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Most of the poor in Sub-Saharan Africa live in rural areas where agriculture is the main income source. This agriculture is characterized by low performance and its productivity growth has been identified as a key driver of poverty reduction. In Niger, as in many other African countries, productivity is even lower among female peasants. To build policy interventions to improve agricultural productivity among women, it is important to measure the potential gap between men and women and understand the determinants that explain the gap. This paper uses the Oaxaca-Blinder decomposition methodology at the aggregate and detailed levels to identify the factors that explain the productivity gap. The analysis finds that in Niger on average plots managed by women produce 19 percent less per hectare than plots managed by men. It also finds that the gender gap tends to be widest among Niger's most productive farmers. The primary factors that contribute to the gender productivity gap in Niger are: (i) farm labor, with women facing significant challenges in accessing, using, and supervising male farm labor; (ii) the quantity and quality of fertilizer use, with men using more inorganic fertilizer per hectare than women; and (iii) land ownership and characteristics, with men owning more land and enjoying higher returns to ownership than women.

Agriculture --- Communities & Human Settlements --- Gender --- Gender & Development --- Gender & Health --- Gender & Law --- Housing & Human Habitats --- Labor Policies --- Productivity Gap --- Social Protections and Labor

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Nonseparable household models outline the links between agricultural production and household consumption, yet empirical extensions to investigate the effect of production on dietary diversity and diet composition are limited. Although a significant literature has investigated the calorie-income elasticity abstracting from production, this paper provides an empirical application of the nonseparable household model linking the effect of exogenous variation in planting season production decisions via climate variability on household dietary diversity. Using exogenous variation in degree days, rainfall, and agricultural capital stocks as instruments, the effect of production on household dietary diversity at harvest is estimated. The empirical specifications estimate production effects on dietary diversity using both agricultural revenue and crop production diversity. Significant effects of agricultural revenue and crop production diversity on dietary diversity are estimated. The dietary diversity-production elasticities imply that a 10 percent increase in agricultural revenue or crop diversity results in a 1.8 percent or 2.4 percent increase in dietary diversity, respectively. These results illustrate that agricultural income growth or increased crop diversity may not be sufficient to ensure improved dietary diversity. Increases in agricultural revenue do change diet composition. Estimates of the effect of agricultural income on share of calories by food groups indicate relatively large changes in diet composition. On average, a 10 percent increase in agricultural revenue makes households 7.2 percent more likely to consume vegetables and 3.5 percent more likely to consume fish, and increases the share of tubers consumed by 5.2 percent.

Crop Diversity --- Dietary Diversity --- Economic Theory & Research --- Food & Beverage Industry --- Industry --- Macroeconomics and Economic Growth --- Nutrition --- Poverty Reduction --- Rainfall --- Rural Development --- Rural Development Knowledge and Information Systems --- Rural Poverty Reduction --- Temperature

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Several developing countries are currently implementing phone surveys in response to immediate data needs to monitor the socioeconomic impact of COVID-19. However, phone surveys are often subject to coverage and non-response bias that can compromise the representativeness of the sample and the external validity of the estimates obtained from the survey. Using data from high-frequency phone surveys in Ethiopia, Malawi, Nigeria, and Uganda, this study investigates the magnitude and source of biases present in these four surveys and explores the effectiveness of techniques applied to reduce bias. Varying levels of coverage and non-response bias are found in all four countries. The successfully contacted samples in these four countries were biased toward wealthier households with higher living standards. Left unaddressed, this bias would result in biased estimates from the interviewed sample that do not fully reflect the situation of poorer households in the country. However, phone survey biases can be substantially reduced by applying survey weight adjustments using information from the representative survey from which the sample is drawn. Applying these methods to the four surveys resulted in a substantial reduction in bias, although the bias was not fully eradicated. This highlights one of the potential advantages of drawing phone survey samples from existing face-to-face, representative surveys over random digit dialing or using lists from telecom providers where such adjustment methods can be more limited.

Business Cycles and Stabilization Policies --- Coronavirus --- Coverage Bias --- COVID-19 --- Disease Control and Prevention --- Health, Nutrition and Population --- Inequality --- Living Standards --- Macroeconomics and Economic Growth --- Nonresponse Bias --- Pandemic Impact --- Poverty Reduction --- Sample Representativeness --- Statistical and Mathematical Sciences --- Survey Methodology --- Weighting Methods

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This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study surveys. Several unit-level methods are considered as well as a method that combines unit and area level information, which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that the model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared errors. The results from design-based validation show that all small area estimation methods represent an improvement, in terms of mean squared errors, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias are unknown ex ante, methods that rely only on aggregated covariates should be used with caution, but they may be an alternative to traditional area level models when these are not applicable.

Household Survey --- Nested Error Model --- Parametric Bootstrap --- Poverty Mapping --- Poverty Reduction --- Small Area Estimate --- Small Area Estimation Poverty Mapping

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This paper investigates how land size measurements vary across three common land measurement methods (farmer estimated, Global Positioning System (GPS), and compass and rope), and the effect of land size measurement error on the inverse farm size relationship and input demand functions. The analysis utilizes plot-level ata from the second wave of the Nigeria General Household Survey Panel, as well as a supplementary land validation survey covering a subsample of General Household Survey Panel plots. Using this data, both GPS and self-reported farmer estimates can be compared with the gold standard compass and rope measurements on the same plots. The findings indicate that GPS measurements are more reliable than farmer estimates, where self-reported measurement bias leads to over-reporting land sizes of small plots and under-reporting of large plots. The error observed across land measurement methods is nonlinear and results in biased estimates of the inverse land size relationship. Input emand functions that rely on self-reported land measures significantly underestimate the effect of land on input utilization, including fertilizer and household labor.

Agriculture. --- E-Business. --- Education. --- Land Measurement. --- Private Sector Development. --- Rural Development Knowledge and Information Systems. --- Rural Development. --- Science and Technology Development. --- Science Education. --- Scientific Research and Science Parks. --- Standards and Technical Regulations. --- Survey Methods.