Income Elasticity and the Gender Gap: A Challenging MDG for the MENA Countries

Noha M.F. Emara

doi:10.1515/rmeef-2012-0034

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Income Elasticity and the Gender Gap: A Challenging MDG for the MENA Countries

Noha M.F. Emara

Published/Copyright: November 27, 2014

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From the journal Review of Middle East Economics and Finance Volume 10 Issue 3

Abstract

The gender equality target is still considered one of the most challenging goals for most Middle East and North African (MENA) Countries. Using panel least square with regional dummies (LSDV) for 22 MENA countries over the period 1990–2007, the study concludes that with less than 5 years for the Millennium Development Goals (MDGs) to be concluded, a significant acceleration in economic growth is required for the MENA countries to achieve the gender goal if these countries depended solely on economic growth. As a policy implication, the increase in economic growth in the MENA countries needs to be complemented with other factors boosting the achievability of the gender equality such as the government spending on education, infrastructure, and encouragement of international trade. All three factors proved to have a statistical significant and important impact on closing the gender gap.

Keywords: MDGs; MENA; income elasticity; development; economic growth

Appendix

Table 8:

Summary statistics

Variable	Observations	Mean	Standard deviation	Min	Max
GDP per capita (constant 2000, US$)	344	4,301.6	6,195.5	364	29,128
Female population (% of total)	388	30.10	8.33	17	51
Urban population (% of total)	388	66.33	19.71	21	98
Agriculture value added (% of GDP)	255	9.63	7.81	1	32
Government expenditure (% of GDP)	344	18.35	8.81	6	57
Telephone lines (per 100 people)	396	14.64	12.99	1	55
Trade (% GDP)	347	71.62	38.37	27	191

Table 9:

Summary statistics by country

Country	Variable	GDP per capita (constant 2000, US$)	Female population (% of total)	Urban population (% of total)	Agriculture value added (% of GDP)	Government expenditure (% of GDP)	Telephone lines (per 100 people)	Trade (% GDP)
1- Algeria	Mean	1,842.83	50	58.67	10.16	15.33	5.6	56.83
	Standard deviation	176.31	0	4.18	1.17	1.86	2.16	8.70
	Min	1,659	50	53	8	12	3	50
	Max	2,135	50	64	11	17	9	71
2- Bahrain	Mean	10,554.83	42.33	88	1	18.67	24.83	151.67
	Standard deviation	2,987.62	0.52	0	0	4.72	2.64	24.10
	Min	4,925	42	88	1	10	20	116
	Max	13,476	43	88	1	24	27	191
3- Cyprus	Mean	11,320.17	50.5	68.5	n.a.	9.67	48.5	54
	Standard deviation	1,535.63	0.55	1.05	n.a.	8.71	5.61	49.99
	Min	9,309	50	67	n.a.	17	39	88
	Max	13,454	51	70	n.a.	18	55	108
4- Djibouti	Mean	868.5	50	81.67	3.83	26.83	0.83	100
	Standard deviation	136.33	0	3.78	0.41	7.76	0.41	16.49
	Min	761	50	76	3	12	1	85
	Max	1,111	50	86	4	35	1	128
5- Egypt	Mean	1,352.5	50	43	16.5	11.33	7.67	50.83
	Standard deviation	183.32	0	0	1.38	1.03	4.50	9.06
	Min	1,135	50	43	14	10	3	39
	Max	1,617	50	43	18	13	14	63
6- Iran	Mean	1,618.33	49	62.5	14.5	13.5	15.17	43.67
	Standard deviation	245.21	0	3.94	3.21	1.52	10.38	8.69
	Min	1,398	49	57	10	12	4	33
	Max	2,023	49	67	19	16	32	56
7- Iraq	Mean	154.67	27.83	38.33	3.33	n.a.	1.83	n.a.
	Standard deviation	245.85	25.06	34.63	3.66	n.a.	1.72	n.a.
	Min	717	50	68	5	n.a.	3	n.a.
	Max	551	50	70	7	n.a.	4	n.a.
8- Israel	Mean	18,177.83	50.67	91.17	n.a.	27.67	42.5	75.5
	Standard deviation	1,762.10	0.52	0.75	n.a.	1.03	4.42	7.50
	Min	15,516	50	90	n.a.	26	35	64
	Max	20,532	51	92	n.a.	29	47	87
9- Jordan	Mean	1,817	48.17	77	4	23.17	10	127.33
	Standard deviation	230.02	0.41	2	2.19	1.47	2.37	14.83
	Min	1,584	48	73	2	21	7	108
	Max	2,225	49	78	8	25	13	147
10- Kuwait	Mean	12,537.17	40.17	98	1	30.17	15.83	94.17
	Standard deviation	7,653.25	1.60	0	0	14.55	5.74	10.30
	Min	16,700	39	98	1	15	7	86
	Max	18,860	43	98	1	57	21	114
11- Lebanon	Mean	4,441.5	51	85.17	5.33	16.5	14.83	69.17
	Standard deviation	592.33	0	1.47	2.73	1.87	1.83	17.45
	Min	3,484	51	83	5	15	13	53
	Max	7,170	51	87	7	20	17	101
12- Libya	Mean	3,357	47.83	76.33	n.a.	16.83	8.16	40.17
	Standard deviation	3,682.90	0.41	0.52	n.a.	11.01	3.37	22.99
	Min	6,371	47	76	n.a.	17	5	40
	Max	7,081	48	77	n.a.	26	13	64
13- Morocco	Mean	1,357.5	50.5	52.33	17	16.83	4.16	61.17
	Standard deviation	170.44	0.55	2.16	2	1.47	1.17	7.08
	Min	1,197	50	49	14	15	2	56
	Max	1,642	51	55	21	19	5	75
14- Oman	Mean	7,750	42.33	71	2.17	21.5	8.33	83.33
	Standard deviation	872.68	1.21	2	1.17	4.80	1.51	10.42
	Min	6,516	41	67	2	12	6	67
	Max	8,941	44	72	3	25	10	96
15- Palestine	Mean	831.33	49	70.83	n.a.	17	3.8	87
	Standard deviation	553.17	0	1.60	n.a.	11.35	2.4	5
	Min	290	49	68	n.a.	18	3	18
	Max	1,374	49	72	n.a.	30	9	33
16- Qatar	Mean	9,663.17	32.17	94.33	n.a.	16.67	23.5	64.47
	Standard deviation	12,221.52	3.54	0.82	n.a.	11.24	2.51	34.63
	Min	27,814	26	93	n.a.	11	20	79
	Max	29,128	35	95	n.a.	32	26	90
17- Saudi Arabia	Mean	9,277.67	44.5	79.5	5	25.83	12.5	72
	Standard deviation	327.77	0.55	1.87	1.10	2.93	3.62	12.57
	Min	8,996	44	77	3	22	9	63
	Max	9,907	45	82	6	31	17	96
18- Syria	Mean	1,165.17	50	51.5	27.17	12.83	9.5	68
	Standard deviation	95.30	0	1.87	5.11	1.17	4.59	6.60
	Min	988	50	49	19	11	4	59
	Max	1,258	50	54	32	14	16	78
19- Tunisia	Mean	1,963.83	49.67	62.67	12.83	15.83	8.5	93.67
	Standard deviation	372.59	0.52	2.58	1.72	0.41	3.73	6.12
	Min	1,548	49	59	11	15	4	88
	Max	2,526	50	66	16	16	13	105
20- Turkey	Mean	3,949.67	50	64	13.33	11.83	23.33	43.67
	Standard deviation	551.59	0	2.90	3.01	0.41	5.35	7.20
	Min	3,348	50	60	10	11	14	31
	Max	4,918	50	68	17	12	28	49
21- United Arab Emirates	Mean	21,349.33	32.83	78.33	2.67	14.83	28.67	132.67
	Standard deviation	2,587.96	0.98	0.52	1.03	4.02	3.61	17.40
	Min	16,343	32	78	1	7	22	108
	Max	23,851	34	79	4	17	31	152
22- Yemen	Mean	498.67	49.17	25.33	13.83	12.33	1.83	61.17
	Standard deviation	44.89	0.41	3.01	8.70	6.53	0.98	35.72
	Min	444	49	21	10	13	1	34
	Max	552	50	29	23	18	3	104

Table 10:

Gender Parity Index (GPI) in primary education. Estimation method: panel least squares with dummies variables (LSDV). Dependent variable: GPI primary

	[1]	[1a]	[7]	[7a]
Constant	−0.177***	−0.330***	−0.304***	−0.365***
Constant	(0.040)	(0.047)	(0.081)	(0.064)
Per capita GDP	0.016***	0.026***	0.003	0.0005
Per capita GDP	(0.006)	(0.005)	(0.006)	(0.006)
Female pop.			0.013**	0.0002
Female pop.			(0.006)	(0.0003)
Urban population			0.038**	0.002***
Urban population			(0.019)	(0.0004)
Agriculture			−0.016**	−0.016
Agriculture			(0.006)	(0.006)
Govt. exp.			0.010	0.0002
Govt. exp.			(0.01)	(0.001)
Telephone			0.008***	0.008***
Telephone			(0.008)	(0.008)
Trade			–0.000	–0.0002
Trade			(0.000)	(0.0002)
Regional indicator	−0.012		–0.021***
Regional indicator	(0.003)		(0.004)
Countries/observations	22/344	22/344	16/245	16/245
Adjusted R²	0.117	0.104	0.523	0.489

^[1]

Table 11:

Gender Parity Index in Secondary Education (GPIS). Estimation method: panel least squares with dummies variables (LSDV). Dependent variable: GPI secondary

	[1]	[1a]	[7]	[7a]
Constant	−0.327***	−0.621***	−1.384***	−0.740***
Constant	(0.063)	(0.086)	(0.397)	(0.121)
Per capita GDP	0.054***	0.055***	0.022***	0.0193*
Per capita GDP	(0.009)	(0.009)	(0.008)	(0.011)
Female pop.			0.007***	−0.0005
Female pop.			(0.010)	(0.001)
Urban population			0.266	0.0047***
Urban population			(0.093)	(0.001)
Agriculture			−0.001***	−0.001
Agriculture			(0.021)	(0.022)
Govt. expenditure			0.018	−0.0003
Govt. expenditure			(0.017)	(0.002)
Telephone			0.010***	0.007***
Telephone			(0.002)	(0.002)
Trade			0.000	0.0002
Trade			(0.000)	(0.0003)
Regional indicator	−0.036***		−0.051***
Regional indicator	(0.007)		(0.006)
Countries/observations	22/343	22/343	16/107	16/107
Adjusted R²	0.212	0.116	0.534	0.364

^[2]

Table 12:

Gender Parity Index in Tertiary Education (GPIT). Estimation method: panel least squares with dummies variables (LSDV). Dependent variable: GPI tertiary

	[1]	[1a]	[7]	[7a]
Constant	−1.161***	−1.451***	−1.424***	−1.103***
Constant	(0.187)	(0.086)	(0.374)	(0.214)
Per capita GDP	0.154***	0.176***	0.109***	0.058***
Per capita GDP	(0.026)	(0.080)	(0.037)	(0.020)
Female pop.			0.015	−0.003**
Female pop.			(0.015)	(–0.001)
Urban population			−0.003	0.004***
Urban population			(0.046)	(0.001)
Agriculture			−0.060	−0.021***
Agriculture			(0.057)	(0.004)
Govt. exp.			0.113**	0.0102**
Govt. exp.			(0.051)	(0.004)
Telephone			0.019***	0.025***
Telephone			(0.003)	(0.003)
Trade			0.004***	0.003***
Trade			(0.001)	(0.001)
Regional indicator	−0.022		−0.035**
Regional indicator	(0.018)		(0.016)
Countries/observations	22/344	22/344	16/245	16/245
Adjusted R²	0.245	0.205	0.418	0.574

^[3]

References

Baliamoune-Lutz, M. 2007. “Globalisation and Gender Inequality: Is Africa Different?” Journal of African Economies16(2):301–48.10.1093/jae/ejl037Search in Google Scholar

Bloom, D. E., and J. D.Sachs. 1998. “Geography, Demography, and Economic Growth in Africa.” Brookings Papers on Economic Activity: 2, Brookings Institution, pp. 207–95.Search in Google Scholar

Bourguignon, F., C.Diaw-Bonilla, and H.Löfgren. 2008. “Aid; Service Delivery and the Millennium Development Goals in an Economy-Wide Framework.” In The Impact of Macroeconomic Policies and Income Distribution, edited by F. Bourguignon, M. Bussolo and L. Pereira da Silva. World Bank Policy Research Working Paper 4683, Washington, DC: Palgrave MacMillan and World Bank.10.1596/1813-9450-4683Search in Google Scholar

Browne, S., and M.Maya. 2003. “Financing the MDGs: Cameroon, Uganda and Philippines.” Development Policy JournalMarch, 23–36.Search in Google Scholar

Chatterjee, S. 2006. Growth and MDG Attainments: A Technical Note Based on Cross- Country Data. Manila (mimeo): Asian Development Bank.Search in Google Scholar

Chen, S., and M.Ravallion. 2007. “Absolute Poverty Measures for the Developing World, 1981–2004.” Policy Research Working Paper Series 4211, The World Bank.10.1596/1813-9450-4211Search in Google Scholar

Dijkstra, A. G. 2002. “Revisiting UNDP’s GDI and GEM: Towards an Alternative.” Social Indicators Research57(3):301–38.10.1023/A:1014726207604Search in Google Scholar

Dollar, D., and R.Gatti. 1999. Gender Inequality, Income and Growth: Are Good Times Good for Women?Washington, DC: World Bank.Search in Google Scholar

Emara, N. 2014. “Effect of Income Elasticity on MDG Health Indicators: The Case of MENA Countries.” Review of Middle East Economics and Finance10(1):21.10.1515/rmeef-2012-0038Search in Google Scholar

Forsythe, N., R. P.Korzeniewicz, and V.Durrant. 2000. “Gender Inequalities and Economic Growth: A Longitudinal Evaluation.” Economic Development and Cultural Change48(3):573–617.10.1086/452611Search in Google Scholar

Gyimah-Brempong, K., and M.Wilson. 2004. “Health Human Capital and Economic Growth in Sub-Saharan African and OECD Countries.” The Quarterly Review of Economics and Finance44(2):296–320.10.1016/j.qref.2003.07.002Search in Google Scholar

Haddad, L., H.Alderman, S.Appleton, L.Song, and Y.Yohannes. 2003. “Reducing Child Malnutrition: How Far Does Income Growth Take Us?” The World Bank Economic Review17:107–31.10.1093/wber/lhg012Search in Google Scholar

Lantican, C. P., C. H.Gladwin, and J. L.Seale. 1996. “Income and Gender Inequalities in Asia: Testing Alternative Theories of Development.” Economic Development and Cultural Change (Chicago)44(2):235–63.10.1086/452212Search in Google Scholar

Melamed, C., K.Higgins, and A.Summer. 2010. “Economic Growth and the MDGs.” Overseas Development Institute, Policy Brief, June, United Kingdom.Search in Google Scholar

Panda, M., and A.Ganesh-Kumar. 2007. “Impact of Economic Growth on Achieving MDGs.” Report submitted to the UN Economic and Social Commission for Asia and Pacific (ESCAP), Bangkok, 2007.Search in Google Scholar

Ram, R. 2006. “Growth Elasticity of Poverty: Alternative Estimates and a Note of Caution.” Kyklos59(4):601–10.10.1111/j.1467-6435.2006.00351.xSearch in Google Scholar

Sachs, J. D. 2001. “Tropical Underdevelopment.” NBER working paper no. 8119 (February).10.3386/w8119Search in Google Scholar

Seguino, S. 2006. The Road to Gender Equality: Global Trends and the Way Forward. MPRA Paper. Munich: University Library of Munich.Search in Google Scholar

Seguino, S. 2007. “The Great Equalizer?: Globalization Effects on Gender Equity in Well-Being in Latin America and the Caribbean.” In Globalization and the Myth of Free Trade, Chapter 8, edited by A.Shaikh. London: Routledge.Search in Google Scholar

Tandon, A. 2005. “Attaining Millennium Development Goals in Health: Isn’t Economic Growth Enough?” ERD Policy Brief No. 35, Asian Development Bank, Manila.Search in Google Scholar

UNESCO Institute of Statistics. http://www.uis.unesco.org/Pages/default.aspxSearch in Google Scholar

UNICEF. 2005. “Promoting girls’ Education in Yemen.” February 15. http://www.unicef.org/infobycountry/yemen_25167.htmlSearch in Google Scholar

UNICEF. 2010. “Narrowing the Gaps to Meet The Goals”, The Convention on the Rights of the Child. http://www.unicef.org/media/files/Narrowing_the_Gaps_to_Meet_the_Goals_090310_2a.pdfSearch in Google Scholar

UNICEF. 2013. “Gender Equality and Basic Education.” http://www.unicef.org/djibouti/education_equality.htmlSearch in Google Scholar

World Bank. 2006. Global Economic Prospects. Washington, DC: World Bank.Search in Google Scholar

World Economic Forum. 2011. Gender Gap Report. New York: World Economic Forum.Search in Google Scholar

Published Online: 2014-11-27

Published in Print: 2014-12-1

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Articles in the same Issue

https://doi.org/10.1515/rmeef-2012-0034

Keywords for this article

MDGs; MENA; income elasticity; development; economic growth