Education

Poverty Dynamics and Risk and Vulnerability

• “Smallholder Identities and Social Networks: The Challenge of Improving Productivity and Welfare,” Christopher B. Barrett. In The Social Economics of Poverty: Identities, Groups, Communities and Networks, Christopher B. Barrett, editor, London: Routledge, 2005. This paper proposes a general framework for resolving the puzzle of how to reconcile the mass of recent evidence on the salutary effects of social capital at the individual level with the casual, larger-scale observation that social embeddedness appears negatively correlated with productivity and material measures of welfare. It advances an analytical framework that not only explains individual productivity or technology adoption behavior as a function of the characteristics or behaviors of others, but that also explains the aggregate properties of social systems characterized by persistently low productivity. Examples from Kenya and Madagascar are used to illustrate the phenomena discussed.
  
  
• “Better Technology, Better Plots or Better Farmers? Identifying Changes In Productivity And Risk Among Malagasy Rice Farmers,” Christopher B. Barrett, Christine M. Moser, Oloro V. McHugh, and Joeli Barison, Forthcoming in American Journal of Agricultural Economics 86(4):869-888 (November). It is often difficult to determine the extent to which observed output gains are due to a new technology itself, rather than to the skill of the farmer or the quality of the plot on which the new technology is tried. We introduce a method for properly attributing observed productivity and risk changes among new production methods, farmers and plots by controlling for farmer and plot heterogeneity using differential production and yield functions. Results from Madagascar show that the new system of rice intensification (SRI) is indeed a superior technology. Although about half of the observed productivity gains appear due to farmer characteristics rather than SRI itself, the technology generates estimated average output gains of more than 84 percent. The increased estimated yield risk associated with SRI would nonetheless make it unattractive to many farmers within the standard range of relative risk aversion.
  
  
• “Welfare Dynamics in Rural Kenya and Madagascar ,” Christopher B. Barrett, Paswel Phiri Marenya, John G. McPeak, Bart Minten, Festus M. Murithi, Willis Oluoch-Kosura, Frank Place, Jean Claude Randrianarisoa, Jhon Rasambainarivo and Justine Wangila, February 2006. This paper presents comparative qualitative and quantitative evidence from rural Kenya and Madagascar in an attempt to untangle the causality behind persistent poverty. We find striking differences in welfare dynamics depending on whether one uses total income, including stochastic terms and inevitable measurement error, or the predictable, structural component of income based on a household’s asset holdings. Our results suggest the existence of multiple dynamic asset and structural income equilibria, consistent with the poverty traps hypothesis. Furthermore, we find supporting evidence of locally increasing returns to assets and of risk management behaviour consistent with poor households' defence of a critical asset threshold through asset smoothing.
  

• Comprehensive descriptive and statistical report: This report will significantly expand upon the preliminary report from the first phase of our education research project. It will cover in detail all the main aspects of the study, including: primary enrollment; grade repetition and dropout during primary and lower secondary school cycles; transitions from primary to secondary school; performance on 2004 academic and life skills tests; indicators of public and private school quality; school management practices; community-school interactions; parents’ perceptions about education and school quality and awareness of education policies. The report will be descriptive as well as statistical, that is, it will apply statistical tests of significance where appropriate.
  
  
• School enrollment and school choice: This study will use the detailed data on local schools and on households to measure the importance of factors such as family background and school availability and quality on the decision to enroll a child in school and the choice of school. Among the school characteristics to be considered are costs and availability of textbooks, two factors that recent education policy in Madagascar has sought to change. An important consideration to be addressed is whether there are differences in quality among public and private primary schools, how this affects the choice of school, and how much the availability of private alternatives affects the rate of primary enrollment. The analysis will use standard techniques for analyzing discrete choice, for example multinomial logit or probit.
  
  
• Progress through school: This paper will examine the determinants of education ‘trajectories.’ It will consider the role of family background (e.g., parental education and wealth), school availability and school quality, child health, and initial performance on tests (measured in the 1998 PASEC survey) in determining how long a child stays in school and the reasons for early dropout or nontransition from the primary to college level. It will also consider the impacts of unanticipated events such as parental illness or death and poor harvests or enterprise losses on the ability of boys and girls to stay in school. Hence the paper aims to identify the reasons for one of the most important problems for education policymakers in Madagascar: very high dropout rates, especially in primary school. The analysis will use appropriate econometric techniques such as ordered or sequential probit and duration analysis.
  
  
• Determinants of scholastic achievement (test performance): This will be a multivariate regression analysis of the determinants of children’s achievement on standard academic (math and French) tests. These tests were administered to all children in the 14-17 age range in the sample, not just those were still in school. The study will determine the relative importance of years of schooling, school quality, and parental and household factors (in particular, parental education) for determining how well children learn. Since the survey included in the testing sample children who had left school, the analysis will be able to address the question of how well children retain knowledge after they leave school—an important question in an environment where so many children do not even finish their primary schooling. Finally, for the subsample of children who were also tested in 1998, the analysis will measure the determinants of how much children gain in knowledge relative to initial levels of academic skills. That is, it will consider the role of early test performance as a predictor of later academic performance.
  
  
• Acquisition of “life skills”: This analysis will measure the determinants of basic practical knowledge as measured by the ‘life-skills’ tests given to 14-17 children in the sample. The tests measure knowledge of good health practices, agricultural knowledge, knowledge of civic and government institutions, etc. Schools potentially have an important role to play in transmitting this knowledge in addition to teaching more academic skills, but impact is not known. As with the academic tests, these tests were given to all such children in the sample, not just those were still in school. The regressions will include household factors such as parental schooling and wealth as well as variables representing school attainment and school quality. Therefore the analysis will address the question of how useful schools in Madagascar are for the acquisition of important non-academic knowledge. It should provide insights into whether and how school curricula should be changed to better address these topics.
  

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