Table 4.

Lasso estimates on the main drivers of economic growth in sub-Saharan Africa

	All countries			Low income	Countries		Middle	High income	Countries
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Variables	DSL	POLR	POIVLR	DSL	POLR	POIVLR	DSL	POLR	POIVLR
Manufacturing value addition	0.062*	0.066*	0.066*	0.125***	0.114***	0.114***	0.008	0.010	0.010
	(0.034)	(0.034)	(0.034)	(0.028)	(0.024)	(0.024)	(0.007)	(0.008)	(0.008)
Population (urban)	0.273***	0.309***	0.255**	0.318**	0.349**	0.349**	0.110	0.152	0.152
	(0.089)	(0.104)	(0.114)	(0.155)	(0.170)	(0.170)	(0.300)	(0.264)	(0.264)
Financial development	0.142***	0.133***	0.144***	0.165***	0.142***	0.142***	0.040	0.047	0.047
	(0.031)	(0.027)	(0.030)	(0.047)	(0.045)	(0.045)	(0.034)	(0.030)	(0.030)
Government expenditure	0.032***	0.034***	0.065**	0.058***	0.059***	0.059***	0.036*	0.023	0.023
	(0.011)	(0.011)	(0.029)	(0.020)	(0.018)	(0.018)	(0.020)	(0.022)	(0.022)
Macroeconomic management	0.757***	0.654**	0.737***	1.493***	0.825**	0.825**	0.885*	0.602	0.602
	(0.274)	(0.261)	(0.269)	(0.511)	(0.371)	(0.371)	(0.497)	(0.438)	(0.438)
Globalization	0.090**	0.078*	0.063	0.002	0.019	0.019	− 0.002	0.036***	0.036***
	(0.041)	(0.041)	(0.042)	(0.031)	(0.028)	(0.028)	(0.023)	(0.013)	(0.013)
Social inclusion	3.308***	2.497***	3.007***	− 1.502	− 1.017	− 1.017	3.231**	1.352	1.352
	(0.904)	(0.825)	(1.006)	(1.447)	(1.222)	(1.222)	(1.425)	(1.045)	(1.045)
Observations	1,720	1,720	1,720	798	798	798	350	350	350
Variables of interest	7	7	7	7	7	7	7	7	7
Controls	56	56	56	66	66	66	66	66	66
Controls selected	39	39	46	25	25	25	27	27	27
Instruments	–	–	12	–	–	7	–	–	7
Wald statistics	89.43***	81.72***	78.11***	38.22***	49.72***	49.72***	16.28**	24.29***	24.29***
Wald p value	0.000	0.000	0.000	0.000	0.000	0.000	0.022	0.001	0.001
Countries	42	42	42	20	20	20	22	22	22

DSL Double-selection lasso, POLR partialing-out linear lasso regression, POIVLR partialing-out instrumental variable linear regression. Robust standard errors in parentheses

*p < 0.1; **p < 0.05; ***p < 0.01;