Abstract
We study how participation in various social protection schemes can mitigate the negative relationship between adverse rainfall shocks and agricultural production, thus acting as a tool for climate change adaptation. We use panel data from Ethiopia, analyzing the influence of these programs on the technical efficiency of smallholder farmers and how these effects on agricultural production change in the presence adverse rainfall shocks. We find heterogeneous effects of social protection. Public works are negatively associated with productive efficiency, especially in the presence of negative shocks. Recipients of free food display higher sales and profits while cash transfers are more neutral to production and positively associated with farming profitability.
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Notes
Stated preferences of PSNP beneficiaries highlight the importance of the insurance function of in-kind transfers in rural Ethiopia: even though most PSNP payments were paid in cash, and even though the transaction costs associated with food payments were higher than payments received as cash, the majority of the beneficiary households stated that they prefer their payments only or partly in food, with higher food prices inducing shifts in stated preferences toward in-kind transfers, while more food-secure households and those closer to food markets and financial services are more likely to prefer cash (Hirvonen and Hoddinott 2021).
Ethiopia is administratively divided into four levels: regions, zones, woredas (districts) and kebele (wards)
The definition used includes all costs of purchased inputs (labor, seeds, land rental, etc.) as well as any other expenses related to farm production. The opportunity cost of household labor is not included in this definition.
The value of farm production, sales, profits and the measures of inputs used in this analysis refer to both crop and livestock production. We provide more details concerning the specific indicators in Table S1 in the Supplementary Material file. In the applied agricultural economics literature, crop and livestock production functions are more commonly estimated separately, typically because either researchers are interested on a specific commodity (e.g. maize), because different agricultural outputs have different production functions, or because the available data do not allow the researcher to estimate complex production functions. However, several papers have estimated stochastic frontier models for mixed systems and we follow this approach (Wang et al. 1996; Battese et al. 1997; Anríquez and Daidone 2010; Huang and Lai 2012; Ogundari 2014; Melo-Becerra and Orozco-Gallo 2017)
CHIRPS data have been retrieved from the Climate Hazards Group InfraRed Precipitation with Station data,
available at https://www.chc.ucsb.edu/data/chirps.
As a measure of sensitivity we use the Normalized difference vegetation index (NDVI). NDVI data have been retrieved from the Earth Observatory of NASA, available at https://www.earthobservatory.nasa.gov/features/MeasuringVegetation. Deviations of the NDVI were standardized in a similar way to the rainfall anomalies, by subtracting the long-run mean and dividing by the standard deviation of the indicator, calculated at woreda level.
We use a Cobb-Douglas specification as the main functional specification, but test the robustness of the results to alternative (i.e. translog) specifications
This correction essentially consists in creating an intercept (by creating a dummy variable for the use of an input) and then adding 1 to the value before taking the log. In principle, this gives unbiased coefficients, but does not handle negative values, which is important in our case.
All the results presented below were estimated using the commands sfpanel (Belotti et al. 2013).
The ILO World Social Protection database estimates that approximately 7.4% of the population are receive social protection benefits, which would imply a total of approximately 8.3 million people. The PSNP alone is estimated to reach approximately 8 million people.
We carried out the analysis excluding the Gambella, Somali, and Benishangul-Gumuz regions from the sample and the results remained unchanged. Results are available upon request.
We are aware that the Kutlu et al. (2019) estimator can address endogeneity concerns in a stochastic frontier model with longitudinal data. However, we opted for the simpler cross-sectional approach by Karakaplan and Kutlu (2017) to avoid inefficiency issues that unavoidably arise in a short panel when a fixed effects estimator, like the true fixed effects model á la Kutlu et al. (2019) is adopted.
Table S2 in the Supplementary Material reports the results of the three probit estimates.
A negative coefficient means that the variable is associated with a lower level of inefficiency (i.e. more efficient).
The model with the interaction did not converge for the True Random Effects model. However, as can be seen in table S10 in the Supplementary Material file, the translog version of this regression does converge and results are overall very similar.
A worsening of the rainfall conditions is a reduction in the CHIRPS deviation variable, hence the effect on inefficiency becomes positive.
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Acknowledgements
We would like to express our gratitude to two anonymous reviewers for their helpful comments, which contributed to improve substantially the quality of the manuscript. We thank Ana Paula de la O Campos, Nicholas Sitko, Anubhab Gupta and participants at Virginia Tech Ag Econ seminar series for their suggestions on earlier versions of the article. We are also indebted to the editor for his guidance through the reviewing process. All remaining errors are ours. While carrying out the research and writing the article, both authors were employed by the Food and Agriculture Organization of the United Nations (FAO). At the country level, FAO is a key development partner working with governments on social protection programs and policies.
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Daidone, S., Fontes, F.P. The role of social protection in mitigating the effects of rainfall shocks. Evidence from Ethiopia. J Prod Anal 60, 315–332 (2023). https://doi.org/10.1007/s11123-023-00688-x
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DOI: https://doi.org/10.1007/s11123-023-00688-x