Poverty Persistence and Intra-Household Heterogeneity in Occupations: Evidence from Urban Ethiopia

Abstract

Previous studies of poverty in developing countries have to a great extent focussed on the characteristics of the household head and used these as proxies for the underlying ability of the household to generate income. This paper uses five rounds of panel data to investigate the persistence of poverty in urban Ethiopia, with a particular focus on the role of intra-household heterogeneity in occupations. Dynamic probit and system generalised method of moments regression results suggest that international remittances and labour market status of non-head household members are important determinants of households' poverty status. Results also show that controlling for these variables and the “initial conditions problem” encountered in nonlinear dynamic probit models reduces the magnitude of estimated poverty persistence significantly for urban Ethiopia. These findings have important implications for identifying the poor and formulating effective poverty reduction and targeting strategies.

JEL Classification::

• I32
• R11
• R20

I would like to thank two anonymous reviewers, the editor (Frances Stewart), Arne Bigsten, Tessa Bold, Luc Christiansen, Stefan Dercon, Dick Durevall, Marcel Fafchamps, Lennart Flood, Augustin Fosu, Gunnar Kohlin, Wim Naude, Mans Soderbom, Francis Teal, Joakim Westerlund as well as staff seminar participants at UNU-WIDER, participants at the Centre for the Studies of African Economies (CSAE) staff seminar series at the University of Oxford, and seminar participants at the University of Gothenburg for constructive comments. Part of this research was done while I was a Visiting Scholar at the Department of Agricultural and Resource Economics, University of California Berkeley. The views expressed in this paper, as well as all errors and omissions, are my own.

Notes

¹ See, for example, Dercon & Krishnan (1998), Dercon (2004), Dercon et al. (2005), Harrower & Hoddinott (2005), Barrett et al. (2006), Dercon (2006, 2008), Beegle et al. (2008), and Litchfield & McGregor (2008).

² See Baulch & McCulloch (2003) for applications in rural Pakistan, and Bigsten & Shimeles (2008) for applications in rural and urban Ethiopia.

³ See Biewen (2009) for possible reasons for poverty persistence in the context of industrialised countries.

⁴ Data were collected in 1995 as well. However, because the dynamic probit model is sensitive to the spacing of the data collection points, I excluded data collected in 1995 in order to maintain fairly even spacing between rounds.

⁵ Refer to AAU & UG (1995) for details on sampling design. While tracking original households, unfortunately, household splits were not followed.

⁶ Households in the other cities were not surveyed due to resource constraints.

⁷ Addis Ababa was chosen because of the fact that about 60% of the households in the sample are located there. Moreover, the city contains diverse cultures and socioeconomic groups, which makes it a good representative of other cities when it comes to patterns of household consumption.

⁸ Ravallion (1998) provides a detailed discussion on the use of poverty lines as deflators.

⁹ I use poverty lines constructed from the survey rather than the ones constructed by the Ministry of Finance and Economic Development of Ethiopia because: (i) the ministry's poverty line, which was constructed in 1995/1996 (see MoFED, 2012) is too general and was constructed to analyse poverty in both urban and rural areas, which may have different consumption patterns, while I use a basket of goods anchored in the lowest 40% of the distribution in urban Ethiopia, which makes poverty comparison in urban Ethiopia relatively easy; (ii) to analyse poverty in 2010/2011 and adjust consumption for spatial and temporal price differences, the national poverty line was deflated using the national and regional consumer price indices, which are too aggregated and general, while I use price data in the respective cities to adjust for spatial and temporal price differences; and (iii) with the basket of goods and poverty lines I used, it is relatively straightforward to compare poverty figures over time with earlier studies (e.g. Dercon & Tadesse, 1999; Tadesse, 1999; Gebremedhin & Whelan, 2005), who analysed poverty using the same data-set.

¹⁰ Following standard practice, I used adult equivalent units constructed by Dercon & Krishnan (1998) for Ethiopia to adjust for household size and composition. One Ethiopian birr was about USD 0.20 in 1994 and 0.10 USD in 2009.

¹¹ A large part of the attrition in the survey seems to have been a result of poor tracking. For instance, it is possible to see original panel households not surveyed in one wave but tracked and surveyed in subsequent years. For the dynamic probit analysis, a balanced panel of the data containing 366 households observed in all waves is used.

¹² In addition, I performed Wald tests for the joint significance of the differences in all slope coefficients and intercepts and did not reject the hypothesis of equality of the coefficients from the two samples.

¹³ Remittances are expressed in nominal terms using current prices.

¹⁴ Table 3 shows the flow of international remittances only in the form of cash. The reported values could be much higher if in-kind remittances were included.

¹⁵ The marginal effects of the dummy variables represent the increase in the probability of poverty due to a change from zero to one in the dummy explanatory variables. For continuous variables, it represents the change in the probability of poverty due to a one unit change in the continuous variable.

¹⁶ There were very few observations for employer members other than heads, which made controlling for “employer members” in the regressions problematic.

¹⁷ Results are available from the author upon request.

¹⁸ Controlling for endogeneity of the remittance and the labour market status of household members actually reduced the coefficient of the lagged consumption variable by only about 1.9 percentage points.

¹⁹ The null hypothesis that the population moment conditions are correct is not rejected at the commonly accepted 5% level of significance in column [2].

²⁰ Regression results are available upon request.

Additional information

Funding

Financial support from the Swedish International Development Cooperation Agency (Sida) and the Swedish Research Council Formas through the Human Cooperation to Manage Natural Resources (COMMONS) programme is gratefully acknowledged.