Research Article
Year 2020,
Volume: 18 Issue: 3, 116 - 136, 30.09.2020
Abstract
Bu çalışmada amaç Türkiye’de 1987 – 2019 dönemi işsizliğini trend ve devresel bileşenlerine ayırarak yapısal işsizliğin seyrini inceleyebilmektir. Bu ayrıştırma zamana bağlı olarak değişen NAIRU (enflasyonu hızlandırmayan işsizlik oranı) kavramı bağlamında gerçekleştirilmektedir. Yapısal işsizliğin ekonominin potansiyel üretim düzeyindeki performansı ile uyumlu olan işsizlik oranı olduğu; bu nedenle zamana bağlı olarak değişen NAIRU ile temsil edilebileceği kabul edilmektedir. Çalışmada, incelenen dönemde Türkiye işgücü piyasasındaki gelişmeler devresel faktörler kadar yapısal faktörlerin de önemli olduğunu vurgulamaktadır. Bu nedenle gerekli iktisat politikaları açısından işsizlik oranındaki artışın ne kadarının yapısal olduğunun incelenmesi önem kazanmaktadır. İndirgenmiş form hızlandırıcı Phillips eğrisi aracılığı ile tahmin edilen durum uzayı modelinden elde edilen yapısal işsizlik değerleri Türkiye’de, özellikle son dönemde, oldukça yüksek bir yapısal işsizlik sorununun varlığına işaret etmektedir. Beveridge eğrisi ve Hodrick – Prescott filtresi yöntemleri de bu tespiti doğrular sonuçlar üretmekte ve işsizlik açığının kapanmaya oldukça yakın olduğunu ortaya koymaktadır.
References
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(2014) “A parsimonious approach to incorporating economic information in measures of potential output”, BIS, WP44. Bouvet, F. (2012) “The Beveridge curve in Europe: New evidence using national and regional data”, Applied Economics, 44(27): 3585-3604. Böheim, R. (2017) “The labor market in Austria: 2000-2016”, IZA World of Labor, 408(12): 1-10. Darvas, Z. and Simon, A. (2015) “Filling the gap: Open economy considerations for more reliable potential output estimates”, Bruegel Working Paper Series, 2015/11. Driver, R., Greenslade, J. and Pierse, R. (2003) “The role of expectations in estimates of the NAIRU in the United States and the United Kingdom”, Bank of England, WP180. Durbin J. and Koopman, J. (2012) “Time series analysis by state space methods”, Oxford University Press, London. Ebeke, C. and Everaert. G. (2014) “Unemployment and structural unemployment in the Baltics”, IMF, WP/14/153. Estrella, A. and Mishkin, F. (1999) “Rethinking the role of NAIRU in monetary policy: Implications of model formulation and uncertainty”, In Monetary Policy Rules, NBER, New York: 405-436. Friedman, M. (1968) “The role of monetary policy”, American Economic Review, 58(1): 1-17. Fronckova, K., Prazak, P. and Soukal, I. (2019) “Review of Kalman filter employment in the NAIRU estimation”, Systems, 7(1): 1-19. Gerlach, S. and Yiu, S. (2004) “Estimating output gaps in Asia: A cross-country study”, Journal of Japanese and International Economies, 8(1): 115-136. Gianella, C., Koske, I., Rusticelli, E. and Chatal, O. (2008) “What drives the NAIRU? Evidence from a panel of OECD countries”, OECD, WP649. Gordon, R. (1997) “The time-varying NAIRU and its implications for economic policy. Journal of Economic Perspectives”, 11(1): 11-32. Greenslade, J., Pierse, R. and Saleheen, J. (2003) “A Kalman filter approach to estimating the UK NAIRU”, Bank of England, WP179. Guichard, S. and Rusticelli, E. (2011). “Reassessing the NAIRUs after the crisis”, OECD, WP918. Hodrick, R. and Prescott, C. (1997) “Postwar US business cycles: An empirical investigation”, Journal of Money Credit and Banking, 29(1): 1-16. IMF (2013) “World Economic Outlook: Hopes, Realities, Risks”, Washington Kalman, R. (1960) “A new approach to linear filtering and prediction problems”, Journal of Basic Engineering, 82(1): 35-45. Kalman, R. and Bucy, R. (1961) “New results in linear filtering and prediction theory”, Journal of Basic Engineering, 83(1): 95-108. Klinger, S. and Weber, E. (2016) “Decomposing Beveridge curve Dynamics by correlated unobserved components”, Oxford Bulletin of Economics and Statistics, 78(6): 877-894. Kuttner, K. (1994) “Estimating potential output as a latent variable”, Journal of Economics and Business Statistics, 12(3): 361-368. Laubach, T. (2001) “Measuring the NAIRU: Evidence from seven economies”, Review of Economics and Statistics, 83(2): 218-231. Melolinna, M.; Toth, M. (2016) “Output gaps, inflation and financial cycles in the United Kingdom”, Bank of England, WP585. Modigliani, F. and Papademos, L. (1975) “Targets for monetary policy in the coming year”, Brooking Papers on Economic Activity, (1): 141-163. Orlandi, F. (2012) “Structural unemployment and its determinants in the EU countries”, European Economy Economic Papers, 455. Phelps, S. (1967) “Phillips curves, expectations of inflation and optimal unemployment over time”, Economica, 34(135): 254-281. Pissarides, C. (2000) “Equilibrium Unemployment Theory”, MIT Press, Cambridge. Richardson, P., Boone, L., Giorno, C., Meacci, M., Rae, D. and Turner, D. (2000) “The concept, policy use and measurement of structural unemployment: Estimating time varying NAIRU across 21OECD countries”, OECD, WP250. Rummel, O. (2015) “Estimating the output gap for Kenya: A practical guide to some state-space and Kalman filter trend-cycle decompositions”, Bank of England, Centre for Central Banking Studies. Turner, D., Boone, L., Giorno, C., Meacci, M., Rae, D. and Richardson, P.(2001) “Estimating the structural rate of unemployment for the OECD countries”, OECD, WP33. Us, V. (2014) “Estimating NAIRU for the Turkish economy using extended Kalman filter approach”, Central Bank Review, 14(3): 63-94. Wall, H. and Zoega, G. (1997) “The British Beveridge curve: A tale of ten regions”, FRB St. Louis, WP2001-007B.
Year 2020,
Volume: 18 Issue: 3, 116 - 136, 30.09.2020
Abstract
This study aims to determine the course of structural unemployment by decomposing trend and cyclical components of 1987-2019 actual unemployment in Turkey. This decomposition will be carried out in the context of time-varying NAIRU (Non-Accelerating Inflation Rate of Unemployment). It is accepted that structural unemployment is the unemployment that is consistent with the performance of the economy at the potential output level and, therefore, can be proxied by the time-varying NAIRU. The developments in Turkish labor markets during the investigation period emphasize that the importance of structural factors besides the cyclical ones. Therefore, it is crucial to examine how much of the increase in the unemployment rate is structural in the Turkish economy. Structural unemployment rate figures obtained through the state-space model estimated by using reduced form accelerationist Phillips curve indicate the presence of high and increasing structural unemployment problems in Turkey, specifically during the last years. Alternative models like Beveridge curve and Hodrick – Prescott filtering also produce results confirming this determination and reveal that the unemployment gap is almost closing in the country.
References
- Alichi, A. (2015) “A new methodology for estimating the output gap in the United States”, IMF, WP/15/144. Alichi, A., Bizimana, O., Laxton, D., Tanyeri, K., Wang, H., Yao, J. and Zhang, F. (2017) “Multivariate filter estimation of potential output for the United States”, IMF, WP/17/106. Ball, L. and Mankiw, G. (2002) “The NAIRU in theory and practice”, Journal of Economic Perspectives, 16(4): 115-136. Barnichon, R. and Figura, A. (2010) “What drives movements in the unemployment rate? Decomposition of the Beveridge curve”, Federal Reserve Board, Discussion Paper 2010-48. Blanchard, O and Diamond, P. (1989) “The Beveridge curve”, Brookings Papers on Economic Activity. 1(3): 1-76. Blanchard, O. (2018) “Should we reject the natural rate hypothesis?”, Journal of Economic Perspectives, 32(1): 97-120. Bonthuis, B., Jarvis, B. and Vanhasa, J. (2013) “What’s going on behind the Euro area Beveridge curve(s)?”,European Central Bank, WP-1586. Borio, C., Disyatat, P. and Juselius, M. (2014) “A parsimonious approach to incorporating economic information in measures of potential output”, BIS, WP44. Bouvet, F. (2012) “The Beveridge curve in Europe: New evidence using national and regional data”, Applied Economics, 44(27): 3585-3604. Böheim, R. (2017) “The labor market in Austria: 2000-2016”, IZA World of Labor, 408(12): 1-10. Darvas, Z. and Simon, A. (2015) “Filling the gap: Open economy considerations for more reliable potential output estimates”, Bruegel Working Paper Series, 2015/11. Driver, R., Greenslade, J. and Pierse, R. (2003) “The role of expectations in estimates of the NAIRU in the United States and the United Kingdom”, Bank of England, WP180. Durbin J. and Koopman, J. (2012) “Time series analysis by state space methods”, Oxford University Press, London. Ebeke, C. and Everaert. G. (2014) “Unemployment and structural unemployment in the Baltics”, IMF, WP/14/153. Estrella, A. and Mishkin, F. (1999) “Rethinking the role of NAIRU in monetary policy: Implications of model formulation and uncertainty”, In Monetary Policy Rules, NBER, New York: 405-436. Friedman, M. (1968) “The role of monetary policy”, American Economic Review, 58(1): 1-17. Fronckova, K., Prazak, P. and Soukal, I. (2019) “Review of Kalman filter employment in the NAIRU estimation”, Systems, 7(1): 1-19. Gerlach, S. and Yiu, S. (2004) “Estimating output gaps in Asia: A cross-country study”, Journal of Japanese and International Economies, 8(1): 115-136. Gianella, C., Koske, I., Rusticelli, E. and Chatal, O. (2008) “What drives the NAIRU? Evidence from a panel of OECD countries”, OECD, WP649. Gordon, R. (1997) “The time-varying NAIRU and its implications for economic policy. Journal of Economic Perspectives”, 11(1): 11-32. Greenslade, J., Pierse, R. and Saleheen, J. (2003) “A Kalman filter approach to estimating the UK NAIRU”, Bank of England, WP179. Guichard, S. and Rusticelli, E. (2011). “Reassessing the NAIRUs after the crisis”, OECD, WP918. Hodrick, R. and Prescott, C. (1997) “Postwar US business cycles: An empirical investigation”, Journal of Money Credit and Banking, 29(1): 1-16. IMF (2013) “World Economic Outlook: Hopes, Realities, Risks”, Washington Kalman, R. (1960) “A new approach to linear filtering and prediction problems”, Journal of Basic Engineering, 82(1): 35-45. Kalman, R. and Bucy, R. (1961) “New results in linear filtering and prediction theory”, Journal of Basic Engineering, 83(1): 95-108. Klinger, S. and Weber, E. (2016) “Decomposing Beveridge curve Dynamics by correlated unobserved components”, Oxford Bulletin of Economics and Statistics, 78(6): 877-894. Kuttner, K. (1994) “Estimating potential output as a latent variable”, Journal of Economics and Business Statistics, 12(3): 361-368. Laubach, T. (2001) “Measuring the NAIRU: Evidence from seven economies”, Review of Economics and Statistics, 83(2): 218-231. Melolinna, M.; Toth, M. (2016) “Output gaps, inflation and financial cycles in the United Kingdom”, Bank of England, WP585. Modigliani, F. and Papademos, L. (1975) “Targets for monetary policy in the coming year”, Brooking Papers on Economic Activity, (1): 141-163. Orlandi, F. (2012) “Structural unemployment and its determinants in the EU countries”, European Economy Economic Papers, 455. Phelps, S. (1967) “Phillips curves, expectations of inflation and optimal unemployment over time”, Economica, 34(135): 254-281. Pissarides, C. (2000) “Equilibrium Unemployment Theory”, MIT Press, Cambridge. Richardson, P., Boone, L., Giorno, C., Meacci, M., Rae, D. and Turner, D. (2000) “The concept, policy use and measurement of structural unemployment: Estimating time varying NAIRU across 21OECD countries”, OECD, WP250. Rummel, O. (2015) “Estimating the output gap for Kenya: A practical guide to some state-space and Kalman filter trend-cycle decompositions”, Bank of England, Centre for Central Banking Studies. Turner, D., Boone, L., Giorno, C., Meacci, M., Rae, D. and Richardson, P.(2001) “Estimating the structural rate of unemployment for the OECD countries”, OECD, WP33. Us, V. (2014) “Estimating NAIRU for the Turkish economy using extended Kalman filter approach”, Central Bank Review, 14(3): 63-94. Wall, H. and Zoega, G. (1997) “The British Beveridge curve: A tale of ten regions”, FRB St. Louis, WP2001-007B.
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Details
| Primary Language | English |
|---|---|
| Subjects | Economics |
| Journal Section | Articles |
| Authors | |
| Publication Date | September 30, 2020 |
| Published in Issue | Year 2020 Volume: 18 Issue: 3 |