Weather shocks and food price seasonality in Sub-Saharan Africa: Evidence from Niger

Kakpo, Ange; Mills, Bradford F.; Brunelin, Stéphanie

doi:10.1016/j.foodpol.2022.102347

Cited by 12 publications

(4 citation statements)

References 25 publications

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Section: Hypothesesmentioning

confidence: 99%

“…In each of these stages, farmers face different frictions. The relatively large reliance of a large part of the Malian population (62% of all employed people work in agriculture; World Bank, 2021) on agriculture as an income‐generating activity leaves farmers more vulnerable to seasonal price fluctuations from harvest cycles (Gilbert et al, 2017; Kakpo et al, 2022). Hence, one can also assume that the credit demand of farmers is following such agricultural cycles, because farmers have different needs for finance during the different stages of the harvest season, for example, to purchase seeds or fertilizers, invest in storage facilities or hire workers.…”

Section: Hypothesesmentioning

confidence: 99%

“…The weather unpredictability contributes to higher volatility of the agricultural output, affects food prices, and makes it more difficult for Malian farmers to obtain agricultural loans. This restricts farmers' ability to grow their businesses and strengthen their resilience even further (Kakpo et al, 2022; World Bank, 2022b). According to the World Bank (2015a, 2015b), Malian banks play a crucial role as the primary providers of agricultural credit in Mali.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the role of interest rates, macroeconomic environment, agricultural cycle, and gender on loan demand in the agricultural sector: Evidence from Mali

Ölkers,

Mußhoff

2023

Agribusiness

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Formal credit plays an important role for the development of the agriculture sector in developing countries because many farmers are characterized as liquidity constrained. Access to credit can increase farmers' purchasing power for inputs and agricultural technology, thus raising the overall productivity. Farmers in Mali are particularly vulnerable to shocks, such as heavy precipitation events. Access to liquidity to increase the resilience of the agricultural sector is essential. Therefore, higher financing volumes are required, which make the analysis of loan demand in agriculture of interest. The purpose of this paper is to empirically investigate the role of the interest rate, the macroeconomic environment, the agricultural cycle and the gender of the farmer on the loan demand in the agricultural sector from a country in the Sahel. Unique and comprehensive loan data at the farm level, provided by a commercial Malian bank, is used for this analysis. The analysis covers the period from 2010 to 2020. Two different estimation strategies are combined. First, an ordinary least square regression is applied with the granted loan amount as the dependent variable. Second, the machine learning technique, least absolute shrinkage and selection operator, is applied to select the most relevant features to be used as explanatory variables in the estimation. The results reveal that the interest rate, the gross value added, the farmer's gender as well as the agricultural cycle have statistically significant effects on the granted loan demand in agriculture. These results are of interest to policymakers, who deal with financial inclusion as well as market failures, and agricultural financial institutions who could incorporate such information in the design of future loan products to stimulate farmers' loan demand, especially for female farmers. [EconLit Citations: G20, G21, O13, O16, Q14, Q18].

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Section: Hypothesesmentioning

confidence: 99%

Section: Hypothesesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the role of interest rates, macroeconomic environment, agricultural cycle, and gender on loan demand in the agricultural sector: Evidence from Mali

Ölkers,

Mußhoff

2023

Agribusiness

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“…Our main explanatory variable is drought, a proxy of weather shock, defined as the number of dry months within the last year or, alternatively, within the last growing season before the survey. Drawing on the existing literature (Burke and Emerick, 2016;Lee et al, 2019;Kakpo et al, 2022), we calculate this drought variable as follows. First, for each EA-month of the year before the survey, we generate rainfall z-scores:…”

Section: Weather Datamentioning

confidence: 99%