Three essays on economic development in sub-Saharan Africa

Anderson, Patrese

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https://hdl.handle.net/2142/113951 Copy

Description

Title

Three essays on economic development in sub-Saharan Africa

Author(s)

Anderson, Patrese

Issue Date

2021-09-15

Director of Research (if dissertation) or Advisor (if thesis)

Baylis, Kathy

Doctoral Committee Chair(s)

Baylis, Kathy

Committee Member(s)

• Michelson, Hope
• Hutchins, Jared
• Evans , Tom

Department of Study

Agr & Consumer Economics

Discipline

Agricultural & Applied Econ

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• food security
• market access
• price prediction
• machine learning

Abstract

The frequency and severity of droughts, floods, and abnormal temperatures is increasing throughout sub-Saharan Africa, where agricultural crop production is primarily rainfed. An extensive body of research links climate shocks to decreases in staple crop production. As most households throughout sub-Saharan Africa are dependent on staple crops for both income and consumption, these shocks are often detrimental to household welfare. In theory, by facilitating the movement of agricultural commodities from surplus to deficit areas, well-functioning markets should mitigate such detrimental effects and prohibit food shortfalls caused by climate shocks. However, in developing countries, due to lack of market integration, price volatility, and other market failures, this does not often happen. This dissertation contains three papers on the interactions between markets and rural households in sub-Saharan Africa. Specifically, the focus is on factors that contribute to food access and availability for rural households, staple food price transmission occurring between both urban and rural markets, and mechanisms through which rural households cope with the long-term effects of climate change. Food markets play a critical role in facilitating access to well-balanced and nutritious food options year-round. Yet, their role in mitigating household food insecurity is understudied in rural developing areas. Rural food markets are heterogeneous across different dimensions; some are small and informal with a few traders, and others are large and highly formalized by rules and governing entities. Due to heterogeneity over multiple dimensions, it is not clear which characteristics of markets matter most for supporting household food security. In the first paper of this dissertation, I use primary data from Zambia to construct a taxonomy of rural market characteristics on four dimensions: formality, competition, access, and product sourcing. To understand the associations that different market types have with surrounding households, the results from the taxonomy are incorporated into several fixed effects specifications. The empirical analysis provides evidence that rural households support a substantial proportion of their dietary diversity through the consumption of foods purchased from local markets. Specifically, I find statistically significant and positive associations occurring between a household’s proximity to large and highly formalized markets and several household food security measures. This result holds for households located both near and far from tarmac roads, which suggests that markets play a vital role in household food security in remote areas and areas with better accessibility to district towns and urban centers. Food security includes both physical and economic access to food. For many households, in both rural and urban areas, affordability is a pivotal factor in determining food access. In the second paper, I use machine learning and traditional econometric methods to construct predictive models of weekly maize prices across 28 markets located in both urban and rural areas of Malawi. I use the errors from the predictive models to make an inference on the degree of price transmission occurring within Malawian maize markets. In this paper the power and potential benefits for using machine learning models to predict maize prices in developing countries is demonstrated. These benefits include the ability to accommodate many covariates while still maintaining high predictive accuracy. Additionally, this paper provides evidence that, contrary to theory, both rural and urban markets play a key role in explaining price transmission occurring between markets. The third paper of this dissertation focuses on the links between rainfall variability and migration. Using data from the Malawi Longitudinal Study of Families and Health (MLSFH) and its supplementary Migration and Health in Malawi (MHM) survey, a dataset of migration histories linking rainfall at the destination and origin to each migrant move was constructed. This data set is used in several linear probability models. These models provide evidence that the probability of an individual’s decision to migrate is positively affected by favorable weather conditions at both the destination and the origin.

Graduation Semester

2021-12

Type of Resource

Thesis

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http://hdl.handle.net/2142/113951

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Copyright 2021 Patrese Anderson

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