Work in progress

Measures and mediators of household vulnerability to poverty in the Sahel

with Elinor Benami (Virginia Tech), Odyssia Ng and Stephanie Brunelin (World Bank Sahel Adaptive Social Protection Program)

Abstract

Adverse weather conditions pose existential problems to many households dependent on rainfed agriculture for their food security and livelihoods. One posited input to help break the link between variation in weather conditions and poverty rests in first estimating household-level vulnerability to these conditions. Although many new products have emerged to detect changing environmental conditions from a combination of on-the-ground sensors and remotely sensed indicators, relating how much livelihoods change in response to changing weather conditions has remained challenging. This work overcomes this challenge by using household consumption data from the Living Standards Measurement Study (LSMS) survey. In particular, we explore the relationship between household consumption and anomalous weather conditions and then leverage our findings to estimate vulnerability, defined as the probability of household consumption falling below the international poverty line. Three years of available consumption data combined with spatially explicit agro-environmental data reveals that approximately 32% (41%) of the population is identified as vulnerable when evaluating conditions at a spatial resolution of administrative 2 units (20km buffers of expected locations). Furthermore, vulnerability varies significantly (∼ 32%) when accounting for additional household characteristics (e.g., household size, age, and education level of the household head). Policymakers and practitioners can leverage our analysis for effective survey planning and essential information selection for vulnerability assessments. Additionally, researchers may find it helpful to examine the stationarity and historic distribution of weather measures across various time frames. If there is limited variation, opting for a shorter historical time frame and the widely adopted Gaussian normal distribution can be a practical choice.

Each point in the graph represents the vulnerability estimate derived from models based on various input assumptions. The primary driver of the change in the household vulnerability to poverty estimate is the spatial resolution, which shifts the estimate from 30% to 40%.

Futures-based forecasts of cotton prices: Beyond historical averages

with Olga Isengildina-Massa (Virginia Tech) and Shamar L. Stewart (Virginia Tech)

Abstract

This study proposed several alternative specifications for a futures-based forecasting model of cotton cash prices with the aim of improving current approaches constrained by restrictive assumptions and limited information sets. In lieu of historic averages used in current models, our approaches recommend using rolling regressions and including current market information reflected in the deviation of the current basis from its 5-year average. We found that regression approaches with and without the basis deviation term offer a significant improvement (up to 38%) in producing more accurate and informative forecasts of cotton prices at almost all horizons.

Root Mean Square Percentage Errors from Benchmark and Alternative Models show that forecasts from rolling regression models 2 and 3 outperform the forecasts from benchmark model at almost all forecast horizons

Evaluating student learning outcomes from participation in student-managed investment fund

with Olga Isengildina-Massa (Virginia Tech)

Abstract

Experiential learning is becoming increasingly important in higher education. It refers to an active and participatory learning experience that utilizes games, case studies, and simulated realities to illustrate key concepts, facilitating more effective student learning. There is a rich body of literature that describes various types of experiential learning programs, discussing their structure, design, and adoption strategies. However, only a few studies have evaluated the impact of such teaching/learning techniques on student learning outcomes. Most of these studies heavily rely on indirect measures, such as self-reported or peer-assessed outcomes, which are susceptible to personal biases such as individual perceptions, emotions, memory, expectations, beliefs, and assumptions. These measures may not always accurately reflect students' true learning outcomes. Hence, in this paper, we suggest combining knowledge-testing surveys with grading rubrics to assess the impact on student disciplinary knowledge, professional development, and soft skills. Our work provides guidance on developing a relatively unbiased and objective assessment framework for experiential learning programs, which can greatly benefit higher education institutions. This evidence-based approach can be particularly valuable in decision-making processes and maintaining consistent program quality.

Sensing Drought in the Sahel for Household Resilience

Data Science for the Public Good Program

with Poonam Tajanpure, Riley Rudd, Milind Gupta, Catherine Back, Elinor Benami, and Susan Chen (Virginia Tech)

Without effective social protection, extreme weather in sub-Saharan Africa causes people to resort to harmful coping strategies that perpetuate the poverty cycle, including removing children from schools, skipping meals, and selling assets. Prior research shows an alarming increase in both the frequency and severity of droughts in the Sahel region of Africa. Our research is centered on Niger, a country characterized by high poverty rates, food insecurity, and poor living standards. Given these circumstances, the World Bank is actively seeking proactive approaches to social protection that leverage our understanding of the intricate connections between environmental and social conditions. This understanding is pivotal in determining when, where, and how to deploy environmental data to allocate funds effectively. Therefore, the primary objective of this project is to compare two commonly used drought indicators, namely rainfall and vegetation index, in Niger. We seek to estimate the correlation between these indicators and food security. Our findings reveal that the strength and direction of the relationship between drought indicators and food insecurity fluctuate from year to year and depend on the specific indicator chosen. It's important to note that the limited relationship observed may be attributed to the high degree of aggregation across spatial units, leading to the loss of some variation in underlying conditions.

Project: https://sahel-dspg.shinyapps.io/sahel-shiny/

An Empirical Analysis of Household Demand for Organic and Conventional Flour in the United States: Evidence from the 2014 Nielsen Homescan Data

with Rafael Bakhtavoryan, Jose A. Lopez, and Asli Ogunc (Texas A&M University-Commerce)

Using the 2014 Nielsen Homescan panel data, the Heckman two-stage sample selection model estimates the likelihood of purchasing organic and conventional flour as well as the quantity purchased of each. A number of demographic variables are found to be statistically significant impacting the likelihood of purchasing organic and conventional flour. Own-price elasticities of demand for organic and conventional flour indicate that the demand for both flour types is inelastic. Cross-price elasticities of demand suggest an asymmetric pattern between organic and conventional flour demand. Finally, organic and conventional flour are found to be inferior goods.

Article: https://ageconsearch.umn.edu/record/302412/

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