Data on Economic Analysis: 2017 Social Accounting Matrices (SAMs) for South Africa

Abstract

The purpose of the Social Accounting Matrix (SAM) is to improve the quality of the database for modelling, including, but not limited to, policy analysis, multiplier analysis, price analysis, and Computable General Equilibrium. This article contributes to constructing the 2017 national SAM for South Africa, incorporating regional accounts. Only in Limpopo Province of South Africa are agricultural industries, labour, and households captured at the district level, while agricultural industry, labour, and household accounts in other provinces remain unchanged. The main data sources for constructing a SAM are found from different sources, such as Supply and Use Tables, National Accounts, Census of Commercial Agriculture, Quarterly Labour Force Survey, South Africa Revenue Service, Global Insight (regional explorer), and South Africa Reserve Bank. The dataset recorded that land returns for irrigation agriculture were highest (18.2%) in the Northern Cape Province of South Africa compared to other provinces, whereas the Free State Province of South Africa rainfed agriculture had the largest shares (22%) for payment to land. Regarding intermediate inputs, rainfed agriculture in the Western Cape, Free State, and Kwazulu-Natal Provinces paid approximately 0.4% for using intermediate inputs. In terms of the districts, land returns for irrigation were highest in the Vhembe district of Limpopo Province of South Africa with 0.3%. Despite Mopani district of Limpopo Province of South Africa having the lowest land returns for irrigation agriculture, it has the highest share (1.6%) of payment to land from rainfed agriculture. The manufacturing and community service sectors had a trade deficit, whereas other sectors experienced a trade surplus. The main challenges found in developing a SAM are scarcity of data to attain the information needed for disaggregation for the sub-matrices and insufficient information from different data sources for estimating missing information to ensure the row and column totals of the SAM are consistent and complete.