An assessment on the impact of agricultural spending types on agricultural growth: A case for Zambia, Malawi, South Africa

Figures & data

Figure 1. Changes in agricultural GDP across the three countries since 2000.

Source: World Bank Indicators (2015)

Figure 2. Changes in private investment across the three countries since 2000.

Source: World Bank Indicators (2015)

Figure 3. Changes in net trade across the three countries since 2000.

Source: ITC (2015), calculations based on UN COMTRADE statistics.

Figure 4. Zambia % shares of agricultural expenditure types.

Source: Compiled from various published budget statements obtained from the Ministry of Finance and National Assembly.

Figure 5. Malawi % shares of agricultural expenditure types.

Source: Compiled from various budget statements obtained from the Ministry of Finance

Figure 6. SA % share of agricultural expenditure types.

Source: Compiled from various published budget statements obtained from National Treasury.

Table 1. Long-run impact of agricultural spending on agricultural growth in Zambia

beta	Coeff.	Std. Err.	T-statistic	Prob
z_arL	0.7175947	0.183136	−3.92	0.000
z_ispL	−0.6864784	0.1413458	−4.86	0.000
z_idpL	0.0236264	0.0053496	4.42	0.000
z_pspL	−0.3526457	0.0778973	−4.53	0.000
z_topL	0.0334943	0.0251106	1.33	0.182
z_investL	−0.04567	0.1725153	−0.26	0.791
_cons	−0.0428803
R-squared = 0.9540 Adj R-squared = 0.9195 F (6, 8) = 27.65 (Prob > F = 0.0001) Durbin-Watson statistic = 3.276352

Source: Calculations from own study

Table 2. Long-run impact of agricultural spending on agricultural growth in Malawi

beta	Coeff.	Std. Err.	T-statistic	Prob
m_areL	−0.641043	0.2305186	2.78	0.005
m_ispL	0.4726954	0.1209913	−3.91	0.000
m_idpL	0.3510447	0.1569521	2.24	0.025
m_pspL	0.1815169	0.0997052	1.82	0.069
m_investL	−2.353927	0.3597868	−6.54	0.000
_cons	9.482369
R-squared = 0.5297 Adj R-squared = 0.1717 Durbin-Watson statistic = 1.761199

Source: Calculations from own study

Table 3. Long-run impact of agricultural spending on agricultural growth in SA

beta	Coeff.	Std. Err.	T-statistic	Prob
s_caspL0.2225259	0.0141086	15.77		0.000
s_idpL	−0.3060379	0.0066272	−46.18	0.000
s_arL	1.261912	0.0556384	22.68	0.000
s_aeL	0.067004	0.0121573	5.51	0.000
s_topL	0.0287591	0.0207113	1.39	0.165
_cons	−16.07048
R-squared = 0.6729 Adj R-squared = 0.4275 Durbin-Watson statistic = 2.723109

Source: Calculations from own study

Table 4. A comparison of government spending priorities against the impact on growth

Rank	1	2	3	4
ZAMBIA
Government Priority	ISP	PSP	IDP	AR
Impact on Growth	AR	IDP	PSP (negative)	ISP (negative)
MALAWI
Government Priority	ISP	IDP	ARE	PSP
Impact on Growth	ISP	IDP	PSP	ARE (negative)
SOUTH AFRICA
Government Priority	CASP	AR	IDP	AE
Impact on Growth	AR	CASP	AE	IDP (negative)

ITC. (2015). International trade centre calculations based on UN COMTRADE statistics. Retrieved August 4, 2014. Available online: http://www.trademap.org.

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