Estimating Okun’s Law for Malta

KORI YAHIA, Abdellah (2018): Estimating Okun’s Law for Malta.

Abstract

The results presented point to a number of interesting observations on the relationship between real economic activity and the labour market in Malta. First, developments in the real economy affect the domestic labour market with a lag. Second, the relationship between output and unemployment is relatively weak in Malta compared with other EU economies, although the link has become more pronounced in recent years perhaps due to the changing structure of employment and the labour market. Empirical estimates suggest that the rate of output growth consistent with a stable unemployment rate is around 1.5% to 2.0%, with an Okun’s coefficient of around 0.2. This implies that GDP growth in excess of 1.5%-2.0% lowers the unemployment rate. More specifically, a 1 percentage point increase in GDP above this threshold lowers the unemployment rate by around 0.2 percentage point. Finally, the relationship appears to be more pronounced during economic recessions. This finding would strengthen the call for prudent fiscal policy during the business cycle to create fiscal space in good times, thereby allowing room for manoeuvre in times of subdued demand to stimulate economic activity and avoid job losses. In addition, the asymmetric relationship between unemployment and output implies that the pace of job creation following a recession may be insufficient to absorb the newly unemployed. Hence, a more proactive approach should be pursued to provide appropriate training and incentives to the unemployed to upgrade their skills to meet the changing requirements of the new industries, thereby facilitating their re-employment.

• Item Type: MPRA Paper
• Language: English
• Keywords: Dynamic Linear Models, Bayesian Techniques, Unemployment, Okun Coefficient, Simulation Techniques
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation

References

[1] Lancaster D., Tulip P., (2015). Okun’s Law and Potential Output, Research Discussion Paper, Reserve Bank of Australia.

[2] Zanin L., Marra G, (2012). A comparative study of the use of generalized additive model and generalized linear models in tourism research, Int J Tour Res 14:451–468.

[3] Adouka L., Bouguell Z., (2010). Estimation de la loi de Okun en Algérie à l’aide de modèle ECM, Roa Iktissadia Review, Université Eloued, N 05.

[4] Furceri D., (2012). Unemployment and labor market issues in Algeria, IMF Working Paper, https://www.imf.org/external/pubs/ft/wp/2012/wp1299.pdf.

[5] Zanin L., Marra G, (2012). A comparative study of the use of generalized additive model and generalized linear models in tourism research, Int J Tour Res 14:451–468.

[6] Lancaster D., Tulip P., (2015). Okun’s Law and Potential Output, Research Discussion Paper, Reserve Bank of Australia.

[7] Marth S., (2015). How strong is the correlation between unemployment and growth really? Socio-economic Sciences and Humanities Europe moving towards a new path of economic growth and social development - Collaborative project.

[8] Knotek E., (2007). How useful is Okun's law. Economic Review, Federal Reserve Bank of Kansas City, 4: PP. 73-103.

[9] Pesaran M. H., Pesaran B., (1997). Working with Microfit 4.0: Interactive Econometric Analysis, Oxford University Press, Oxford.

[10] Wilson E. J., Chaudhri D. P., (2004). A Perspective on Food Policies Evolution and Poverty in the Indian Republic (1950-2001), presented in 33rd conference of Economics, University of Sydney, 27-30 September 2004.

[11] Duasa J., UASA (2007). Determinants of Malaysian Trade Balance: An ARDL Bound Testing Approach, Global Economic Review, Vol. 36, No. 1, 89_102, March 2007.

[12] Narayan P. K., (2004). Reformulating Critical Values for the Bounds F-statistics Approach to Cointegration: An Application to the Tourism Demand Model for Fiji, discussion papers (Victoria: Department of Economics, Monash University).

[13] Pesaran M. H., Shin Y., (1995). Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis, DAE Working Paper Series No. 9514 (Cambridge: Department of Applied Economics, University of Cambridge).

[14] Pesaran M. H., Shin Y., (1995). Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis, DAE Working Paper Series No. 9514 (Cambridge: Department of Applied Economics, University of Cambridge).

[15] Schott S. L., Blocker A. W., Bonassi F. V., Chipman H. A., George E. I., and McCulloch R. E., (2013). Bayes and big data: The consensus Monte Carlo algorithm. In Bayes 250.

[16] Schott S. L., Blocker A. W., Bonassi F. V., Chipman H. A., George E. I., and McCulloch R. E., (2013). Bayes and big data: The consensus Monte Carlo algorithm. In Bayes 250.

[17] Schott S. L., Blocker A. W., Bonassi F. V., Chipman H. A., George E. I., and McCulloch R. E., (2013). Bayes and big data: The consensus Monte Carlo algorithm. In Bayes 250.

[18] Alamro H., Al-dala’ien Q., 2014. Modeling the relationship between GDP and unemployment for Okun’s law specific to Jordan, Aqaba Special Economic Zone (ASEZA).

[19] Bahmani-Oskooee M., Chi Wing Ng R., (2002). Long-Run Demand for Money in Hong Kong: An Application of the ARDL Model, International Journal of Business and Economics, 1, pp. 147-155.