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January, 2015

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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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GLOBAL FACTOR TRADE: Statistical Tests on Technology and Absorption 4

In a zero trade cost world with perfect specialization, we have the following parameter restrictions, a0i = = 0. If there are trade costs that increase with distance, these parameter restrictions cease to hold. We can statistically test for the existence of trade costs simply by estimating this equation and testing whether a0i = = 0 for all i. Not surprisingly, the data resoundingly reject this hypothesis.

We therefore decided to use a gravity model as the basis for our demand predictions. One of the problems that we faced in implementation, however, was how to calculate the distance of any individual country to the ROW. In all specifications we calculated this distance as the GDP-weighted average distance from a particular country to all the other countries in the ROW. In some sectors we found large systematic errors in predicting trade with the ROW. This may be the result of mis-measurement of distance or the fact that the true ROW is some multiple of our sample of countries. We therefore added a dummy variable corresponding to the exporting country being the ROW and a dummy corresponding to the importing country being the ROW.

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GLOBAL FACTOR TRADE: Statistical Tests on Technology and Absorption 3

As we noted earlier, there is good reason to believe that there are efficiency differences, even among the rich countries. A convenient specification is to allow for Hicks-Neutral technical differences (P3). If we denote these differences by 8c, then we can econometrically identify these technical differences by estimating:
where exp(0 c) = 8c. Estimation of this specification requires us to choose a normalization for the 0 c. A convenient one is to set 0 US equal to zero (or equivalently 8US = 1)

We have also suggested that it might be possible that production might be characterized by a continuum of goods DFS model in which industry input coefficients in tradables depend on country capital abundance (P4). The latter feature may arise also if FPE breaks down and countries are in different production cones (P4), in which case this will affect production coefficients in non-traded sectors as well. These models can be easily implemented. We postulate that input coefficients are characterized by the following equation:

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GLOBAL FACTOR TRADE: Statistical Tests on Technology and Absorption 2

Nonetheless, both because the principal concern of trade economists here is in measures of net factor trade, and also for direct comparability with prior studies, in Section V we will go on to implement each of the models of technology and absorption. In doing so, we will gain a rich view of the role played by each change in improving the working of the HOV model. For reference, we will indicate the production specification associated with the distinct models of technology. www.easyloans-now.com

Our first model of technology (P1) is the standard starting point in all investigations of HOV: it postulates that all countries use identical production techniques in all sectors. This can be tested directly using our data. For any countries c and c’, it should be the case that Bc = Bc’ . We reject this restriction by inspection.

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GLOBAL FACTOR TRADE: Statistical Tests on Technology and Absorption

We would also like to note, though, that the desire to bring new data sources to bear on the problem has carried a cost. Specifically, the factors available to us for this study are limited to capital and aggregate labor. We would very much have liked to be able to distinguish skilled and unskilled workers, but unfortunately the number of skilled and unskilled workers by industry is not available for most countries.

We would like to note how the reader should think about this factor “aggregate labor,” and why we do not believe this presents too great a problem for our study. There are at least a couple of interpretations that can be given. A first fact about our labor variable is that under most specifications the OECD countries are judged scarce in labor while the ROW is abundant in it. This suggests that one appropriate interpretation is that our variable labor is a very rough proxy for unskilled or semi-skilled labor. Note, though, that in most of our later implementations, labor is converted to efficiency units. If this is an appropriate way to merge skilled and unskilled, then the fact that these OECD countries are scarce in it suggests that this is true, even when we convert all labor to common efficiency units. We have little doubt that if it were possible to distinguish highly-skilled labor separately for our study that the US and some of the other OECD countries would be judged abundant in that factor. If you need that money quickly, you surely need a reliable online lender to give you a hand. Our quick loans cash might be coming your way in just a few hours, if that’s how soon you want it, because we always try to meet our customers in the middle. Apply for a loan with this and see how fast it happens!

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GLOBAL FACTOR TRADE: Data Sources and Issues 2

The basis for our data set is the OECD’s Input-Output Datase [OECD (1995)]. This database provides input-output tables, gross output, net output, intermediate input usage, domestic absorption and trade data for ten OECD countries.10 Significantly, all of this data is designed to be compatible across countries. We constructed the country endowment data and the matrices of direct factor input requirements using the OECD’s Inter-Sectoral Database and the OECD’s STAN Database. Hence for all countries, we have data on technology, net output, endowments, absorption, and trade. By construction, these satisfy:
We also have data for 20 other countries that we refer to as the “Rest of the World” or ROW.11 Data on capital is derived from the Summers and Heston Database while that for labor is from the International Labor Organization. For countries that do not report labor force data for 1985 we took a labor force number corresponding to the closest year and assumed that the labor force grew at the same rate as the population. Gross output data is taken from the UN’s Industrial Statistics Yearbook, as modified by DWBS. Net output is calculated by multiplying gross output by the GDP weighted average input-output matrix for the OECD and subtracting this from the gross output vector. Bilateral trade flows for manufacturing between each of our ten OECD countries as well as between each country and the ROW was drawn from Feenstra, Lipsey, and Bowen (1997) and scaled so that bilateral industry import totals match country totals from the IO tables. Bilateral imports for non-manufacturing sectors are set equal to the share of manufacturing imports from that country times total non-manufacturing imports in that sector. ROW absorption was then set to satisfy condition 2. If you are struggling to make ends meet, we can help you by offering reliable instant money loans online. We helped thousands of people facing financial troubles. Thanks to our timely assistance, you will be able to afford that purchase. Our rates are fair, and application is easy – come find us at http://get-instant-loans.com/.

In sum, this data set provides us with 10 sets of compatible technology matrices, output vectors, trade vectors, absorption vectors, and endowment vectors. In addition we have a data set for the ROW that is comparable in quality to that used in earlier studies.

Data Issues

We would emphasize several characteristics of the data to underscore its advantage over prior data sets. The first draws on the nature of the tests considered. The prior work is uniform in rejecting the simplest HOV model. Hence the most interesting work has gone on to consider alternative hypotheses. Importantly, the most prominent of these theories concern alterations in assumptions about technological similarity across countries (e.g. Hicks-neutral technical differences) and the structure of absorption (e.g. a home bias in demand). Yet typically these studies have only a single observation on technology (that of the US) and no observations whatsoever about the structure of absorption.

The technological and absorption parameters are chosen to best fit the statistical model, but these yield little confidence that they truly do reflect the economic parameters of interest.14 Our construction of the technology matrices allows us to test the theories of technological difference directly on the relevant data and similarly for our hypotheses about absorption. This ability to directly test the cross-country theories of interest greatly enhances our confidence that the estimated technology and absorption parameters indeed do correspond to the economic variables of interest.

A second issue is the consistency with which the data is handled. In part this corresponds to the fact that we are able to rely to a great extent on data sources constructed by the OECD with the explicit aim to be as consistent as practicable across sources. In addition, the OECD has made great efforts to insure that the mapping between output data and trade categories is sound. Finally, the consistency extends also to conditions we impose on the data which should hold as simple identities, but which have failed to hold in previous studies because of the inconsistencies in disparate data sets. These restrictions include that each country actually uses its own raw technology matrix, reflected in BcYc / Vc.

GLOBAL FACTOR TRADE: Data Sources and Issues

Among the more outlandish simplifications in the HOV model is the assumption that international trade is wholly costless. This is false on its face and overwhelmingly refuted by the data [McCallum (1995), Engel and Rogers (1995)]. There is a highly successful model of trade volumes known as the “gravity” model that does take trade costs into account, typically proxied by distance [Anderson (1979), Bergstrand (1985), Frankel, et al. (1996)]. However, the gravity model has not appeared previously in empirical tests of the HOV factor content predictions. The reason is that the bilateral trade relations posited in the gravity model are not typically well defined in a many-country HOV model [see Deardorff (1998) and Trefler (1998)]. However, they are well defined in the production model we have developed precisely because all countries feature perfect specialization in tradables.9 In this case, the demand for imports bilaterally has to be amended to account for bilateral distance. Let dcc, be the distance between countries c and c’. Then a simple way to introduce trade costs is to posit that import demand in country c for products from c’ takes the form of a standard gravity equation:
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