Towards an Understanding of Household Vulnerability in Rural Kenya

Christiaensen, Luc; Subbarao, K.

doi:10.1093/jae/eji008

Cited by 199 publications

(165 citation statements)

References 23 publications

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“…Highly educated individuals have higher probability of gaining more stable and/or better-paying jobs. More-educated individuals are likely to be more adaptive to varying circumstances and have higher coping capability (Glewwe and Hall, 1998;Christiaensen and Subbarao, 2005). This is confirmed by our results comparing the determinants of vulnerability, chronic poverty, transitory poverty and chronic non-poverty.…”

Section: Discussionsupporting

confidence: 84%

Estimation of Vulnerability to Poverty Using a Multilevel Longitudinal Model: Evidence from the Philippines

Mina¹,

Imai

2016

The Journal of Development Studies

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Using the panel data for the Philippines in 2003-2009, we estimate a three-level random coefficient model to measure household vulnerability and to decompose it into idiosyncratic and covariate components. We correct heterogeneity bias using Bell and Jones's (2015) 'within-between' formulation. A majority of the poor and 18 percent of the non-poor are found to be vulnerable to unobservable shocks, while both groups of households are more susceptible to idiosyncratic shocks than to covariate shocks. Adequate safety nets should be provided for vulnerable households that lack access to infrastructure, or are larger in size with more dependents and less-educated heads.The JEL codes: C23, I32, O15

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Section: Discussionsupporting

confidence: 84%

Estimation of Vulnerability to Poverty Using a Multilevel Longitudinal Model: Evidence from the Philippines

Mina¹,

Imai

2016

The Journal of Development Studies

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“…In other words, an additional member in the family increases the vulnerability level by 17%; its impact on poverty is higher than that of aggregate risk. This is similar to the results obtained by other studies as well [37][38][39][40] . Moreover, it reduces idiosyncratic risks.…”

Section: Resultssupporting

confidence: 93%

“…These variables are age of the household head (AGE), years of education of the household head (EDU), family size (SIZE), crop-diversification (CRP NO ), number of big and small ruminants owned by the households (RUM B and RUM S ), access to self-help groups (SHGs) and presence of migrant members in the family (MIG). Previous studies report that combinations of these variables associated with demography, agriculture and economic capacity are the major determinants of vulnerability, and are likely to exhibit either positive or negative relationship with the outcome vulnerability 7,24,[37][38][39][40] . It is expected that an inverse relationship exists between the educational qualification of the household head and vulnerability level 7,24 .…”

Section: Study Area Data and Variablesmentioning

confidence: 99%

Can Developmental Interventions Reduce Households’ Vulnerability? Empirical Evidence from Rural India

Patnaik¹,

Das²,

Bahinipati³

et al. 2017

Current Science

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Vulnerability is a multidimensional concept incorporating notions of risk and poverty. While it has been established that higher incidence of poverty in developing countries exacerbates vulnerability, the role of risk requires closer inspection. Developmental interventions in these countries target poverty reduction, which in turn, could reduce vulnerability. However, a key question is whether developmental interventions reduce the vulnerability and risks faced by households. To answer this, the present study empirically examines the impact of developmental interventions on the vulnerability of households in a rural Indian setting. The major advantage hence is that it not only looks into the impact on aggregate vulnerability but also its different components such as poverty, covariate, idiosyncratic and unexplained risks. Empirical analysis is based on a survey of 800 households in the drought-prone villages of western Odisha, India, where a key developmental intervention, Western Orissa Rural Livelihood Project was implemented during the last decade. Adopting 'vulnerability as expected utility' approach, this study reveals three major findings. First, both aggregate risk and poverty are the dominant sources of vulnerability, with the former accounting for a sizable share. Second, the households that benefited from livelihood interventions are found less vulnerable. Third, the other major determinants of vulnerability are education, access to social network, family size and crop-diversification. From a policy perspective, results support continuation of these programmes, but realigning these also target risk reduction.

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“…First, when comparing cross sections of the NBS and RCRE surveys with overlapping years from cross section surveys not using a diary method, it is apparent that some high and low income households are under-represented. 11 Poorer illiterate households are likely to be under-represented because enumerators find it difficult to implement and monitor the diary-based survey, and refusal rates are likely to be high among affluent households who find the diary reporting method a costly use of their time. Second, much of the difference between levels and trends from the NBS and RCRE surveys can be explained by differences in the valuation of home-produced grain and treatment of taxes and fees.…”

Section: The Rcre Household Surveysmentioning

confidence: 99%

“…Variances for the asymptotically normallydistributed estimators 13 of chronic and transient poverty are analytically computed taking full account of the survey design, viz, taking into account sampling stratification and clustering 14 . Figure 5 graphs the conditional standard deviation of poverty gaps g t = 11 The cross-sections used were the rural samples of the 1993, 1997 and 2000 China Health and Nutrition Survey (CHNS) and a survey conducted in 2000 by the Center for Chinese Agricultural Policy (CCAP) with Scott Rozelle (UC Davis) and Loren Brandt (University of Toronto). 12 This rounds up to a national poverty line of 850 RMB per capita in 2002 that is deflated to 1990 using provincial price deflators.…”

Section: Jr and Ede Poverty Gapsmentioning

confidence: 99%

Chronic and transient poverty: Measurement and estimation, with evidence from China

Duclos

Araar

Giles

2010

Journal of Development Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Chronic and Transient Poverty: Measurement and Estimation, with Evidence from ChinaJean-Yves Duclos Abdelkrim Araar John Giles The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit company supported by Deutsche Post World Net. The center is associated with the University of Bonn and offers a stimulating research environment through its research networks, research support, and visitors and doctoral programs. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. The paper contributes to the measurement of poverty and vulnerability in three ways. First, we propose a new approach to separating poverty into chronic and transient components. Second, we provide corrections for the statistical biases introduced when using a small number of periods to estimate the importance of vulnerability and transient poverty. Third, we apply these tools to the measurement of chronic and transient poverty in China using a rich panel data set that extends over approximately 17 years. We find that alternative measurement techniques yield significantly different estimates of the relative importance of chronic and transient poverty, and that precision of estimates is enhanced with simple statistical corrections. JEL Classification: C15, D31, D63, I32

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Towards an Understanding of Household Vulnerability in Rural Kenya

Cited by 199 publications

References 23 publications

Estimation of Vulnerability to Poverty Using a Multilevel Longitudinal Model: Evidence from the Philippines

Estimation of Vulnerability to Poverty Using a Multilevel Longitudinal Model: Evidence from the Philippines

Can Developmental Interventions Reduce Households’ Vulnerability? Empirical Evidence from Rural India

Chronic and transient poverty: Measurement and estimation, with evidence from China

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