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The authors would like to thank Susan Collins, Aart Kraay, Roberto Rigobon, Dani Rodrik, and other participants in the 2003 Brookings Institution Trade Forum (Washington, DC); and Peter Clark, Judith Gold, Jan Gottschalk, Ernesto Hernandez-Catá, Timothy Lane, and Doris Ross for useful comments and discussions. They owe special thanks to Roberto Rigobon for very generously sharing a GAUSS program to implement the IH estimator. They would also like to thank Chi Nguyen, Gustavo Bagattini, and Aung Win for their excellent research assistance.
This finding may be related to the specific openness measure used, the Sachs-Warner measure.
See for example Bosworth and Collins (2003) for evidence of an equal role of factor accumulation and TFP growth in accounting for growth differences (across countries and over time).
There is however, a substantial literature on the determinants of productivity differences across industrial countries, as well as national studies on the sources of inter-industry productivity differences.
The sample is reduced to 60 countries when net present value of debt indicators are used.
Shares of physical and human capital of one-third each are also consistent with the data on income shares available for a few (mostly advanced) countries. The similarity of income shares in countries where they can be measured appropriately suggests that assuming them to be the same for all countries is not a serious simplification. The assumption that income shares weights are fixed over time is also consistent with the available data for the OECD countries. Given the lack of data on weights for developing countries, we avoid arbitrary choices by assuming that industrial countries weights also apply to developing countries, which is likely to be a rough approximation.
Alternative measures of capital stocks using international price measures of investment (PWT; Easterly and Levine, 2001) were not available for several countries and for the latter half of the past decade. See Bosworth and Collins (2003) for a discussion of the advantages of using national price measures of investment in a growth accounting context. Of course, any such calculation can provide only a rough estimation of the capital stock data.
We prefer a specification where human capital has a separate role as a factor of production (following MRW and subsequent studies) rather than exclusively augmenting the labor force as in Bosworth and Collins (2003). The problem with the latter specification is that it constrains the income share of human capital to be equal to the labor share (i.e., 1−α), thus giving a larger weight (0.6–0.7) to human capital contribution than suggested by previous studies (0.3–0.4). The residual TFP growth is systematically lower in Bosworth and Collins’ growth accounts as a result of the larger share assumed for human capital contribution.
However, the presence of lagged income in the estimation reduces the actual estimation sample to 9 periods, or 1972–98.
The presence of the fixed effects introduces a correlation between the lagged income variable and the residual, which biases the results. In particular, the coefficient on the lagged income variable is negatively biased.
For practical estimation purposes, heteroskedasticity is treated as a multiple-regime process, with one regime per time period. To ensure identification, we normalize the data by dividing the residual of each variable by its standard deviation prior to estimation. The estimated coefficients are then renormalized to ensure comparability with the other methods.
For each variable, outliers are defined as observations that deviate from the mean by more than five times the standard deviation.
Augmented by the rate of technical progress (2 percent) and by the rate of depreciation (3 percent), as in Mankiw, Romer, and Weil (1992).
The schooling, investment, fiscal balance, openness and debt variables are instrumented in the IV, difference and system GMM estimations.
Equation (4) implies that, for any particular methodology employed, we expect the coefficient in the growth regression to be a weighted average of the coefficients of the regressions for the three growth components. The weights would equal 1, alpha, and beta for the coefficients in the TFP, physical capital, and human capital regressions, respectively.
Thus, as an example, looking at the top left cells in Tables 3 and 4 (first row) indicates that the OLS growth regression using the debt/export variable yields an insignificant coefficient of 0.73 for low debt (below the threshold) and a significantly (at the 1 percent level) negative coefficient of −1.24 for high debt (above the threshold). For the IH method, only the high debt coefficient, −1.88, (obtained by linear estimation on the sample restricted to high debt observations) is reported.
Note that the direct comparison of IH and OLS results is not feasible here. Normally one would expect IH to deliver smaller estimates of the effect of debt on growth than OLS, because the former method eliminates the endogeneity bias. However, in Table 4 the two methods are applied to different specifications: the IH specification is estimated only on the high debt sub-sample, and all the potentially endogenous explanatory variables—apart from debt indicators—are lagged (to control for endogeneity). As expected, OLS estimates of the same specification (only on the high-debt sub-sample) were larger than IH ones.
The within county variability is even higher than the between variability, when evaluated in a wider sample of about 100 developing countries (the dataset employed by PPR, 2002, which was not constrained by the availability of data for physical and human capital).
These estimates should be considered as indicative. Our model was designed to investigate growth and the set of control variable necessary to explain debt might be different from ours. Further research would be necessary to properly estimate the effect of growth on indebtedness controlling for endogeneity. Such an analysis is beyond the scope of the paper.