal-sector workers into the informal sector puts additional pressure on the informal labor market (Bourguignon and Morrisson, 1992; Morley, 1995; Walton and Manuelyan, 1998; Lustig and Walton, 1998).

  • Relative price changes. After a currency depreciation, the price of tradables rises relative to nontradables, leading to a fall in earnings of those employed in the nontrade sector. At the same time, there may be an increase in the demand for exports, and consequently, employment and earnings in the sectors producing exportables increase, thereby offsetting some of the losses due to the decline in GDP. The exchange rate change may affect the price of imported food, increasing domestic food prices; this increase in turn hurts poor individuals and households that are net consumers of food (Sahn and others, 1997).

  • Fiscal retrenchment. Spending cuts affect the volume of publicly provided critical social services, including social assistance outlays, and limit the access of the poor to these services at a time when their incomes are declining (Lanjouw and Ravallion, 1999).

  • Changes in assets. Wealth effects or changes in the value of assets have a significant impact on income distribution (Datt and Ravallion, 1998; Blejer and Guerrero, 1990). Changes in interest rates, as well as in asset and real estate prices, affect the wealth of the better off.1

1 Trade liberalization, the removal of price subsidies, and privatization are likely to affect social groups asymmetrically over the medium term. Easterly (2001) shows that IMF or World Bank adjustment programs tend to reduce the impact of recessions on the poor. The poor also benefit less from expansions in the presence of an adjustment program.

With regard to policy implications, the empirical analysis will shed light on (1) the main channels through which financial crises are likely to have an impact on poverty, as well as the magnitude of the impact; (2) the short-run policy instruments that can be used to shelter the poor before, during, and after financial crises; and (3) the characteristics of poverty and inequality that should be taken into account in the policy responses to crises.

II. The Methodology

A. The Cross-Country Analysis

The cross-country analysis will be carried out by analogy with the differences-in-differences methodology used conventionally in microdata analysis. The empirical literature on currency crises and leading indicators (summarized in Box 2), also uses methodologies conventionally applied to the analysis of microeconomic phenomena, such as the event analysis borrowed from the microfinance literature. In a nutshell, the methodology consists of examining outcomes, such as the impact of a financial crisis on poverty, using observations in a treatment group (i.e., the crisis-stricken countries) relative to a control group (i.e., countries unaffected by the crisis) that are not randomly assigned. In other words, the methodology (1) assesses precrisis and postcrisis average changes in poverty and income distribution indicators in countries affected by financial crises; and (2) compares these changes in poverty and income distribution indicators relative to a sample of control countries that have not been affected by financial crises.4 All relevant variables are defined as differences between the crisis-affected countries under examination and the control group.5

The Financial Crisis Literature: An Overview

There have been important developments in the literature on currency and banking crises (e.g., Eichengreen, Rose and Wyplosz, 1995; Flood and Marion, 1997; Milesi-Ferretti and Razin, 1996 and 1998; Kaminsky, Lizondo, and Reinhart, 1997). Financial crises are attributed to rapid reversals in international capital flows and prompted chiefly by changes in international investment conditions. Flow reversals are likely to trigger sudden current account adjustments, and subsequently currency and banking crises (e.g., Frankel and Rose, 1996; Eichengreen and Rose, 1998).

A first generation of currency crisis models—pioneered by Krugman (1979)—explained the collapse of exchange rate regimes on the grounds that weak fundamentals lead foreign investors to pull resources out of the country, and as a result the depletion of foreign reserves needed to sustain the currency leads to the collapse of the exchange rate regime. A second generation of models suggests that currency crises may also occur despite sound fundamentals, as in the case of self-fulfilling expectations (Obstfeld, 1996), speculative attacks, and changes in market sentiment (Frankel and Rose, 1996; Flood and Marion, 1997).

Identifying crises

The currency/banking crisis literature favors the event analysis methodology for identifying crises. Frankel and Rose (1996) define a currency crash “as a nominal depreciation of the currency of at least 25 percent that is also a 10 percent increase in the rate of depreciation” (p. 3). A three-year window is also considered between crisis episodes to avoid counting the same crisis twice. Eichengreen, Rose, and Wyplosz (1995) define a currency crisis not only in terms of large nominal depreciations, but also in terms of speculative attacks that are successfully warded off. Noncrisis observations are defined as “tranquil” observations. The methodology allows for the analysis of the chronology of crisis episodes and their characteristics. It also allows for multivariate analysis of the crisis episodes and other macroeconomic variables. Kaminsky, Lizondo, and Reinhart (1997) also use event analysis and construct an index of currency market turbulence defined as a weighted average of exchange rate changes and reserve changes.

The estimating equation can be defined as

(1)ΔPi(t)−ΔPj(t)=a0+a1Δ[Fi(t)−Fj(t)]+a2Δ[Xi(t)−Xj(t)]+ui(t),

where ΔPi(t)=lnPi(t)−lnPi(t−s) denotes the change in a poverty/income distribution indicator (for instance, poverty headcount ratios, Gini coefficient, and income shares, among others) of a crisis-stricken country i between a postcrisis period t and a precrisis period t − s; ΔPj(t) = lnPj(t)−lnPj(t−s) denotes the change in the poverty indicator in a control country j (or control sample) over the time periods defined as precrisis and postcrisis for the crisis-affected country i; ΔFi(t) = lnFi(t)−lnFi(t−s) denotes the change between a postcrisis period t and the precrisis period t − s in the explanatory variables capturing the channels through which financial/economic crises are expected to affect poverty in country i (the same variable is defined for the control country j); ΔXi(t)=lnXi(t)−lnXi(t−s) denotes the change in a set of variables controlling for noncrisis poverty determinants between precrisis and postcrisis periods in the crisis-affected country i (the same variable is defined for the control country j); and ui(t) is an error term.

B. The Microlevel Analysis

The cross-country approach described above is complemented with microlevel analysis to assess the effect of financial crises on poverty. In particular, cross-sectional Mexican household survey data are used to estimate the probability of being poor before and in the wake of the 1994—95 Financial crisis.6 A two-step strategy is followed for the microlevel empirical analysis: first, the factors affecting the probability of being poor in each year (i.e., before and after the crisis) are estimated using a logit model; then a logit regression is estimated using the pooled data set, in order to assess the impact over time of the financial crisis on the stability of the relevant parameter estimates. Exogenous variables are chosen among the set of structural factors that are deemed to affect poverty (i.e., household socioeconomic characteristics and demographics, among other factors) and those that are more likely to proxy the impact of financial crises on the living conditions of the population.

The underlying model can be specified in terms of an unobservable latent variable λ*i measuring deprivation, lack of welfare, or poverty in its multidimensional form.7 The probability of being poor can specified and estimated as

(2)P(di=1)=P(λi*≥0)=P(εi<E(λi*/xi))=F(xi’β),

where di is a binary variable equal to one if household i lies below the poverty line at time t (t = 1992, 1994, 1996) and zero otherwise.8 The vector of independent variables xi includes individual control variables, as well as variables proxying for the fiscal and macroeconomic policy stance (i.e., public transfers, unemployment, level of wages and salaries, among others).

We first compare the parameter vector β estimated before and after the crisis to assess the impact of the crisis on the logistic regression coefficients. This effect is measured by changes in the odds ratios Ω.9 We then use the pooled data set of the two years to estimate the following logit model:

(3)pi,t=P(di,t=1)=F(xi,t′β+zi,t′xi,tγ),

where di,t is the probability of being poor in period t(t = 1992 and 1996, or alternatively 1994 and 1996) for household i. This probability can be defined as a function of the set of independent variables used in the previous step, and of a dummy variable zi,t that assumes a unit value for the postcrisis year, and zero otherwise. Hypothesis testing on the significance of vector γ of parameter estimates allows for the assessment of the impact of the financial crisis on the link between poverty and its causal factors.

Some caution is needed in the interpretation of the results of equation (3). The estimate of γ does not account solely for the effects of financial crisis. In fact, this parameter measures the change in the factors underlying the probability of being poor in the period of analysis. Other factors could be responsible for a change in the structure of the poverty risk between 1994 and 1996. During this period there were major reforms that affected agriculture and the rural areas, large changes in commodity prices, and NAFTA came into effect (Lustig, 1998). However, given the relatively short period of time, it is very unlikely that profound modifications of the structure of poverty would have taken place in the absence of the crisis and, therefore, we refer to the estimate of γ as a first approximation for the impact of the 1994–95 financial crisis on the probability of being poor in Mexico.

III. The Cross-Country Regressions

A. Identifying a Financial Crisis and Selecting a Control Group

Financial crises are conventionally characterized by currency crashes. Recent studies have attempted to define financial crises by focusing on event analysis and leading indicators (i.e., Kaminsky, Lizondo, and Reinhart, 1997). In line with this body of literature, we have used Frankel and Rose’s (1996) définition of a currency crash “as a nominal depreciation of the currency of at least 25 percent that is also a 10 percent increase in the rate of depreciation” (p. 3). The Frankel-Rose methodology has been used for a number of reasons. First, it focuses on currency crises, rather than balance of payments and banking crises, and therefore country-specific information, which is hard to come by and/or quantify, is not required. Second, low-frequency (annual) data are used, given the availability of poverty indicators. Third, information is not needed on changes in nominal interest rates, which are not market determined in most countries in the sample, and on foreign exchange reserves.10

We have also examined an alternative definition of financial crisis that takes account of the association between currency crashes and income losses. However, most definitions of financial crises, summarized in Box 2, are based exclusively on currency crashes or indicators of exchange rate pressure.11 The alternative definition considered in the sensitivity analysis that follows focuses on those currency crash episodes in which the rate of growth of GDP per capita was negative between the crisis year and the precrisis year. Motivation for this alternative definition is that depreciations may be expansionary, particularly if the economy has been in a recession due to, for example, high interest rates to defend a currency peg; in this case, a currency crash may not necessarily lead to a fall in average income. Also, as discussed later, the economy may recover from the exchange rate depreciation during the year in which the crisis episode takes place, leading therefore to no average income losses in the crisis year relative to the precrisis year.

Several options were entertained but we have opted for treating the sample of OECD countries that did not experience a financial crisis in the period under examination as the control group. This is due to two main reasons. First, unlike for most developing countries, information on the relevant indicators is available for most OECD countries on a yearly basis. Crisis episodes have been identified for different time periods, thereby requiring information on these indicators for the control group for all the years in which a crisis episode was identified in the treatment group. Second, the quality of the data for these OECD countries is typically higher than for most developing countries.12 Despite the data constraints, we are aware that the choice of the OECD group as the control group has some pitfalls. Although OECD and non-OECD countries are inherently different, the methodology analyzes the difference in changes between the control and crisis countries, rather than at the differences in levels. The methodology would be invalidated if these two groups differed significantly in their responses to crises. In other words, the question is whether the impact on poverty and income distribution would be significantly different in the OECD countries if they experienced the same crisis episodes as the treatment group.

Problems would arise if the channels through which crises affect poverty and income distribution were significantly different in the OECD group (before and after the crisis episodes) and in the treatment group before the crisis.

To address this issue, we performed a simple specification test consisting of rewriting equation (1) as:

(4)ΔPi(t)=β0+β1ΔPj(t)+β2ΔFi(t)+β3ΔFj(t)+β4 ΔXi(t)+β5ΔXj(t)+vi(t),

and testing the following hypothesis:

(5)H0:β1=1, β2+β3=0, and β4+β5=0.

Acceptance of this hypothesis, based on standard F-tests (reported below), allows for the definition of the main variables as differences relative to the control group. If this is the case, the control group provides a valid representation of the behavior of the crisis-stricken countries in the absence of the crisis.

B. The Macrolevel Data

Data on bilateral exchange rates are available from the IMF’s International Financial Statistics (IFS). Annual data have been collected for developing and industrial countries since the late 1960s. The poverty incidence data are available from the World Bank (Chen and Ravallion, 2000). Information based on household expenditure/income surveys is available on mean household income, poverty headcount ratios, and poverty gaps for a sample of developing countries starting with the early 1980s.13 The income distribution data used are available from the Deininger and Squire (1998) database. Information is available on the Gini coefficient and the distribution of income per quintile for developing and industrial countries starting with the early 1980s. The caveats in using these cross-country data are well documented (Deininger and Squire, 1998; Chen and Ravallion, 2000; Ravallion, 2000).

After identifying the crisis episodes using the methodology above and matching these episodes with the available data on poverty incidence and income distribution, we are left with at most 65 observations in the sample. The construction of the database is described in detail in Appendix I. Our sample contains a cross-section of crisis episodes, covering a variety of countries, mainly in the developing world. Data on the relevant macroeconomic indicators are available for most crisis-stricken countries. Nevertheless, information is not always available for the poverty and income distribution indicators for all the countries identified as having had a crisis episode. Collection of internationally comparable time series for poverty/inequality indicators is a relatively recent endeavor and information for the 1970s and 1980s is not readily available. The sample is much smaller for the poverty incidence indicators than for the income inequality indicators. As a result, caution is recommended in interpreting the parameter estimates reported below.

C. Financial Crises and Poverty: Preliminary Findings

In the sample under examination, financial crises—defined as currency crashes—are associated with sizeable changes in the macroeconomic indicators used to capture the main channels through which crises are expected to affect poverty and income distribution (Table 1). For example, consumer price inflation increases in the crisis year by nearly 62 percent relative to the precrisis year. Formal unemployment rises by 1.1 percent in crisis years relative to precrisis years. GDP per capita rises by nearly 1 percent relative to precrisis years. Government spending on education and health care also decline slightly.14

Table 1.

Financial Crisis Episodes: Summary Statistics

(All variables are defined as rates of change (in percent) in the crisis year relative to precrisîs year)

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Sources: World Bank and IMF data sets; and IMF staff calculations.

Financial crises are also associated with a deterioration in poverty indicators. On average, poverty headcount ratios increase during financial crises. Notwithstanding the increase in the incidence of poverty, the poor in the lowest income quintile do not suffer the greatest income losses during crises (Table 1). The main losers in terms of changes in income shares are not the poorest (lowest income quintile) but those in the second (lowest) income quintile. The income share of the highest income quintile also falls in crisis years relative to precrisis years. 15 It can be argued that the very poor may find income in informal-sector activities, thereby protecting themselves from income losses due to financial crises.16 The poor also tend to recover their income losses faster than the wealthy in the recovery periods following financial crises.

The association between crises and poverty/distribution indicators is stronger if financial crises are followed by average income losses. Based on the alternative definition of financial crisis, which focuses on currency crashes that are also associated with average income losses, GDP per capita contracts by 1.4 percent on average in the crisis year relative to the precrisis year. Inflation increases by nearly 92 percent and unemployment increases by nearly 1.6 percent relative to the precrisis year. Based on the Gini coefficient, inequality also increases by 0.63 percent relative to the precrisis year. The fall in the income share of the highest quintile is lower (−0.03 percent) and the increase in the income share of the fourth quintile (nearly 2 percent) is higher, relative to the financial crisis episodes defined as currency crashes alone.

D. The Cross-Country Evidence

Because of the limited sample size, the association between each channel and poverty/income distribution indicators is estimated separately.17 Parameter estimates are reported in Tables 2 and 3 for a variety of variables capturing the channels through which financial crises affect poverty:

  • A fall in GDP per capita in the wake of financial crisis is associated with an increase in the incidence of poverty and a deterioration in income distribution, measured by the Gini coefficient (Table 2).18 A fall in per capita income is associated with falling mean household income, as expected, and an increase in income inequality, measured by the Gini coefficient.19 Declining per capita income explains about 15–30 percent of the observed change in the poverty and inequality indicators. Because the Gini coefficient is a summary statistic that is too sensitive to changes in the middle of the income distribution, we also focused on income shares.20 The deterioration in income distribution as a result of crisis-induced average income losses is due to a more-than-proportional fall in the income share of the lowest income quintiles, and an increase in the income share of the highest quintile.

  • A rise in inflation is associated with an increase in the income share of the middle-income quintile. In the aftermath of a financial crisis, rising inflation is associated with a fall in the income share of the highest quintile and an increase in the income share of the middle-income quintile. The correlation between changes in inflation and in poverty indicators is not statistically significant at classical levels.

  • The analysis for formal unemployment is inconclusive. The association between changes in formal unemployment and in indicators of poverty and income distribution is not statistically significant at classical levels. The lower number of observations would also compromise the statistical validity of the results.

  • Fiscal retrenchment in the aftermath of crises is associated with a deterioration in the distribution of income.21 An increase in government spending on education, health care, and social security programs is associated with a rise in the income share of the lowest quintiles. The elasticities are small in magnitude, reflecting, at least in part, the fact that outlays on social programs are often poorly targeted. Higher spending on health care programs is also associated with a reduction in the incidence of poverty.22 23 This provides evidence in support of preserving social spending programs from cuts in the aftermath of financial crises. Incidentally, Dollar and Kraay (2000) show that a rise in inflation and a fall in government spending have an adverse impact on the income of the poor, controlling for changes in mean income.

Table 2.

Income, Inflation, Unemployment, and Poverty 1/

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All models are estimated by OLS and include an intercept. The rate of change in per capita GDP is used as a control variable in all models, except when it is the main transmission mechanism under examination (first column). In this case, inflation is used as the control variable. Heteroscedasticity-consistent t-ratios in parentheses.

Note: (***), (**), and (*) denote significance at the 1 percent, 5 percent, and 10 percent levels, respectively. The specification test is an F-test. Significant values of the F-test reject the specification restrictions.
Table 3.

Public Spending and Poverty 1/

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All models are estimated by OLS and include an intercept. The rate of change in per capita GDP is used as a control variable in all models. Heteroscedasticity-consistent t-ratios in parentheses.

Note: (*). (**), and (***) denote significance at the 1, 5, and 10 percent levels, respectively. The specification test is an F-test. Significant values of the F-test reject the specification restrictions.

E. Robustness Analysis

A variety of robustness checks have been carried out and can be summarized as follows:

  • The parameter estimates reported above do not account for the impact on poverty of differences in initial levels of inequality within countries. This may affect the impact of changes of income on the incidence of poverty. Typically, the higher the level of inequality in a country, the lower the elasticity of poverty incidence to economic growth. The equations were reestimated for the sample of low-inequality countries, defined as those with a Gini coefficient less than 0.45. Parameter estimates are typically higher for the low-inequality sample, as expected. Significance levels are comparable to those reported for the full sample.

  • The baseline results are robust to alternative definitions of financial crisis. In this case, the crisis episodes in which per capita GDP rises, rather than falls, in the aftermath of crises are eliminated from the sample. The elasticities are slightly higher when currency crashes are associated with average income losses, as expected.

The caveats

The cross-country analysis provides preliminary, but by no means conclusive, evidence that financial crises are correlated with poverty and changes in income distribution, and the empirical results should be interpreted with caution. The cross-country analysis suffers from well-known caveats:

  • The use of low-frequency data does not allow for a detailed analysis of when crises peak and bottom out during the year in which they are identified. As discussed above, economic recovery during, as opposed to after, the crisis year affects indicators constructed on an annual basis.

  • Data on income distribution is hard to come by for a large sample of countries. Therefore, in certain cases, it was not possible to match the years when crises occurred and those for which data are available. This may cause some discrepancies in the empirical association between financial crises and poverty.

  • Data on income distribution by quintile do not allow for the analysis of intraquintile income distribution. As shown in the case of the Mexican crisis described below, the association between crises and poverty is likely to be affected by changes in income distribution within the lowest quintiles, particularly in countries where the poor are clustered below that income threshold.

IV. The Mexican Experience

A. The 1994–95 Mexican Crisis

Mexico was hit particularly hard by the financial crisis of 1994–95. Following the nominal depreciation of the peso by nearly 47 percent between 1994 and 95, consumer price inflation soared to 52 percent at end-1995, and real GDP fell by more than 6 percent, recovering to the precrisis level in 1997 (Table 4). Concomitantly, fiscal policy was tightened, including some cuts in health and education expenditures. The labor market was affected by the slowdown in economic activity: open unemployment doubled to 7.4 percent in 1995. By end-1996, the economy had started to recover and the rate of open unemployment fell back to 4.7 percent.

Table 4.

Mexico: Selected Indicators

(Percent changes)

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Source: Mexican authorities; and IMF staff estimates.

Nonfinancial public sector.

B. The 1994–95 Crisis: The Microlevel Data

A number of studies have found that the impact of the Mexican crisis on poverty and income distribution was mixed.24 Our results confirm these findings, but go beyond previous studies in that we use a survey that is representative of households both in urban and in rural areas, where poverty is concentrated. Moreover, the use of expenditure data to calculate poverty lines, as opposed to income data, is preferable because it serves as a better proxy for permanent income.

Based on the microlevel data, available from the 1992, 1994, and 1996 National Income and Expenditure Surveys conducted by the Mexican Statistical Institute,25 average monthly household income in constant 1994 prices fell by 31 percent between 1994 and 1996, while household consumption experienced a decline of 25 percent during the same period (Table 5).26 The number of households with unemployed, self-employed or pensioner household heads rose between 1992 and 1996, in line with worsening conditions in the labor market.

Table 5.

Mexico: Descriptive Statistics

(Percentage values, unless otherwise speficied)

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Source: IMF staff estimates based on 1992, 1994 and 1996 ENIGH.

Local currency at constant 1994 prices.

Other than farmers.

Mexican microdata show an increase in the incidence of poverty27 and in the poverty gap28 relative to the precrisis period. Higher poverty incidence in the aftermath of the 1994–95 financial crisis resulted from two separate factors: (1) the increase in the number of households that were lying slightly above the poverty line before the crisis and did not benefit from effective social safety nets preventing them from falling into poverty; (2) the worsening of the living conditions of those households that were already classified as poor in 1992 and in 1994. Relevant results of the analysis can be summarized as follows (Table 6):

  • The poverty headcount ratio, defining the incidence of poverty, rose to nearly 17 percent of the population in 1996, from 10.6 percent in 1994, reversing the gains made between 1992 and 1994.29 However, the characteristics of poor households did not change significantly relative to the precrisis period. Poverty rates are higher among households headed by farmers or self-employed persons; less-educated individuals; those living in rural areas, the southern states, and the Yucatán peninsula; and households with numerous family members.

  • The poverty gap, defining the income shortfall of the poor, increased in the 1994–96 period, although this increase was insufficient to reverse the gains made in reducing the depth of poverty between 1992 and 1994. This result was determined by the increase in poverty depth for those household groups that had experienced the largest reduction in the poverty gap in the 1992–94 period. Thus, in the aftermath of the 1994–95 crisis, some of the poor households that had climbed closer to the poverty line in the 1992–94 period may have experienced a sharp reduction in their living conditions. In addition, those households that became poor as a result of the crisis could have experienced a large drop in their consumption levels, which brought them far below the poverty line. The poverty gap remained highest after the crisis for households headed by farmers, self-employed, elderly, and less educated heads; for those living in rural areas, the Yucatán peninsula, and the southern states; and for larger households.

  • The households that were already poor before the crisis were not necessarily the hardest hit by the crisis. The increase in poverty rates was worst for single-parent households and those headed by individuals with middle school or high school educations, by pensioners, by the self-employed, and by employees. Note that the gains in poverty reduction for the self-employed between 1992 and 1994 were reversed by 1996, while the large increase in poverty among the unemployed observed in 1994 persisted after the crisis. In the wake of the crisis, the poverty gap increased relatively more for single-parent and single-person households, and those headed by individuals with no schooling, elderly above 75 years of age, and in those living in the Yucatán peninsula. For these households, the depth of poverty increased, implying that they were especially hard hit by the crisis and therefore fell deeper into poverty.

Table 6.

Poverty Incidence and Poverty Gap 1/

(In percent, unless otherwise speficied)

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Source: IMF staff estimates based on 1992, 1994, and 1996 ENIGH.

Poverty is measured as consumption relative to a basic basket as defined by INEGI in 1992.

Other than farmers.

All the estimates of income inequality presented in Table 7 point to a significant reduction in the differences between the upper and the lower tail of the income distribution in the 1992–96 period.30 This is unlike the cross-country evidence reported above, and the evidence of some Latin American countries hit by recession in the late 1980s and in the early 1990s (Lustig, 2000).31 In Mexico, the income and expenditure shares of the lowest quintile increased relative to the precrisis period by over 10 percent, while the income and expenditure shares of the highest quintile decreased by over 2 percent between 1994 and 1996 (Figure 1). This confirms the results presented in Cunningham and Maloney (2000).32 It is also important to note that monthly average expenditures of the poorest 20 percent of the population, despite its growing share in total income, fell in absolute terms from M$433 in 1994 to M$386 in 1996 measured in 1994 Mexican pesos. Given their margin of survival, this may be extremely significant and should continue to merit the attention of public policy. When looking at the subsample of poor households, one notes that the average expenditures loss between 1994 and 1996 was 1.6 percent, but the poorest 10 percent of the poor experienced an expenditure loss of 12 percent. This confirms the fact that the depth of poverty increased despite an improvement in income distribution (Table 8).

Figure 1.
Figure 1.

Mexico. Distribution of Equivalent Expenditure in 1994 Pesos

Citation: IMF Working Papers 2002, 004; 10.5089/9781451842050.001.A001

Table 7.

Mexico: Inequality Measures

(Percentage values)

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Source: IMF staff estimates based on 1992. 1994, and 1996 ENIGH.

Inequality avertion parameter.

Table 8.

Mexico: Changes in Average Income and Expenditure by Decile Subsample of Poor Households

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Source: IMF staff estimates based on 1992, 1994 and 1996 ENIGH.

Changes in income distribution can be attributed, at least in part, to a disproportionate fall in the income of the richest deciles relative to the precrisis period. In particular, as shown in Table 9, average wages for the richest decile fell by nearly 41 percent, relative to an average drop in wages of 34 percent.33 The decrease in profits was 25 percent on average, with the greatest decrease among the richest 50 percent of the population, suggesting a possible channel for the fall in the relative income of the wealthy.34 Average transfers fell by 13 percent for the poorest decile, compared to a drop by 37 percent for the richest decile between 1994 and 1996.

Table 9.

Mexico: Changes in Average Income and Expenditure by Expenditure Decile

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Source: IMF staff estimates based on 1992, 1994 and 1996 ENIGH.

To assess the impact of transfers on poverty, a simulation was performed by excluding all transfers from household income and then comparing the resulting poverty headcount with that calculated with after-transfer income. The results imply that transfers kept only a slightly higher share of the population out of poverty in 1996 than in 1994. In 1994, 4.5 percent of the population was kept out of poverty because of transfers, against 6.1 percent of the population in 1996. This points to the fact that the targeting of transfers did not improve substantially after the crisis, nor did transfers prevent many people from becoming poor given the large increase in the number of poor.35 This is also confirmed by the fact that the shares of transfers in total income remained highest in the top deciles.36

C. The Determinants of Poverty: The Empirical Findings

The results of the logit estimations allow for comparisons of the probability of being poor for the precrisis and postcrisis periods as follows:

  • In 1992, 1994, and 1996, the probability of being poor was found to be higher for larger households; for those living in rural regions, in the southern states and in the Yucatán peninsula; and for households headed by less-educated individuals, by the self-employed, or by farmers (Table 10). The risk of being poor is significantly lower for those households headed by pensioners and more-educated individuals, and for household heads in the 60- to 74-year-old range. A higher share of nondurable and food consumption in total household expenditures is generally associated with a higher risk of poverty.

  • The 1994–95 crisis changed slightly the profile of poverty risk by household characteristics. When comparing the logit results for 1994 and 1996, we find that the probability of being poor increased for households headed by employees and pensioners.37 Households that were disproportionately hit by the crisis include those headed by individuals having a middle-school or high-school education, by those aged between 40 and 59 years, and by those living in the South and the Yucatán peninsula. Urban households were affected more adversely by the crisis than rural households. The probability of being poor fell for households headed by farmers, by adults aged 60 and older, and by those with elementary school education. Residents in the central states and those with three or four household members also experienced a moderate decline in their relative risk of poverty. Gender of the household head was found to have no significant impact on the risk of poverty once all other determinants are held constant.

  • Home ownership further became a protection against poverty after the crisis. Because other sources of income, including labor income, typically fall during crises, owning a home can protect the household from the risk of falling into poverty as homeowners do not need to spend their income on rent.38 The relative risk of poverty was also reduced for individuals living in households headed by farmers, or with more than three family members. In these cases, consumption of self-production and pooling of household resources across family members could have helped to protect from declining household welfare.

  • The regression analysis using the pooled data for both 1994 and 1996 confirms the previous results and sheds some light on the gap in poverty incidence between urban and rural areas (Table 11). The pooled regression analysis shows that the risk of becoming poor in the aftermath of the crisis increased disproportionately for those resident in urban areas, for the households in the Yucatán, and for those that are headed by either very young or very old individuals. Despite the long-term trend towards widening inequality between rural and urban areas, as documented in other empirical studies (Bouillon, Legovini and Lustig, 1998), rural households were better protected than urban households from the risk of poverty during the 1994–95 financial crisis, once all the other determinants of the probability of being poor are held constant. A possible explanation for this result is that higher unemployment and soaring inflation had a stronger impact on the living conditions of the urban poor, particularly those households slightly above the poverty line. At the same time, the incidence of poverty remained much higher in rural areas than in urban areas: the relative risk of poverty for households living in rural areas was more than twice that of urban households.

Table 10.

Results of the Estimates of the Logit Model—Dependent Variable: Probability of Being Poor

(Percent values, unless otherwise speficied, consumption-based definition of poverty)

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Source: Data provided by the 1992, 1994 and 1996 Mexican income and expenditure surveys (ENIGH), and IMF stair estimations.

Other than farmers.

Note: (*), (**), and (***) denote statistical significanceat the 10, 5, and 1 percent levels, respectively.
Table 11.

Results of the Estimates of the Pooled Logit Model—Dependent Variable: Probability of Being Poor

(Percent values, unless otherwise specified; consumption-based definition of poverty

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Source: Data provided by the 1992 and 1996 Mexican income and expenditure surveys (ENIGH), and IMF staff estimations.

Other than farmers.

Note; (*), (**), and (***) denote statistical significance at the 10, 5, and 1 percent levels, respectively.