Economics & Sociology

Following recent advances in the panel time-series data analysis, this paper estimates the aggregate production function for Slovenia using industry-level data, thus allowing for variable non-stationarity, cross-industry heterogeneity and dependence. The production function parameter estimates are then used to calculate the joint (product and labor) market imperfections parameter developed by Dobbelaere and Mairesse (2013). The results illustrate that: 1) a constant return-to-scale assumption can be imposed on the aggregate production function, 2) industry-level output elasticities with respect to inputs are heterogenous, 3) the joint market imperfections parameter indicates that, on average, Slovenia´s producers´ output markets can be characterized as imperfect, and 4) the labor markets features the ˝efficient-bargaining˝ labor model characteristics.

1. Abramowitz, M. (1956). Resource and Output Trends in the United States since 1870. American Economic Review, 46(2), 5-23.

2. Baccianti, C. (2013). Estimation of Sectoral Elasticities of Substitution Along the International Technology Frontier. ZEW Discussion Paper No. 13-092, Centre for European Economic Research.

3. Baltagi, B.H. (2005). Econometric Analysis of Panel Data models. Third Edition. Chichester: John Wiley & Sons Ltd.

4. Banerjee, A., & Duflo, E. (2005). Growth theory through the lens of development economics. In: Handbook of economic growth, edited by Aghion, O., and Durlauf, S., 473-552. New York: North Holland.

5. Banerjee, A., & Carrion-i-Silvestre, J. L. (2017). Testing for Panel Cointegration Using Common Correlated Effects Estimator. J. Time Ser. Anal., 38(4), 610-636.

6. Baptist, S., & Hepburn C. (2013). Intermediate inputs and economic productivity. Philosophical Transactions of the Royal Society, A: Mathematical, Physical and Engineering Sciences, 371: 20110565.

7. Baumol, W.J. (1967). Macroeconomics of unbalanced growth: the anatomy of urban crisis. American Economic Review, 57(3), 415-426.

8. Baumol, W.J., Blackman, S.A.B., & Wolff, E.N. (1985). Unbalanced growth revisited: asymptotic stagnancy and new evidence. American Economic Review, 75(4), 806-817.

9. Benos N., Mylonidis, N., & Zotou, S. (2017). Estimating production functions for the US states: the role of public and human capital. Empirical Economics, 52(2), 691-721.

10. Bournakis, I., Christopoulos, D., & Mallick, S. (2017). Knowledge Spillovers and Output per Worker: An Industry-Level Analysis for OECD Countries. Economic Inquiry, 56(2), 1028-1046.

11. Carrion-i-Silvestre, J. L., & Surdeanu, L. (2012). Estimation of production functions: The Spanish regional case. XV Encuentro de Economia Aplicada. La Coruña, Spain, June 7-8. Retrieved from http://encuentros.alde.es/anteriores/xveea/trabajos/c/pdf/193.p

12. Carrion-i-Silvestre, J. L., & Surdeanu, L. (2016). Productivity, Infrastructure and Human Capital in the Spanish Regions. Spatial Economic Analysis, 11(4), 365-391.

13. Chudik, A., & Pesaran, M.H. (2013). Large Panel Data Models with Cross-Sectional Dependence: A Survey. Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper No. 153.

14. Chudik, A., Pesaran, M.H., & Tosetti, E. (2011). Weak and Strong Cross Section Dependence and Estimation of Large Panels. Econometrics Journal, 14(1), C45-C90.

15. Constantini, M., & Destefanis, S. (2009). Cointegration analysis for cross-sectionally dependent panels: The case of regional production functions. Economic Modelling, 26(2), 320-327.

16. Damoah, A. K. (2017). Markups, Markets Imperfections, and Trade Openess: Evidence from Ghana. Working Papers - Economics N. 15/2017, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.

17. D´Auria, F., Denis, C., Havik, K., Mc Morrow, K., Planas, C., Raciborski, R., Röger, W., & Rossi, A. (2010). The production function methodology for calculating potential growth rates and output gaps. European Commission Economic and Financial Affairs Dir

18. De Loecker, J., & Warzynski, F. (2012). Markups and Firm-Level Export Status. American Economic Review, 102(6), 2437-2471.

19. Dobbelaere, S., & Mairesse, J. (2013). Panel Data Estimates of the Production Function and Product and Labor Market Imperfections. Journal of Applied Econometrics, 28(1): 1-46.

20. Dobbelaere, S., Kiyota, K., & Mairesse, J. (2015). Product and labor market imperfections and scale economies: Micro-evidence on France, Japan and the Netherlands. Journal of Comparative Economics, 43(2), 290-322.

21. Dobbelaere, S., & Kiyota, K. (2017). Labor market imperfections, markups and productivity in multinationals and exporters. TI Discussion Paper Series, Vol. 17, No. 113/V. Amsterdam: Tinbergen Institute.

22. Durlauf, S.N. (1993). Nonergodic economic growth. Review of Economic Studies, 60(2), 349-366.

23. Durlauf, S.N., Kourtellos, A., & Minkin A. (2001). The Local Solow Growth model. European Economic Review, 45(4-6), 928-940.

24. Eberhardt, M. (2012). Estimating panel time-series models with heterogeneous slopes. The Stata Journal, 12(1), 61-71.

25. Eberhardt, M. (2017). XTCD Stata routine. Retrieved from https://sites.google.com/site/medevecon/code#TOC-xtcd.

26. Eberhardt, M., & Bond, S. (2009). Cross-section dependence in nonstationary panel models: a novel estimator. MPRA Paper No. 17870.

27. Eberhardt, M., & Teal, F. (2010a). Growth and Development in an Empirical Dual Economy. Paper presented at the CSAE Conference 2010, Economic Development in Africa. Retrieved from http://ex-iis.csae.ox.ac.uk/conferences/2010-EdiA/papers/275-Eberhardt.pdf.

28. Eberhardt, M., & Teal, F. (2010b). Productivity Analysis in Global Manufacturing Production. University of Oxford Department of Economics Discussion Paper Series No. 515.

29. Eberhardt, M., Banerjee, A., & Reade, J.J. (2010). Panel Estimation for Worriers. University of Oxford Department of Economics Series Working Papers No. 514.

30. Eberhardt, M., & Teal, F. (2012). Structural Change and Cross-Country Growth Empirics. The World Bank Economic Review, 27(2), 229-271.

31. Eberhardt, M., & Teal, F. (2013). No Mangoes in the Tundra: Spatial heterogeneity in Agricultural Productivity Analysis. Oxford Bulletin of Economics and Statistics, 75(6), 914-939.

32. Eberhardt, M., & Teal, F. (2017). The Magnitude of the Task Ahead: Macro Implications of Heterogeneous Technology. Discussion Papers 2017-16, University of Nottingham, GEP.

33. Eurostat. (2008). Statistical Classification of Economic Activities in the European Community, Rev. 2. Luxembourg: Eurostat.

34. Fanti, L., & Gori, L. (2013). Efficient bargaining versus right to manage: A stability analysis in a Cournot duopoly with trade unions. Economic Modelling, 20, 205-211.

35. Hall, R.E. (1988). The relationship between price and marginal cost in US industry. Journal of Political Economy, 96(5), 921-947.

36. Hall, B.H., Mairesse, J., & Mohnen, P. (2009). Measuring the returns to R&D. NBER Working Paper Series No. 15622.

37. Han, J. (2016). Essays on Treatments of Cross-Section Dependence in Panel Data Models. A PhD Thesis. Rice University. Retrieved from https://scholarship.rice.edu/handle/1911/96543.

38. Holly, S., Pesaran, M. H., & Yamagata, T. (2010). A spatio-temporal model of house prices in the USA. Journal of Econometrics, 158(1), 160-173.

39. Jemec. N. (2012). Output gap in Slovenia: What can we learn from different methods? Banka Slovenije Prikazi in analize 4/12, Ljubljana: Bank of Slovenia.

40. Jongen, E. L. W. (2004). An Analysis of Past and Future GDP Growth. IER Working Paper No. 2004. Ljubljana: Institute for Economic Research.

41. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 65(1), 9-15.

42. Krüger, J.J. (2008). Productivity and Structural Change: A Review of the Literature. Journal of Economic Surveys, 22(2), 330-363.

43. Laitner, J. (2000). Structural change and economic growth. Review of Economic Studies, 67(3), 545-561.

44. Lewandowski, P. (2007). PESCADF: Stata module to perform Pesaran's CADF panel unit root test in presence of cross section dependence. Retrieved from http://econpapers.repec.org/software/bocbocode/s456732.htm.

45. Lewis, W.A. (1954). Economic development with unlimited supplies of labor. The Manchester School, 22(2), 139-191.

46. Mankiw, N.G., Romer, D., & Weil, D.N. (1992). A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics, 107(2), 407-437.

47. Molnar, M. (2010). Measuring Competition in Slovenian Industries: Estimation of Mark-ups. OECD Economics Department Working Papers, No. 787, OECD Publishing, Paris.

48. Mundlak, Y. (1993). On the Empirical Aspects of Economic Growth Theory. American Economic Review, 83(2), 415-420.

49. Mundlak, Y., Butzer, R., & Larson, D.F. (2008). Heterogenous Technology and Panel Data. The Case of the Agricultural Production Function. Journal of Development Economics, 99(1), 139-149.

50. Norrbin, S.C. (1993). The Relation between Price and Marginal Cost in U.S. Industry: A Contradiction. Journal of Political Economy, 101(6), 1149-1164.

51. Novak, M., & Bojnec, Š. (2005). Human Capital and Regional Economic Growth in Slovenia. Managing Global Transitions, 3(2), 157-177.

52. Novak, M. (2003). The Returns to Education: Some Empirical Findings for Slovenia. Managing Global Transitions, 1(2), 153-167.

53. Pedroni, P. (2007). Social Capital, Barriers to Production and Capital Shares: Implications for the Importance of Parameter Heterogeneity from a Nonstationary Panel Approach. Journal of Applied Econometrics, 22(2), 429-451.

54. Pesaran M.H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. IZA Discussion Paper No. 1240.

55. Pesaran M.H. (2006). Estimation and Inference in Large Heterogeneous panels with a multifactor error structure. Econometrics, 74(4), 967-2012.

56. Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Journal of Applied Econometrics, 22(2), 265-312.

57. Pesaran, M. H. (2015). Testing Weak Cross-Sectional Dependence in Large Panels. Econometric Reviews, 34(6-10), 1089-1117.

58. Pesaran, M.H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogenous panels. Journal of Econometrics, 68(1), 79–113.

59. Pesaran, M. H., Tosetti, E. (2011). Large panels with common factors and spatial correlation. Journal of Econometrics, 161(2), 182-202.

60. Phillips, P.C.B., & Moon, H.R. (1999). Linear regression limit theory for nonstationary data. Econometrica, 67(5), 1057-1112.

61. Roeger, W. (1995). Can imperfect competition explain the differences between primal and dual productivity measures? Estimates for U.S.Manufacturing. The Journal of Political Economy, 103(2), 316-330.

62. Solow, R.M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-320.

63. Statistical Office of the Republic of Slovenia. (2017). Methodological Explanation – Non-financial Assets. Available at: http://www.stat.si/statweb/File/DocSysFile/8101, referred on 03/06/2018.

64. Temple, J. (2005). Dual economy models: A primer for growth economists. The Manchester School, 73(4), 435-478.

65. Uy, T., Yi, K-M., & Zhang, J. (2013). Structural Change in an Open Economy. Journal of Monetary Economics, 60(6), 667-682.

66. Vollrath, D. (2009). The dual economy in long-run development. Journal of Economic Growth. 14(4), 287–312.

67. Westerlund, J. (2007). Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748.

68. Wursten, J. (2016). XTQPTEST: Stata module to perform Born & Breitung Bias-corrected LM-based test for serial correlation. Statistical Software Components S458219, Boston College Department of Economics,

69. Wursten, J. (2017). XTCDF: Stata module to perform Pesaran's CD-test for cross-sectional dependence in panel context. Statistical Software Components S458385, Boston College Department of Economics.