Anderson, T. and C. Hsiao, 1982, “Formulation and estimation of dynamic models using panel data,” Journal of Econometrics, Elsevier, vol. 18(1), pp. 47–82 (January).
Arellano, M. and S. Bond, 1991, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, Wiley Blackwell, vol. 58(2), pp. 277–97 (April).
ASDA’A Burson-Marsteller (2013), Arab Youth Survey, presentation sourced at http://www.slideshare.net/BMGlobalNews/asdaa-bursonmarsteller-arab-youth-survey-2013
Baker, D., D. Glynn, A. Howell and J. Schmitt, 2007, “Are Protective Labor Market Institutions at the Root of Unemployment? A Critical Review of the Evidence,” Capitalism and Society, 2(1), pp.1–71.
Baldwin-Edwards, M, 2011, “Labour Immigration and Labour markets in the GCC Countries: National Pattenrs and Trends”, London School of Economics Kuwait Programme on Development, Governance and Globalisation in the Gulf States Working Paper 15
Banerjee, A.., Dolado, J., Mestre, R., 1998, “Error-Correction Mechanism Tests for Cointegration in a Single Equation Framework”, Journal of Time Series Analysis 19, pp. 267–283.
Behar, A. and J. Hodge, 2008, “The Employment Effects of Mergers in a Declining Industry: The Case of South African Gold Mining,” The B.E Journal of Economic Analysis and Policy, 8(1).
Behar, A and J Mok, 2013, “Does Public-Sector Employment Fully Crowd Out Private-Sector Employment?” Centre for the Study of African Economies Working Paper WPS/2013-20, Oxford.
Behar, A and J Mok, 2015 “Does Public Employment Reduce Unemployment?”, Topics in Middle Eastern and African Economies, Vol. 7(2).
Bernal-Verdugo, L., D. Furceri and D. Guillaume, 2012a, “Labor market Flexibility and Unemployment; New Empirical Evidence of Static and Dynamic Effects,” IMF Working Paper WP/12/64 (Washington: International Monetary Fund).
Bernal-Verdugo, L., D. Furceri and D. Guillaume, 2012b, “Crises, Labor Market Policy, and Unemployment,” IMF Working Paper WP/12/65 (Washington: International Monetary Fund).
Blanchard, O and J Wolfers, 2000, “The Role Shocks and Institutions in the Rise of European Unemployment: The Aggregate Evidence,” The Economic Journal, 110, C1–C33.
Bruno, G. (2005), “Approximating the bias of the LSDV Estimator for Dynamic Unbalanced Panel Data Models”, Economics Letters 87, pp. 361–66.
Chami, R., Y Abdih, A Behar, S Cevik, L Dougherty-Choux, D Furceri, N Janus, and P Zimand (2012), “A Template for Analyzing and Projecting Labor Market Indicators”, IMF Technical Notes and Manuals 12/01.
Center for Mediterranean Integration, World Bank, European Investment Bank, and Islamic Educational Scientific and Cultural Organization ((2013), Transforming Arab Economies:Traveling the Knowledge and Innovation Road, World Bank, Washington D.C.
Crivelli, E., D. Furceri, and J. Toujas-Bernate, 2012, “Can Policies Affect Employment Intensity of Growth? A Cross-Country Analysis,” IMF Working Paper 12/218.
Ericsson, N., and J. MacKinnon, J., 2002, “Distributions of error correction tests for cointegration”, Econometrics Journal 5, pp. 285–318.
Espinoza, R. (2013), “Government Spending, Subsidies and Economic Efficiency in the GCC,” in R. Espinoza, G. Fayad and A. Prasad (eds.), The Macroeconomics of the Arab States of the Gulf, Oxford University Press.
Freeman, R. 2005, “Labor Market Institutions without Blinders: The Debate over Flexibility and Labor Market Performance,” International Economic Journal, 19(2), pp.129–45.
Giammarioli, N, J Messina, T. Steinberger and C. Strozzi, 2002, “European Labor Share Dynamics: An Institutional Perspective,” EUI Working Paper ECO No. 2002/13 (San Domenico: European University Institute).
Hanouz, M. and M. Dusek, 2013, “The Arab Competitiveness Report,” Insight Report, European Bank for Reconstruction and Development and World Economic Forum.
ILO and UNDP, 2012, Rethinking Economic Growth: Towards Productive and Inclusive Arab Societies, International Labour Organization, Beirut.
IMF, 2010 “Unemployment Dynamics During Recessions and Recoveries: Okun’s Law and Beyond”, chapter 3 in World Economic Outlook, April 2010, International Monetary Fund.
IMF (2011), Gulf Cooperation Council Countries: Enhancing Economic Outcomes in an Uncertain Global Economy, International Monetary Fund.
IMF (2014b), Labor market Reforms to Boost Employment and Productivity in the GCC – An Update, Paper prepared for the Gulf Cooperation Council Annual Meeting of Ministers of Finance and Central Bank Governors, Kuwait City
Kapsos (2005), “The Employment Intensity of Growth: Trends and Macroeconomic Determinants,” ILO Employment Strategy Paper, International Labor Organization, Geneva.
Kiviet, J (1995), “On bias, inconsistency, and efficiency of various estimators in dynamic panel data models”, Journal of Econometrics 68: 53–78.
Kwiatkowski-Phillips-Schmidt-Shin (1992), “Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root: How Sure are We that Economic Time Series have a unit root?”, Journal of Econometrics, vol.54 issue 1–3 pp. 159–178
Mullis, V., M Martin, G Ruddock, C O’Sullivan and C Preuschoff, 2009, TIMSS 2011 Assessment Frameworks, Lynch School of Education, Boston College.
Nickel, S., 1986, “Dynamic Models of Labour Demand”, in O Ashenfelter and R Layard (eds.), Handbook of Labor Economics Volume 1, Elsevier, Amsterdam.
Nickell, S., 1997, “Unemployment and Labor Market Rigidities: Europe vs North America,” Journal of Economic Perspectives, 11(3), pp.55–74.
Pesaran, H., Smith, R., 1995. “Estimating Long-Run Eelationships from Dynamic Heterogeneous Panels”, Journal of Econometrics, 68, pp 79–113.
Pedroni, P., 1999, “Critical Values for Contegration Tests in Heterogeneous Panels with Multiple Regressors”, Oxford Bulletin of Economics and Statistics, Special Issue, pp 653–670.
Stock, J., 1987, “Unit Roots, Structural Breaks, and Trends”, R. Engle and D McFadden (eds.), Handbook of Econometrics, Volume IV, Elsevier, Amsterdam. Taylor and Sarno (1998)
Taylor, M, and L Sarno (1998), “The Behavior of Real Exchange rates During the post-Bretton Woods Period”, Journal of International Economics, Vol 46, pp 281–312
Vermeulen, P., 2007, “Can Adjustment Costs Explain the Variability and Counter-Cyclicality of the Labour Share at the Firm and Aggregate Level? European Central Bank Working Paper No 772 (June).
Westerlund, J., 2007, “Testing for Error Correction in Panel Data”, Oxford Bulletin of Economics and Statiatics 69(6) pp. 709–48.
World Bank, 2011, Investing for Growth and Jobs, Economic Developments and Prospects, September 2011, World Bank Middle East and North Africa Region.
Zellner, A. 1962, “An Efficient Method of Esdtimating Seemingly Unrelated Regressions and Tests for Aggregation Bias”, Journal of the American Statistical Association, Vol 57 No. 298, pp. 348–68.
Thank you to Robert Blotevogel, Davide Furceri, Gaelle Pierre, Padamja Khandelwal and participants in the MCD Departmental Seminar Series for their comments on a 2013 draft of this paper; to the GCC country teams for verifying or helping to assemble the data; and to Sarah Knight for editorial assistance and extracting information from Arabic sources.
Kapsos (2005) finds elasticities of approximately unity for the Middle East, placing them amongst the highest in the world, while those for North Africa are also relatively high. Combined, Crivelli et al (2012) estimate an elasticity of approximately 0.25 for the Middle East and North Africa (and the subset limited to oil exporters), placing these estimates low relative to other regions.
Numbers include public sector estimates based on 2013 data for Oman.
This equilibrium correction formulation is isomorphic to the Autoregressive Distributed Lag (ARDL) format (Hendry, 1995).
The most striking example is Qatar. We also ran regressions only using Qatar’s data since 2006 or omitting Qatar entirely.
Limited time series preclude such testing for Qatar. Elsewhere, for nationals, critical values are high enough to exceed 1% significance levels when no trend term is included, and 10% or better significance for when trends are included (except for Saudi Arabia). For expatriates, significance levels are 1% for three countries and 5% for Oman when no trend is included, but including a trend term means we fail to reject the null at 10% for two countries. For output, the null hypothesis is strongly rejected in all cases except for Kuwait when we include a trend term. Detailed results are available on request.
This is distinct from a Vector Error Correction Model in which there may be more than one cointegrating relationship among the variables.
This approach automatically removes gross statistical outliers, if any, and then uses two sequences of iterative regressions that weight the remaining observations according to the size of the estimated residuals. The estimation procedure has efficiency losses relative to OLS, but these are countered by having additional observations.
As is well known in the dynamic panel data literature at least since Nickell (1981), the lagged dependent variable is by construction endogenous. For long time series, this is not an issue – the resulting bias is of the order 1/T or a modest 5 percent in our case. The use of GMM estimators used by Arellano and Bond (1991) and successors is inappropriate for our data because they are designed for panels that have large number of cross sectional units relative to time series units. Instead, a small sample bias approximation, the Least Squares Dummy Variable Corrected (LSDVC) estimator due to Kiviet (1995) and Bruno (2005) is implemented. Nonetheless, one disadvantage is the approximation is reliant on potentially inconsistent starting values. Moreover, this class of estimators is strictly speaking designed for data that, although persistent, is stationary.
As is the case for panel unit root tests, a number of panel cointegration tests have been developed (Pedroni, 1999). However, these are untested on panels with a small cross-sectional dimension. Westerlund (2007) develops panel ECM tests, but they are based on separate estimates for each country and hence need a large T dimension. Applying these, we found large variation across country estimates but they tended to support cointegration for both expatriates and nationals.
The year term p has been placed inside the cointegrating vector, which implies the cointegrating relationship includes a trend term, but could just as easily have been left outside, which would mean that there is a linear trend in first differences and hence a quadratic trend in levels. The unconstrained estimation of the ECM does not distinguish between the two. Similarly, the country specific intercepts could be inside or outside the cointegrating vector.
In light of this, we estimated specifications in which the lagged output term is omitted. The estimates were similarly imprecise and yielded negligible short-run elasticities together with very slow adjustment to a low long-run elasticity.
For each country, these are based on the dummies in the regressions but further calibrated to nest the constant and such that the data matches the forecast in 2014.
For all six GCC countries, non-oil growth aggregated using purchasing power parity GDP weights is expected to average 4¾ percent per annum from 2015 to 2020 inclusive.
This is because a change in the long-run coefficient immediately generates a disequilibrium between actual employment and that implied by the cointegrating relationship. Even for our modest increase, a staggered period of adjustment is needed to avert a spike in employment.
This rate is calculated by subtracting nationals’ private and public employment figures from the labor force. Not all GCC countries publish unemployment numbers. Our calculations yield unemployment rates of 12¾ percent in 2013 and 2014.
Both papers refer to medium- to long-run employment elasticities, and Crivelli et al discourage the application of their results to short-run elasticities. However, preliminary work on short-run Okun coefficients indicates short-run elasticities are larger in countries with more flexible labor markets (Ball, 2013).