Data for: Impact of the business environment on output and productivity in Africa

Published: 06-12-2016| Version 1 | DOI: 10.17632/td8tggggz2.1
Elhadj Bah,
Lei Fang


Abstract of associated article: We develop a general equilibrium model to assess the quantitative effects of the business environment, including regulations, crime, corruption, infrastructure and access to finance, on output and total factor productivity (TFP) in Sub-Saharan Africa. The first four dimensions are modeled as a tax on output and the finance dimension is modeled as a borrowing constraint. The model is simulated for a sample of Sub-Saharan African countries using the country-specific financial development and the country-specific joint distribution between productivity and taxes. We find that the simulated output and TFP are highly correlated with those in the data and the model accounts for 48% of the variation of output in the data. Access to finance alone accounts for 39% and the other four dimensions account for 11% of the dispersion in output.


Steps to reproduce

The files for the calibration to the US are: UScalibration.m (main file) , calibratefun.m, mclear1.m, b_max_2cost.m, parameters.m, and results2.m The US distribution of establishments is in “US establishment Census.xlsx” The results are saved in resultUS14.mat 2. With the calibrated model, we conduct some experiments in BLmainExper.m. Results are reported in table 6. 3. For the simulations to African countries, we use BLmain1.m. This file use copuladraws1 to get the sample of taxes. For each country, we draw the tax and verify if the number of establishments is more than 100. If yes, we simulate the model, if not we go to the next country. The results are reported in section 6.2. The data needed are: resultsUS14.mat, sizes.mat, panel.mat, finance.mat, adj.mat Panel.mat contains the business environment statistics for all countries and all establishments. We use countrytax.m to extract the data for each country. The country order in the data files above are: AGO, BDI, BEN, BFA, BWA, CIV, CMR, COG, CPV, DRC, ETH, GAB, GHA, GIN, GMB, GNB, KEN, LBR, LSO, MDG, MLI, MRT, MUS, NER, NGA, RWA, SEN, UGA, ZAF, ZMB. 4. The calibration to Nigeria and the calibration with Pareto distribution can be easily adapted from the US Calibration. The simulations with these alternative calibrations use the same files as in point 3. 5. Figure 5, 6, and 7 are created with figdatavsmodel.m