Comparative Advantage Calculation: Unpacking Unit Labor Costs and Sectoral Trade Balances

Introduction

In the realm of international trade, understanding the dynamics that govern trade balances is crucial for policymakers and economists alike. Misconceptions, often fueled by what some term “modern mercantilist” viewpoints, can lead to calls for protectionist measures and managed trade, especially when imbalances like Japan’s manufacturing trade surpluses emerge. However, economic theory posits that more fundamental, and often less obvious, factors are at play. This article delves into the core economic principles of comparative and absolute advantage to shed light on the sectoral trade balances of the world’s leading industrial nations, the Group of Seven (G-7), with a particular focus on the United States and Japan.

Sectoral trade balances are not solely determined by macroeconomic currents; they are also deeply rooted in microeconomic foundations. While macroeconomic factors like aggregate demand, savings-investment balances, and exchange rates shape the overall current account balance, microeconomic elements, notably comparative advantage, dictate sectoral surpluses and deficits. Nations gain comparative advantage in sectors where they exhibit superior productivity or lower costs relative to others. These sectors naturally evolve into net exporters, even amidst balanced overall trade. Conversely, sectors at a comparative disadvantage tend towards net imports.

Figure 1: Real Effective Exchange Rate and Net Exports for the United States and Japan (Source: International Monetary Fund)

The interplay between macro and micro factors is evident in the impact of real exchange rates. Exchange rate fluctuations influence absolute advantage, uniformly affecting the competitiveness of all sectors within an economy. For example, a real appreciation of the U.S. dollar can diminish surpluses in sectors where the U.S. holds a comparative advantage, potentially pushing some into deficit, while simultaneously exacerbating deficits in sectors already at a disadvantage. The aggregate outcome is a widened overall current account deficit. Comparative Advantage Calculation therefore becomes essential in dissecting these complex interactions and understanding the true drivers of trade balances.

Numerous studies have explored the macroeconomic determinants of current accounts and microeconomic aspects of sectoral trade balances, often employing the Heckscher-Ohlin-Samuelson (HOS) model. However, integrated perspectives that assess the relative significance of both micro and macroeconomic factors remain limited. This article adopts a multi-commodity Ricardian model as its analytical framework to bridge this gap. The Ricardian model, with its emphasis on relative unit labor cost, provides a robust platform to analyze both comparative advantage and the influence of real exchange rates.

The core concept is that relative unit labor cost—the cornerstone of the Ricardian model—is shaped by sector-specific variables such as productivity and wages, as well as by the real exchange rate. Compared to the HOS model, where comparative advantage stems from factor endowments, the Ricardian model’s micro-level approach offers both advantages and disadvantages. Its primary advantage lies in its ability to incorporate technological differences between countries, which are demonstrably significant at the sectoral level. Persistent disparities in labor and total factor productivity across industries and nations underscore this point. Furthermore, empirical support for the HOS model has been somewhat limited.

The main drawback of the Ricardian model is its implication of complete specialization, a scenario rarely observed in reality where import-competing sectors seldom vanish entirely. To accommodate incomplete specialization within a Ricardian framework, additional assumptions, such as product differentiation, are necessary. Thus, the pure Ricardian model offers a foundational, albeit partial, explanation for actual trade patterns. Despite this practical challenge, prior applications of this model have yielded surprisingly successful results, although such studies are not abundant.

This article will first lay out the analytical framework rooted in the Ricardian model. It will then proceed with an empirical analysis of sectoral trade balances within the G-7 countries. Finally, it will present time-series and cross-sectional regressions that correlate relative unit labor costs with sectoral trade balances, providing a statistical underpinning to the theoretical framework. Through this process, the article aims to illuminate the mechanics of comparative advantage calculation and its role in shaping international trade.

I. Analytical Framework: Deconstructing Comparative Advantage

The analysis begins with a two-country, multi-good Ricardian model, inspired by the seminal work of Dornbusch, Fischer, and Samuelson (DFS). In this model, comparative advantage is fundamentally defined by comparing domestic and foreign labor productivities across different sectors. Let’s denote ‘a’ as the factor input per unit of output, which can be measured in two ways:

Labor-only input: ( a = frac{L}{Q} )
Labor and capital input: ( a = frac{L^alpha K^{1-alpha}}{Q} )

Where:

( Q ) represents value added.
( L ) is labor employment.
( K ) is capital stock.
( alpha ) is labor’s share of income.

Using either measure of factor input, we can establish a chain of comparative advantage by ranking sectors in descending order of home-country comparative advantage relative to the foreign country (denoted by *):

( frac{a_1^}{a_1} > frac{a_2^}{a_2} > … > frac{a_i^}{a_i} > … > frac{a_n^}{a_n} )

This chain illustrates the relative productivity advantage of the home country across sectors. The crucial question then becomes: where is the break point in this chain? Which goods will be produced domestically and which will be imported? This is determined by relative wages (domestic wage ( w ) and foreign wage ( w^* )) and the exchange rate ( e ). These factors combine to determine the relative unit labor cost (( c_i )) in a common currency:

( c_i = frac{e cdot w_i^ cdot a_i^}{w_i cdot a_i} )

According to the Ricardian model, the home country will specialize in and export goods where ( c_i > 1 ), indicating a cost advantage. Conversely, it will import goods where ( c_i < 1 ), signifying a cost disadvantage. The model is closed by incorporating a market equilibrium condition, typically current account balance, which subsequently determines the ratio of home to foreign real wages. While the basic DFS model does not include capital movements, these can be integrated. In such a case, goods market equilibrium would align with balance of payments equilibrium (current account plus capital account equals zero), rather than solely a zero current account as in the original DFS framework.

Several key aspects of ( c_i ) are worth noting for a comprehensive comparative advantage calculation:

Sectoral Wage Disparities: Traditional Ricardian models, including DFS, often assume perfect competition and homogenous labor, leading to wage equalization across sectors within a country. However, this analysis acknowledges sectoral wage disparities to account for variations in education levels across sectors and labor market imperfections. Comparative advantage, therefore, can reflect wage differences in addition to productivity differences between sectors and countries.
Exchange Rate Influence: Exchange rate fluctuations can simultaneously alter the competitiveness of all sectors, thereby shifting the point at which the chain of comparative advantage is broken. The long-run equilibrium exchange rate can be defined as the rate that achieves aggregate current account balance. Deviations from this equilibrium, driven by macroeconomic factors like savings-investment imbalances, can lead to overall external deficits or surpluses.
Unit Labor Cost as a Competitiveness Gauge: Unit labor cost may be an imperfect measure of competitiveness if quality differences are not accurately captured or if labor is not the sole factor of production. Quality variations between domestic and foreign products might mean that the critical value of ( c_i ) deviates from 1. For instance, if Japanese products are perceived as superior in reliability and service, they might remain competitive even with higher unit costs. The presence of other production factors suggests that total factor productivity, as in equation (1b), should ideally be considered alongside labor productivity (equation 1a). Both measures are explored in the empirical analysis that follows.

To further dissect the components of relative unit labor cost, we can decompose it into:

( c_i = frac{a_i^}{a_i} cdot frac{w_i^/w^}{w_i/w} cdot frac{w^/w cdot e}{PPP} )

Where ( PPP ) is the implicit purchasing-power-parity exchange rate. This decomposition reveals that relative unit labor costs are influenced by four key factors:

Relative Labor Productivities (( frac{a_i^*}{a_i} )): Differences in how efficiently labor is used in sector ( i ) between the foreign and home country.
Relative Sectoral Wage Divergences (( frac{w_i^/w^}{w_i/w} )): The ratio of sectoral wage deviations from the aggregate wage level in both countries. This captures how wages in sector ( i ) compare to the average wage within each economy.
Overall Wage Ratios at PPP Exchange Rates (( frac{w^*/w cdot e}{PPP} )): The ratio of aggregate wage levels between the foreign and home country, adjusted by the market exchange rate ( e ) and compared against the purchasing power parity exchange rate ( PPP ).
Gap Between PPP and Market Exchange Rate (( frac{1}{PPP/e} )): Reflects the macroeconomic alignment of exchange rates. A significant gap indicates potential over or undervaluation of currencies relative to purchasing power parity.

The first two factors are microeconomic in nature, driven by sector-specific conditions, while the latter two are macroeconomic, reflecting broader economic conditions and exchange rate dynamics. For example, the pressure on the U.S. automobile sector from Japanese imports could arise from:

High Japanese Automobile Productivity: Japanese automakers are highly efficient.
Elevated U.S. Auto Worker Wages: U.S. auto workers earn wages above the average for traded goods sectors.
Misalignment of Overall U.S. Wages: U.S. aggregate wages might be disproportionate to overall productivity levels.
Dollar Overvaluation: The U.S. dollar might be overvalued relative to its equilibrium level.

Understanding these decomposed factors is crucial for a nuanced comparative advantage calculation and for formulating effective trade and economic policies. The following sections will apply this framework to empirical data to assess its explanatory power in real-world trade scenarios.

II. Data and Method: Empirical Application to G-7 Countries

To empirically test the Ricardian framework and conduct a robust comparative advantage calculation, this study utilizes data primarily from the OECD’s International Sectoral Data Base (ISDB) (1993). The ISDB offers a consistent and comprehensive dataset encompassing trade, value added, factor use (labor and capital), and labor compensation for most OECD countries, disaggregated into approximately 20 sectors. While the ISDB spans from 1960–92, more complete and reliable data are available for the period 1970–89, which forms the primary focus of this analysis.

Table 1. Sectoral Comparisons of Labor Productivity, Expressed as Ratio of G-7 Averages

Table 1: Sectoral Labor Productivity Ratios (Source: OECD ISDB and Author’s Calculations)

The dataset, while extensive, has limitations. Notably, it presents significant gaps in service sector data, thereby orienting the analysis predominantly towards goods-producing sectors. This is a constraint, especially given the increasing prominence of trade in services and the growing U.S. comparative advantage in this domain. Nevertheless, the ISDB facilitates the calculation of sectoral productivities, wage rates, and consequently, unit factor costs. It also provides variables in U.S. dollars at PPP exchange rates, enabling comparisons of real outputs across countries.

The scope of this study is confined to the G-7 countries: the United States, Japan, Germany, France, the United Kingdom, Italy, and Canada. To adapt the Ricardian model to a multi-country context, the analysis calculates average G-7 unit factor costs and benchmarks each country against this G-7 average.

Using this rich dataset, it becomes possible to calculate unit labor costs (and unit factor costs using total factor productivity), their constituent components (productivity and wages), and the impacts of exchange rates. Relative unit labor cost is then correlated with observed trade patterns. While Jorgenson and Kuroda (1990) have presented similar disaggregated comparisons of international competitiveness for the U.S. and Japan, their work did not extend to assessing trade patterns or include other countries. Although the ISDB contains consistent trade data, this study utilizes OECD trade statistics directly provided on diskette, as the ISDB lacks bilateral trade flow data. This OECD bilateral data is used to construct intra-G-7 trade balances, ensuring consistency with unit labor cost calculations.

Table 1 presents calculated labor productivities for the United States, Japan, and West Germany (as ratios of the G-7 average) for both the total economy and at a more disaggregated sectoral level. Labor productivity is calculated as real value added in dollars at PPP exchange rates, divided by total employment. The PPP exchange rate used is an aggregate for the entire economy. While sectoral PPPs would be theoretically preferable for international productivity level comparisons, as emphasized by Hooper and Larin (1989), obtaining these for the sectoral disaggregation used here is challenging, and prior studies suggest that aggregate PPPs do not significantly alter the results. This issue is more pertinent for point-in-time productivity comparisons than for tracking changes over time.

Table 2. Sectoral Comparisons of Total Factor Productivity, Expressed as Ratio of G-7 Averages

Table 2: Sectoral Total Factor Productivity Ratios (Source: OECD ISDB and Author’s Calculations)

Total factor productivity, shown in Table 2, is calculated as per equation (1b), assuming a uniform labor share (( alpha )) of 0.7 across all industries and countries for international comparability. This approach mirrors methodologies used by Wolff (1993) and Meyer-zu-Schlochtern (1988).

The labor productivity findings align with those reported by Wolff (1993) and Pilat and Van Ark (1992). The U.S. consistently maintains a productivity lead over other G-7 nations throughout the period, both economy-wide and in most sectors. Japan’s productivity has seen rapid growth but remains behind the U.S. in most sectors, with the catch-up rate decelerating, especially when considering total factor productivity. Germany’s relative productivity levels have generally remained slightly below the G-7 average since 1980, following increases in the 1970s.

As Wolff (1993) noted, there appears to be some convergence in overall productivity levels among G-7 countries, but this convergence is less pronounced in certain sectors. For example, U.S. agricultural productivity remains significantly higher than the G-7 average, particularly compared to Japan. The U.S. also exhibits a substantial lead in mining. The manufacturing sector presents a mixed picture. While the U.S. remains more productive in manufacturing than Japan and Germany, Japan has made significant gains, especially in labor productivity. The U.S. labor productivity level surpassed the G-7 average across all manufacturing categories throughout the period in Table 1, although the extent of U.S. absolute advantage varies. The U.S. advantage is most pronounced in machinery and equipment, and food, beverages, and tobacco, and weakest in basic metals and paper and printing. Japan’s relative productivity shows greater sectoral variation than the U.S. In chemicals and basic metals, Japan had a considerable absolute advantage over the U.S. by 1989. Conversely, Japan’s productivity in sectors like textiles, nonmetallic minerals, and food and beverages is below the G-7 average. Germany’s manufacturing productivity shows less dispersion, with most sectors declining relative to the G-7 average during 1970-89.

Table 3. Sectoral Wages as Ratio of Total Economy Wages

Table 3: Sectoral Wage Ratios (Source: OECD ISDB and Author’s Calculations)

Another key component in comparative advantage calculation is sectoral relative labor compensation (( w_i/w ) in equation (3)). Labor compensation per employee (referred to as “wages”) is derived from the ISDB by dividing total labor compensation by the number of employees. Table 3 presents sectoral wages as ratios of the economy-wide average wage. Wage dispersion is notable and varies across countries. Manufacturing wages in the U.S. are significantly above the U.S. average, unlike in Japan or Germany. Agricultural wages are below average in all three countries, most markedly in the U.S. Mining wages are high in both the U.S. and Germany, possibly due to strong labor unions, a factor potentially also influencing basic metal wages in all three nations. High relative U.S. wages are particularly evident in machinery and equipment, offsetting much of the U.S. productivity advantage in this sector, especially against Japan.

Finally, competitiveness is determined by combining productivity with relative factor prices and converting to a common currency. Given the similarity in patterns between labor and total factor productivity, and the likely smaller inter-country capital cost differences compared to labor costs, subsequent calculations focus on unit labor cost. Table 4 presents U.S., Japanese, and German unit labor costs as ratios of the G-7 average in current dollars for selected years. The theoretical critical value for relative unit labor cost is unity, but practical factors like quality differences and measurement errors can obscure this.

Table 4. Sectoral Relative Unit Labor Costs and Intra-G-7 Trade Balances, Measured in Dollars Expressed as Ratio of G-7 Averages

Table 4: Sectoral Relative Unit Labor Costs and Trade Balances (Source: OECD ISDB, OECD Trade Statistics, and Author’s Calculations)

Table 4 reveals the combined effects of comparative advantage and exchange rate movements on unit labor cost. The U.S.’s substantial productivity advantage in agriculture and mining manifests in low U.S. unit labor costs and high German and Japanese costs in these sectors, although high U.S. mining wages moderate the U.S. advantage. Exchange rate effects are apparent across all sectors. For example, U.S. total manufacturing unit labor cost was high in 1970 and 1985—periods of dollar overvaluation relative to PPP—but low in 1980 and 1989. By 1989, U.S. manufacturing unit labor cost was below the G-7 average. German manufacturing unit labor cost moves inversely with the U.S., reflecting the dollar/deutsche mark exchange rate’s influence. Japan’s manufacturing comparative advantage is evident in unit labor costs below unity until the late 1980s, when a strong yen offset Japanese manufacturing competitiveness. Japan’s sectoral productivity dispersion is reflected in relative unit labor costs. In basic metals and chemicals, sectors with Japan’s highest relative productivity, unit labor costs remained below unity even in 1989, albeit higher than in previous years. U.S. manufacturing competitiveness appears largely driven by exchange rate fluctuations over the sample period, except for basic metals, where the U.S.’s comparative disadvantage is so significant that unit labor cost remains above unity even in 1989, reflecting weak productivity and high wages.

Trade balances in Table 4 are scaled by sectoral value added and lagged by two years relative to unit labor cost data to account for typical exchange rate lags. Trade balances are deflated by sectoral value added to indicate trade balance size relative to sector resources, though this is not theoretically implied. The Ricardian model’s complete specialization implication suggests net export to domestic production ratios should be near one or negative infinity. However, observed trade balances are typically much smaller than Ricardian theory predicts.

Two main data limitations exist. First, trade flows are nominal, not in constant prices. Ideally, constant price trade flows are preferable, but unavailable at this disaggregated level. If sectoral trade balance variations are price-driven rather than volume-driven, model performance suffers, especially in sectors with low value added as a price component and high intermediate input price variability. Oil-intensive sectors like mining (including oil) and chemicals may yield misleading results. Second, sectors are aggregated at the two-digit SIC level, encompassing diverse products. For example, chemicals include industrial chemicals, petroleum refining, and pharmaceuticals. Data availability necessitated this aggregation level, warranting cautious interpretation of results.

Based on Table 4, sectoral unit labor costs appear to explain some time-series and cross-sectional variations in sectoral trade balances, but puzzles remain. Time-series behavior of trade balances since the late 1970s is largely explained by unit labor cost changes. Exchange rate movements significantly influenced the 1980s, and trade balances responded as expected. For instance, U.S. unit labor costs rose sharply from 1980 to 1985, then declined precipitously from 1985 to 1989, mirrored by Japanese and German unit labor costs. Correspondingly, the U.S. trade balance deteriorated significantly between 1982 and 1987, then largely returned to 1982 levels by 1991. This pattern is evident across agriculture, total manufacturing, and manufacturing subsectors, except for mining, which is heavily influenced by oil prices. Japanese and German trade balances largely mirrored the U.S. path in the 1980s.

However, pre-late 1970s sectoral trade balance behavior is less easily explained. In the early 1970s, U.S. manufacturing deficits remained stable or widened despite declining U.S. relative unit labor costs, particularly puzzling for machinery and equipment, and chemicals. Conversely, German and Japanese manufacturing trade balances showed surprisingly little adverse reaction to their declining manufacturing competitiveness in the early 1970s, as indicated by unit labor costs. A potential explanation is that U.S. manufacturing unit labor cost, while improving through the 1970s, remained relatively high until late in the decade, indicating weak competitiveness during significant world trade expansion. The early 1970s may represent a disequilibrium period with overly high U.S. relative unit labor costs. In this view, widening U.S. manufacturing trade deficits and declining U.S. relative unit labor costs were responses to a disequilibrium real exchange rate. Alternatively, unquantified competitiveness dimensions like product quality may have played a role, with other countries catching up to the U.S. in the 1970s.

Cross-sectionally, some trade balances are puzzling, while others align with relative unit labor costs. U.S. comparative advantage and Japanese and German disadvantage in agriculture are reflected in U.S. surpluses and deficits for the others. Low Japanese manufacturing unit labor costs (until the late 1980s) and generally high U.S. counterparts (except during dollar depreciations) align with Japanese surpluses and U.S. deficits in manufacturing. German manufacturing unit labor cost fluctuated narrowly around unity, consistent with relatively small German surpluses. At a disaggregated level, Japan’s high textile unit labor costs correlate with weak net exports, while low costs in machinery and equipment, and basic metals, correlate with surpluses. However, Japan also appears to be a low-cost producer of paper and printing, food and beverages, and chemicals, yet has sectoral deficits in these industries, possibly due to higher Japanese raw material costs. The U.S. had less dispersion in sectoral manufacturing unit labor cost than Japan, mirrored by less dispersion in disaggregated manufacturing trade balances. The U.S.’s greatest comparative disadvantage was in basic metals, also its largest deficit sector. A major anomaly is food and beverages, where the U.S. had trade surpluses without a clear cost advantage, potentially reflecting lower raw material costs. Germany’s manufacturing competitiveness and trade pattern relationship appears weaker than in the U.S. or Japan. Germany had chronic surpluses in machinery and equipment, basic metals, and chemicals, but these are not sectors of unusual German comparative advantage based on unit labor costs, potentially reflecting measurement issues, other comparative advantage influences, and trade policies.

The next section presents a more rigorous statistical analysis of the relationship between relative unit labor costs and sectoral trade balances.

III. Statistical Analysis of Intra-G-7 Sectoral Trade Balances: Regression Insights

This section delves into a more rigorous statistical examination of the time-series and cross-sectional relationships between sectoral trade balances and relative unit labor costs using regression analysis. Mining is excluded from this analysis due to the data concerns previously discussed. The sample encompasses agriculture and seven manufacturing sectors over the period 1970–89.

Time-Series Regressions: Unveiling Dynamic Relationships

To capture the dynamic relationship over time, net exports relative to sectoral value added are regressed against relative unit labor cost (domestic relative to total G-7 GDP), an activity variable, and a time trend. All variables are transformed into logarithms, except for the trade balance. Let ( TB{ij} ) represent the sectoral trade balance (intra-G-7) divided by sectoral GDP, ( C{ij} ) be the logarithm of unit labor cost of sector ( j ) in country ( i ) relative to the G-7 average unit labor cost, ( Y_i ) be the logarithm of country ( i )’s total GDP divided by total G-7 GDP, and ( T ) be a time trend. The individual sectoral equations are specified as:

( TB_{ij} = a + b1 C{ij} + b2 C^{-1}{ij} + b3 C^{-2}{ij} + b4 C^{-3}{ij} + b_5 Y_i + b_6 T )

The coefficients on relative unit labor cost are estimated using a polynomial distributed lag to account for potential lagged effects.

Table 5. Time-Series Regression Coefficients: Sectoral Trade Balance on Relative Sectoral Unit Labor Costs

Table 5: Time-Series Regression Coefficients (Source: Author’s calculations from regression analysis)

Table 5 presents the sum of coefficients on the relative unit labor cost variable for each sector and country. Detailed regression results are available in Appendix Table A1. The coefficient represents the semi-elasticity of the trade balance with respect to relative unit labor costs, indicating trade responsiveness to competitiveness changes. A negative coefficient signifies a normal trade response. The key finding is that the U.S. and Japan exhibit the highest trade flow responsiveness to competitiveness. For total manufacturing and agriculture, both countries have relatively large and statistically significant coefficients (at the 1 percent level). Each also shows statistically significant negative coefficients for most manufacturing sectors. Paper and printing are exceptions for both, as are chemicals for Japan and nonmetallic minerals for the U.S. However, Japan’s chemical sector deficit is unlikely due to Japanese trade barriers, as Japan has both comparative and absolute advantage in chemicals. Indeed, for all countries except the U.S., the unit labor cost coefficient for chemicals is insignificant. This may reflect pricing and aggregation issues. Considering agriculture and seven manufacturing sectors, statistically significant negative coefficients are observed in six sectors for the U.S. and Japan, four for France, two for Germany and Italy, one for the U.K., and none for Canada.

Appendix Table A1 provides other coefficient details. Domestic total GDP relative to G-7 total GDP is included to represent relative incomes, expected to have a negative sign due to import demand effects. This is generally confirmed, with varying statistical significance. The U.S., Canada, Germany, and Italy tend to show high income responsiveness, while Japan’s is low. A time trend captures structural changes not reflected in measured relative unit labor costs, as in Bosworth (1993). However, as trade balances are intra-G-7, structural change does not represent rising developing country trade shares. As in Bosworth (1993), the U.S. shows negative trend growth in trade balances, while Japan has a strong positive time trend. Canada also has a large positive time trend, though the Canadian regressions’ t-statistics may be unreliable due to high serial correlation.

Cross-Sectional Regressions: Sectoral Variations in Trade Responsiveness

To analyze the role of relative unit labor costs across sectors rather than over time, cross-sectional regressions of the form:

( TB_i = a + b_i C_i )

were conducted for each country using ten-year averages. However, the small sample size of eight sectors (agriculture and seven manufacturing subsectors) limits the robustness of these regressions. Theory predicts a negative coefficient on relative unit labor costs. Results are in Table 6. Most countries show a negative coefficient, but statistical significance is low. Italy is the only country with significant coefficients in both periods, though the U.S. has a marginally significant coefficient for 1980–89. Japan has negative coefficients for both subperiods, but they are insignificant.

Table 6. Cross-Sectional Regression Coefficients: Sectoral Trade Balance on Relative Sectoral Unit Labor Costs (Agriculture and Seven Manufacturing Sectors), Ten-Year Averages

Table 6: Cross-Sectional Regression Coefficients (Source: Author’s calculations from regression analysis)

Pooled Regressions: Combining Time-Series and Cross-Sectional Insights

Pooled cross-sectional time-series regressions were performed for each country, covering 1970–89 and eight sectors (agriculture and seven manufacturing industries). The specification mirrors equation (4), but unit labor cost coefficients were not constrained to a polynomial distributed lag due to panel estimation procedure limitations. Two pooling models were used: a fixed-effect model, allowing sector-specific constants but constraining other coefficients to be uniform, and a simple ordinary least-squares (OLS) model, constraining all coefficients to be uniform across sectors. Table 7 presents unit labor cost variable results. Model choice significantly impacts results for some countries (especially Canada and Italy), sometimes reversing coefficient signs. Japan, the U.S., Germany, and the U.K. have statistically significant negative coefficients in both models, while France and Italy have a significant negative coefficient in one model. Japan’s coefficient is among the largest in the fixed-effect model and about average in the simple OLS model. Generally, the fixed-effect model emphasizes time-series behavior, while cross-sectional behavior is more reflected in the simple OLS model.

Table 7. Pooled Regression Coefficients: Sectoral Trade Balance on Relative Sectoral Unit Labor Costs (Agriculture and Seven Manufacturing Sectors), 1970-89

Table 7: Pooled Regression Coefficients (Source: Author’s calculations from regression analysis)

IV. Conclusions: Comparative Advantage and Trade Patterns in the G-7

This study has employed a Ricardian framework to elucidate the roles of microeconomic and macroeconomic factors in shaping the time-series and cross-sectional behavior of sectoral trade balances. Through empirical analysis focusing on comparative advantage calculation and its relationship to sectoral trade balances across G-7 countries, particularly the U.S. and Japan, several key conclusions emerge.

Japanese overall productivity has shown robust growth relative to other nations but exhibits significant dispersion across sectors. By the late 1980s, Japan’s productivity in many sectors remained below that of the U.S. and the G-7 average, yet in certain manufacturing sectors, Japan achieved the highest productivity among the G-7. U.S. productivity in agriculture and aggregate manufacturing consistently outpaced other G-7 countries. Sectoral wage analysis revealed that U.S. manufacturing wages were relatively high compared to the aggregate economy, whereas Japanese manufacturing wages tended to be slightly below average, enhancing Japan’s manufacturing competitiveness. Exchange rate fluctuations emerged as a significant factor in the medium-term dynamics of unit labor cost, particularly in the 1980s, where the rise and fall of the dollar profoundly influenced the competitiveness of the U.S., Japan, and Germany.

Statistical analysis of relative unit labor cost effects on sectoral trade balances indicated that time-series changes in sectoral trade balances are largely explained by unit labor cost evolution. However, reconciling the levels of these balances with unit labor cost levels proved more challenging. Time-series regressions highlighted the U.S. and Japan as having the most responsive trade flows to competitiveness.

Cross-sectional regressions were less conclusive, potentially due to data limitations affecting level assessments more acutely than change assessments. Sectoral nonlabor costs, such as raw materials, likely vary less over time than across sectors, as do government policies affecting specific sectors. The limited number of observations in cross-sectional regressions (eight sectors) also poses a constraint. Pooled regressions, reflecting both time-series and cross-sectional dynamics, provided mixed support for the theory of comparative advantage, with sectoral trade balances generally negatively related to relative unit labor costs across all countries except Canada. Specification choices in pooling models significantly influenced results.

Overall, regression results suggest that Japan’s trading pattern aligns more closely with the Ricardian model than many other countries, challenging the conventional view of Japanese trade as unresponsive to market mechanisms. This finding, indicating that a substantial portion of Japan’s trade is consistent with a Ricardian framework, complements Saxonhouse’s (1983 and 1989) results using the Heckscher-Ohlin-Samuelson model, suggesting that both technological differences and factor endowments are crucial in explaining Japanese comparative advantage. While this does not negate the existence of protectionist measures in Japan, it implies that comparative advantage plays a more significant role in shaping Japanese trade patterns than in many other economies. Consequently, this analysis does not support the notion that Japan’s sectoral trade balances necessitate “management” due to an alleged unresponsiveness to normal market forces.

Future research should address data limitations by further disaggregating data, extending analysis to the service sector, and using trade volumes instead of values. Incorporating raw material and capital costs, and transportation costs, is also crucial. An alternative approach focusing on bilateral relationships like U.S.-Japan trade, with detailed sectoral and policy information, could further illuminate the relationship between trade patterns and comparative advantage.

APPENDIX

Table A1. Time-Series Regressions of Sectoral Trade Balances, 1970-89

Table A1: Detailed Time-Series Regression Results (Source: Author’s calculations from regression analysis)

REFERENCES

*Stephen Golub is an Associate Professor at Swarthmore College where he has taught since 1981. He received his Ph.D. in Economics from Yale University. This paper was written while he was a visiting scholar in the World Economic Studies Division of the IMF Research Department. Comments from David T. Coe, Robert P. Ford, Jeffrey Frankel, Tamim Bayoumi, Manmohan S. Kumar, Swarthmore colleagues, and Janet Ceglowski were helpful. Yutong Li provided very capable research assistance. Bernard Menendez of the OECD Statistics Department provided the trade data used in this paper.

1See Golub (1994) for a review of the literature on the microeconomic and macroeconomic aspects of trade balances for the United States and Japan.

2 Ceglowski (1989) examined the responsiveness of U.S. sectoral exports and imports to sector-specific real exchange rates. Stone (1979) provided disaggregated elasticities of export supply and import demand for the United States, the European Community, and Japan. Lenz (1991) qualitatively assessed trade balances for various sectors but lacked an analytical framework.

3The most famous of these studies is MacDougall (1951). For a discussion of MacDougall’s article and further results, see Deardorff (1984).

4The multicountry multicommodity case was first presented in Jones (1961).

5No adjustment is made for hours worked, as no sectoral data for this factor exist. Japan’s relative productivity would be lowered by such an adjustment because Japanese workers work more hours than do those of other G-7 countries (McKinsey (1992)). Allowing for hours worked would not have much effect on comparative advantage to the extent that Japanese workers work longer hours in all industries. In any case, hours worked has a strong cyclical component, which could cloud the underlying comparative advantages.

6 Pilat and Van Ark (1992) develop unit value ratios—essentially sectoral PPPs—by using micro data from the census of manufactures for the United States, Japan, and Germany. However, they do not obtain results for productivity for most sectors that are much different from Wolff (1993), who uses the same OECD data as used here. Moreover, the results in this paper for total manufacturing productivity are very similar to those of Hooper and Larin (1989, Table 5), suggesting that the precise choice of a PPP index does not make a significant difference.

7This way of calculating total factor productivity is similar to Wolff (1993) and Meyer‐zu‐Schlochtern (1988).

8Italy (paper) and Canada (machinery) each have one statistically significant positive coefficient, although the Canadian equation has high serial correlation and the significance test is therefore unreliable.