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Combining Sustainable Agricultural Practices Pays Off: Evidence on Welfare Effects from Northern Ghana









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    How do extreme weather events affect livestock herders’ welfare? Evidence from Kyrgyzstan 2018
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    This paper examines the impact of the harsh 2012 winter on livestock herding households in Kyrgyzstan and identifies policy options to increase household resilience to such shocks. While existing studies mostly focus on rainfall shocks in tropical or dry climate areas, this analysis examines the exceptionally harsh winter that hit Kyrgyzstan in 2012, which resulted in the death of 25 000 animals. Using a unique household panel survey, merged with observed temperature data, the analysis finds that, on average, the negative effects of the winter shock on household welfare are significant and persistent over time, leading to a 5 percent and a 8 percent decrease in households’ food consumption expenditure in the short- (2011–2013) and medium-run (2011–2016), respectively. When disaggregating by income quantiles, the evidence shows that negative impact is concentrated in the upper quantiles of the welfare distribution. Several policy options are identified as effective in mitigating the negative welfare impacts of the weather shock. First, supporting households to restock their herds following weather shocks is found to significantly improve medium-term welfare by 10 percent relative to those that did not restock. Restocking efforts can be addressed in a holistic manner that takes into account immediate household needs, while simultaneously building long-term resilience in the livestock sector. This may include mitigating animal losses through the development of local forage markets that increase the availability of winter forage, combined with efforts to improve the genetic pool of livestock species through breeding programmes that select for resiliency traits. Second, results show that households living in regions with higher access to public veterinary services had significantly better welfare outcomes following the winter shock. Improvements of veterinary services and strengthening community-based organizations focusing on livestock and pasture development may help herding households to cope with weather shocks.
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    Welfare impacts of climate shocks: evidence from Tanzania 2016
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    Sub-Saharan Africa (SSA) remains the world’s most food-insecure region characterized by high levels of child mortality and poverty and low levels of human and physical capital (FAO, 2009). Countries in SSA, including Tanzania, heavily depend on a smallholder-based agricultural sector, which makes their welfare and food security particularly vulnerable to climate change (Barrios et al., 2008). The goal of this study is to provide a comprehensive analysis of the impact of weather risk on rural hou seholds’ welfare in Tanzania using nationally representative household panel data together with a set of novel weather variation indicators based on interpolated gridded and re-analysis weather data that capture the peculiar features of short term and long term variations in rainfall and temperature. In particular, we estimate the impact of weather shocks on a rich set of welfare indicators (including total income, total expenditure, food expenditure and its share in total expenditure and calori e intake) and investigate whether and how they vary by different definitions of shocks - capturing changes in levels and variations over different time periods. We find that both rainfall and maximum temperature variability exert a negative impact on welfare (i.e. no consumption smoothing) and that households that have adopted SLM practices are able to achieve income-smoothing. We also find that the most vulnerable rural households are much more affected by a rainfall deficit compared to the hou seholds in the top income quantile. Results underline the key role extension services play in enhancing adaptive capacity to reduce vulnerability to adverse weather conditions, as well as the importance of targeting the most vulnerable households in policy interventions to improve food security in the face of weather shocks.
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    Adaptation to Climate Risk and Food Security: Evidence from Smallholder Farmers in Ethiopia 2015
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    This paper explores the impact of climate risk on the adoption of risk decreasing practices and other input choices and evaluates their impact on subjective and objective measures of household welfare (namely net crop income and a food insecurity indicator). The analysis is conducted primarily using a novel data set that combines data from the large-scale and representative Ethiopia Socioeconomic Survey (ERSS), 2011/12 with historical climate and biophysical data. We employ a multivariate probit model on plot level observations to model simultaneous and interdependent adoption decisions and utilize a conditional mixed process estimator (CMP) and instrumental variable (IV) method for the impact estimates. Findings show that there is interdependency between the adoption decisions of different farm management practices which may be attributed to complementarities or substitutability between the practices. Greater riskiness, reflected in the coefficients of variation and higher temperature , increases use of risk reducing inputs such as climate-smart agriculture (CSA) inputs, but decrease use of modern inputs such as chemical fertilizer. Even if higher climate risk does generate higher incentive to adopt, results also confirm the importance of other conventional constraints to adoption that need to be addressed. Yield enhancing inputs such as chemical fertilizer and improved seed are mainly adopted by wealthier households and households having access to credit and extension servic es whereas risk reducing inputs are frequently used by households with lower level of wealth and limited access to credit and households with stable land tenure. Moreover, the CMP and IV estimations showed that the adoption of CSA and modern inputs have positive and statistically significant impacts on the objective measure of food security (net crop income) but no impact is observed for the subjective food security indicator.

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