Economic growth or social expenditure: what is more effective in decreasing poverty and income inequality in the EU – a panel VAR approach

Velkovska, Ivana; Trenovski, Borce

doi:10.3326/pse.47.1.5.

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Economic growth or social expenditure: what is more effective in decreasing poverty and income inequality in the EU – a panel VAR approach

Ivana Velkovska*

Borce Trenovski*

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https://doi.org/10.3326/pse.47.1.5

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Table 1
Literature review

Study	Period	States	Methodology	Results
Dafermos and Papatheodorou, 2010	1994-2007	14 EU members	Panel model	Social expenditure significantly decreases poverty and inequality
Nelson, 2013	1990-2008	28 EU members	Panel model	European social protection inadequate for redistribution
Cyrek, 2019	2007-2013	All EU members	Panel model	Effectiveness of social protection declined during euro zone crisis
Andrés-Sánchez, Belzunegui-Eraso and Valls-Fonayet, 2020	2011-2015	28 EU members	Panel model	The southern member states are less effective in tackling poverty and inequality
Molina-Morales, Amate-Fortes and Guarnido-Rueda, 2014	1996-2006	27 EU members	Panel model	The social protection expenditure is dependent on the political will of the country
Mansi et al, 2020	2009 -2018	EU and WB	Multiple regression analysis using the fixed effect model	Economic growth does have a significant impact on reducing poverty
Bosco and Poggi, 2019	2008-2011	26 EU countries	Dynamic three-level model	The risk of poverty is negatively related to the size of the structural social expenditure
Da Silva and Andrade, 2016	2003- 2013	EU-27	Nonparametric panel data model	Results show different effectiveness for different countries and for different levels of social transfers which above a saturation point result in inefficiency.
Sanchez and Perez-Corral, 2018	2005-2014	28 EU members	Dynamic panel models	The results show the existence of a negative correlation between public social expenditure as a whole and income inequality.
Hidalgo-Hidalgo and Iturbe-Ormaetxe, 2018	2005	17 EU members	Cross-section analysis	Public expenditure in primary education has a strong effect on raising individuals above the poverty line
Leventi, Sutherland and Tasseva, 2018	2013	7 EU members	Microsimulation models	Most cost-effectively in most countries are increasing child benefits and social assistance
Doina and Viorica, 2017	2015	27 EU members	Regression analysis	The expenditure made by the state have a significant influence on poverty reduction. The greatest influence is made by expenses for social protection and they are followed by the health care, business and education-related expenses
Cammeraat, 2020	1990-2015	22 EU and OECD members	2SLS regression models	Social expenditure reduces poverty and inequality without being harmful for GDP growth. Targeted schemes are most effective in reducing poverty, while social expenditure types with a universal character are more effective in reducing inequality.
Antonelli & De Bonis, 2017	2013	22 EU members	Cross section analysis	States that have the highest levels of social expenditure are also the ones with highest effectiveness of social expenditure
Caminada and Goudswaard, 2010	1990-2007	OECD and EU-15	Cross country analysis	If pensions are treated as transfers, we find a strong relationship between levels of social spending and antipoverty effects of social transfers and taxes. Social spending seems to be an important determinant of a country’s poverty outcome. Each percentage point of social expenditure alleviates poverty in both EU15 and non-EU15 countries by 0.7 percentage point on average
Caminada and Goudswaard, 2009	2005, 2006	OECD and EU-15	Cross country analysis	They do not find a strong relationship between levels of social Spending and antipoverty effects of social transfers and taxes. At the program level, family programs and child support alleviate poverty to a large extent.
Fonayet, Eraso and Sánchez, 2020	2007-2015	EU	Panel model	Correlation between social expenditure and the levels of poverty is not strong
Bayar & Sasmaz, 2018	2005-2014	Selected CE and EU countries	Causality analysis	There is no causal interaction between social expenditures and poverty in this sample

Table A1
pVAR results for model 1

Panel vector autoregression
GMM Estimation
Initial weight matrix: Identity
GMM weight matrix: Robust
No. of obs. = 180
No. of panels = 30
Ave. no. of T = 6.000
Variables	Coef.	Std. err.	z	P>\|z\|	[95% Conf. Interval]
dlogSPE
dlogSPE L1	-0.22	0.24	-0.93	0.35	-0.69	0.24
dlogSPE L2	-0.20	0.15	-1.29	0.19	-0.50	0.10
dPVR L1	0.00	0.00	1.97	0.04	0.00	0.01
dPVR L2	0.00	0.00	0.56	0.57	-0.00	0.00
dINQ L1	0.00	0.00	-1.21	0.22	-0.01	0.00
dINQ L2	0.00	0.00	-1.56	0.11	-0.01	0.00
dlogGDP L1	0.09	0.17	0.54	0.59	-0.24	0.42
dlogGDP L2	0.07	0.10	0.65	0.51	-0.14	0.28
dPVR
dlogSPE L1	-3.49	8.51	-0.41	0.68	-20.19	13.20
dlogSPE L2	-0.18	4.66	-0.04	0.96	-9.33	8.96
dPVR L1	0.05	0.19	0.29	0.77	-0.32	0.44
dPVR L2	0.02	0.09	0.22	0.82	-0.16	0.20
dINQ L1	0.01	0.24	0.07	0.94	-0.46	0.50
dINQ L2	0.16	0.15	1.09	0.27	-0.13	0.47
dlogGDP L1	-5.88	4.02	-1.46	0.14	-13.77	2.01
dlogGDP L2	-7.87	3.01	-2.61	0.00	-13.78	-1.97
dINQ
dlogSPE L1	-5.99	5.90	-1.01	0.31	-17.56	5.58
dlogSPE L2	-5.90	4.42	-1.33	0.18	-14.58	2.77
dPVR L1	0.19	0.14	1.38	0.16	-0.08	0.47
dPVR L2	0.12	0.07	1.70	0.08	-0.01	0.26
dINQ L1	-0.17	0.16	-1.07	0.28	-0.50	0.14
dINQ L2	-0.03	0.15	-0.24	0.81	-0.33	0.26
dlogGDP L1	-2.18	3.18	-0.68	0.49	-8.42	4.06
dlogGDP L2	8.04	2.57	3.12	0.00	2.99	13.09
dlogGDP
dlogSPE L1	0.01	0.26	0.07	0.94	-0.50	0.54
dlogSPE L2	-0.06	0.14	-0.46	0.64	-0.35	0.21
dPVR L1	0.00	0.00	0.45	0.65	-0.00	0.00
dPVR L2	0.00	0.00	1.34	0.18	-0.00	0.00
dINQ L1	-0.00	0.00	-0.33	0.74	-0.01	0.00
dINQ L2	-0.00	0.00	-1.08	0.28	-0.01	0.00
dlogGDP L1	0.32	0.22	1.42	0.15	-0.12	0.76
dlogGDP L2	0.01	0.08	0.22	0.82	-0.14	0.18

Source: Authors’ calculation using STATA 13.

Table A3
pVAR results for model 2

GMM Estimation
Initial weight matrix: Identity
GMM weight matrix: Robust
No. of obs. = 180
No. of panels= 30
Ave. no. of T = 6.000
Variables	Coef.	Std. Err.	z	P>\|z\|	[95% Conf. Interval]
dlogSPE
dlogSPE L1	-0.29	0.28	-1.06	0.29	-0.85	0.25
dlogSPE L2	-0.23	0.18	-1.28	0.20	-0.58	0.12
dPVR L1	0.00	0.00	1.76	0.07	0.00	0.01
dPVR L2	0.00	0.00	0.21	0.83	0.00	0.00
dlogGDP L1	0.13	0.17	0.74	0.46	-0.21	0.47
dlogGDP L2	0.12	0.10	1.14	0.25	-0.08	0.34
dPVR
dlogSPE L1	-2.59	9.71	-0.27	0.78	-21.62	6.43
dlogSPE L2	0.17	4.86	0.04	0.97	-9.35	9.69
dPVR L1	0.05	0.18	0.30	0.76	-.313	0.42
dPVR L2	0.03	0.08	0.46	0.64	-0.12	0.19
dlogGDP L1	-6.37	3.76	-1.69	0.09	-13.75	0.99
dlogGDP L2	-8.54	2.84	-3.00	0.00	-14.13	-2.96
dlogGDP
dlogSPE L1	0.00	0.28	-0.02	0.98	-0.57	0.55
dlogSPE L2	-0.07	0.15	-0.51	0.60	-0.37	0.21
dPVR L1	0.00	0.00	0.45	0.65	0.00	0.00
dPVR L2	0.00	0.00	1.22	0.22	0.00	0.00
dlogGDP L1	0.33	0.22	1.51	0.13	-0.09	0.77
dlogGDP L2	0.03	0.08	0.46	0.64	-0.12	0.20

Source: Authors’ calculation using STATA 13.

Table A5
pVAR results for model 3

GMM Estimation
Initial weight matrix: Identity
GMM weight matrix: Robust
No. of obs. = 180
No. of panels= 30
Ave. no. of T = 6.000
Variables	Coef.	Std. Err.	z	P>\|z\|	[95% Conf. Interval]
dlogSPE
dlogSPE L1	-0.19	0.22	-0.88	0.38	-0.63	0.24
dlogSPE L2	-0.18	0.15	-1.19	0.23	-0.47	0.11
dINQ L1	-0.00	0.00	-0.75	0.45	-0.01	0.00
dINQ L2	-0.00	0.00	-1.37	0.17	-0.01	0.00
dlogGDP L1	0.10	0.16	0.61	0.53	-0.22	0.42
dlogGDP L2	0.04	0.10	0.44	0.66	-0.15	0.24
dINQ
dlogSPE L1	-4.98	5.72	-0.87	0.38	-16.21	6.24
dlogSPE L2	-5.37	4.09	-1.31	0.18	-13.38	2.64
dINQ L1	-0.09	0.14	-0.67	0.50	-0.37	0.18
dINQ L2	0.02	0.12	0.20	0.84	-0.22	0.27
dlogGDP L1	-1.86	3.15	-0.59	0.55	-8.04	4.32
dlogGDP L2	7.25	2.68	2.70	0.00	1.99	12.51
dlogGDP
dlogSPE L1	0.03	0.26	0.12	0.90	-0.49	0.55
dlogSPE L2	-0.06	0.14	-0.44	0.66	-0.35	0.22
dINQ L1	-0.00	0.00	-0.20	0.84	0.00	0.00
dINQ L2	-0.00	0.00	-0.99	0.32	0.00	0.00
dlogGDP L1	0.32	0.22	1.44	0.15	-0.11	0.77
dlogGDP L2	0.01	0.08	0.13	0.89	-0.15	0.17

Source: Authors’ calculation using STATA 13.

Abrigo, M. and Love, I., 2016. Estimation of panel vector autoregression in Stata. Stata Journal, 16(3), pp. 778-804 [CrossRef]

Andrés-Sánchez, J., Belzunegui-Eraso, Á. and Valls-Fonayet, F., 2020. Pattern recognition in social expenditure and social expenditure performance in EU 28 countries. Acta Oeconomica, 70(1), pp. 37-61 [CrossRef]

Andrews, D. W. K. and Lu, B., 2001. Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics, 101(1), pp. 123-164 [CrossRef]

Antonelli, M. and De Bonis, V., 2017. Social SPEnding, Welfare and Redistribution: A Comparative Analysis of 22 European Countries. Modern Economy, 8(11), pp. 1291-1313 [CrossRef]

Atkinson, A. B., 2000. A European social agenda: poverty benchmarking and social transfers. EUROMOD Working Paper, No. EM3/00.

Atkinson, A. B., 2015. Inequality: What Can Be Done? Harvard University Press [CrossRef]

Bayar, Y. and Sasmaz, M. U., 2018. Social Expenditures and Poverty in Central and Eastern European Union Countries: A Causality Analysis. In: D. Procházka, eds. The Impact of Globalization on International Finance and Accounting, Springer Proceedings in Business and Economics. Cham: Springer, pp. 257-263 [CrossRef]

Becker, W. [et al.], 2020. Wrapping up the Europe 2020 strategy: A multidimensional indicator analysis. Environmental and Sustainability Indicators, 8 [CrossRef]

Bosco, B. and Poggi, A., 2019. Middle Class, Government Effectiveness and Poverty in the EU: A Dynamic Multilevel Analysis. The Review of Income and Wealth, 66(1), pp. 94-125 [CrossRef]

Caminada, K. and Goudswaard, K. P., 2009. Effectiveness of Poverty Reduction in the EU: A Descriptive Analysis. Poverty & Public Policy, 1(2), pp. 1-49 [CrossRef]

Caminada, K. and Goudswaard, K. P., 2010. How well is social expenditure targeted to the poor? In: P. Saunders and R. Sainsbury, eds. Social Security, Poverty and Social Exclusion in Rich and Poorer Countries. Mortsel: Intersentia, pp. 97-112.

Cammeraat, E., 2020. The relationship between different social expenditure schemes and poverty, inequality and economic growth. International Social Security Review, 73(2) [CrossRef]

Canova, F. and Ciccarelli, M., 2013. Panel Vector Autoregressive Models: A Survey. ECB Working Paper, No. 1507 [CrossRef]

Cyrek, M., 2009. Government social SPEnding in the EU countries: efficiency in poverty and income inequality reduction. Equilibrium. Quarterly Journal of Economics and Economic Policy, 14(3), pp. 405-424 [CrossRef]

Da Silva, N. B. and Andrade, J. S., 2016. The relationship between social transfers and poverty reduction: A nonparametric approach for the EU-27. GEMF Working Papers, No. 2016-09.

Dafermos, Y. and Papatheodorou, C., 2010. The Impact of Economic Growth and Social Protection on Inequality and Poverty: Empirical Evidence from EU Countries. 1st International Conference in Political Economy, September 10-12, 2010, Rethymno, Crete.

De Bonis, V. and Antonelli, M. A., 2018. Welfare State and Social SPEnding: Assessing the Effectiveness and the Efficiency of European Social Policies in 22 EU countries. Public Finance Research Papers, No. 32.

De Hoyos, R. and Sarafidis, V., 2006. Testing for Cross-sectional Dependence in Panel Data Models. The Stata Journal, 6(4), pp. 482-496 [CrossRef]

Dees, S. and Guntner, J., 2014. Analysing and Forecasting Price Dynamics Across Euro Area Countries and Sectors: A Panel VAR Approach. ECB Working Paper, No. 1724 [CrossRef]

Doina, J. and Viorica, C., 2017. The Statistical Analysis of the Relationship Between Poverty and Public Expenses in the European Union Countries. “Ovidius” University Annals, Economic Sciences Series, 17(2).

European Commission, 2019. Joint Employment Report 2019.

European Commission, 2022. Employment, Social Affairs & Inclusion.

Fana, M. [et al.], 2020. The COVID confinement measures and EU labour markets. Publications Office of the European Union, EUR 30190 EN 30190 IT.

Ferrera, M., 2007. Democratization and Social Policy in Southern Europe. From expansion to “recalibration” (updated version 2007). Geneva: United Nations Research Institute for Social Development (UNISRID).

Fonayet, F. V., Eraso, A. B. and Sánchez, J. D. A., 2020. Efficiency of Social Expenditure Levels in Reducing Poverty Risk in the EU-28. Poverty and Public Policy, 12(1), pp. 43-62 [CrossRef]

Frees, E. W., 2004. Longitudinal and Panel Data: Analysis and Applications in the Social Sciences. Cambridge: Cambridge University Press [CrossRef]

Harris, R. D. F. and Tzavalis, E., 1999. Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics, 91, pp. 201-226 [CrossRef]

Hidalgo-Hidalgo, M. and Iturbe-Ormaetxe, I., 2018. Long-run effects of public expenditure on poverty. The Journal of Economic Inequality, 16, pp. 1-22 [CrossRef]

Hsiao, C., 2003. The Analysis of Panel Data. Cambridge University Press [CrossRef]

Leventi, C., Sutherland, H. and Tasseva, I. V., 2019. Improving poverty reduction in Europe: What works best where? Journal of European Social Policy, 29(1), pp. 9-43 [CrossRef]

Levin, A., Lin, C.-F. and Chu, C.-S. J., 2002. Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), pp. 1-24 [CrossRef]

Mansi, E. [et al.], 2020. Poverty-A Challenge for Economic Development? Evidences from Western Balkan Countries and the European Union. Sustainability, 12 (18), 7554 [CrossRef]

Molina-Morales, A., Amate-Fortes, I. and Guarnido-Rueda, A., 2013. Social Expenditure in the European Union: Does Inequality Matter? Journal of Economic Issues, 47(3), pp. 745-764 [CrossRef]

Nelson, K., 2013. Social Assistance and EU Poverty Thresholds 1990–2008. Are European Welfare Systems Providing Just and Fair Protection Against Low Income? European Sociological Review, 29(2), pp. 386-401 [CrossRef]

Petrevski, G., Trenovski, B. and Tashevska, B., 2019. The effectiveness of fiscal and monetary policies in a small open economy–the case of Macedonia. Post-Communist Economies, 31(6), pp. 805-821 [CrossRef]

Picketty, T., 2014. Capital in the 21st Century. The Belknap Press of Harvard University Press.

Sánchez, A. and Pérez-Corral, A. L., 2018. Government Social Expenditure and Income Inequalities in the European Union. Hacienda Pública Española / Review of Public Economics, 227(4), pp. 133-156 [CrossRef]

Stiglitz, J. E., 2012. The Price of Inequality. W.W. Norton and Company.

Van Lancker, W. and Van Mechelen, N., 2015. Universalism under siege? Exploring the association between targeting, child benefits and child poverty across 26 countries. Social Science Research, 50, pp. 50-65 [CrossRef]

Vidangos, I., 2009. Fluctuations in individual labor income: a panel VAR analysis. Finance and Economics Discussion Series, No. 2009-09.

Wooldridge, J. M., 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press.

World Bank, 2003. World Development Report 2004: Making Services Work for Poor People. Washington, D.C.: The International Bank for Reconstruction and Development : The World Bank.

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