Capital Abundance and Developing Country Production Patterns

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statistically significant without controlling for skill to negative and statistically insignificant with skill controlled for, and the estimated effect of (K/L) on fabricated metal products (381) turns from positive and statistically significant to positive and statistically insignificant. The estimated effects of the skill variable are statistically significant in 14 of the 28 industries, with nine of the 14 showing a positive estimated coefficient, suggesting that most manufacturing industries would see output share to increase with skill. 5.2 Identifying a Subsample with Conditional FPE Estimating a single Rybczynski equation (9) is valid only if factor prices are conditionally equalized in the sample. Otherwise countries would produce different sets of goods and the estimated Rybczynski effect would switch signs with respect to different levels of capital abundance. The literature offers a simple test of conditional FPE. Under conditional FPE, countries produce the same set of goods. A change in factor supplies will be fully absorbed by outputshare adjustments, leaving factor prices unchanged. As a result, production techniques will be insensitive to factor-supply changes. This suggests, in the two-factor case, the following regression equation for testing the conditional FPE hypothesis: ( ) ( ) K K = α it + β t + ν ict . (10) L ict L ct In regression equation (10), the dependent variable is industry capital intensity, and the independent variable is country capital abundance. Pooling data across country and industry for any time t, we have industry capital intensity (K/L) ict dependent on industry-specific fixed effects α it . 8 Under the null hypothesis of conditional FPE, we have β t = 0. The error term ν ict is assumed to be zero-mean random technology shocks. If there is no conditional FPE, then β t ̸= 0. In particular, non-FPE models (e.g. Dornbusch, Fischer, and Samuelson, 1980) predict 8 The regression does not include country-specific fixed effects because neutral technology differences across countries do not affect capital intensity. 13
that β t > 0: the goods produced by a labor-abundant country are more labor-intensive than the goods produced by a capital-abundant country. Table 5 reports results from estimating (10). The first row of Table 5 reports the results for the full sample of 14 countries. We find that the estimated β is positive and statistically significant at the 1 percent level, clearly rejecting conditional FPE in the full sample. 9 One reason for factor prices not equalized is that countries have very different capital abundance. To see if conditional FPE holds for a subsample of countries which have similar capital abundance, we apply regression (10) first to a pair of most labor-abundant countries, India and Indonesia. The estimated β is positive, but the statistical significance level is only 10 percent. If we add to the sample the next labor-abundant country, Egypt, then the estimated β becomes statistically indifferent from zero. Continuing this experiment, we find that the estimated β is indifferent from zero for the subsample of the seven most labor-abundant countries in 1992. This is true in 1982 as well. These results suggest that conditional FPE holds among the seven most labor-abundant countries in our sample. Their capital abundance is similar enough for them to produce similar goods. Table 5 also shows some evidence that the three most capital-abundant countries (Korea, Hong Kong, and Singapore) form a single cone of diversification. 5.3 Estimating Rybczynski Effects: Subsample Given conditional FPE in the subsample of the seven most labor-abundant countries, we can run a single Rybczynski regression on this subsample. Table 6 reports the results. 10 We find the 9 We also use the decomposition method of Hanson and Slaughter (2002). The method is to decompose the changes in factor supply into output-mix changes, generalized changes in production techniques, and idiosyncratic changes in production techniques. In their examination of U.S. states, Hanson and Slaughter find that statespecific changes in production techniques play a relatively small role and interpret it as evidence of conditional FPE among U.S. states. Our results show that country-specific changes in production techniques play a relatively large role (40% for labor and 29% for capital, averaged over the 14 countries for the period 1982-1992), which supports our rejection of conditional FPE for countries in our sample. 10 The industries in Table 6 are ranked in ascending order of average capital intensity of the subsample. The second and third columns compare the industry capital intensities between the full sample and the subsample. The average capital intensity of the 7 most labor-abundant country is significantly lower than that of the full sample, but the ranking is largely the same. 14
Page 1 and 2: Capital Abundance and Developing Co
Page 3 and 4: industry distribution of this outpu
Page 5 and 6: a pooled regression with industry f
Page 7 and 8: and good Y 2 are identically priced
Page 9 and 10: function with respect to factor j
Page 11 and 12: n∑ γ ij lnp jct = d ic + d it +
Page 13: 5 Empirical Results 5.1 Estimating
Page 17 and 18: signs of the effects. Capital abund
Page 19 and 20: References Bernstein, Jeffrey R. an
Page 21 and 22: .2 354 .1 gsy 0 361 390 356 384 332
Page 23 and 24: Table 1. Countries in the Sample Ca
Page 25 and 26: 25 354 0.0004 (0.0019) 26 371 −0.
Page 27 and 28: 26 371 −0.018 (0.006)*** 27 351
Page 29 and 30: Table 6. Random-Effects Panel Regre
Page 31 and 32: Table 7. Random-Effects Panel Regre

Capital Abundance and Developing Country Production Patterns

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