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ASIAN CRISIS: Introduction

This paper develops a model of financial and currency crises led by moral hazard, with special reference to the recent Asian events, and presents a preliminary empirical analysis of the extent to which the 1997/98 crisis was related to regional macroeconomic and structural weaknesses.

Our interpretation of the origins and causes of the Asian meltdown focuses on moral hazard as the common source of overinvestment, excessive external borrowing, and current account deficits in a poorly supervised and regulated economy. In our model, private agents act under the presumption that there exists public guarantees on corporate and financial investment, so that the return on domestic assets is perceived as implicitly insured against adverse circumstances. To the extent that foreign creditors are willing to lend against future bail-out revenue, unprofitable projects and cash shortfalls are re-financed through external borrowing. Such a process — referred to as ‘evergreening’ — translates into an unsustainable path of current account deficits.

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GLOBAL FACTOR TRADE: Conclusion

While our analysis has adjusted for the fact that average input coefficients shift with country factor proportions, we have not adjusted for differences in factor intensity between export and average sectors. This will tend to result in apparent missing trade.

Finally, and most obviously, trade barriers, demand irregularities, and non-neutral technological differences really do exist. Hence, it would be astonishing if we could ignore all of these and describe global factor trade flows perfectly. The real surprise is just how well we do.

Conclusion

The empirical validity of the factor proportions theory has been a focus of research for nearly one-half century. In the process, researchers have accumulated a great deal of experience that has informed our work. Leontief’s (1953) seminal work provided the first true factor content study. The work of Maskus (1985) and Bowen, Leamer and Sveikauskas (1987) is extremely important not only for the methodological contributions, but also for the extraordinary energy they brought to their studies. The same could be said of the work of Trefler (1993, 1995, 1997), which (among other contributions) provides extremely lucid characterizations of anomalies in the data. These important contributions notwithstanding, this half-century of empirical research failed to produce a set of simple departures that allow the theory to match the salient features of the international data.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade 7


In Table 8 we repeat our trade results obtained above and also present our results when recast in Trefler units. The switch to Trefler units matters little. Now the coefficient on predicted factor trade actually rises from 0.82 to 0.88. Our variance ratio test statistic falls a little but overall the same basic picture emerges.. Clearly our results are robust to this specification.

Xavier Gabaix (1997) has suggested a second weighting scheme for evaluating factor content studies. If one deflates both sides of the HOV trade equation by the country’s share of absorption, one eliminates all size-based variation from the data. This adjustment is tantamount to projecting each country’s endowment point on to the same iso-income line. The results also appear in Table 8. Once again we see a steady rise in the slope coefficient as we move from T3 to T7. The final specification has a slope coefficient of 0.83, again quite similar to our primary specification.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade 6

Robustness Checks

There are a variety of robustness checks that we would like to make. The first notes that specifications T6 and T7 have included the ROW point even though both force the ROW production model to fit perfectly. We have already provided reasons for believing that adjustment of the ROW technology is appropriate. Nonetheless, it would be troubling if the steady improvement in the model owed solely to inclusion of the ROW points once this adjustment is made. Our check on this is to return to models T4 through T7, excluding ROW in each case. The results are presented in Table 6. Exclusion of ROW does tend to reduce the slope coefficients in each case. And the improvement of T6′ over T5′ seems somewhat less substantial than that of T6 over T5. Nonetheless, the key observation is that the results are broadly consistent across the two sets of tests.

Most importantly, the slope coefficient and the trade variance ratio rise consistently across both sets of tests, beginning and culminating at very similar levels. Even if we exclude ROW, the model correctly predicts the direction of net factor trade 90 percent of the time and the measured factor trade is over three-fourths the level predicted. Thus the results are highly robust to exclusion of ROW.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade 5


Corrections on ROW Technology: T6

We have seen that production model P5 works quite well for most countries. There are a few countries for which the fit of the production model is less satisfying. There are relatively large prediction errors (ca. 10 percent) for both factors in Canada, for capital in Denmark, and for labor in Italy. Given the simplicity of the framework, the magnitude of these errors is not surprising. Since we would like to preserve this simplicity, neither do these errors immediately call for a revision of our framework. Here

There is one case, however, in which a closer review is appropriate. For the ten OECD countries, we have data on technology which enters into our broader estimation exercise. But this is not the case for ROW. The technology for ROW is projected from the OECD data based on the aggregate ROW endowments and the capital to labor ratio. Because the gap in capital to labor ratios between the ten and the ROW is large, there is a good measure of uncertainty about the adequacy of this projection. As it turns out, the prediction errors for ROW are large: the estimated technology matrix under-predicts labor usage by 9 percent, and over-predicts capital usage by 12 percent. Moreover, these errors may well matter because ROW is predicted to be the largest net trader in both factors and because its technology will matter for the implied factor content of absorption of all other countries.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade 4

The D-F-S Continuum Model with Industry Variation in Factor Employment: P4 and T4

As we discussed in the section on estimating the technologies, there is a robust feature of the data that has been completely ignored in formal tests of the HOV model: capital to labor input ratios by industry vary positively with country factor abundance. We consider this first within the framework of the Dornbusch-Fischer-Samuelson (1980) continuum model, as this allows us to conserve yet a while longer the assumption of (approximate) factor price equalization.
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Consider production specification P4, as in Figure 9. The production slope coefficient remains at 0.89, but the median production error falls slightly to 5 percent. What is most surprising is how the continuum model affects the trade specification T4. A plot appears as Figure 10. The proportion of correct sign tests rises sharply to 86 percent (19 of 22) — significantly better than a coin flip at the 1 percent level. The variance ratio remains relatively low, although at 7 percent it is much higher than in any of the previous tests. The most impressive statistic is the slope coefficient of 0.17, where all of the previous trade slopes were zero. Clearly, allowing country capital to labor ratios to affect industry coefficients is moving us dramatically in the right direction.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade 3


If we exclude the US as well, the slope falls to about 0.90. The R2 in each case is respectably above 0.9. Also, in both cases, the median production errors are approximately 20 percent. The ROW continues to be a huge outlier, given its significantly lower productivity. These results suggest that use of an average technology matrix is a substantial improvement over using that of the US, since median production errors fall by one-third to one-half. Nonetheless, the fact that prediction errors are still on the order of 20 percent for the OECD group, and much larger for the ROW, suggests that there remains a lot of room for improvement.
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Examination of T2 can be brief. The sign test correctly predicts the direction of net factor trade only 45 percent of the time. The variance ratio continues to be essentially zero, again indicating strong missing trade. The Slope Test coefficient is -0.006. In short, factor abundance continues to provide essentially no information about which factors a country will be measured to export. These statistics are reinforced by the pictures in Figure 5 and Figure 6. Overall, this model is a complete empirical failure.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade 2

The Simple HOV Model Employing US Technology: P1 and T1

We have the same point of departure as prior studies: an assumption that all countries share a common technology matrix and an implementation that uses that of the United States. However, our study is the first to examine directly the production component of this model. As one can see in Table 4, specification P1 fails miserably, but in an interesting way. A plot of P1 for all countries appears as Figure 3. The US is excluded, since it fits perfectly by construction. A glance at the plot reveals two key facts. First, for all countries and factors, measured factor content of production is always less than predicted. Second, this gap is most severe for ROW.
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This carries a simple message: if these countries used the US technology matrix to produce their actual output, they would need much less of each factor than they actually employ. The slope coefficient of measured on predicted factor trade is only 0.24. Excluding the ROW raises the slope coefficient to 0.67, still well short of the theoretical prediction of unity. The results by factor are presented in Table 5. The median prediction error is 34 percent for capital and 42 percent for labor. Thus our direct data on production suggest strongly that adjusting for productivity differences will be an important component in getting HOV to work.

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GLOBAL FACTOR TRADE: Implications for Net Factor Trade


The reason is that the trade literature is replete with proposed amendments to the HOV model that in the end do not help us to understand actual factor service flows. We have already evaluated the hypotheses statistically, so in this section we examine the extent to which these hypotheses help us to understand real world factor trade flows. In order to understand the economic significance of our models, we conduct tests of the HOV model of production and trade under a variety of specifications, as developed in Section II and summarized in Table 2. Here our tests are designed not for model selection, but rather to help us see the economic implications for the HOV model of each of the hypotheses that we have considered. We will begin by working primarily on the production side. Once we have made the major improvements we anticipate in that area, we move on to consider an amendment to the absorption model.
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