local climatic conditions on household consumption. A case of South Africa

Description

The primary data source for this study is the nationally representative 2017 South African National Income Dynamic Study (NIDS), wave 5 data (Southern Africa Labour and Development Research Unit (SALDRU), 2018. NIDS, wave 5, was administered to over 47,000 individuals in 13,700 households. However, for our study, we limit our sample to 7,135 households with complete information on all the relevant variables. NIDS wave 5 collected various socioeconomic variables at the individual and household levels central to our study. These include demographic characteristics (age, gender, and race), income, consumption, education level, assets, household debt, and employment status. In addition, we use geospatial climate data, mainly temperature and precipitation, produced by the Climate Research Unit (CRU) at the University of East Anglia (https://www.uea.ac.uk/web/groups-and-centres/climatic-research-unit/data )(Harris et al. 2020). The climate data combines data from more than 4,000 weather stations worldwide and satellite data to estimate monthly(yearly) average climate data from 1901-2020. The advantage of this database is that it is provided at fine spatial resolution (0.5x0.5 degree) grids, which allows us to extract the climate data for different geographical levels. We aggregate the data to 52 districts following the geographical boundaries used in the 2011 Census, which matches the district boundaries in the 2017 NIDS data. Finally, we merge the climate data for 2017 with 2017 NIDS, wave 5 data based on the 52 districts in South Africa so that each household is assigned the average yearly climate data for its corresponding district. The data is merged and is in STATA 16 format. We included a dofile that we used to analyse the data

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Run commands in the dofile

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