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The Revealed Competitiveness of U.S. Exports1

Massimo Del Gattoa, Filippo di Maurob, Joseph Gruberc, and Benjamin R. Mandelc

NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors. Recent IFDPs are available on the Web at http://www.federalreserve.gov/pubs/ifdp/. This paper can be downloaded without charge from the Social Science Research Network electronic library at http://www.ssrn.com/.

Abstract:

The U.S. share of world merchandise exports has declined sharply over the last decade. Using data at the level of detailed industries, this paper analyzes the decline in U.S. share against the backdrop of alternative measures of the competitiveness of the U.S. economy. We document the following facts: (i) only a few industries contributed to the decline in any meaningful way, (ii) a large part of the drop was driven by the changing size of U.S. export industries and not the size of U.S. sales within those industries, (iii) in a gravity framework, the majority of the decline in the U.S. export share within industries was due to the declining U.S. share of world income, and (iv) in a computed structural measure of firm productivity, average U.S. export productivity has generally maintained its high level versus other countries over time. Overall, our analysis suggests that the dismal performance of the U.S. market share is not a sufficient statistic for competitiveness.

Keywords: Trade competitiveness, gravity model, firm productivity

JEL classification: F14, F17


1  Introduction

The U.S. share of world merchandise exports has declined sharply over the last decade. Using data at the level of detailed industries, this paper analyzes the decline in U.S. share against the backdrop of alternative measures of the competitiveness of the U.S. economy.

Usual suspects for a given country's decline in export share might include: unfavorable relative price movements, crowding out from the proliferation of low-cost exporters from developing countries, uneven reductions in trade costs and barriers around the world, or possibly the deterioriating productivity of exporting firms compared to foreign rivals. Disentangling these factors presents several complications. First, relative prices are only weakly (unconditionally) correlated with U.S. market share, and are thus not very helpful in explaining its recent dynamics. This is evidenced by the accelerating drop in share during the 2000's amidst a decline in the value of the broad real dollar. Second, in many instances, and particularly for international comparisons, trade costs and firm productivity are difficult to measure directly.2 And third, export shares may additionally reflect the idiosyncratic composition of the U.S. export bundle, which may have little to do with the ability of U.S. exporters within a given industry to compete.

To tackle these issues, Section 2 begins by decomposing the decline in share into detailed industry groups. We find that only a handful of industries contributed to the decline in any meaningful way. Moreover, a large part of the drop was driven by the changing size of U.S. export industries rather than the size of U.S. sales within those industries. This means that U.S. exporters happened to be specialized in industries that grew slowly as a share of world trade. Together, these observations offer our first suggestion that the fall in aggregate U.S. share has little to do with the underlying productivity of U.S. exporting firms.

We then present two measures of trade competitiveness which, insofar as they are inferred from actual trade flows, we refer to as revealed competitiveness. The first measure, in Section 3, is derived as a residual from a standard gravity equation. The objective of the exercise is to purge bilateral trade flows of the effect of national income and geography, wherein the residual contains information about the relative productivity and unmeasured trade costs of exporters. We find that the majority of the decline in the U.S. export share is in fact due to the declining share of U.S. income in the world. The residual, which we view as a 'purer' measure of competitiveness, is declining but not as dramatically.

Our second approach, in Section 4, is derived from a structural model and builds on the multi-country, multi-sector version of Melitz-Ottaviano (2008).3 In that framework, the overall competitiveness of a country in a given sector is the outcome of a process of firm selection driven by: (1) the degree of 'accessibility' (i.e. trade costs) of the country and the size of its domestic market, as well as (2) the exogenous ability of the country to generate low cost firms, which depends on structural and technological factors. We extend previous empirical applications of that model by using richer product level detail, and additionally employ an innovative approach by Novy (2009) to compute competitiveness indicators which are comparable over time. Consistent with our gravity residual exercise we find that, notwithstanding significant heterogeneity across sectors, U.S. export productivity has generally maintained its high level versus other countries over time. Overall, our analysis suggests that the dismal performance of the U.S. market share is not a sufficient statistic for competitiveness.


2  The State of U.S. Export Share

From 1980 to 2009 the U.S. share of world exports fell by almost one third, declining from about 11 percent to just over 8 percent of world exports. In this section we examine the decline in the U.S. share using NBER-UN bilateral trade data from Feenstra, Lipsey, Deng, Ma and Mo (2005).

As shown in Figure 1, the U.S. share of world merchandise exports rose slightly from 1986 to 1999, increasing from about 10 $ \frac{1}{2}$ to 12 $ \frac{1}{2}$ percent of world exports, before falling 4 percentage points between 1999 and 2009. The bilateral trade data run through 2004 and, in Figure 2, we observe that every industry group at SITC 1-digit aggregation registered a decrease over the period from 1984 to 2004, with many of the larger changes occuring in the early 2000's. The largest declines in share were recorded among the basic materials categories (SITC 0 through 4), which account for approximately 25 percent of U.S. exports, and in machinery and transportation equipment (SITC 7), which account for almost half of U.S. export sales. It is interesting to note that the timing of the decline in U.S. share differs over SITC categories. The fall in basic material shares is gradual and persistent, while decline in machinery & transportation equipment is abrupt, primarily occurring after 1999.

The decline in market share is machinery and transportation equipment is particularly notable given the importance of the sector in overall U.S. exports. The fall in the U.S. share of machinery and transportation equipment is examined further in Table 1, which breaks the category into SITC 2-digit subcategories. Although the decline in U.S. share is apparent across most 2-digit machinery categories, the fall in the U.S. share of office machine and computer exports is particularly striking, with U.S. share of world exports falling from a third of the total to just under one tenth. As with overall exports, there is some dispersion in the timing of the decline in shares across subcategories. Whereas the fall in computers is steep and steady over the entire period, in most other categories of machinery the U.S. was able to maintain or expand export share through 1999 before shares plummeted sharply.

A more meaningful way of decomposing the decline in the aggregate export share is to compute the appropriately weighted contribution from disaggregate categories of goods. The change in aggregate export share can be expressed as the sum of changes across product categories ($ i$) as a ratio of the change in world exports:

$\displaystyle \frac{\Delta X_{US}}{\Delta X_{WORLD}}=\sum\limits_{i}\frac{\Delta X_{US}^{i} }{\Delta X_{WORLD}} $

Figure 3 depicts the contributions to the change in aggregate export share for each 1-digit SITC code over the period from 1984 to 2004. Food & live animals provided the largest contribution to the decline in share, accounting for almost one fourth of the aggregate decline. Almost as large were the contributions of machinery & transportation and crude materials, also each contributing about one fourth to the overall decline in share. The importance of raw materials for the decline in U.S. share raises a note of caution in interpreting aggregate export share statistics. Commodity prices fell over most the period under consideration, and since the exports of the United States are relatively commodity intensive, so did the U.S. share of world exports.

The importance of commodities is further illustrated in Figure 4, which depicts the top 10 contributors to the aggregate decline among 4-digit SITC codes. Corn and soybeans contribute a combined one sixth of the overall decline. However, the 4-digit data also reveals that a number of categories of manufactured goods also contributed to the decline, including motor vehicle parts and digital processing units (computers). The take away message is that a true measure of developments in U.S. competitiveness is more likely to be found by looking at U.S. export performance within relatively narrowly defined categories.

The importance of foods for the explaining the overall decline in U.S. share is somewhat surprising given foods relatively small share in U.S. exports and, as shown in Figure 2, the lack of an abnormally large fall in the U.S. share of food specific exports. However, it is important to note that the contribution of each individual category to the fall in the U.S. aggregate share occurs along both an intensive and an extensive margin. The decline in the U.S. aggregate share reflects both an intensive decline in market share within each category, as well as an extensive decline stemming from changes in the size of each category in world exports. For instance, corn (SITC 0440) contributes to the decline in U.S. aggregate share both as the U.S. captures a smaller proportion of the corn-specific export market and also as corn's share of overall world exports declines.

One established method of assessing the importance of composition for changes in trade shares is constant market share analysis (see ECB (2005) for a detailed description).4 Constant market share analysis separates changes in aggregate market share into two components, a commodity effect and competitiveness effect defined as follows:5

$\displaystyle \frac{\Delta X_{US}^{i}}{\Delta X_{WORLD}}$ $\displaystyle =\underbrace{\sum\limits_{i} \frac{X_{US}^{i}}{X_{WORLD}^{i}}.\left( \Delta\frac{X_{WORLD}^{i}}{X_{WORLD} }\right) }+\underbrace{\sum\limits_{i}\left( \Delta\frac{X_{US}^{i} }{X_{WORLD}^{i}}\right) .\frac{X_{WORLD}^{i}}{X_{WORLD}}}$
 
Commodity Effect
Competitiveness Effect

The commodity effect measures the effect of composition on the change in the aggregate export share, by weighting the change in the composition of world exports by the initial composition of the U.S. export bundle. The competitiveness effect measures the portion of the change in the aggregate share that is due to changes in the within category share of U.S. exports.

Figure 5 decomposes the contribution of each 1-digit SITC export category to the change in the aggregate export share (the blue bars) into components due to commodity (the green bars) and competitiveness (the red bars) effects over the 1984 to 2006 period. The large negative contributions of food and live animals and crude materials largely reflect the declining importance of these goods in world exports (signified by negative commodity effects), although U.S. exports also suffered a negative competitiveness effect in each case. In contrast the negative contribution to the aggregate recorded by the machinery and transportation sector is completely due to a decline in U.S. competitiveness, as the sector has greatly increased its weight in the world exports over the time frame under consideration.

In summary, interpreting the decline in the U.S. export share is complicated by compositional effects. The primary drivers of the decline in aggregate U.S. share were raw commodities, with negative contributions that largely derived from their declining weight in the world export basket. That said, the U.S. did experience a large decline in the share in the machinery and transportation sector, which was not reflected in the composition of U.S. exports but rather declines within detailed sub-categories. Here the evidence of a fall in U.S. competitiveness is more compelling. Against this background, the following sections focus on U.S. export performance within industries and attmept to identify its drivers. We suggest two alternative empirical methodologies to parse out a narrower definition of competitiveness: exporter productivity purged of geographical and relative market size considerations. This strategy is termed, "revealed competitiveness," which derives from the fact that it is inferred from observable trade flows.


3  Reduced Form Revealed Competitiveness

One possible explanation for the decline in U.S. export share is simply that the U.S. now accounts for a smaller share of global output. As China and other emerging economies expand rapidly and become more integrated into the global economy, it is natural that the U.S. share of world exports would fall without necessarily indicating any decline in the productivity of U.S. exporters. As shown in Figure 6, the fall in the U.S. share of global exports of about 4 percentage points over the past decade corresponds to a decrease in the U.S. share of global output of about 3 1/2 percentage points on a PPP basis. The relatively tight correlation between export share and income holds true for many other countries as well. Across the G7, France, Italy, and Japan have experienced declines in export share that broadly match their declining share of world output. On the other hand, Germany has more less maintained export share even as its share of world income has declined, while Canada and the UK have suffered much steeper falloffs in exports than income. In percentage terms, the export share growth of China has outpaced its income share, and the same holds for India.

Figure 6 strongly suggests that changes in market share may be conflating competitiveness effects with income dynamics. Specifically, country characteristics such as size may be influencing market share but have little to do with the underlying ability of a country's exporters to compete. To control for such characteristics, our first approach is to non-parametrically estimate trade flows minus the contribution of country size, geography and certain trade costs. A derivative of the gravity equation is a natural candidate to do so. Previous studies such as Baier and Bergstrand (2001), and more recently Whalley and Xin (2009) and Novy (2009), use gravity to decompose the levels of bilateral trade flows into contributions from income, trade costs or otherwise. Each finds that exporter and importer income plays a substantial, even dominant, role in explaining trade. Our approach extends this logic to the case of relative trade performance, where the gravity equation is 'folded' by dividing through by a reference exporter. In the particular case where the reference country is the entire world, the gravity equation converts neatly into an expression for market share in terms of relative exporter size, relative geographic characteristics and relative productivity.

Our approach to 'decomposing' the share into factors that have to do with competitiveness and those that don't involves simply looking at the time variation in the residual of a panel gravity estimation. The intuition is that if a country is increasingly outperforming the average exporter's performance (i.e., a country exports more relative to its own size and more to distant countries over time) then its residual will grow over time. We posit that this residual contains information about changes in the underlying productivity of exporters. In this section, we do not apply a structural interpretation to that productivity, it is merely contained in the residual. In the section that follows, we apply a structure that allows for more specific interpretation of the residual and, moreover, is consistent with the reduced form gravity equation herein.

To be concrete, define $ T_{s}^{lh}$ as country $ l$'s exports to country $ h$ in sector $ s$ in a given period $ t$:

$\displaystyle T_{st}^{lh}=D_{t}^{l}D_{t}^{h}r_{st}^{l}r_{st}^{h}\rho_{s}^{lh}\phi _{t}$ (1)

Equation (1) corresponds to a generic gravity model, where bilateral trade is a function of country size ($ D$), latent country-specific multilateral resistance ($ r$), geographic characteristics ($ \rho$) and global shocks ($ \phi$). Exploiting the multiplicative form of the equation, we cancel out importer-specific terms by dividing through by total exports to country $ h$ in industry $ s$.

$\displaystyle \frac{T_{st}^{lh}}{\sum_{l}T_{st}^{lh}}=\frac{D_{t}^{l}r_{st}^{l}\rho_{s} ^{lh}}{\sum_{l}D_{t}^{l}r_{st}^{l}\rho_{s}^{lh}~}$ (2)

The intuition for this reduced form is that the change in a given importer's income or multilateral resistance will affect the level of that country's imports but not how the new imports are allocated across exporters. Moreover, a global shock affecting all exporters will not affect their relative performance and hence the $ \phi$ terms cancel out as well. The method of taking ratios of the gravity equation has three ostensible benefits. First, for our purpose of relating the share of U.S. exports to underlying productivity measures, equation (2) is expressed in the correct units of share owing to income, trade costs and productivity. Second, the size of the data matrix used in the estimation is reduced by folding in the importer-specific terms. Third, multilateral resistance terms (as defined in Anderson and van Wincoop (2003)) associated with importers cancel out, sparing the need to approximate them using fixed effects.6

Denoting the geometric mean of a given variable by $ \overline{X}=\prod _{l}X^{\frac{1}{n}}$, taking logs, and allowing for a mean-zero perturbation ( $ \varepsilon$), we can rewrite the above expression as:7

$\displaystyle \ln\frac{T_{st}^{lh}}{\sum_{l}T_{st}^{lh}}=\ln\frac{1}{n_{s}}+\ln\frac {D_{t}^{l}}{\overline{D_{t}}}+\ln\frac{\rho_{s}^{lh}}{\overline{\rho}_{s}^{h} }+\ln\frac{r_{st}^{l}}{\overline{r_{st}}}+\varepsilon_{st}^{lh}$ (3)

The log of country $ l$'s market share in destination market $ h$ is a positive function of its relative income, its geographic proximity and its relative productivity. Again, this specification is isomorphic to a standard gravity model, though specified in relative terms. With the additional assumptions that $ \rho$ and $ n$ are constant over time, variation in exporter multilateral resistance and productivity is identified as the residual of a model with exporter relative income and country-pair fixed effects on the right-hand side. That is, the actual market share changes over time relative to the changes in the gravity model prediction contains information about the evolution of relative exporter productivity. This is what we will refer to as the share-to-model ratio, or simply the growth in the model residual by exporting country, averaged across industries:

$\displaystyle \bigtriangleup\ln\frac{r_{t}^{l}}{\overline{r_{t}}}=\overline{\left( \bigtriangleup\ln\frac{T_{st}^{lh}}{\sum_{l}T_{st}^{lh}}-\bigtriangleup \ln\widehat{\frac{T_{st}^{lh}}{\sum_{l}T_{st}^{lh}}}\right) }$ (4)

The assumption of a time-invariant $ \rho$ is similar to standard gravity approaches using variables such as distance, common border and common language that don't tend to change much. The implication of this assumption, however, is that decreases in trade costs due to changing trade policy will also be captured in the residual term. In our implementation we add dummies for significant shifts in policy (e.g., NAFTA, EMU) as well as over the course of our sample to try to control for changing trade costs, but nonetheless the residual likely captures elements of falling trade costs in addition to relative productivity. As such, in this section we jointly estimate relative performance due to these factors, both of which fit into a reasonable, if broad, definition of export competitiveness; in the following section we use a structural model to parse the residual more finely.

The assumption of a constant number of trading partners per importer ($ n$) may be less benign. Due to the seminal work of Feenstra (1994), there has been much study of the increase in product variety within even narrowly defined product categories. We address this empirically in two ways. First, every specification below contains time fixed effects which would soak up a secular trend in varieties. Secondly, most specifications contain country-pair fixed effects or exporter-time fixed effects which would pick up at least a portion of the level differences in $ n$ by country. We note that a disproportionate rise in relative product variety to certain countries over time would decrease the term $ ln(1/n)$ and hence work against the finding of rising productivity in the residual. For the most prolific traders in terms of their number of trading partners, which includes the U.S., we thus take our estimates of the rising residual as an underestimate of the true change in productivity and trade costs.

3.1  Reduced Form Revealed Competitiveness: Data & Specification

The data used in the estimation are bilateral trade flows as described in the previous section, nominal GDP data from the Penn World Table which are converted into international dollars at PPP exchange rates, dummy variables for NAFTA, EU and EMI trade flows, the distance between capital cities, as well as common border and common language dummies. We follow previous studies by truncating the data at $10,000 per annual bilateral flow to avoid potential distortions from errors of units in the data and implausibly small trade values. We run each gravity regression at the SITC 4-digit level and constrain ourselves to products with over 1,000 exporter-importer-year observations. The amount of data lost due to concordance issues for income and distance data will vary by specification since the use of fixed effects often obviates the use of those variables, but the most punitive cut of the data still accounts for over 83 percent of global trade value between 1980 and 2004.

Our estimator is OLS on the log-linear specification of (3). Cognizant of the fact that there are many different ways to take that equation to data, we try an array of five different panel specifications with varying degrees of control for multilateral resistance terms. Again, our objective is to compute various indexes of the change in the residual (4) which will be informative of the portion of U.S. share decline not explained by gravity controls such as income and geography. The differences among these five regressions are the treatment of the $ \rho$ terms (which in some cases are country-pair fixed effects and in others are the standard distance, border and language controls), the measure of country-specific variables $ D$, as well as the subset of data used for the estimation.

The specifications are described in Table 2. In specification (i), we regress the exporter's share of global sales in each SITC product on the exporter's relative nominal income (recall that the importer-specific terms cancel by dividing by a reference exporter), exporter-importer fixed effects, year fixed effects, and dummies for NAFTA and EMU. An actual measure of income is used to control for the trends described in the previous section. The exporter-importer FE is a static measure of trade costs which wipes out variation in border, distance and language, and arguably includes many more unmeasured (and unchanging) barriers to trade. To control for some large policy changes during our sample which we do not view as endogenous to competitiveness, dummies for post-NAFTA and post Euro years are included for the appropriate countries. Finally, year fixed effects soak up secular trends in $ n$.

Specification (ii) uses the same regressors as (i), but on a subset of the data that has observations for at least 20 of the 25 years in the sample (i.e., within each exporter-importer-SITC cell). It is informative to constrain ourselves to the subset of bilateral trade flows that are balanced over the course of the sample for at least two reasons. First, the average results statistics reported across products may be skewed by compositional changes over time in the unbalanced panel. Secondly, our linear-in-logs specification potentially introduces selection bias by dropping the observations with zero trade flows. One possible way to assess the sensitivity of the results to loosening the data truncation at zero would be to tighten it further; that is, any selection bias caused by dropping zero values would be enhanced by dropping sporadic ones.

Specification (iii) uses exporter-year fixed effects in the place of GDP. Since these fixed effects also approximate changes to the multilateral resistance terms of the exporter, they may in fact be soaking up some of the information on competitiveness intended to be measured. As such, the robustness of the result consists of a similar profile of residual changes in specifications (i) through (iii), due to the following trade-off: in the first two there is likely some omitted variable bias since implicit indexes of multilateral resistance (as defined in Anderson and van Wincoop (2003)) are themselves a function of geographic variables included in the regression. On the other hand, the appropriate control for multilateral resistance removes from the residual at least some information on the relative performance of exporters. Specifications (iv) and (v) check the robustness of the results to more standard gravity specifications, by unfolding (3) into levels and incorporating conventional measures of static trade costs. Specification (vi) uses a an alternative data source on bilateral international trade flows aggregated into broader ISIC 2-digit sectors.8

3.2  Reduced Form Revealed Competitiveness: Results

After controlling for model factors in several alternative formulations of the gravity model, we find that the U.S. export share is only in slight decline. In our benchmark specification (i), the majority of the roughly 20 percent decline in aggregate U.S. export share is explained by the model with about a 6 percent decline in the residual.9

Table 3 shows the estimates of control variables for (3) estimated across all products.10 As expected, exporter GDP share is positively related to export share, with a 1 percent decrease in relative income decreasing export share by roughly 0.4-0.6 percent. These magnitudes are similar to the coefficients on GDP in the level regressions and slightly lower than those using the ISIC data. The effect of NAFTA and the introduction of the euro are both positive and significant, with coefficients ranging from 0.4-1.5 and 0.1-0.5, respectively. Measures of distance, language and border have the expected sign.

An index of market share changes for the U.S., along with an index of model predicted values, are shown in Figure 7. The index in each year is a geometric mean of share changes across U.S. destination countries and products, where each change in share is weighted by the SITC-importer value in the year 2000.11 Despite a widening of the gap between the two indices in the early period, the model prediction broadly follows the share trend. Since there are not many time-varying regressors in our gravity estimation, this result is closely related to the observation in Figure 6 that U.S. income share and trade share have similar dynamics.

To construct a statistic for the overall percent change in market share due to the gravity residual, the ratio of actual to predicted share is averaged across time periods in the early part of the sample (1980-1992) and the latter part (1993-2004) and the log-difference of these two ratios is taken for each exporter-importer-SITC group. The average of those statistics across destinations and 4-digit product groups is shown in Figure 8 for the G20 plus Singapore, Taiwan and Hong Kong.12 Across all products, the U.S. is in the middle of the pack with decreases in its residual of 6 percent. This can be interpreted as a decrease in U.S. export market share of 6 percent that is not accounted for by the dynamics of income, and is notably smaller than the overall share decline of approximately 20 percent over that period. This suggests that U.S. relative productivity competitiveness, albeit in slight decline by this measure, did not decline by nearly as much as its fall in share might suggest. This result is consistent across product categories, shown in Table 4, even for SITC 7 (machinery and transportation) where U.S. share performance was particularly grave, as well as for other specifications shown in Appendix B. For other exporters, clear winners and losers emerge. Indonesia, China, India and Mexico had among the highest increases in their gravity residual by a large margin, as their export growth far outpaced the increase in their income shares. On the other hand, certain large Asian exporters had dramatic falls in their residuals presumably due to the rise of China and large increases in Mexican exports to the U.S. over the sample period. European countries and Canada had more moderate changes in their export performance and, with a few exceptions, tended to lag behind the rest of the world.

In summary, this reduced form exercise strongly supports the story that exporter income shares are an important determinant of trade shares. Beyond that, however, it is difficult to know whether the gravity residual reflects the actual evolution in the underlying productivity of exporters rather than other factors, such as evolving trade costs. In the following section we take a different approach to identifying relative cost competitiveness across countries by modelling the micro-foundations of trade shares explicitly.


4  Structural Revealed Competitiveness

In this section we build on a multi-country multi-sector version of the Melitz-Ottaviano (2008) model to obtain a (computable) structural equation for the relative competitiveness of a country. A full description of the reference model is reported in Corcos et al. (FEEM, 2010), although its main properties are summarized in Appendix A.

The model yields the following expression for aggregate bilateral trade from country $ l$ to country $ h$ in a given sector $ s$13:

$\displaystyle T_{s}^{lh}=\Upsilon_{s} ~ \rho_{s}^{lh} ~ E_{s}^{l} ~ [max(m)_{s} ^{l}]^{-\gamma_{s}} ~ D^{h} ~ [m_{s}^{hh}]^{\gamma_{s}+2}$ (5)

where $ \gamma_{s}$ is the shape parameter of the (Pareto) marginal cost distribution in sector $ s$; $ \Upsilon_{s} \equiv\frac{1}{2 \upsilon_{s} \left( \gamma_{s}+2\right) }$ is a bundling sectoral parameter playing no role in subsequent analysis; $ E_{s}^{l}$ is the number of entrants in country $ l$ - sector $ s$; $ max(m)_{s}^{l}$ is the upper bound of the exogenous marginal cost distribution in country $ l$ - sector $ s$ (exogenous cost cutoff); $ \rho _{s}^{lh} \in(0,1]$ is a measure of trade freeness between country $ l$ and country $ h$ in sector $ s$; $ D^{h}$ is country size (i.e population and, by extension, GDP); $ m_{s}^{hh}$ is the endogenous maximum possible marginal cost for a generic domestic firm producing and selling in country $ h$ - sector $ s$ (endogenous cost cutoff).

Equation (5) expresses exports from $ l$ to $ h$ in a given sector as a function of bilateral trade freeness [ $ \rho_{s}^{lh}$] and a set of country characteristics specific to the exporting [ $ max(m)_{s}^{l}$, $ E_{s}^{l}$] or the importing [$ D^{h}$, $ m_{s}^{hh}$] country.14

It is worth noting how, bearing in mind equations (13) and (14), the vector of inverse endogenous cutoffs $ \bm{M_{s}}^{-1}$ (with generic element $ 1/m_{s}^{hh}$) can be interpreted, once ordered, as a country ranking in terms of actual competitiveness. On the other hand, the vector of inverse exogenous cutoffs $ \Psi_{S}^{-1}$, with generic element $ 1/\psi_{s}^{h}\equiv\left[ \omega_{s}^{h} f_{s}^{h} \left( max(m)_{s}^{h}\right) ^{\gamma_{s}}\right] ^{-1}$ , can be thought of, once ordered, as a country ranking in terms of the exogenous ability to generate low cost firms. Given $ \Psi_{S}^{-1}$, a country's position in $ \bm{M_{s}}^{-1}$ is an inverse function of home market size ($ \bm{D}$) and trade freeness ( $ \bm{P_{S}}$). As explained in Appendix A, to stress this relationship between $ 1/max(m)_{s}^{h}$ and $ 1/m_{s}^{hh}$, we refer to the former as the Producer (Marginal Cost) Competitiveness of country $ h$ and to the latter as its Overall (Marginal Cost) Competitiveness (henceforth OC and PC respectively).

Equation (5) provides us with the chance to derive an analytical expression that can be used to infer the vector $ \bm{M_{s}}^{-1}$ of the OC of the countries from observed bilateral trade flows. To this aim, we start by noting that only $ T_{s}^{lh}$ and $ D^{h}$ are observable. Thus we first of all need to purge (5) of the unobservable terms. However, consider that the terms in (5) are specific to both the origin and the destination country [i.e. $ \rho_{s}^{lh} $], or either to the former (i.e. $ [max(m)_{s}^{l}]^{-\gamma_{s}}~E_{s}^{l}$) or the latter (i.e. $ [m_{s}^{hh}]^{\gamma_{s}+2}D^{h}$) only. To isolate OC, we can therefore use country $ l$'s exports to a reference country $ f$ (UK in the application), to transform equation (5) into a prediction of relative (instead of absolute) trade flows:

$\displaystyle \frac{T_{s}^{lh}/D^{h}}{T_{s}^{lf}/D^{f}}=\frac{\rho_{s}^{lh}}{\rho_{s}^{lf} }\left[ \frac{m_{s}^{hh}}{m_{s}^{ff}}\right] ^{\gamma_{s}+2}$ (6)

This expression, in which measurable terms are grouped on the left hand side, expresses measurable (relative) trade flows as a function of trade freeness and OC, both in relative terms.

Using a tilde to indicate that a variable is expressed in relative terms ( $ \tilde{\rho}_{s}^{lh}=\rho_{s}^{lh}/\rho_{s}^{lf}$; $ \tilde{D}^{h} =D^{h}/D^{f}$; $ \tilde{m}_{s}^{hh}\equiv\frac{m_{s}^{hh}}{m_{s}^{ff}}$), relative average marginal costs in a given country-sector can be written as

$\displaystyle \tilde{\bar{m}}_{s}^{hh} \equiv\left( \frac{\tilde{T}_{s}^{lh}}{\tilde{D} ^{h}}\frac{1}{\tilde{\rho}_{s}^{lh}}\right) ^{\frac{1}{\gamma_{s}+2} }$ (7)

where we also used the fact that, under the Pareto assumption, $ \overline {m}_{s}^{h}=\frac{\gamma_{s}}{\gamma_{s}+1}m_{s}^{hh}$ , and thus $ \tilde {\bar{m}}_{s}^{hh}\equiv\frac{m_{s}^{hh}}{m_{s}^{ff}}$.

Bilateral trade costs - or more precisely the degree of trade freeness $ \tilde{\rho}_{s}^{lh}$ - are however unknown. To deal with this issue, we derive - as suggested by Novy (2009) - a very simple form for bilateral trade freeness, which exploits the structure of the reference model without the need to estimate a gravity equation. From (6), bilateral trade freeness between country $ l$ and country $ h$ can be in fact expressed as

$\displaystyle \tilde{\Omega}_{s}^{lh} \equiv\frac{\tilde{T}_{s}^{lh}~\tilde{T}_{s}^{hl} }{\tilde{T}_{s}^{ll}~\tilde{T}_{s}^{hh}} = \frac{\tilde{\rho}_{s}^{lh} ~\tilde{\rho}_{s}^{hl}}{\tilde{\rho}_{s}^{ll}~\tilde{\rho}_{s}^{hh} }.$ (8)

The intuition behind (8) is (Novy, 2009) straightforward. If bilateral trade flows between two countries increase relative to domestic trade flows, it must have become relatively easier for the two countries to trade with each other. This is captured by an increase in $ \widetilde{\Omega }_{s}^{lh}$, and vice versa.

Assuming $ \tilde{\rho}_{s}^{lh}=\tilde{\rho}_{s}^{lh}$, (8) can be plugged into (7) in order to obtain the following measure of Revealed Overall Competitiveness (henceforth ROC)15:

$\displaystyle ROC \equiv(\tilde{\bar{m}}_{s}^{hh})^{-1} = \frac{\tilde{D}^{h} ~ \widetilde{\Omega}_{s}^{lh}} {\tilde{T}_{s}^{lh}}.$ (9)

Equation (9) does not require econometrics. The advantage over gravity estimates16 is that $ \widetilde{\Omega}_{s}^{lh}$ can be calculated not only for cross-sectional data but also for time series and panel data. Thus, the evolution of the resulting country rankings can in this case be trusted. Note also that, although $ \widetilde{\Omega}_{s}^{lh}=\widetilde{\Omega}_{s}^{hl}$, $ \tilde {T}_{s}^{lh} $ normally differs from $ \tilde{T}_{s}^{hl}$. Thus, what equation (9) suggests is that the difference in $ \tilde{T}_{s}^{lh}$ respect to $ \tilde{T}_{s}^{hl}$ has to be traced back to differences in relative costs ( $ \tilde{\bar{m}}_{s}^{hh}/\tilde{\bar{m}}_{s}^{ll}$) and market size ( $ \tilde{D}^{h}/\tilde{D}^{l}$).

Finally, it is worth noting that the idea of "revealed" competitiveness associated with (7) is more general than more conventional measures of aggregate total factor productivity ($ tfp$). To see this, consider equation (12): our measure of "overall competitiveness" is a composition of "inverse $ tfp$" ($ c$) and input costs ( $ w_{x,s}^{l}$), as well as input shares ( $ \beta_{x,s}^{l}$). Although a country could have high $ tfp$ (i.e. low $ c$) in sector $ s$, that may not be sufficient to be competitive in international markets. It could be that international differences in input costs (such as capital, labour and intermediates) are a disadvantage to that country.

Moreover, a country's domestic value added (DVA) content of exports might be low, which would dampen the link between a country's $ tfp$ and its export performance. The importance of $ tfp$ in determining the international competitiveness of a country decreases with the degree of international fragmentation of the production process in the country. By definition, $ tfp$ is meant to measure the output differences which are not explained by different input choices and occurs, instead, through marginal product increases. Due to this physical nature, firms' $ tfp$ (and thus a country's $ tfp$) is invariant to different choices concerning whether to outsource phases of the production process and whether to buy intermediates domestically or abroad. Whilst $ tfp$ is not affected by these choices, marginal costs are. For given quantities of intermediate inputs used in production, the possibility to import them from abroad offers a chance to reduce marginal costs (see equation (12)). In the aggregate, this results in an improved capacity to target the international consumers of the final good $ s$ at relatively low prices. Since it is expressed in units of marginal costs, ROC is a measure of competitiveness which is "naturally" linked to the concept of DVA.

Moreover, since the international structure of ROC (vector $ M_{s}$) results from a combination of forces (such as trade costs and market size) affecting the degree of international competition for final goods, ROC is informative of a given country's ability to sell good $ s$ at low prices to the international market; in contrast, $ tfp$ is informative of that country's ability to sell good $ s$ at low prices domestically.

4.2  Structural Revealed Competitiveness: Data & Specification

As equation (9) derives country $ h$'s ROC from its bilateral trade flows with a given country $ l$, for each country $ h$ (and industry $ s$) we compute $ \tilde{\bar{m}}_{s}^{hh}$ as many times as the number of its commercial partners. In other words, our reported ROCs are obtained considering, for each country, all the country pairs for which bilateral trade flows are available. A single value for $ \tilde{\bar{m}} _{s}^{hh}$ is then obtained as a weighted average in which each country is assigned its share on country $ h$'s total imports as weight.17

As in section 3, we focus on two periods (1980-1991 and 1992-2004). Data on bilateral flows are obtained from the CEPII TradeProd database. The choice is driven by the fact that, in contrast to the bilateral trade data used above, TradeProd reports reliable internal trade flows. Trade flows are provided in nominal dollars at the 3-digit level of the ISIC Rev.2 classification. Again, we truncate the data at $10,000 per annual bilateral flow; this has no remarkable effects on the results. As above, data on country GDP from the Penn World Tables are converted into international dollars at PPP exchange rates. We use United Kingdom as our reference country since it has the highest number of observations as importer or exporter. Consistent with the reducted form exercise, results are presented for the G20 country group with the exception of Saudi Arabia, for which information on internal trade flows is unavailable.

4.2  Structural Revealed Competitiveness: Results

In this section we focus on average percentage changes in ROC from the early to the late period. Our main results are synthesized in Figure 9, though readers are directed to Appendix B for additional detail on countries and industries.

Figure 9 reports the average ROC percentage change for those G20 countries for which ROC is available for at least 23 out of our 28 sectors. For each country, the sectors are weighted using the product's average share in the country's export bundle during the late period. Standardization is by sector and with respect to the whole G20. A slight decline (-5.58%) characterizes the evolution of the average ROC variation in the U.S. Overall, Figure 9 confirms the exceptional competitiveness growth of certain emerging market competitors such as China and Mexico but also that of other, more traditional, competitors such as Canada and Australia. Among EU countries, only UK, Spain and Austria show a positive variation in ROC. In particular, Italy is the worst performing G20 country, followed by Portugal and traditional U.S. competitors like France and Germany.

Throughout the paper we have interpreted the gravity residual and structural cost estimates as largely reflecting latent exporter productivity. However, other factors likely contribute to export performance in excess of what might be predicted by these frameworks. As mentioned in the model description, the structure of production within certain regions of the global economy could be playing an important role. For example, regions that are relatively intensive in cross-border production sharing would record higher exports for a given unit of output independent of exporter productivity. Indeed, this may be behind some of the high measures of performance that we estimate for China and Mexico over the sample period. In principle, though, the dynamic trade cost measure in the structural analysis (which compares international to intranational trade flows) captures some of the increasing incidence of production sharing. The fact that East Asian countries, excluding China, had competitiveness losses in the reduced form estimates and competitiveness gains in the structural estimates is consistent with the reality of large flows of goods passing through China for final assembly.


5  Conclusion

The U.S. share of global exports has fallen by roughly 20 percent over the last decade. This paper aims to deconstruct the drivers of the decline in share. First, we document that the distribution of the decline is quite uneven, with a minority of categories contributing disproportionately. Second, when controlling for the relative decline in the U.S. share of global output, driven by a large extent by rapid growth in emerging market economies, the fall in share is far less pronounced. We formalize this notion within a gravity framework and assess changes in competitiveness through the evolution of the estimation residuals in an array of empirical specifications. We find that, accounting for income share and other controls, the decline in U.S. export share is largely explained by model factors.

We then take a more structural approach to examining the evolution of U.S. competitiveness, deriving an expression for U.S. export share from a heterogeneous firms model in the style of Melitz-Ottaviano (2005). This approach confirms the outcome of our gravity model exercise, that the U.S. has generally maintained its level of competitiveness within detailed product categories, despite the fall in the overall share. All together this analysis points to the inadequacy of the aggregate export share as an indicator of country export competitiveness.


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Figure 1. U.S. Share of World Merchandise Exports

Data for Figure 1 immediately follows

Data for Figure 1

YearUSA Export Share
198011.05
198111.60
198211.50
198311.28
198411.71
198511.32
198611.02
198710.44
198811.58
198912.02
199011.42
199111.92
199211.90
199312.79
199412.39
199511.83
199612.11
199712.46
199812.52
199912.34
200012.29
200111.90
200210.80
20039.71
20048.98
20058.68
20068.48
20078.34
20088.07
20098.55

Figure 2. U.S. Share of World Merchanside Exports, by SITC 1-Digit Sector

Data for Figure 2 immediately follows

Data for Figure 2

Sitc CodeUnited States: 1984United States: 1989United States: 1994United States: 1999United States: 2004United States: SITCUnited States: dshare
Food & Live Animals (0)0.1536750.1382570.1209570.1164740.098210-0.05546
Beverages & Tobacco (1)0.1548720.1593890.1620490.1298040.0848881-0.06998
Crude Materials (2)0.1816140.1709070.1619180.1399260.1227512-0.05886
Mineral Fuels (3)0.0266240.0344690.0303250.0231390.0209473-0.00568
Animal & Vegetable Products (4)0.1223110.1022780.0979830.0943490.0500534-0.07226
Chemicals (5)0.1525350.1380340.1429770.1476510.1236875-0.02885
Manufactured Goods by Material (6)0.0618440.0632320.0759220.0903830.06572360.00388
Machinery & Transportation (7)0.179440.1540520.1622660.1645440.1115917-0.06785
Misc. Manufactured (8)0.1060050.0973030.1125810.1189210.0882178-0.01779
Other (9)0.1037360.1449190.149050.0882940.0969069-0.00683

Figure 3. SITC 1-Digit Contributions to the Aggregate Share Decline (Percentage Points)

Data for Figure 3 immediately follows

Data for Figure 3

Corn-0.00322
Motor vehicle parts and accessories-0.00253
Soybeans-0.00218
Lignite-0.00198
Digital processing units-0.00176
Oscilloscopes & spectrum analyzers-0.00116
Office and data processing machines-0.00107
Aircraft parts-0.00105
Cotton, not carded or combed-0.0009
Fuel oils-0.0009

Figure 4. Top 10 4-Digit Contributions to the Aggregate Share Decline (Percentage Points)

Figure 4 depicts the top 10 contributors to the aggregate decline among 4-digit SITC codes. Corn and soybeans contribute a combined one sixth of the overall decline. However, the 4-digit data also reveals that a number of categories of manufactured goods also contributed to the decline, including motor vehicle parts and digital processing units (computers). The take away message is that a true measure of developments in U.S. competitiveness is more likely to be found by looking at U.S. export performance within relatively narrowly defined categories.

Figure 5. Commodity and Competitiveness Contributions to the Aggregate Share Decline (Percentage Points)

Figure 5 decomposes the contribution of each 1-digit SITC export category to the change in the aggregate export share (the blue bars) into components due to commodity (the green bars) and competitiveness (the red bars) effects over the 1984 to 2006 period. The large negative contributions of food and live animals and crude materials largely reflect the declining importance of these goods in world exports (signified by negative commodity effects), although U.S. exports also suffered a negative competitiveness effect in each case. In contrast the negative contribution to the aggregate recorded by the machinery and transportation sector is completely due to a decline in U.S. competitiveness, as the sector has greatly increased its weight in the world exports over the time frame under consideration.

Figure 6a. Export and GDP Shares

In Figure 6, the fall in the U.S. share of global exports of about 4 percentage points over the past decade corresponds to a decrease in the U.S. share of global output of about 3 1/2 percentage points on a PPP basis. The relatively tight correlation between export share and income holds true for many other countries as well. Across the G7, France, Italy, and Japan have experienced declines in export share that broadly match their declining share of world output. On the other hand, Germany has more less maintained export share even as its share of world income has declined, while Canada and the UK have suffered much steeper falloffs in exports than income. In percentage terms, the export share growth of China has outpaced its income share, and the same holds for India. Figure 6 strongly suggests that changes in market share may be conflating competitiveness effects with income dynamics. Specifically, country characteristics such as size may be influencing market share but have little to do with the underlying ability of a country's exporters to compete. To control for such characteristics, our first approach is to non-parametrically estimate trade flows minus the contribution of country size, geography and certain trade costs. A derivative of the gravity equation is a natural candidate to do so. Previous studies such as Baier and Bergstrand (2001), and more recently Whalley and Xin (2009) and Novy (2009), use gravity to decompose the levels of bilateral trade flows into contributions from income, trade costs or otherwise. Each finds that exporter and importer income plays a substantial, even dominant, role in explaining trade. Our approach extends this logic to the case of relative trade performance, where the gravity equation is 'folded' by dividing through by a reference exporter. In the particular case where the reference country is the entire world, the gravity equation converts neatly into an expression for market share in terms of relative exporter size, relative geographic characteristics and relative productivity.

Figure 6b. Export and GDP Shares (cont'd)

In Figure 6, the fall in the U.S. share of global exports of about 4 percentage points over the past decade corresponds to a decrease in the U.S. share of global output of about 3 1/2 percentage points on a PPP basis. The relatively tight correlation between export share and income holds true for many other countries as well. Across the G7, France, Italy, and Japan have experienced declines in export share that broadly match their declining share of world output. On the other hand, Germany has more less maintained export share even as its share of world income has declined, while Canada and the UK have suffered much steeper falloffs in exports than income. In percentage terms, the export share growth of China has outpaced its income share, and the same holds for India. Figure 6 strongly suggests that changes in market share may be conflating competitiveness effects with income dynamics. Specifically, country characteristics such as size may be influencing market share but have little to do with the underlying ability of a country's exporters to compete. To control for such characteristics, our first approach is to non-parametrically estimate trade flows minus the contribution of country size, geography and certain trade costs. A derivative of the gravity equation is a natural candidate to do so. Previous studies such as Baier and Bergstrand (2001), and more recently Whalley and Xin (2009) and Novy (2009), use gravity to decompose the levels of bilateral trade flows into contributions from income, trade costs or otherwise. Each finds that exporter and importer income plays a substantial, even dominant, role in explaining trade. Our approach extends this logic to the case of relative trade performance, where the gravity equation is 'folded' by dividing through by a reference exporter. In the particular case where the reference country is the entire world, the gravity equation converts neatly into an expression for market share in terms of relative exporter size, relative geographic characteristics and relative productivity.

Figure 7. Predicted adn Actual Market Share Indices

Data for Figure 7 immediately follows

Data for Figure 7

yeargravity modelmarket share
1980100100
1981100.21722102.10952
198299.51950771100.4635452
1983101.797718298.66312793
1984107.195440490.57168588
1985106.367162787.49859763
1986104.047135380.88658236
1987102.02222278.71434892
198899.3748269881.02090762
198997.3948332483.07720205
199095.7027343780.27475048
199192.1915828883.45226593
199290.149244383.09862861
199389.2354104385.25630943
199488.1257502583.54162601
199586.3329640581.61488043
199685.6154939682.69111953
199785.0231631682.80315773
199884.9240516680.99820622
199984.2200907279.00458927
200076.8376354974.17610529
200175.8216114471.86184047
200275.3902774568.15747039
200374.6786083163.70143721
200474.8327001560.84569541

Figure 8. Reduced Form Measure of Competitiveness, Change Between Early and Late Sample (Percentage Points)

Data for Figure 8 immediately follows

Data for Figure 8

 UN Comtrade (SITC)Chelem (ISIC)
Indonesia0.504986530.71561738
China0.46475020.68373595
India0.390437160.56362496
Turkey0.36769720.6390655
Mexico0.360517690.72107074
Saudi Arabia0.309192030.19683399
Spain0.187729610.01071516
Italy0.146077550.0618769
South Africa0.136598860.86433848
Rest of World0.079679030.00450146
Australia0.045039120.05880159
France0.02608082-0.17244206
All0.0214243-0.02268058
Other EU0.01706058-0.13606494
Germany0.01055769-0.16091488
Brazil-0.05609352-0.12218161
USA-0.06091851-0.02762394
Canada-0.10314775-0.1176693
Korea-0.115841-0.46735922
UK-0.12033621-0.36014848
Argentina-0.1203525-0.13767552
Singapore-0.29100903-0.50380707
Japan-0.29132421-0.55357127
Taiwan-0.2922583-0.49385896
Hong Kong-0.40803643-0.23891996

Figure 9. Structural Measure of Competitiveness, Change Between Early and Late Sample (Index)

Data for Figure 9 immediately follows

Data for Figure 9

countryavg
China80.55
Australia67.09
Canada57.44
Mexico56.55
Indonesia50.61
Taiwan47.24
Korea46.75
India38.86
Austria38.77
Spain27.72
UK20.05
Finland0.48
Turkey0.43
Greece-0.03
South Africa-2.25
Ireland-2.69
Denmark-4.47
USA-5.58
Sweden-7.64
Germany-9.54
Argentina-9.6
France-18.32
Portugal-33.16
Japan-50.16
Italy-65.52

Table 1. U.S. Export Share in Machinery and Equipment Categories

SITCDescriptionU.S. Export Share: 1984U.S. Export Share: 1989U.S. Export Share: 1994U.S. Export Share: 1999U.S. Export Share: 2004
71POWER GENERATING MACHINERY AND EQUIPMENT0.250.230.230.260.20
72MACHINERY SPECIALIZED FOR PARTICULAR INDUSTRIES0.160.120.140.160.13
73METALWORKING MACHINERY0.100.100.130.140.12
74GENERAL INDUSTRIAL MACHINERY AND MACHINE PARTS0.170.150.170.180.12
75OFFICE MACHINES AND AUTOMATIC DATA PROCESSING MACHINES0.330.240.200.140.09
76TELECOMMUNICATIONS AND SOUND EQUIPMENT0.080.080.110.130.06
77ELECTRICAL MACHINERY AND ELECTRICAL PARTS THEREOF 0.200.160.170.180.12
78ROAD VEHICLES 0.140.100.110.100.08
79TRANSPORT EQUIPMENT0.210.300.290.320.24

Table 2. Gravity Regression Specifications

Dependent(i): Export share(ii): Export share(iii): Export share(iv): Export sales(v): Export sales(vi): Export share
DExporter GDP shareExporter GDP shareExporter-year FEExporter GDP, Importer GDPExporter GDP, Importer GDPExporter GDP share
ρCountry-pair FE, NAFTA, EMUCountry-pair FE, NAFTA, EMUCountry-pair FE, NAFTA, EMUCountry-pair FE, NAFTA, EMUDistance, language, border, NAFTA, EMUCountry-pair FE, NAFTA, EMU
nYear FEYear FEYear FEYear FEYear FEYear FE
SampleFullBalancedFullFullFullFull
DataSITC-4SITC-4SITC-4SITC-4SITC-4ISIC-2

Table 3. Estimates of Control Variables in the Gravity Regression

Dependent var. --->Export Share (i)Export Share (ii)Export Share (iii)Export Volume (iv)Export Volume (v)Memo: ISIC Industries
Exporter GDP share 0.430**
(0.001)		0.644**
(0.005)		   0.890**
0.008
Exporter GDP     0.682**
(0.005)		 0.345**
(0.001)		 
Importer GDP     0.522**
(0.004)		0.478**
(0.001)		 
NAFTA 0.484**
(0.011)
0.424**
(0.011)		1.486**
(0.010)		0.922**
(0.012)		1.281**
(0.009)		1.192**
0.055
EMU 0.417**
(0.004)		0.305**
(0.005)		0.074**
(0.005)		0.332**
(0.005)		 0.461**
(0.004)		 0.191**
0.011
Distance      -0.263**
(0.001)		 
Common Language      0.142**
(0.002)		 
Common Border      0.409**
(0.002)		 
Exporter-Importer FEYesYesNoYesNoYes
Exporter-Year FENoNoYesNoNoNo
Year-FEYesYesYesYesYesYes
Balanced panelNoYesNoNoNoNo
N11,638,4014,526,16312,672,55111,253,72710,101,0642,998,339
R-squared0.400.440.200.250.200.60
RMSE1.651.371.891.671.741.69

Table 4. Evolution of the Gravity Residual (Earlyl Sample to Late Sample)

 All SITC012345678
Indonesia50%29%9%17%36%71%36%57%91%48%
China46%26%5%27%7%9%21%45%65%64%
India39%33%18%16%41%49%66%49%9%44%
Turkey37%7%10%7%-19%5%-4%57%48%56%
Mexico36%22%62%6%-17%0%24%37%53%47%
Saudi Arabia31%54%29%5%5%30%46%66%-16%13%
Spain19%13%6%22%17%31%20%15%21%24%
Italy15%5%10%13%-21%19%10%22%12%17%
South Africa14%3%40%8%50%4%11%8%31%11%
Rest of World8%-2%6%4%22%2%4%11%11%11%
Australia5%5%27%3%2%-7%3%2%4%10%
France3%2%-10%2%15%3%2%-2%7%2%
Other EU2%0%3%5%1%7%4%-4%5%2%
Germany1%6%6%17%-1%-1%-8%-2%7%-4%
Brazil-6%-7%-6%18%-40%-20%-5%-3%-9%-13%
USA-6%-9%-6%-4%-16%2%-2%-9%-6%-4%
Canada-10%-12%2%-4%-4%7%-23%-19%-9%5%
Korea-12%-27%-23%-2%50%-35%17%-18%13%-58%
UK-12%-8%-1%-1%-19%17%-21%-13%-11%-11%
Argentina-12%1%27%-7%16%11%8%-17%-48%-20%
Singapore-29%-37%-3%-42%-33%-47%-6%-42%-22%-34%
Japan-29%-49%-23%-31%-10%-53%-9%-44%-20%-42%
Taiwan-29%-100%-15%-25%-32%-29%-10%-23%-14%-57%
Hong Kong-41%-52%-26%-63%-19%-61%-46%-42%-28%-46%

Appendix A:  Short Description of the Reference Model

The theoretical background is the framework developed by Del Gatto et al. (2006), also used in Ottaviano et al. (2009). While the reader is redirected to those papers, and in particular to Corcos-DelGatto-Mion-Ottaviano (FEEM, 2010)18 for an extensive exposition, here we report a short description of the logic behind the model and the key equations for the application.

The model is a multi-country multi-sector version of Melitz and Ottaviano (2008) encompassing S industries (with no inter-industry linkages)19 active in $ N$ countries, indexed $ l=1,...h..,N$. Each country-industry is endowed with given amounts of labor $ L^{l}$ and capital $ K^{l}$ (factors are geographically immobile) and the output of each industry is horizontally differentiated in a large set of varieties.

Consumers maximize a quasi-linear utility function with quadratic sub-utility, as in Ottaviano et al. (2002). Under this hypothesis, the demand of a generic variety in a given country is positive only provided that its selling price is lower than a certain (cutoff) level $ max(p)_{s}^{l} $. This level is higher when: consumers like the differentiated good a lot, varieties are very differentiated, the average price is high, the number of competing varieties is small.

Firms compete in a monopolistic market and each variety is supplied by one and only one firm. Each firm is negligible to the market and does not compete directly with the other firms. However, given the demand structure, firms interact indirectly through an aggregate demand effect, as the total output of the industry has an influence on firms' profit.

Firms in a given sector share the same (Cobb-Douglas) technology but are heterogeneous in terms of Unit Input Requirement (UIR) $ c$, defined as inverse 'total factor productivity' (tfp) (i.e. $ c=\frac{1}{tfp}$). $ c$ is used to identify the firm. Accordingly, the marginal cost faced by a generic firm $ c$ active in country $ l$ and sector $ s$ is:

$\displaystyle m_{s}^{l} \equiv m(c)^{l}_{s} = c ~ \omega_{s}^{l}$ (12)

where $ \omega_{s}^{l} = B \prod_{x \in X} \left( w^{l}_{x,s}/\beta _{x,s}\right) ^{\beta_{x,s}}$ , with $ w^{l}_{x,s}$ and $ \beta_{x,s}$ denoting input $ x$'s cost and share (in country $ l$ - sector $ s $) respectively, and $ X=\left\{ k,l,m\right\} $ (i.e. capital, labour, and intermediates) and $ \sum_{x \in X} \beta_{x,s} = 1$. $ B$ is the bundle of parameters associated with the Cobb-Douglas.20

National markets are segmented but firms can export and, as production faces constant returns to scale, they independently maximize the profits earned in different destination countries. Exporting firms incur a per-unit trade cost, encompassing not only carriage in a strict sense, but all those "impediments to trade" whose amount is related to the quantity exported. For each delivered unit from country $ l$ to country $ h$, $ \tau_{s}^{lh}>1$ units have to be shipped. Moreover, we also allow for costly trade within a country with $ \tau_{s}^{lh}>\tau_{s}^{ll}\geq1$.

Firm heterogeneity is modeled as follows. In order to enter the market, each firm has to make an irreversible investment in terms of labor and capital. This "sunk cost of entry" amounts to $ \omega_{s}^{l} f_{s}^{l}$. Only once this cost has been payed, and production started, a firm is allowed to observe its own marginal cost $ m^{l}_{s}$. This is modeled as the outcome of a draw from a common and known Pareto distribution $ \left[ \frac{m^{l}_{s} }{max(m)_{s}^{l}}\right] ^{\gamma_{s}}$, with support $ [0,max(m)_{s}^{l}]$ varying across countries.21

Only those firms whose cost draw is good enough to enable them to sell to market $ h$ at a price below the price cutoff $ max(p)_{s}^{h}$ earn non-negative profits and can afford to serve that market. Let $ m_{s}^{hh}$ denote the marginal cost inclusive of trade frictions faced by a producer in country $ h$-industry $ s$ that is just indifferent between serving its local market or not. Then, by definition $ m_{s}^{hh}=max(p)_{s}^{hh}$. A firm, wherever located, can serve market $ h$ only provided that its delivered cost does not exceed $ m_{s}^{hh}$. In other words: firm $ c$ producing in country $ l$ is able to target market $ h$ when $ \tau_{s}^{lh}m_{s}^{l}<m_{s}^{hh}$, it is not able to target market $ h$ when $ \tau_{s}^{lh}m_{s}^{l}>m_{s}^{hh}$, it is indifferent between serving or not market $ h$ when $ \tau_{s}^{lh}m_{s} ^{l}=m_{s}^{hh}$. Thus, $ m_{s}^{hh}$ measures the 'cutoff cost' in country $ h$-industry $ s$.

The analytical solution in terms of the $ N \times S$ equilibrium cost cutoffs is:

$\displaystyle \begin{matrix}\bm{M_{1}}^{\gamma_{1}+2} & = & \Phi_{1} & \bm{P_{1}}^{-1} & \bm{D}^{-1} & \bm{\Psi_{1}}\\ \vdots & & \vdots & \vdots & \vdots & \vdots\\ \bm{M_{s}}^{\gamma_{s}+2} & = & \Phi_{s} & \bm{P_{s}}^{-1} & \bm{D}^{-1} & \bm{\Psi_{s}}\\ \vdots & & \vdots & \vdots & \vdots & \vdots\\ \bm{M_{S}}^{\gamma_{S}+2} & = & \Phi_{S} & \bm{P_{S}}^{-1} & \bm{D}^{-1} & \bm{\Psi_{S}}\end{matrix}$ (13)

where:

Each row of (13) states, for each country in a given sector, the marginal cost above which a firm is not productive enough to serve the domestic market from therein and, since $ max(m)_{s}^{lh}=m_{s}^{hh}/\tau _{s}^{lh}$, from anywhere.23

Overall ( $ 1/m_{s}^{hh}$) and producer ( $ 1/\psi_{s}^{h}$) competitiveness.  By Cramer's rule, the $ h$-th generic element (i.e. the cutoff level in country $ h$-industry $ s$) of $ \bm{M_{s}}$ can be expressed as

$\displaystyle m_{s}^{hh}=\left[ \frac{\Phi_{s}}{D^{h}} \frac{\sum_{l=1}^{M}\left\vert R_{s}^{lh}\right\vert \psi_{s}^{l}} {\left\vert \bm{P_{s}} \right\vert }\right] ^{\frac{1}{\gamma_{s} +2}}$ (14)

where $ \left\vert \bm{P_{s}} \right\vert $ is the determinant of the trade freeness matrix in sector $ s$ and $ \left\vert R_{s}^{lh}\right\vert $ is the corresponding cofactor.

Equation (14) entails a relationship between $ m_{s}^{hh}$ and $ \psi_{s}^{l}$, basically two measures for the "competitiveness" of a country. In this relationship:

From a practical point of view, (14) can be used to see how much of the actual competitiveness of a country, measured in terms of marginal costs (i.e. $ 1/m_{s}^{hh}$), can be traced back to its exogenous competitiveness, expressed in terms of a mixture of "traditional" competitive advantages (i.e. factor prices and technology) and entry costs. To highlight this relationship, we refer, for each sector $ s$, to $ 1/m_{s}^{hh}$ and $ 1/\psi_{s}^{h}$ as respectively "overall" and "producer" competitiveness of country $ h$ (OC and PC respectively), with $ \bm{M_{s}}^{-1} $ and $ \bm{\Psi_{s}}^{-1}$ denoting (once ordered) the corresponding country-rankings.


Appendix B.  Alternative Gravity Specifications

Table B1. Percent Change of the Gravity Regression Residual From the Early Period (1980-1992) to the Late Period (1993-2004) - Specification (ii)

 All SITC012345678
Change in Residual: China45%24%6%19%-30%-5%10%41%68%83%
# of Observations: China4,8035024329444187521,4575291,160
Change in Residual: India39%31%35%11%3%76%48%42%12%61%
# of Observations: India2,3353211719427219754293523
Change in Residual: Turkey38%12%37%-13%-10%-7%17%51%68%78%
# of Observations: Turkey1,22833725121286833070265
Change in Residual: Indonesia38%11%16%9%-7%69%23%51%29%77%
# of Observations: Indonesia1,0102272314118347624621221
Change in Residual: Mexico32%12%63%6%-45%-30%21%31%66%34%
# of Observations: Mexico1,22714432106156232252265173
Change in Residual: Spain27%20%2%26%5%11%34%30%27%24%
# of Observations: Spain7,00759610431762571,0732,1861,5971,006
Change in Residual: Italy17%4%19%6%-15%32%14%25%16%16%
# of Observations: Italy15,316798125472107572,0734,5204,6022,546
Change in Residual: Australia6%3%25%10%2%-7%-1%1%12%12%
# of Observations: Australia3,180656553364932331692623393
Change in Residual: South Africa4%-7%91%-4%47%-52%-20%-11%57%43%
# of Observations: South Africa1,1662041523125310835812398
Change in Residual: Other EU4%0%-2%2%-3%5%8%0%6%8%
# of Observations: Other EU45,6103,9205572,1564573306,93912,45012,0596,677
Change in Residual: Saudi Arabia4%53%92%-16%-23%.27%74%-27%8%
# of Observations: Saudi Arabia179722341047123115
Change in Residual: Rest of World3%-6%0%-2%11%1%-3%8%8%9%
# of Observations: Rest of World29,9125,1544172,6123712113,5126,7835,0185,730
Change in Residual: France3%7%-16%-3%20%1%4%0%6%3%
# of Observations: France17,9371,4763037091521012,8784,7554,7622,791
Change in Residual: Canada2%-2%-5%3%34%11%-10%-9%5%27%
# of Observations: Canada3,7734483742034184338401,044480
Change in Residual: Brazil1%-8%-17%23%-30%-7%2%2%4%-11%
# of Observations: Brazil3,4674155824117454591,138767320
Change in Residual: Argentina-5%-1%46%-13%48%6%11%-13%-34%-23%
# of Observations: Argentina1,15532027107173719823612882
Change in Residual: USA-6%-4%3%-8%-5%-5%-3%-7%-11%0%
# of Observations: USA20,6781,7092381,1922561663,2124,6636,2392,906
Change in Residual: Germany-7%0%0%8%-5%-10%-16%-7%-4%-8%
# of Observations: Germany21,8151,2771759472321874,0045,8086,1693,000
Change in Residual: UK-9%-5%2%3%-25%28%-17%-9%-10%-6%
# of Observations: UK18,2771,187291675207763,0454,8285,1132,834
Change in Residual: Japan-25%-37%-5%-28%6%-33%3%-38%-20%-36%
# of Observations: Japan13,7113065535879361,8493,6015,2052,192
Change in Residual: Singapore-27%-28%-18%-42%-2%-54%-12%-42%-19%-31%
# of Observations: Singapore3,9503304220456694668071,232720
Change in Residual: Korea-28%-39%30%-17%55%-144%18%-25%-4%-75%
# of Observations: Korea4,04712110911014151,460947988
Change in Residual: Taiwan-38%-95%-14%-20%-2%-3%-22%-30%-22%-65%
# of Observations: Taiwan4,961193131371044091,5211,4661,204
Change in Residual: Hong Kong-41%-44%-34%-57%15%-105%-47%-46%-22%-47%
# of Observations: Hong Kong3,5951601467781658818251,460

Table B2. Percent Change of the Gravity Regression Residual From the Early Period (1980-1992) to the Late Period (1993-2004) - Specification (iii)

 All SITC012345678
Change in Residual: South Africa14%12%14%8%18%13%18%16%15%8%
# of Observations: South Africa7,179903130768117389812,1931,262737
Change in Residual: Indonesia9%3%8%8%19%5%7%12%19%4%
# of Observations: Indonesia6,68871772535901556832,1167681,499
Change in Residual: Mexico9%3%15%5%3%12%11%8%11%7%
# of Observations: Mexico6,74849210946193291,3671,6181,597948
Change in Residual: Saudi Arabia8%-8%-28%12%-1%22%17%12%1%7%
# of Observations: Saudi Arabia2,1561941116116518527520329205
Change in Residual: Turkey6%-3%1%10%11%-6%11%8%15%-2%
# of Observations: Turkey7,6651,11310354755627292,5311,2771,211
Change in Residual: Rest of World6%3%9%6%7%8%7%5%10%5%
# of Observations: Rest of World181,19321,9172,35313,8043,0441,48522,93144,44038,13531,393
Change in Residual: Hong Kong6%1%19%5%16%10%11%6%8%2%
# of Observations: Hong Kong11,9925576439642411,0233,2732,9823,537
Change in Residual: India6%3%7%5%10%4%8%5%12%-2%
# of Observations: India11,4761,01811073936821,5973,6792,2551,885
Change in Residual: Singapore5%-4%16%8%-6%-1%5%3%8%6%
# of Observations: Singapore14,4781,1561356592443501,6913,0964,5982,424
Change in Residual: Argentina4%1%6%7%-22%1%4%3%6%7%
# of Observations: Argentina5,5151,081108455901868271,228970529
Change in Residual: Korea4%3%-15%5%-5%31%-2%-1%8%8%
# of Observations: Korea15,44250672420113221,7395,1104,5792,841
Change in Residual: Australia3%-1%-6%1%1%-4%5%3%5%3%
# of Observations: Australia11,2421,7641471,0241991001,2132,5102,7291,441
Change in Residual: All2%-1%4%3%2%4%2%1%4%0%
# of Observations: All690,88963,1448,14340,0818,9966,15693,134180,759177,818107,995
Change in Residual: Other EU1%-2%1%2%2%2%1%-1%3%-1%
# of Observations: Other EU127,70610,7371,5506,0731,5201,28218,77133,17736,51917,294
Change in Residual: Brazil0%0%3%-2%-3%4%0%-1%2%3%
# of Observations: Brazil13,5571,3122068331092041,8654,1603,3611,442
Change in Residual: Taiwan0%-6%6%2%-8%1%1%-1%1%1%
# of Observations: Taiwan14,5435903555487481,5984,3474,2532,983
Change in Residual: All0%-2%1%0%-1%0%0%0%0%0%
# of Observations: All230,33920,8082,67812,1512,2701,51132,96360,57859,12837,784
Change in Residual: Japan0%-4%-10%1%-8%7%-5%-1%2%1%
# of Observations: Japan29,1918871309902451333,9137,77210,6354,320
Change in Residual: USA-1%-3%3%3%1%6%-2%-1%0%-4%
# of Observations: USA45,8374,3536463,0596785866,28110,73913,0166,173
Change in Residual: UK-1%-6%-3%-1%-4%2%0%-2%1%-2%
# of Observations: UK40,5832,8506211,8875303376,33210,53411,3765,902
Canada-1%-5%-2%-4%-11%4%1%0%0%-7%
# of Observations: Canada15,3411,6241271,1581461041,7643,5644,6732,026
Change in Residual: France-3%-9%0%2%-4%1%-1%-4%-1%-3%
# of Observations: France41,5103,7096051,8904643606,02710,58511,5386,137
Change in Residual: China-3%3%0%3%18%9%4%-3%-1%-14%
# of Observations: China20,3561,4691531,100242972,6976,1264,3774,030
Change in Residual: Italy-4%-6%-3%2%-10%-6%-3%-6%-4%-5%
# of Observations: Italy37,6782,2923661,5223962415,13910,70211,0025,851
Change in Residual: Spain-4%-10%1%-1%5%6%-6%-9%1%-1%
# of Observations: Spain22,8131,9032901,0462911963,4396,7395,5873,187

Table B3. Percent Change of the Gravity Regression Residual From the Early Period (1980-1992) to the Late Period (1993-2004) - Specification (iv)

 All SITC012345678
Change in Residual: China48%31%7%17%18%1%26%49%58%69%
# of Observations: China16,8411,228127925187922,1075,2043,5313,384
Change in Residual: Mexico31%26%53%7%-24%-21%15%30%48%43%
# of Observations: Mexico6,87250010646391271,3631,6941,609984
Change in Residual: Spain18%21%16%16%1%12%18%14%18%27%
# of Observations: Spain23,0011,8812871,0572751903,3846,8885,6213,284
Change in Residual: Italy17%10%29%12%-17%-9%8%27%12%25%
# of Observations: Italy36,3392,2143441,5533802574,89310,44410,4435,643
Change in Residual: Belgium12%9%38%16%2%7%21%2%15%21%
# of Observations: Belgium25,0621,9492431,2293772654,3317,1806,3372,950
Change in Residual: France3%1%-4%0%0%-9%2%1%7%3%
# of Observations: France39,1423,4085251,9124423575,65110,18310,6265,845
Change in Residual: Rest of World3%-1%-2%5%12%11%0%6%2%2%
# of Observations: Rest of World214,34927,8612,89516,4663,0472,07123,71556,11643,35836,870
Change in Residual: USA2%-4%-12%-3%-7%4%1%-3%9%5%
# of Observations: USA41,0243,8595922,7715715855,4569,83611,4445,658
Change in Residual: All0%-3%0%1%1%0%-1%-1%2%-2%
# of Observations: All622,62856,5517,11236,1997,5595,70480,308166,556158,213100,215
Change in Residual: Netherlands-1%2%0%7%-28%-5%-10%-7%6%6%
# of Observations: Netherlands29,0223,1794661,5594825974,9766,9147,2603,438
Change in Residual: Sweden-3%8%5%-11%28%28%0%-9%-5%4%
# of Observations: Sweden17,5357321017931541161,9314,9386,2932,374
Change in Residual: Austria-11%-10%15%-11%4%23%-21%-15%-1%-18%
# of Observations: Austrai15,78273912861789321,8234,8224,8602,589
Change in Residual: Korea-11%-28%-48%3%59%-35%16%-15%12%-57%
# of Observations: Korea14,84147864436107221,6344,9474,3202,798
Change in Residual: Switzerland-11%-26%-11%-14%9%-43%-19%-16%-3%-6%
# of Observations: Switzerland19,748964213639123503,5784,8725,9713,245
Change in Residual: UK-13%-10%-3%-6%-15%5%-21%-14%-11%-12%
# of Observations: UK39,9042,8175642,0065303916,10710,53310,8575,871
Change in Residual: Canada-19%-14%-16%-8%-4%-5%-36%-32%-14%-10%
# of Observations: Canada15,7181,5691251,1311481031,7723,7734,7822,169
Change in Residual: Japan-20%-40%-17%-11%0%-29%4%-32%-16%-34%
# of Observations: Japan27,7448841171,0132331453,6637,4949,7724,274
Change in Residual: Singapore-26%-29%-11%-21%-39%-45%-3%-40%-16%-36%
# of Observations: Singapore12,7941,0591206201963141,3362,8263,9802,240
Change in Residual: Hong Kong-35%-48%-27%-55%-8%-26%-52%-34%-24%-35%
# of Observations: Hong Kong12,2706056141942391,0113,4582,9933,565
Change in Residual: Taiwan-37%-95%-49%-26%-20%-10%-21%-31%-24%-62%
# of Observations: Taiwan14,6406253459085511,5774,4344,1563,034

Table B4. Percent Change of the Gravity Regression Residual From the Early Period (1980-1992) to the Late Period (1993-2004) - Specification (v)

 All SITC012345678
Change in Residual: China61%48%17%11%12%6%26%63%74%93%
# of Observations: China18,4611,3131331,055217982,4285,5943,9893,566
Change in Residual: Mexico21%31%72%-20%-47%-40%15%19%28%36%
# of Observations: Mexico6,75150410745988251,3241,6441,592972
Change in Residual: Spain6%30%43%2%-12%57%4%5%-5%16%
# of Observations: Spain22,1781,8052751,0392671833,2706,6295,4393,140
Change in Residual: Rest of World1%12%26%-6%4%26%4%4%-8%-3%
# of Observations: Rest of World207,43026,9692,69716,0522,8802,03723,26154,22042,65934,740
Change in Residual: Italy1%23%52%-2%-30%22%-4%12%-15%8%
# of Observations: Italy34,2562,0423301,4703432264,6319,8309,9475,277
Change in Residual: Belgium-2%19%58%0%-15%7%9%-11%-9%4%
# of Observations: Belgium24,7991,9092431,2263852644,2867,1236,2792,889
Change in Residual: All-5%11%29%-9%-11%15%-4%-5%-13%-7%
# of Observations: All613,55355,4396,84836,0807,4495,65979,968164,164156,96796,809
Change in Residual: USA-6%9%14%-13%-28%16%-2%-8%-12%-1%
# of Observations: USA43,2704,0845913,0606456185,90910,46211,9245,717
Change in Residual: Korea-6%-10%-21%-6%48%-16%16%-8%18%-53%
# of Observations: Korea14,60847069434108221,6134,8554,2702,730
Change in Residual: Sweden-8%36%44%-18%25%43%1%-11%-19%-2%
# of Observations: Sweden17,3487311037991541161,9414,8606,2352,306
Change in Residual: France-10%13%27%-11%-14%1%-7%-13%-17%-13%
# of Observations: France38,0883,3075071,8944383555,4989,95010,3165,633
Change in Residual: Netherlands-11%12%29%-2%-39%3%-19%-17%-15%-8%
# of Observations: Netherlands28,4993,1054531,5334805954,8676,8027,1683,349
Change in Residual: Switzerland-16%8%33%-21%12%-28%-18%-19%-20%-14%
# of Observations: Switzerland19,667954209643122513,6494,8655,9043,170
Change in Residual: Singapore-19%-10%23%-30%-61%-24%8%-29%-12%-34%
# of Observations: Singapore12,9811,0521116321933041,4482,8724,0542,218
Change in Residual: UK-20%13%32%-10%-28%23%-29%-19%-31%-20%
# of Observations: UK39,4762,8105612,0055183966,02810,48210,7425,703
Change in Residual: Austria-24%-2%54%-23%-10%10%-26%-26%-24%-34%
# of Observations: Austria15,02467812158981261,7194,6164,6512,459
Change in Residual: Canada-26%-1%19%-24%-28%-14%-29%-33%-33%-17%
# of Observations: Canada15,6431,5501231,1301531031,7713,7674,7802,125
Change in Residual: Taiwan-28%-74%-18%-34%-42%-3%-17%-18%-20%-50%
# of Observations: Taiwan15,0176363660191541,6194,5754,2803,071
Change in Residual: Japan-32%-20%13%-18%-8%-19%-1%-41%-36%-45%
# of Observations: Japan27,5989091221,0172391463,6547,4969,6894,180
Change in Residual: Hong Kong-34%-28%6%-68%-34%-41%-48%-30%-31%-35%
# of Observations: Hong Kong12,4596115744247401,0523,5223,0493,564

Table B5a. Structural Estimates of Revealed Overall Competitiveness, Change From Early to Late Period

COUNTRYAll SectorsFoodBeveragesTobaccoTextilesApparelLeatherFootwearWoodFurniturePaperPrintingIndustrial ChemicalsOther ChemicalsPetroleum
China80.695.623.477.145.084.340.6-147.0185.8106.8152.670.1128.5123.6
Australia67.121.450.26.034.80.446.5-9.7-73.5-56.8-23.911.1189.663.515.3
Canada57.427.037.8-3.917.519.7-70.3-6.9114.6-32.666.534.664.90.330.4
Mexico56.6-33.7-33.0-334.338.793.962.39.287.9214.6-152.2-16.4-68.8-58.2160.3
Indonesia50.644.8-86.7-31.49.7-75.58.386.9161.9125.1-132.343.95.5-
Taiwan47.256.013.4126.761.261.0--34.132.355.662.0131.299.236.949.1
Korea46.870.2-26.052.236.0-158.536.1-438.688.755.499.1113.382.053.594.7
India38.924.1-215.725.629.095.2129.042.344.213.419.2-42.214.528.8-66.4
Austria38.820.6-78.9-7.012.666.187.259.6-58.1-31.025.819.7-3.278.026.8
Spain27.725.155.92.511.914.4-12.920.149.61.017.544.314.111.029.7
UK20.19.653.313.02.515.820.243.038.4-15.10.133.216.224.810.0
Finland0.5-6.2-19.4126.716.6-249.087.20.445.8-112.3-29.5-12.10.712.5-18.3
Turkey0.4-15.155.7108.117.034.5-67.214.216.3-180.4-38.4-240.1-54.0-44.5-175.0
Greece0.0-0.4-44.345.118.8--81.514.347.3-65.8-44.2-115.1-142.8-10.7-21.7
South Africa-2.3-74.2-157.8-154.5-494.721.214.2-56.3-221.6-44.6-105.0-289.389.2-150.8-7.5
Ireland-2.786.4118.424.344.840.1106.782.2-206.2-59.0-66.982.6-140.8-
Denmark-4.524.231.9-13.929.4--65.3-7.6203.2-2.014.1-106.578.9-19.8
USA-5.621.245.737.22.18.9-8.642.450.01.55.751.2-46.9-13.442.1
Sweden-7.62.5-28.2-3.725.084.0-324.373.848.5162.2-32.3-4.5-30.582.499.3
Germany-9.510.433.128.038.227.1102.141.043.5-27.623.512.4-168.626.121.0
Argentina-9.612.0-12.6-37.4-24.855.48.0-203.6-38.5-53.5-99.7-52.4-65.742.1
France-18.31.217.921.92.912.679.227.830.4-21.0-5.919.0-57.2-21.611.6
Portugal-33.213.4-103.7-19.17.1-328.7-39.4-40.5-18.6-101.148.8-42.86.3-9.579.2
Japan-50.2-10.714.610.0-12.8-13.6-69.925.121.3-33.8-47.715.4-82.3-53.517.3
Italy-65.5-8.15.8-29.0-11.9-14.4-117.2-72.510.1-113.5-24.49.5-24.1-37.3-18.0
Belgium-42.454.736.031.347.3-81.8-67.8-0.8118.113.1172.2--34.5
Brazil--281.3-214.3-224.7-66.7-23.1---205.027.2-356.9-46.5-204.4--
Hong Kong-172.3288.0--76.0-----93.7---
Netherlands-24.4120.849.8-5.2---59.1-62.588.039.7179.080.7-156.9
Singapore--375.0--------85.4176.5150.6--387.1-334.4

Table B5b. Structural Estimates of Revealed Overall Competitiveness, Change From Early to Late Period (cont'd)

COUNTRYAll SectorsFuelsRubberPlasticPotteryGlassNon-metal MineralsIron & SteelOther MetalFabricated MetalOther MachineryElectric MachineryTransportScientific EquipmentOther Mnfg.
China80.6-107.2125.977.084.1106.794.133.9185.9108.859.711.863.141.1
Australia67.125.4-33.6-226.9-10.6-120.228.0145.4183.211.834.8-27.5-99.355.710.2
Canada57.446.619.7-63.327.132.1-17.8-13.6133.820.149.230.955.842.630.1
Mexico56.635.2-24.3-4.593.131.6-40.9-18.878.1266.2149.9-84.8--
Indonesia50.6-33.16.1-26.090.6-34.9-208.3--96.6147.5103.4-131.156.841.7
Taiwan47.23.3111.251.5-478.468.240.576.25.7148.5-60.613.869.640.9
Korea46.856.1122.2107.1-11.755.581.148.092.576.548.155.944.751.931.4
India38.9-294.640.349.1-11.3-65.367.47.6-28.8107.623.5-15.625.342.2-
Austria38.832.268.247.915.041.9-3.236.6-13.91.392.7-21.1121.0-0.9-
Spain27.742.167.961.125.928.429.3-2.3-24.7-9.7-9.214.559.126.032.2
UK20.185.73.637.524.29.315.911.90.6-32.722.966.212.2-55.837.1
Finland0.5-188.593.4-37.79.54.5-2.851.717.7-35.3-10.813.038.126.130.8
Turkey0.464.5-85.13.66.735.016.1-20.3-20.2-128.016.110.2-0.6-25.738.1
Greece0.0--223.633.2-72.522.9166.0-118.6152.7-22.636.3-35.8-19.261.936.7
South Africa-2.3-175.4-133.9-357.8-13.4-449.5-441.1114.878.9-200.012.2-176.9-46.937.412.9
Ireland-2.7-136.391.347.427.147.8-205.9-74.2-273.2152.998.4-22.7-
Denmark-4.566.633.5-113.054.9-27.33.7150.5--31.6-74.856.257.7-425.123.2
USA-5.649.3-10.855.816.125.624.0-18.2-50.40.1-9.4-19.9-1.934.629.9
Sweden-7.633.268.76.677.512.612.823.241.3-43.8-55.913.9-28.6-7.7-378.9
Germany-9.531.445.738.017.924.1-0.212.9-12.3-28.05.8-4.3-6.530.839.6
Argentina-9.6--93.5-138.810.0-19.9-61.5-104.8-58.3-54.3-29.0-238.514.532.024.1
France-18.340.8-66.030.2-0.9-4.33.5-48.6-59.0-3.8-26.0-33.2-0.130.7
Portugal-33.2-91.559.538.929.225.619.49.8-81.910.4-75.753.466.9-149.716.5
Japan-50.241.8-62.221.3-2.86.8-7.0-64.4-82.6-64.9-49.9-88.7-32.729.025.6
Italy-65.5--21.49.647.5-1.4-58.4-16.4-102.4-160.6-149.5-64.3-0.322.2-193.8
Belgium-51.8-52.04.7-17.3-97.6--294.3-6.7172.1126.7---
Brazil---256.2-5.6-14.4---176.8---178.6-247.8-413.55.7-
Hong Kong------27.5--------
Netherlands-44.0-31.58.374.1-2.3182.4--26.2-148.8124.5--
Singapore---49.448.8-86.0--97.2---14.5--

Figure B1. Comparison of Gravity Residual Changes Using UN-NBER and CEPII Data

Data for Figure B1 immediately follows

Data for Figure B1

 UN-NBER (SITC 4-digit)CEPII (ISIC 2-digit)
Indonesia0.504986530.71561738
China0.46475020.68373595
India0.390437160.56362496
Turkey0.36769720.6390655
Mexico0.360517690.72107074
Saudi Arabia0.309192030.19683399
Spain0.187729610.01071516
Italy0.146077550.0618769
Rest of World0.079679030.00450146
Australia0.045039120.05880159
France0.02608082-0.17244206
All0.0214243-0.02268058
Other EU0.01706058-0.13606494
Germany0.01055769-0.16091488
Brazil-0.05609352-0.12218161
USA-0.06091851-0.02762394
Canada-0.10314775-0.1176693
Korea-0.115841-0.46735922
UK-0.12033621-0.36014848
Argentina-0.1203525-0.13767552
Singapore-0.29100903-0.50380707
Japan-0.29132421-0.55357127
Taiwan-0.2922583-0.49385896
Hong Kong-0.40803643-0.23891996

Footnotes

1.  The authors thank Brian Andrew for excellent research assistance. The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System, any other person associated with the Federal Reserve System, or the European Central Bank. Return to text

a.  G.d'Annunzio University and CRENoS Return to text

b.  European Central Bank Return to text

c.  Federal Reserve Board Return to text

2.  Measures of aggregate $ tfp$, comparable across countries, are usually obtained as the residual component of GDP growth that cannot be explained by the growth of production inputs. One of the drawbacks of the growth accounting approach is that the role of the sectoral composition of output is ruled out by assumption. By assuming that GDP is produced by a single sector, one cannot disentangle $ tfp$ differences (across countries) due to sectoral specialization from $ tfp$ differences due to other factors. Return to text

3.  That model was first brought to the data by Del Gatto, Mion and Ottaviano (2006) and further developed by Ottaviano, Taglioni and di Mauro (2009). Return to text

4.  Constant market share analysis is beset by a number of well documented theoretical problems (see Richardson (1971) for an overview). However, the approach remains illustrative and simple to implement even if interpretation is complicated by relative price changes and other issues. Return to text

5.  The constant market share approach often includes an additional ''market effect'' related to the geographical pattern of trade. For ease of exposition we have focused only on the commodity effect, in a sense wrapping the market effect into our measurement of the competitiveness effect. With declining trade costs it is likely that the market effect has become a less pronounced determinate of aggregate share in any case. Return to text

6.  Other examples of cancelling out the importer fixed effects in a gravity framework include: Head and Mayer (2000), Martin, Mayer and Thoenig (2008) and Head, Mayer and Ries (2010). Return to text

7.  Expression (3) imposes separability across right-hand side ratios with the assumption that $ \ln\sum T=\sum\ln T$. In practice, this may have the effect of overestimating the share of each exporter (i.e., since the shares as decomposed on the right-hand side will add up to more than 1), but little impact on the relative size of the shares. Return to text

8.  Specification (vi) confirms the consistency of the reduced form results with the empirical exercise in the following section. While the reduced form regressions use the Feentra et al. (2005) data described above, the methodology in the next section additionally requires data on sectoral intra-national trade, which necessitates using an alternative data set. Those data are described below.  Return to text

9.  Results for the remaining five specifications can be found in Appendix B. Return to text

10.  As mentioned, the gravity residuals are estimated at the SITC 4-digit level for specifications (i)-(v) and at the ISIC 2-digit level for specification (vi). In the table, due to computational constraints on such a large dataset, we present aggregate control variables estimated without product fixed effects. As such, the coefficients can be interpreted as simple averages across SITC products, or in the case of specification (vi), ISIC products. Return to text

11.  For the sake of comparability, the predicted and actual market share changes are aggregated over exactly the same SITC-destination pairs. The index of share change does not exactly match that in Figure 1, since: (a) it is a geometric index, whereas simply adding up share across products as in Figure 1 is analogous to an arithmetic mean, and (ii) because the index is matched in each period (i.e., the trade flow had to occur in both time t and t-1 for it to be included), the composition of items in the Figure 2 index will be a subset of those in Figure 1. Overall, the magnitude of the drop of the geometric index seems reasonably close to the aggregate drop and the dynamics of contractions in the early 1980's and 2000's parallel one another. Return to text

12.  This list corresponds well with the top twenty exporters by size in 1980. In the table, the category 'Other EU' includes: Austria, Belguim, Denmark, Finland, Ireland, Netherlands, Portugal and Sweden. Return to text

13.  The number of exporters from $ l$ to $ h$ amounts to $ E_{s}^{l}\left[ \frac{m_{s}^{l} }{max(m)_{s}^{lh}}\right] ^{\gamma_{s}}$ . Each exporter from $ l$ to $ h$ generates f.o.b. export sales equal to $ p^{lh}(c)q^{lh}(c)$. Aggregating over all exporters yields equation (5). Return to text

14.  Note that the adjustment of $ T_{s}^{lh}$ takes place along both the 'extensive margin' (number of exporters) and the 'intensive margin' (per capita exports). Return to text

15.  Since the exponent $ \frac{1}{\gamma_{s}+2}$ plays no role in determining the country rankings, as it only entails a re-scaling by sector, it will be omitted hereinafter. Return to text

16.  Equation (6) could be interpreted as a gravity equation and estimated as

$\displaystyle ln\left( \frac{\tilde{T}_{s}^{lh}}{\tilde{D}^{h}}\right) =imp_{s}~-\beta _{s}ln\tilde{X}_{s}^{lh}$ (10)

where vector $ \tilde{X}_{s}^{lh}$ includes bilateral distances, as well as a number of dummies controlling for the presence of border effects (contiguity, language indicators, etc.), and $ imp_{s}$ is a (destination) country-sector dummy capturing the ROC. Estimation of (10) provides us with information on trade costs, through $ \hat{\beta}{s}$, and, at the same time, with information on ROC. More precisely, the fixed effects in (10) can be estimated (see Fadinger and Fleiss, 2008) as

$\displaystyle \tilde{\bar{m}}_{s}^{hh}=exp\left[ ln\left( \frac{\tilde{T}_{s}^{lh}} {\tilde{D}^{h}}\right) -\hat{\beta}_{s}ln\bar{\tilde{X}}_{s}^{lh}\right]$ (11)

where the bar refers to the mean across exporting countries. Return to text

17.  With this specification, zeros-missings in bilateral trade do not translate one-to-one into zeros in $ \tilde{\bar{m}}_{s}^{hh}$. The latter can instead be due to missing information on GDP and/or internal trade in country $ h$. Return to text

18.  The paper is downloadable at http://www.feem.it/userfiles/attach/2009121116814115-09.pdf.  Return to text

19.  As inter-industry linkages are ruled out, the $ s$ index could be omitted and the model presented as an "industry-model", with all the equations referring to a generic industry. However, the $ s$ index will reveal useful in subsequent analysis, as country, industry, and country-industry specific variables (parameters) coexist in the model. Return to text

20.  Equation (12) expresses the marginal cost associated with a standard Cobb-Douglas production function

$\displaystyle Q(c)^{l}_{s} = c^{-1} \prod_{x \in X} (M_{x})^{\beta_{x,s}} $
where $ M_{x}$ denotes the amount of input $ x$ utilized. Return to text

21.  In a strict sense, the Pareto assumption refers to $ c$ (i.e. the UIR). However, as evident from (12), $ \left[ \frac{m^{l}_{s}}{max(m)_{s}^{l}}\right] ^{\gamma_{s}} \equiv\left[ \frac{c}{max(c)_{s}^{l}}\right] ^{\gamma_{s}}$ for $ [0,max\left( c\right) _{s}^{l}]$. Thus, there is no loss of generality in thinking (and solving) the model in terms of marginal costs. Return to text

22.  Parameter $ \upsilon_{s}$ comes from the utility function and measures the degree of product differentiation between different varieties of good $ s$. When $ \upsilon_{s}=0$, consumers only care about their total consumption level over the varieties of good $ s$. Return to text

23.  Since there are no inter-industry linkages, rows in (13) are independent one another. Return to text

This version is optimized for use by screen readers. Descriptions for all mathematical expressions are provided in LaTex format. A printable pdf version is available. Return to text

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