Abstract
Total Factor Productivity (TFP) accounts for a sizable proportion of the income differences across countries. Two challenges remain to researchers aiming to explain these differences: on the one hand, TFP growth is hard to measure empirically; on the other hand, model uncertainty hampers consensus on its key determinants. This paper combines a non-parametric measure of TFP growth with Bayesian model averaging techniques in order to address both issues. Our empirical findings suggest that the most robust TFP growth determinants are time-invariant unobserved heterogeneity and trade openness. We also investigate the main determinants of two TFP components: efficiency change (i.e., catching up) and technological progress.
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Notes
In order to apply the method to our panel of countries, we consider the panel data version of the approach discussed in Moral-Benito (2012).
Only the country-specific effects are robust determinants of both TFP components, giving support to the importance of unobserved heterogeneity at the country level in TFP growth rates.
Details on the estimation of production frontiers by means of DEA can be found in Chapter 6 of Coelli et al. (1998).
For the sake of brevity we do not present here the results but are available from the authors upon request.
The list of countries, which includes 20 OECD and 47 non-OECD countries, is provided in Table 1.
Table 1 List of countries For instance, in this respect we only include one indicator of trade openness. These concerns are also echoed by Kraay and Tawara (2010) in a different context.
For example, the TFP growth indices for Algeria and Argentina are, respectively, 0.9991 and 1.008. Although these numbers are very similar, translated into growth terms, they indicate that while Algeria’s TFP performance is characterized by a yearly decline of 0.09 % on average, Argentina shows an average TFP growth of 0.18 % each year.
This situation resembles the literature on growth regressions since it is not clear which empirical specification must be preferred a priori.
Note also that the number of models to be estimated in the BMA setting is enormous and might be intractable in practice. In this paper, we consider the MC\(^3\) algorithm in order to overcome this computational issue (see Koop 2003).
This prior is a multivariate normal with mean the MLE of the parameters and variance the inverse of the expected Fisher information matrix for one observation.
For ease of interpretation we include \(\eta _i\) as a component of the error term but it can also be interpreted as a country-specific constant within the traditional fixed effects approach.
Since specification tests within the BMA setting are not available in the literature, we consider the estimates from the full model with all the candidate regressors included to conduct the specification tests in this section.
We also check this conclusion in Sect. 4.1 based on a Hausman test, and looking at the posterior inclusion probability of the country dummies as a whole.
Note that including some of these time invariant variables in our setting would require an additional identifying assumption, namely, independence between such regressors and the effects.
Note that the results in Table 5 for the case of uniform priors on the model space remain qualitatively unaltered when using the dilution priors (e.g., George 1999; Durlauf et al. 2008). In contrast to the uniform prior, the dilution prior assigns uniform probability to neighborhoods rather than models taking into account the correlations among regressors.
Note that this result also emerges in the subsamples of OECD and non-OECD countries; however, to save space these results are not reported but are available upon request.
Sala-i-Martin et al. (2004) note that in most cases, having a ratio of posterior mean to posterior standard deviation around two in absolute value indicates an approximate 95 % Bayesian coverage region that excludes zero.
The significant and negative coefficient on initial GDP gives evidence in favor of a convergence effect in the evolution of TFP across countries. Growth rates of TFP in poorer countries tend to be higher than in richer countries.
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Acknowledgments
We would like to thank Atsu Amegashie, Cristian Bartolucci, Joan Llull, and Jonathan Temple for helpful comments and suggestions. We also thank an Associate Editor and two anonymous referees for insightful suggestions that led to a substantial improvement of the paper.
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Danquah, M., Moral-Benito, E. & Ouattara, B. TFP growth and its determinants: a model averaging approach. Empir Econ 47, 227–251 (2014). https://doi.org/10.1007/s00181-013-0737-y
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DOI: https://doi.org/10.1007/s00181-013-0737-y