Abstract
Using Monte Carlo simulations, this paper evaluates the bias properties of estimators commonly used to estimate growth regressions derived from the Solow model. We explicitly allow for measurement error, country-specific fixed effects and regressor endogeneity. An OLS estimator applied to a single cross-section of variables averaged over time (the between estimator) performs best in terms of the extent of bias on each of the estimated coefficients. Fixed-effects and the Arellano–Bond GMM estimator overstate the speed of convergence under a wide variety of assumptions, while the between estimator understates it. Finally, fixed effects and Arellano–Bond bias towards zero the slope estimates on the human and physical capital accumulation variables, while the between estimator and the Blundell–Bond system GMM estimator bias these coefficients upwards.
References
Acemoglu D., Johnson S., Robinson J.A. (2001) The colonial origins of comparative development: An empirical investigation. American Economic Review 91: 1369–1401
Arellano, M. (2003). Modelling optimal instrumental variables for dynamic panel data. In Invited lecture at european meeting of the econometric society in Venice, CEMFI Working Paper no. 310.
Arellano M., Bond S. (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment questions. Review of Economic Studies 58(2): 277–297
Arellano M., Bover O. (1995) Another look at the instrumental variables estimation of error-components models. Journal of Econometrics 68: 29–51
Barro R. (1991) Economic growth in a cross section of countries. Quarterly Journal of Economics 106(2): 407–443
Barro R. (1997) Determinants of economic growth. MIT Press, Cambridge
Barro, R. J., & Lee, J.-W. (2000). International data on educational attainment: Updates and implications, Working Paper no. 42. Center for International Development at Harvard University.
Barro R., Sala-i-Martin X. (1995) Economic growth. McGraw Hill, New York
Barro R., Sala-i-Martin X. (2003) Economic growth (2nd ed). McGraw Hill, New York
Baumol W. (1986) Productivity growth, convergence and welfare: What the long run data show. American Economic Review 76: 1072–1085
Benhabib J., Spiegel M. (1994) The role of human capital in economic development: Evidence from aggregate cross-country data. Journal of Monetary Economics 34: 143–173
Bernanke, B. S., & Gürkaynak, R. S. (2001). Is growth endogenous? Taking Mankiw, Romer, and Weil seriously. NBER Macroeconomics Annual.
Blundell R., Bond S. (1998) Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87: 115–143
Bond, S., Hoeffler, A., & Temple, J. (2001). GMM estimation of empirical growth models. CEPR Discussion Paper #3048.
Caselli, F. (2004). The missing input: Accounting for cross-country income differences. In P. Aghion & S. Durlauf (Eds.), Handbook of economic growth. North Holland (forthcoming).
Caselli F., Esquivel G., Lefort F. (1996) Reopening the convergence debate: A new look at cross-country growth empirics. Journal of Economic Growth 1(3): 363–389
Caselli, F., & Feyrer, J. (2006). The marginal product of capital, working paper. London School of Economics.
Cragg J.G., Donald S.G. (1993) Testing identifiability and specification in instrumental variable models. Econometric Theory 9: 222–240
DeLong J.B. (1998) Productivity growth, convergence, and welfare: Comment. American Economic Review 78: 1138–1154
Durlauf, S., Johnson, P., & Temple, J. (2005). Growth econometrics. In P. Aghion & S. Durlauf (Eds.), Handbook of economic growth (Vol. 1, part A, chapter 8, pp. 555–677). Amsterdam: North Holland.
Easterly W., Loayza N., Montiel P. (1997) Has Latin America’s post-reform growth been disappointing?. Journal of International Economics 43: 287–311
Forbes K. (2000) A reassessment of the releationship between inequality and growth. American Economic Review 90: 869–887
Frankel J.A., Romer D. (1999) Does trade cause growth?. American Economic Review 89(3): 379–399
Griliches Z., Hausman J. (1986) Errors in variables in panel data. Journal of Econometrics 31(1): 93–118
Hall R., Jones C.I. (1999) Why do some countries produce so much more output per worker than others?. Quarterly Journal of Economics 114(1): 83–116
Hauk, W. R., & Wacziarg, R. (2004). A Monte Carlo study of growth regressions. NBER Technical Working Paper #T0296
Heston, A., Summers, R., & Aten, B. (2002). Penn world table version 6.1. Center for International Comparisons at the University of Pennsylvania (CICUP).
Islam N. (1995) Growth empirics: A panel data approach. Quarterly Journal of Economics 110(4): 1127–1170
Islam N. (2000) Small sample performance of dynamic panel estimators in estimating the growth convergence equation: A Monte Carlo study. Advances in Econometrics 15: 317–339
Klenow P.J., Rodríguez-Clare A. (1997) The neoclassical revival in growth economics: Has it gone too far?. In: Bernanke B., Rotemberg J. (eds) NBER macroeconomics annual 1997. MIT Press, Cambridge, MA, pp 73–102
Klepper S., Leamer E. (1984) Consistent sets of estimates for regressions with errors in all variables. Econometrica 52(1): 163–184
Knight M., Loayza N., Villanueva D. (1993) Testing the Neoclassical theory of economic growth: A panel data approach. International Monetary Fund Staff Papers 40(3): 512–541
Levine R., Loayza N., Beck T. (2000) Financial intermediation and growth: Causality and causes. Journal of Monetary Economics 46: 31–77
Mankiw N.G., Romer D., Weil D.N. (1992) A contribution to the empirics of economic growth. Quarterly Journal of Economics 107(2): 407–437
Solow R. (1956) A contribution to the theory of economic growth. Quarterly Journal of Economics 70(1): 65–94
Staiger D., Stock J.H. (1997) Instrumental variables regression with weak instruments. Econometrica 65: 557–586
Stock J.H., Wright J., Yogo M. (2002) A survey of weak instruments and weak identification in GMM. Journal of Business and Economic Statistics 20(4): 518–529
Stock, J. H., & Yogo, M. (2003). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Festschrift in honor of Thomas Rothenberg. Cambridge, UK: Cambridge University Press (forthcoming).
Temple J. (1998) Robustness tests of the augmented solow model. Journal of Applied Econometrics 13(4): 361–375
Wacziarg R. (2002) Review of easterly’s the elusive quest for growth. Journal of Economic Literature 40(3): 907–918
Wansbeek T. (2001) GMM estimation in panel data models with measurement error. Journal of Econometrics 104: 259–268
Windmeijer F. (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126: 25–51
Acknowledgements
We thank Francesco Caselli, Nazrul Islam, Norman Loayza, David McKenzie and seminar participants at Stanford University, UC Davis, the University of Houston, the IMF and Duke University for useful comments. The data and programs used in this paper are available upon request, and all errors are ours.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Hauk, W.R., Wacziarg, R. A Monte Carlo study of growth regressions. J Econ Growth 14, 103–147 (2009). https://doi.org/10.1007/s10887-009-9040-3
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DOI: https://doi.org/10.1007/s10887-009-9040-3
Keywords
- Growth regressions
- Measurement error
- System-GMM
JEL Classification
- O47
- O57
- C15
- C23