Patterns of Official Development Assistance in Tajikistan: effects on growth and poverty reduction

Abduvaliev, Mubinzhon; Bustillo, Ricardo

doi:10.1590/0034-7329202000206

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Article • Rev. Bras. Polít. Int. 63 (2) • 2020 • https://doi.org/10.1590/0034-7329202000206 copy

Patterns of Official Development Assistance in Tajikistan: effects on growth and poverty reduction

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The aim of this paper is to assess the effect of official development assistance on economic growth and poverty reduction in Tajikistan, as well as to examine the recent role of South-South Cooperation. We used a panel data set on economic growth and poverty estimates in Tajikistan, and found that a 1% increase of official development assistance provoked a 1.6% rise in per capita GDP and a 0.48% decrease in poverty levels in Tajikistan. Despite the increased relevance of South-South Cooperation in Tajikistan, the current bilateral cooperation pattern does not allow us to think South–South aid will create employment and growth opportunities.

Tajikistan; official development assistance; economic growth; poverty measure

Variable	Measurement	Data source
L_GDPpc	Natural logarithm of GDP per capita	World Development Indicators
L_GDPpc	Natural logarithm of GDP per capita	(http://databank.worldbank.org)
L_ODAGDP	Natural logarithm of real	World Development Indicator
	total net official	(http://databank.worldbank.org)
	development assistance in ratio to GDP
L_POV	The log headcount index measures the proportion of the log of population that is poor and lives below the poverty line	World Development Indicator
		(http://databank.worldbank.org)
		Millennium Indicators Databases
L_GINI	The standard measure of income inequality based on Lorenz Curve that ranges from 0% to 100%, with 0 representing perfect equality and 100 representing perfect inequality	World Development Indicator
L_GINI		(http://databank.worldbank.org)
L_YearEdu	log of secondary school enrollment (in percentage) used as a proxy for the measure of investment in human capital	Barro and Lee (1994)Barro, R. J., J. Lee. “Sources of economic growth.” Carnegie Rochester Conference Series on Public Policy 40, (1994): 1-46. doi: https://doi.org/10.1016/0167-2231(94)90002-7 https://doi.org/10.1016/0167-2231(94)900...
		See updated version at:
		www.cid.harvard.edu/ciddata.ciddata.htm
L_GovExp	General government final consumption expenditure (% of GDP)	World Development Indicator
L_GovExp	General government final consumption expenditure (% of GDP)	(http://databank.worldbank.org)
L_OPN	Ratio of the sum of imports and exports to the GDP that provides the measure of openness of economy	World Development Indicator
L_OPN		(http://databank.worldbank.org)
L_CPIACor	Transparency, accountability, and corruption in the public sector rating (1=low to 6=high)	World Bank’s Country Policy and Institutional Assessment
L_LF	Labour Force participation ratio	World Development Indicator
L_LF	Labour Force participation ratio	(http://databank.worldbank.org)

	Strengths	Weaknesses
1	VECM allows us to obtain jointly the long-term and short-term relationships between variables	We can conduct only for the series which are stations in their differences (I)1
2	VECM model would be correctly specified and the interpretation of results are simple yet intuitive	There is much debate on how the lag lengths should be determined
3	VECM allows us to deal with both stationary and non-stationary variables with different orders of integration	It is possible to end up with a model including numerous explanatory variables, with different signs, which has implications for degrees of freedom
4	VECM allows us to examine the serial correlation, functional form, normality and heteroscedasticity
5	VECM allows us to find the first differenced variables and error correction term
6	The advantage of VECM over VAR is that the resulting VAR from VECM representations has more efficient coefficient estimates

Varibale	ADF		DF-GLS	KPSS

	with constant	with constant and trend	Perron-Qu method including a trend	test statistic including trend	robust estimate of variance
LnGDPpc _t	-0.0816	-0.2690	-0.2737	0.1216	0.0886
LnODA _t	-0.0717	-1.2621	-0.7271	0.1473	0.1418
LnEdu _t	-0.4482	-0.4649	-0.3185	0.1005	0.0004
LnGE _t	-0.5098	-1.2262	-0.8548	0.0561	0.0232
LnCPIA _t	-0.0769	-0.3581	-0.3531	0.1476	0.0077
LnOPN _t	-0.2690	-0.3133	-0.3171	0.1473	0.1418
LnGINI _t	-0.4485	-0.4331	-0.4486	0.0854	0.0070
LnPOV _t	-0.1892	-0.1563	-0.2076	0.1285	0.9590
LnLF _t	-0.0138	-0.0425	-0.1322	0.1763	0.0003
Variables’ first difference
Δ LnGDPpc _t	-0.6115*	-0.5965*	-0.6060	0.1633	0.0401
Δ LnODA _t	-1.6068***	-1.6078***	-1.1059	0.0826	0.0279
Δ LnEdu _t	-0.8774	-0.8793*	-0.8791	0.0844	0.0001
Δ LnGE _t	-1.2158***	-1.2280***	-1.2244	0.0575	0.0247
Δ LnCPIA _t	-1.0625***	-1.0978**	-1.0955	0.0790	0.0020
Δ LnOPN _t	-1.0160***	-1.5137***	-1.0190	0.1287	0.0375
Δ LnGINI _t	-1.0754***	-1.0836***	-1.0778	0.0928	0.0029
Δ LnPOV _t	-0.9283***	-1.0159**	-1.0111	0.1225	0.2071
Δ LnLF _t	-0.0806	-0.2800*	-0.2219	0.1631	Mar-02

Maximum likelihood estimates, observations			AIC = -25.5907
1999-2016 (T = 18)			BIC = -22.0292
Determinant of covariance matrix = 1.127486			HQC = -25.0996
	Coefficient	Std. Error		t-ratio	p-value
D_L_GDPpc	1.68651	0.750309		2.24771	0.0412 ***
D_L_ODA/GDP	0.62408	0.433775		1.43871	0.1722*
D_L_CPIACor	−0.03843	0.359568		−0.1069	0.9164
D_L_GovExp	0.80176	0.352051		2.27741	0.0390 **
D_ L_OPN	2.54193	1.31979		1.92601	0.0747 *
D_L_Edu	0.02785	0.08983		0.3101	0.7611
D_l_LF	0.05830	0.02279		2.55731	0.0228 **

Dependent variable POV

Variables	MODEL 1	MODEL 2
Const	−6.61228	−1.33650
Const	(0 3306)	(0.0102**)
L_GDPpc	−0.791557	-0.50117
L_GDPpc	(0.0338 **)	(0.007***)
L_ODA/GDP	−0.030769	-0.48140
L_ODA/GDP	(0.9052)	(0.0625*)
L_CPIACor	1.12763	Jun-33
L_CPIACor	(0.2180)	(0.9992)
L_GovExp	0.652253	0.33745
L_GovExp	(0309**)	(0.1871)
L_GINI	6.21623	0.10627
L_GINI	(0.0001***)	(0.1586)
L_OPN	1.08636	0.59160
L_OPN	(0.02525**)	(0.0178**)
L_Edu	−6.32528	0.01862
L_Edu	(0.0443**)	(0.2166)

Mean dependent var.	3.040299	0.071546
R-squared	0.594105	0.321320
Adjusted R-squared	0.514899	0.282027
Log-likelihood	5.550056	170.04314
Akaike criterion	4.899888
Hannan-Quinn	6.178580
Durbin-Watson	1.966459	1.459002
AIC		-10.8937
BIC		-7.3322
HQC		-10.4026

Variable	Mean	Median	S.D.	Min	Max
L_GDPpc	6.101	6.260	0.705	4.940	7.010
L_OPN	4.671	4.670	0.294	4.220	5.300
L_POV	3.040	3.440	0.866	1.590	3.960
L_GINI	3.450	3.460	0.0654	3.300	3.530
L_Edu	2.350	2.350	0.0164	2.330	2.370
L_GovExp	2.380	2.410	0.210	2.110	2.690
L_ODAGDP	-13.7	-13.7	0.530	-14.4	-12.9
L_CPIACor	0.764	0.693	0.107	0.693	0.916
L_LF	14.80	14.81	0.173	14.51	15-Jan

Centro de Estudos Globais da Universidade de Brasília Centro de Estudos Globais, Instituto de Relações Internacionais, Universidade de Brasília, Campus Universitário Darcy Ribeiro, Brasília - DF - 70910-900 - Brazil, Tel.: + 55 61 31073651 - Brasília - DF - Brazil
E-mail: rbpi@unb.br

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[TFN2] Note: the lag of ADF test is determined by the AIC and BIC values.

[TFN3] Lag order is shown in parenthesis based on AIC and BIC at ADF level. * , ** and *** indicate siginficant at 1%, 5% and 10%, respectively.

[TFN4] For DF-GLS critical values after the first difference as follows: -2.89 (10%), -3.19 (5%), -3.46 (2.5%), -3.77(1%)

[TFN5] For KPSS critical values after the first difference: 0.125 (10%), 0.150 (5%), 0.204 (1%)

L_GDPpc	1.0	-0.6	-1.0	0.5	0.4	-0.0	0.6	0.7	1.0
L_OPN	-0.6	1.0	0.4	-0.8	-0.4	-0.0	-0.3	-0.0	-0.4
L_ODA/GDP	-1.0	0.4	1.0	0.5	-0.4	0.0	-0.5	-0.8	-0.9
L_POV	0.5	-0.8	-0.5	1.0	0.5	-0.1	-0.0	0.1	0.4
L_GINI	0.4	-0.4	-0.4	0.5	1.0	-0.2	-0.1	0.3	0.4
L_Edu	-0.0	-0.0	0.0	-0.1	-0.2	1.0	-0.2	-0.1	0.1
L_GovExp	0.6	-0.3	-0.5	-0.0	-0.1	0.2	1.0	0.5	0.6
L_CPIAcor	0.7	-0.0	-0.8	0.1	0.3	-0.1	0.5	1.0	0.8
L_LF	1.0	-0.4	-0.9	0.4	0.4	0.1	0.6	0.8	1.0
	L_GDPpc	L_OPN	L_ODA/GDP	L_POV	L_GINI	L_Edu	L_GovExp	L_CPIAcor	L_LF