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March, 2015

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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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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.


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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