Globalisation, poverty and corruption: Retarding progress in South Africa

Figures & data

Table 1. Empirical evidence of the relationship between globalisation and poverty reduction.

Author(s)	Data type	Time	Notes on methodology	Results
Trade liberalisation reduces poverty
Desai & Rudra (2018)	Cross-national, time series data for 150 developing countries	2000–10	OLS regression	Countries with trade surpluses in agricultural products because of liberalised trade spend more on social protection by expanding coverage of the extreme poor, eventually reducing poverty. In contrast, developing countries experiencing increased trade in manufacturing experience a decline in social protection coverage, resulting in increased poverty
Olper et al. (2018)	Panel data on child mortality from emerging and developing countries	1960–2010	Addressed potential heterogeneity effects by applying the Synthetic Control Method (SCM)	Trade liberalisation was observed to have significantly reduced poverty for most countries
Ha & Cain (2017)	Panel data on headcount and child mortality rate for over 100 countries	1976–2005	A panel regression technique	Globalisation as measured by trade openness reduces poverty for both indicators
Liyanaarachchi et al. (2016)	Data for Sri Lanka		A macro–micro modelling approach	Trade liberalisation was found to reduce poverty both in the short- and the long-run
Kis-Katos and Sparrow (2015)	Data for 259 districts of Indonesia	1993–2002	Studied the effects of trade liberalisation on poverty through its effect on labour market mechanisms	Reduction in import tariffs on intermediate goods rather than on final inputs drew strong reaction from low-skilled work participation and middle-skilled wages. Intensified competition is a driving force for poverty reduction
Bergh & Nilsson (2014)	Unbalanced panel data (the KOF index of globalisation^a) for 114 countries	1983–2007	Panel regression techniques including fixed effect, a long-run first difference and pooled-OLS regression were applied to address for potential endogeneity of globalisation which had remained unresolved in previous studies	Globalisation reduced absolute poverty and this finding was robust across the different econometric specifications
Tsai & Huang (2007)	Time series data including the income of the bottom quintile of the population and trade as a share of GDP for Taiwan		Regression with cointegration tests	Trade openness alleviated poverty in Taiwan the more open the country’s trade became
The impact of trade liberalisation is either neutral, ambiguous or it does not reduce poverty
Le Goff & Singh (2014)	Cross-country and time-series data on poverty headcount and poverty gap for 30 African countries	1981–2010	Regression analysis using a using the System Generalised Method-of-Moment (GMM) estimator	Trade openness does not increase or decrease poverty for the countries studied, evidence that trade benefits are policy-dependent
Singh & Huang (2011)	Data on financial deepening, headcount poverty, poverty gap, Gini coefficient and income of the poorest quintile for 37 sub-Saharan African countries	1992–2006	Regression using Feasible Generalised Least Squares (FGLS) to address heteroscedasticity	Greater trade openness increased headcount poverty, increased the poverty gap, and caused a decline in the income of the poorest quintile
Attanasio et al. (2004)	Tariff reductions and wage distribution in Colombia	1980s and 1990s	Regression analysis	Tariff reductions caused a rise in unemployment in traded-good sectors
Goldberg & Pavcnik (2003)	Trade and employment data from Brazil and Colombia	1980s and 1990s	Regression analysis	In an environment of more intense competition, firms may reduce their costs by substituting for temporary workers. This might cause a rise in poverty
Nissanke & Thorbecke (2010)	A review of nine empirical papers on globalisation and inequality and poverty in Latin America		Review	The impact of globalisation on poverty and inequality is extremely context specific

^aThe KOF Index of Globalisation is an index of the degree of globalisation of 122 countries, conceived by the Konjunkturforschungsstelle of ETH Zurich, in Switzerland (Swiss Economic Institute, 2018).

Table 2. Data sources and summary statistics.

Source	Definition	Variables	Obs.	Mean	Std. dev.	Min	Max
WDI (2017)	Logarithm of poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)	Log poverty headcount	27	4.1103	0.0214	4.0634	4.1494
	Logarithm of mortality rate, infant (per 1000 live births)	Log mortality Rate	27	3.7521	0.1130	3.5322	3.8979
	Logarithm of life expectancy	Log life expectancy	27	4.0543	0.0626	3.9620	4.1336
	Logarithm of GDP per capita (constant 2010, US$)	Log GDPPC	27	8.4092	0.3519	7.8337	8.9945
	Percentage of population using the Internet	Internet use	27	14.8617	18.3919	0.0133	54.0000
	Logarithm of financial development	Log FD	27	5.9328	0.2343	5.3102	6.2898
	Foreign direct investment	FDI	27	1.3003	1.3345	0.0025	5.9789
	Percentage of mobile cellular subscriptions	Log mobile	27	3.1581	1.7655	0.2558	5.1030
	Secondary school enrolment	Log education	27	4.4839	0.0809	4.2414	4.5933
KOF Swiss Economic Institute (2018)	Globalisation index	Log GLO	27	3.8491	0.1325	3.5812	3.9857
WGI (2018)	Control of corruption	Corruption	27	3.3336	1.0787	2.0000	5.0000
	Rule of law	Institutional quality	27	2.4984	0.6562	1.0000	4.0000

Source: Authors’ own calculations.

Table 3. Variance inflation factor (VIF) results.

Variable	VIF	1/VIF
lnGLO	1.84	0.542067
COR	2.87	0.347991
INT	2.14	0.467111
INQ	1.88	0.531434
lnFD	2.92	0.342795
lnGDPPC	1.56	0.642542
lnMOB	1.43	0.698292
LnEDU	1.17	0.853386
FDI	1.08	0.928105
Mean VIF	1.88

Table 4. Zivot and Andrews unit root test results.

Variables	Levels			1st Difference
	T-statistic	Time break	Decision	T-statistic	Time break	Decision
lnHC	−5.852*	2008	Stationary
lnMR	−2.800	2009	Unit root	−7.748*	2010	Stationary
lnLE	−24.639*	2008	Stationary
lnGLO	−4.587	1997	Unit root	−6.016*	2003	Stationary
COR	−7.056*	1998	Stationary
lnGDPPC	−3.303	2003	Unit root	−6.107*	2003	Stationary
lnFD	−5.495*	2005	Stationary
FDI	−5.194	2001	Unit root	−6.912*	2007	Stationary
INT	−17.799*	2010	Stationary
lnEDU	−4.576	2000	Unit root	−8.707*	2002	Stationary
INQ	−5.208	1998	Unit root	−5.535*	1996	Stationary
lnMOB	−3.169	1997	Unit root	−5.970*	1999	Stationary

*Represents significance at 1% level.

Table 5. ARDL cointegration results and critical values.

Models	Independent variables	Function		F-statistic		Cointegration status
1	lnHC	F(lnGLO, lnCOR, lnGDPPC, INT, lnFD, FDI, lnEDU, INT, lnMOB, INQ)		13.254***		Cointegrated
2	lnMR	F(lnGLO, lnCOR, lnGDPPC, INT, lnFD, FDI, lnEDU, INT, lnMOB, INQ)		17.879***		Cointegrated
3	LnLE	F(lnGLO, lnCOR, lnGDPPC, INT, lnFD, FDI, lnEDU, INT, lnMOB, INQ)		11.502***		Cointegrated
Asymptotic Critical Values (unrestricted intercept and no trend)
Pesaran et al. (2001:300) critical values (Table CI(iii) Case III)		10%		5%		1%
		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
		2.62	3.35	2.26	3.79	3.41	4.68

Note: *, ** and *** denote statistical significance at 10%, 5% and 1% levels respectively.

Table 6. ARDL analysis results (lnHC): long and short run.

Dependent variable = lnHC
Variables	Coefficient	Standard error	T-statistics[Prob]
Long-run analysis
Constant	0.742	0.421	1.761[0.009]
GLO	−0.084	0.038	2.214[0.069]
COR	0.041	0.004	9.582[0.000]
Control variables
lnGDPPC	0.159	0.018	9.032[0.000]
INQ	−0.045	0.007	−6.729[0.001]
lnFD	−0.163	0.011	−14.932[0.000]
FDI	0.076	0.010	7.796[0.000]
lnEDU	−0.002	0.001	−2.924[0.027]
ICT	−0.316	0.032	−9.814[0.000]
INT	0.001	0.000	3.275[0.017]
lnMOB	−0.050	0.014	−3.549[0.012]
R^2	0.9876
Short-run analysis
Constant
ΔGLO	−0.062	0.010	−6.186[0.001]
ΔCOR	0.027	0.002	23.008[0.000]
Control variables
ΔlnGDPPC	0.047	0.003	14.545[0.000]
ΔINQ	−0.036	0.005	−6.6704[0.000]
ΔlnFD	0.076	0.003	23.934[0.000]
ΔFDI	−0.002	0.000	−16.195[0.000]
ΔlnEDU	−0.030	0.008	−3.640[0.011]
ICT
ΔINT	0.001	0.000	14.172[0.000]
ΔlnMOB	−0.064	0.003	−24.828[0.000]
ECMt−1	−0.071	0.002	−31.425[0.000]
R^2	0.986
F-statistic	51.673[0.000]
D. W	2.304
Short-run diagnostic tests
Test	F-statistic	Prob. value
χ^2SERIAL	0.1677	0.089
χ^2ARCH	3.3762	0.079
χ^2RAMSEY	2.4162	0.046

Table 7. ARDL analysis results (lnMR): long and short run.

Dependent variable = lnMR
Variables	Coefficient	Standard error	T-statistics[Prob]
Long-run analysis
Constant	1.7621	0.81890	2.1519[0.057]
GLO	−1.6849	0.68421	2.4625[0.030]
COR	0.0079	0.022052	−0.35998[0.025]
Control variables
lnGDPPC	0.1623	0.15948	1.0182[0.329]
INQ	0.1763	0.066281	2.6602[0.021]
lnFD	−0.0027	0.012912	−0.21337[0.835]
FDI	−0.0015	0.008867	−0.16921[0.868]
lnEDU	1.4077	0.84664	1.6626[0.122]
ICT
INT	0.0017	0.001049	1.6276[0.130]
lnMOB	0.1209	0.12662	0.95529[0.358]
R^2	0.9035
Short-run analysis
Constant	1.7621	0.81890	2.1519[0.057]
ΔGLO	−1.3963	0.44861	3.1125[0.007]
ΔCOR	−0.0664	0.048106	−1.3811[0.187]
Control variables
ΔlnGDPPC	0.13457	0.12647	1.0641[0.304]
ΔlnFD	−0.7024	0.12361	−5.6824[0.000]
ΔFDI	−0.0012	0.007316	−0.16995[0.867]
ΔINQ	0.1461	0.043157	3.3857[0.004]
ΔlnEDU	0.3388	0.37141	0.91231[0.376]
ICT
ΔINT	0.0014	0.8889E−3	1.5928[0.132]
ΔlnMOB	0.1002	0.10193	0.98337[0.341]
ECMt−1	−0.8287	0.15597	−5.3132[0.000]
R^2	0.9054
F-statistic	12.7752[0.000]
D. W	2.6471
Short-run diagnostic tests
Test	F-statistic	Prob. value
χ^2SERIAL	4.2525	0.064
χ^2ARCH	0.16036	0.003
χ^2RAMSEY	2.0876	0.076

Table 8. ARDL analysis results (lnLE): long and short run.

Dependent variable = lnLE
Variables	Coefficient	Standard error	T-statistics[Prob]
Long-run analysis
Constant	3.4733	0.37086	9.3657[0.000]
GLO	1.5448	0.77058	2.0047[0.080]
COR	−0.0073	0.038828	−0.18851[0.055]
Control variables
lnGDPPC	−0.9211	0.39904	−2.3085[0.050]
lnFD	0.6797	0.024602	27.6311[0.000]
FDI	−0.0304	0.026074	−1.1665[0.277]
INQ	0.2718	0.088223	3.0810[0.015]
lnEDU	2.4410	0.78549	3.1076[0.014]
ICT
INT	0.5340	0.00181	0.29483[0.776]
lnMOB	1.0784	0.33984	3.1732[0.013]
R^2	0.9102
Short-run analysis
Constant	1.2524	0.449880	2.7838[0.024]
ΔGLO	−0.0464	0.067844	0.68437[0.504]
ΔCOR	−0.8898	0.004724	−0.18833[0.853]
Control variables
ΔlnGDPPC	−0.0384	0.033872	−1.1354[0.274]
ΔlnFD	0.0446	0.020364	2.1911[0.045]
ΔFDI	−0.0013	0.001445	−0.93847[0.363]
ΔINQ	0.0330	0.012077	2.7361[0.015]
ΔlnEDU	0.1526	0.057746	2.6432[0.018]
ICT
ΔINT	0.8635	0.6500E-3	1.3284[0.204]
ΔlnMOB	0.0739	0.027646	2.6764[0.017]
ECMt−1	−0.1215	0.023865	−5.0941[0.000]
R^2	0.9279
F-statistic	11.4547[0.000]
D. W	1.8493
Short-run diagnostic tests
Test	F-statistic	Prob. value
χ^2SERIAL	0.0076	0.933
χ^2ARCH	6.9141	0.015
χ^2RAMSEY	2.1119	0.189

Figure 1. (A) Plot of cumulative sum of recursive residuals (Model 1). (B) Plot of cumulative sum of squares of recursive residuals (Model 1).

Note: Straight lines represent critical bounds at 5% significance level.

Figure 2. (A) Plot of cumulative sum of recursive residuals (Model 2). (B) Plot of cumulative sum of squares of recursive residuals (Model 2).

Note: Straight lines represent critical bounds at 5% significance level.

Figure 3. (A) Plot of cumulative sum of recursive residuals (Model 3). (B) Plot of cumulative sum of squares of recursive residuals (Model 3).

Note: Straight lines represent critical bounds at 5% significance level.

Table 9. Toda Yamamoto causality test results.

	χ^2	Prob.		χ^2	Prob.
ΔlnMR → ΔlnLE	2.182878	0.1396	ΔlnLE → ΔlnMR	7.291313	0.0069
ΔlnHC → ΔlnLE	2.090599	0.1482	ΔlnLE → ΔlnHC	0.045276	0.8315
ΔGLO → ΔlnLE	0.785379	0.3755	ΔlnLE → ΔGLO	0.001880	0.9654
ΔCOR → ΔlnLE	1.835296	0.1755	ΔlnLE → ΔCOR	0.001911	0.9651
ΔlnHC → ΔlnMR	0.004476	0.9467	ΔlnMR → ΔlnHC	5.288921	0.0215
ΔGLO → ΔlnMR	0.893577	0.3445	ΔlnMR → ΔGLO	0.013947	0.9060
ΔCOR → ΔlnMR	0.165253	0.6844	ΔlnMR → ΔCOR	0.147764	0.7007
ΔGLO → ΔlnHC	0.457662	0.4987	ΔlnHC → ΔGLO	0.293853	0.5878
ΔCOR → ΔlnHC	0.080197	0.7770	ΔlnHC → ΔCOR	0.131983	0.7164
ΔCOR → ΔGLO	1.398093	0.2370	ΔGLO → ΔCOR	3.367715	0.0665

Table 10. Variance decomposition results (lnHC).

Period	LNHC	GLO	COR	LNGDPPC	INQ	LNFD	LNFDI	LNEDU	INT	LNMOB
1	100.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2	68.311740	0.865366	0.129216	16.481910	6.797510	0.385963	0.942674	5.729493	0.191517	0.164616
3	57.776530	1.497676	0.515634	22.704340	7.939065	1.847063	1.342232	6.107837	0.141504	0.128117
4	52.915920	1.324243	0.829904	23.766960	10.756640	2.263296	1.874244	6.014178	0.125626	0.128988
5	51.257100	1.325965	0.818373	23.908630	11.865400	2.278943	1.898015	6.378190	0.145415	0.123964
6	50.484870	1.315468	0.823451	24.056890	12.113390	2.317670	2.129624	6.465504	0.171888	0.121243
7	49.810800	1.309347	1.034046	24.222450	12.169800	2.345019	2.203275	6.576245	0.210581	0.118444
8	49.084600	1.296670	1.247237	24.560700	11.998560	2.408416	2.375505	6.655259	0.257842	0.115204
9	48.380950	1.281815	1.543470	24.839800	11.841000	2.490328	2.504687	6.686404	0.318864	0.112678
10	47.658310	1.259739	1.892165	25.094600	11.653690	2.561030	2.658575	6.722557	0.389013	0.110319

Table 11. Variance decomposition results (lnMR).

Period	LNMR	GLO	COR	INT	INQ	LNEDU	LNFD	LNFDI	LNGDPPC
1	100.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2	80.16816	7.394742	2.086054	2.010177	1.527093	1.347602	0.354489	3.438348	1.673338
3	75.91277	8.964175	1.960760	2.157399	1.792160	1.568959	0.531718	5.252509	1.859548
4	72.64801	8.919575	2.507663	2.083936	1.795996	1.787107	0.518928	7.956260	1.782526
5	70.87407	8.757911	2.820424	2.235006	2.291796	2.146066	0.507319	8.594587	1.772823
6	70.49834	8.806950	2.876062	2.438634	2.279848	2.230738	0.504572	8.595204	1.769656
7	70.27884	8.933321	2.951561	2.451202	2.281177	2.263675	0.508038	8.568768	1.763418
8	70.15475	8.946582	2.984773	2.537653	2.276958	2.260622	0.507088	8.567714	1.763860
9	70.04677	8.975508	3.011976	2.590023	2.276732	2.261678	0.506318	8.569423	1.761569
10	69.95610	8.995550	3.029070	2.657409	2.274074	2.259315	0.506888	8.561950	1.759641

Table 12. Variance decomposition results (lnLE).

Period	LNLE	GLO	COR	INQ	INT	LNEDU	LNFD	LNFDI	LNGDPPC
1	100.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2	94.69130	0.672902	1.769843	0.463700	1.265251	0.170520	0.853577	0.106752	0.006154
3	88.65463	2.350125	2.986296	1.251996	3.008527	0.120486	1.363680	0.254861	0.009395
4	83.58421	4.008996	2.955973	1.741085	5.268558	0.082939	1.476110	0.838814	0.043311
5	79.58829	5.267447	2.636935	1.775353	7.763397	0.060039	1.417532	1.406359	0.084646
6	76.29787	6.032203	2.290769	1.600051	10.37354	0.045368	1.352092	1.889179	0.118924
7	73.49844	6.443330	1.981017	1.363900	12.95793	0.036126	1.344928	2.233862	0.140463
8	71.01530	6.625647	1.720879	1.145151	15.45687	0.029808	1.407205	2.448620	0.150520
9	68.72246	6.666488	1.501030	0.968076	17.85837	0.025215	1.535732	2.569451	0.153180
10	66.53381	6.622501	1.312924	0.835130	20.17408	0.021754	1.716475	2.631734	0.151592

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