How Low Can You Go? Combining Census and Survey Data for Mapping Poverty in South Africa

Alderman, Harold; Babita, Miriam; Demombynes, Gabriel; Makhatha, Nthabiseng; Özler, Berk

doi:10.1093/jae/11.2.169

Cited by 74 publications

(52 citation statements)

References 9 publications

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“…Simelton et al [14] observed that small droughts might have relatively large effects on maize yields in the face of inadequate adaptive capacity and vice versa because high adaptive capacity lowers vulnerability. A variety of socio-economic proxies have been suggested for use in indicator-based approaches for vulnerability assessment, including: level of education and poverty, availability of safety nets and transportation systems [68][69][70][71][72][73][74].…”

Section: Adaptive Capacity Indexmentioning

confidence: 99%

Vulnerability of Maize Yields to Droughts in Uganda

Epule

Ford

Lwasa

et al. 2017

Water

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Climate projections in Sub-Saharan Africa (SSA) forecast an increase in the intensity and frequency of droughts with implications for maize production. While studies have examined how maize might be affected at the continental level, there have been few national or sub-national studies of vulnerability. We develop a vulnerability index that combines sensitivity, exposure and adaptive capacity and that integrates agroecological, climatic and socio-economic variables to evaluate the national and spatial pattern of maize yield vulnerability to droughts in Uganda. The results show that maize yields in the north of Uganda are more vulnerable to droughts than in the south and nationally. Adaptive capacity is higher in the south of the country than in the north. Maize yields also record higher levels of sensitivity and exposure in the north of Uganda than in the south. Latitudinally, it is observed that maize yields in Uganda tend to record higher levels of vulnerability, exposure and sensitivity towards higher latitudes, while in contrast, the adaptive capacity of maize yields is higher towards the lower latitudes. In addition to lower precipitation levels in the north of the country, these observations can also be explained by poor soil quality in most of the north and socio-economic proxies, such as, higher poverty and lower literacy rates in the north of Uganda.

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Section: Adaptive Capacity Indexmentioning

confidence: 99%

Vulnerability of Maize Yields to Droughts in Uganda

Epule

Ford

Lwasa

et al. 2017

Water

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“…The basic idea is to impute income to households in the Population Census (and Population Counts), using a model that predicts income from a household survey. Empirical evidence based on this method has proven to be precise when applied to data from nations like Ecuador, South Africa, Brazil, Panama, Madagascar and Nicaragua (see Elbers et al 2003, Alderman et al 2002, and Elbers et al 2001. In addition, the small-area estimation methodology has key advantages as it benefits from the strengths of both household surveys and census and avoids their weaknesses.…”

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confidence: 99%

Income Inequality and Violent Crime: Evidence from Mexico's Drug War

Enamorado¹,

López-Calva²,

Rodríguez‐Castelán³

et al. 2014

Policy Research Working Papers

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Evidence of a causal effect of inequality on crime is scarce in developing countries. This paper estimates the effect in a unique context: Mexico's Drug War. The analysis exploits a unique dataset containing inequality and crime statistics for more than 2,000 Mexican municipalities over a 20-year period. An instrumental variable for the Gini coefficient combines the initial income distribution at the municipality level with national trends. The results indicate that a one-point increment in the Gini between 2006-2010 translates into an increase of over 10 drug-related homicides per 100,000 inhabitants. These effects are smaller between 1990 and 2005. The fact that the effect found during the Drug War is substantially higher is likely because the cost of crime decreased with the proliferation of gangs (lowering the marginal cost of criminal behavior), which, combined with rising inequality in some municipalities, increased the expected net benefit of criminal acts after 2005.Keywords: Income Inequality; Crime; Instrumental Variables; Mexico JEL classification: C26, D74, H70, I3, O54 1 Princeton University. E-mail: tede@princeton.edu 2 World Bank. E-mail: lflopezcalva@worldbank.org (corresponding author). 3 World Bank. E-mail: crodriguezc@worldbank.org 4 World Bank. E-mail: hwinkler@worldbank.org The authors would like to thank Alan Fuchs, Samuel Freije-Rodríguez and participants of the third Summer Initiative for Research on Poverty, Inequality and Gender of the World Bank for their thoughtful comments, and Eduardo Ortiz-Juarez and Daniel Valderrama for invaluable research assistance. The findings, interpretations and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent. 2 IntroductionThe question of what is the effect of inequality on crime has been a matter of interest among many researchers and policy analysts. While most of the literature on this topic finds a positive effect of inequality on crime, the empirical evidence has fallen short in establishing an unambiguous direction of causality (see Pridermore, 2011), as well as on whether the effect holds for different types of violent crime. Moreover, when focusing on developing countries the available evidence is weaker given that reliable and comparable crime statistics tend to be scarce. In addition, scholars have faced other major challenges when delving into this subject. For example, cross-country studies are usually biased by measurement error and omitted variables problems, and they are also limited by smallsample sizes. Reverse causality is a matter of concern, since increasing crime rates might also affect inequality by, for example, encouraging richer residents to move out of violent locations.Neumayer (2005) points out that focusing on within-country variation could be a remedy to the difficulty to control for confounding factors at the country level and to the small-sample problem that arises in cross-c...

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“…I begin by focusing on the one probit model for equation (1) and the eight separate probit models using equation (2). These results are reported in Table 8.…”

Section: Univariate Estimation Resultsmentioning

confidence: 99%

“…I estimated a probit model using the NCS-R data for whether one has any mental health problem using the speci…cation in equation (1). Based on the results in Tables 8 and 10, it seems to be important to allow for age to a¤ect prevalence ‡exibly.…”

Section: Results Using Ncs-rmentioning

confidence: 99%