"Linguistic distance" as a determinant of bilateral trade.

1. Introduction

Variables accounting for the transaction costs associated with international trade have been included in gravity models since the early application of this model to explain the volume of bilateral international trade. (1) Although Bergstrand (1985, 1989) limits transaction cost variables to adjacency and membership in a preferential trading area, Gould (1994) extends Bergstrand (1985) to explicitly model "costs associated with gaining foreign market information." Gould focuses on the trade-enhancing effects arising from the presence of a stock of foreign immigrants residing in the home country.

Several papers have extended Gould (1994) and all of these have included a variable to account for the effect that a common language between the trading partners would have on transactions costs. (2) Frankel (1997) discusses the role of common language in the gravity framework and has included this variable in subsequent papers. (3) Boisso and Ferrantino (1997) attempt to capture the language effect by constructing a measure of the likelihood that an exporter from one country would encounter an importer in another country who spoke the same language. Their measure of linguistic similarity had no identifiable effect on the volume of trade between pairs of countries. That is, a higher probability that an exporter from one country would encounter an importer in a second country who spoke the same language had no significant effect on the volume of trade between the two countries.

Wagner, Head, and Ries (2002) attempt to provide a measure of the degree of commonality of language between two countries that is very similar to that derived by Boisso and Ferrantino; that is, it is the probability that a randomly chosen person from a Canadian province would speak the same language as a randomly chosen person in the trading partner. These measures are a modification of the practice of using a dummy variable for a common language between trading partners. Hutchinson (2002) demonstrates that the greater the proportion of the population that speaks English as either a first or a second language, the higher the volume of trade (both exports and imports) between the United States and that country. (4)

We attempt to capture the trade effect imposed on residents of a country, native or immigrant, of learning a second language. We examine the effect on international trade that results from what Chiswick and Miller (1998) call "linguistic distance": how "distant" from English a particular language is, in the sense of how difficult it is to learn the foreign language. With the use of a measure of the relative difficulty that a native English-speaking person encounters in learning particular languages, (5) Chiswick and Miller (1998) studied the effect of linguistic distance on English language fluency for immigrants in the United States. Fluency in English is a determining factor in the ability of an immigrant to realize the potential benefits from networking and to effectively use knowledge of their home country tastes and markets to promote trade and commerce between their host country and their country of origin. Thus, in addition to all the other factors that affect the volume of bilateral trade, linguistic distance is hypothesized to affect the volume of bilateral trade by increasing transaction costs and reducing the volume of trade between the United States and trading partners.

Greater difficulty in attaining English language fluency diminishes the ability of residents of foreign countries to develop networks and take advantage of specific knowledge of their home country tastes and markets in promoting trade with the United States. Similarly, the greater the difficulty for a native English speaker to become fluent in a foreign language, the lower will be the volume of trade one would expect to observe between the United States and the country in which the foreign language is spoken. Both effects work in the same direction: the greater difficulty a native English speaker (non-English speaker) has in obtaining fluency in a foreign language (English), the lower the volume of bilateral trade one expects to find between the United States and the country in which the particular foreign language is spoken.

This paper will proceed in the following manner. In the next section we present the model and discuss the data in section 3. Empirical results are presented and discussed in section 4, and these results are related to the trade literature in the final section.

2. Model

We need a model that, controlling for other determinants of trade, will allow us to determine the effect that linguistic distance has on the volume of trade. We use a gravity model based on Gould (1994) which was an extension of Bergstrand (1985). Gould introduces transaction costs into the model. He argues that these costs arise from the lack of knowledge that a person from one country has regarding the laws, customs, tastes and riskiness of markets in another country. Gould attempts to model the effect that a stock of immigrants from a country will have on the cost of obtaining such information. Obtaining such information will result in larger trade volumes between the host country and the immigrant's country of origin. We argue that independent of the effect of immigrants on trade flows, the difficulty of obtaining information and conducting trade depends on the difficulty of learning the foreign country's language as well as the difficulty for someone from the foreign country learning the home country's language. We measure this degree of difficulty with the Chiswick and Miller index discussed in the previous section.

Gould's model assumes identical technologies among N countries that produce products differentiated by country of destination. In the supply side of the model, labor is allocated across industries for each country according to a constant elasticity of transformation technology. Labor is transformed at constant elasticity, but the rate is different when labor is transformed between foreign products than when labor is transformed between foreign and domestic products. Transactions costs are introduced as a wedge between the price that a good sells for in a foreign market and the price that the domestic producer receives for the good. Gould models three types of transaction costs: tariffs and other trade barriers, transport costs, and information costs. As Equation 1 describes, the price received, [P.sup.*.sub.ij], by i for selling a product in the jth market is less than the price the product sells for in the foreign market, [P.sub.ij], because of these three types of transaction costs. The greater the transactions costs, the lower the supply of goods to foreign markets. We shall return to these costs in a moment.

[P.sup.*.sub.ij] = [P.sub.ij]/[T.sub.ij][C.sub.ij][Z.sub.ij] (1)

Consumers in all countries have the same constant elasticity of substitution utility function in which the elasticity of substitution differs when comparing foreign and domestic goods as opposed to comparing two foreign goods. There are N(N - 1) import demand equations to match the N(N - 1) export supply functions, which allows one to solve for equilibrium conditions. Assuming that countries are small allows one to derive price and trade flow quantity equations, which when multiplied yield value of aggregate trade flow equations. The value of aggregate trade flows is described in Equation 2, in which trade flows are determined by country incomes ([Y.sub.i], [Y.sub.j]), transactions costs ([C.sub.ij], [T.sub.ij], (6) and [Z.sub.ij]), export prices ([P.sub.ij]), import prices ([P.sup.*.sub.ij]), a domestic price index ([P.sup.*.sub.ij] + [P.sub.ii]), and a foreign price index ([P.sub.ij] + [P.sub.jj]). (7)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

Gould offers a specific formulation for Z to capture the information effect of a stock of immigrants, which, as Wagner, Head, and Ries (2002) point out, is not derived from theory. (8) We offer an alternative formulation that we believe captures the essence of the effects one would attribute to the presence of immigrants as well as the language difficulty encountered when attempting to take advantage of specific information about tastes, culture, laws, and market risk. We model [Z.sub.ij] as described in Equation 3, where [M.sub.ij] is the stock of immigrants in country i from country j and [ld.sub.ij] is the index of the difficulty for a person whose native tongue is the language of country i to learn the language of country j. This specification captures the positive trade effect resulting from a stock of immigrants and the negative effect resulting from greater difficulty in learning the foreign language.

Z = [ld.sub.ij]/[M.sub.ij] [[differential]X/[differential]M] > 0 [[differential]X/[differential]ld] <0 (3)

The advantage of specifying the model as in Equations 1, 2, and 3 is that it can be estimated in the standard double logarithmic form that is typically used for the gravity model. Use of the double logarithmic specification facilitates comparison with the results of other investigators who have examined trade in the gravity framework.

Gould used a country fixed effects model to determine how important the presence of a stock of immigrants was for trade between the United States and the home country of the immigrants. To determine the effect of linguistic distance, we cannot use a country fixed effects model because, unlike the stock of immigrants, linguistic distance is fixed for a country. Therefore, in addition to the linguistic distance index, we include the standard country-specific gravity model variable, distance. Distance, as measured by the great circle distances between national capitals, will be used as a proxy for transport cost. (9) We also include a time trend to control for possible bias introduced by growth over time in the size of trade and income. (10)

The gravity model hypothesizes that the distance between two countries and their size determines the volume of bilateral trade, exports or imports. Although an imperfect measure, distance reflects the cost of shipping goods between countries, [C.sub.ij]. Thus, the greater the distance, the lower the volume of trade. The larger two countries are, the greater the volume of trade that one would expect to observe because larger countries account for a larger share of world income and consumption. Following Gould, we use income and population to capture the size of trading partners. (11) We also follow Gould by including various characteristics of the immigrant stock, for example, length of stay and the ratio of skilled to unskilled workers in the stock of immigrants from a particular country. Equation 4 presents the estimating equation to be used.

[V.sub.ijt] = F(US [Income.sub.t], [Foreign Income.sub.it], US [Population.sub.t], Foreign [Population.sub.it], US Price [Deflator.sub.t], Foreign Price [Deflator.sub.it], Import/Import Unit [Value.sub.it] [Distance.sub.it], Immigrant [Stock.sub.it], Skilled/Unskilled [Immigrants.sub.it], Period [Stayed.sub.it], Linguistic [Distance.sub.i], [year.sub.t]) + random [error.sub.it] (4)

[V.sub.ijt] is the volume of trade, exports or imports, between country i, the United States, and country j in year t, and except for Unit Value, the other variables are as defined for each regression. Unit Value is the ratio of U.S. export unit value to the foreign import unit value when estimating the export equation and the ratio of the U.S. import unit value to the foreign export unit value when estimating the import equation. All other variables appear in both the export and import equations. The variable year accounts for any general trend not otherwise captured by other included variables.

3. Data

Except for the linguistic distance index, the data are those graciously provided by David Gould and are the data that he used in Gould (1994). Thirty-six of the 47 countries in Gould's data set are classified as non-English speaking. (12) Data for the 36 countries are for all years between 1970 and 1986, except in the cases of El Salvador (1970-1984), Ethiopia (1970-1980), Morocco (1970-1985), Nicaragua (1970-1980), Singapore (1972-1980), Trinidad (1970-1985), Turkey (1970-1984), and Yugoslavia (1970-1983). Trade data were gathered from the International Monetary Fund Directions of Trade statistics (IMF 1990a), and consumer and producer manufactured exports and imports came from OECD (1990) Statistics on Trade in Manufactured Goods and were sorted on the basis of the International Standard Industrial Classification codes. (13) The stock of immigrants and data relating to them were derived from the Immigration and Naturalization Service's public use data on yearly immigration with the 1980 Census as a benchmark. Average length of stay was calculated as the average length of stay for immigrants who arrived between 1970 and 1986. Data for U.S. and foreign import and export unit value indexes, population, GDP, and GDP deflators were taken from the IMF (1990b) International Financial Statistics. (14) A list of countries, consumer and producer manufactures, and data descriptions are provided in Appendix A, Appendix B, and Appendices C.1 and C.2, respectively.

The linguistic distance index is from Chiswick and Miller (1998, appendix B). The index is based on language test scores (LS) that range from 1.0 to 3.0 by increments of 0.25, with a value of 1.0 assigned to a country with a language that is extremely difficult for a native English speaker to learn. (15) In our case, Japanese and Korean were the only two languages assigned the value of 1.0. Chiswick and Miller argue that foreign language fluency is part of an immigrant's human capital that allows him or her to realize higher earnings and to more efficiently search for jobs and consumer goods. We extend this to encompass the ability of a person to communicate with those from a foreign country to gain knowledge of tastes and market information to promote trade between the two countries. Thus, a language that is further from English results in greater transactions costs and lower trade volume between the United States and the country in which the language is spoken.

The linguistic distance index, derived as (1/LS), is used in the estimation of Equation 4, resulting in a variable that varies between 0.33 and 1.0. Thus, higher index values are associated with more difficult languages, that is, greater linguistic distance. The indexes appear along with the list of countries in Appendix A.

4. Results

The results from estimating Equation 4 in double log form, except for the year dummy, appear in Table 1, in which the standard gravity variables have the expected coefficients. In the column for exports, foreign income is positive and significant but less than one, which indicates that a 10% increase in the income of a foreign country results in less than a 10% increase in U.S. exports to that country. Holding income constant, an increase in the population of a foreign country results in lower exports to that country, which is consistent with the implied reduction in the standard of living. A higher U.S. population, holding income constant, results in lower exports, which is consistent with the capacity pressure literature. (16,17)

The negative sign for the ratio of the export unit value to import unit value indicates that if prices of U.S. export goods rise relative to the prices of all goods imported by the foreign country, one would expect lower exports from the United States to that country. Similarly, if prices of U.S. imports generally rise faster than the prices of exports from a foreign country, one would expect the United States to import more from that foreign country; that is, the sign is positive as it appears in the import equation results. (18)

Distance is negative but not significantly different from zero for both exports and imports. The ratio of skilled to unskilled immigrants is negative for both exports and imports, but is only significant for imports. Thus, the United States imports less from countries that have a higher ratio of skilled to unskilled immigrants in the United States. (19) Moreover, the longer the immigrants stay in the United States, the lower is trade with their country of origin.

The stock of immigrants has a positive and significant effect on the volume of exports and imports. A 10% larger stock of immigrants results in approximately a 1% greater volume of both exports and imports. Although the larger coefficient on immigrant stock for imports might reflect the immigrant taste effect, the coefficients are not significantly different. Linguistic distance is positive but insignificant for exports and negative and significant for imports. Thus, the more difficult it is for a native English speaker to learn a language, the lower will be the imports from that country. Exports are not significantly affected by linguistic distance.

Given the sample of countries (see Appendix A), one might ask whether any countries have a unique relationship with the United States and are outliers or distort the results. Figures 1 and 2 contain the log of the ratio of average exports to average GNP and the log of the ratio of average imports to average GNP plotted against the country's linguistic index, respectively. One quickly sees that Japan and South Korea are outliers relative to the other countries. (20) Although we cannot control for all country fixed effects, we can control for this unique relationship between the United States and Japan and South Korea. Thus, we estimate Equation 4 controlling for the specific intercept shift characteristics of Japan and South Korea. We argue that this relationship exists because of the unique ties between these two countries and the United States as a result of wars and the political situation that has prevailed for more than a half century. (21)

Columns 3 and 4 of Table 1, Exports(D) and Imports(D), contain the results from estimating Equation 4 with an intercept shift dummy variable for Japan and South Korea. The coefficients, reported in the notes to Table 1, were large and significantly greater than zero, as one would expect given the evidence in Figures 1 and 2. Results for the standard gravity model variables are little changed from those in columns 1 and 2 of Table 1. The immigrant stock coefficients remain positive and significantly greater than zero. However, the linguistic distance coefficients for both exports and imports are now negative and significant. The coefficient for imports is nearly four times more negative than the value in column 2 of Table 1. Results that were only marginally evident become quite stark when one controls for the special trade characteristics associated with Japan and Korea. We shall include this control in the remainder of our analysis.

Serial correlation resulting from persistence effects in trade are common problems with time series or panel data. We follow Eichengreen and Irwin (1998) in using the Hatanaka 2-Step process to correct for serial correlation and to account for any persistence that might occur as a result of prior trade relations between the United States and the other countries in the sample. The Hatanaka process requires that one obtain estimates of the dependent variable (exports or imports) by an instrumental variable approach. The instrument for the lagged dependent variable is the predicted value from a regression of the dependent variable on current and lagged values of the independent variables. The predicted value for the dependent variable is used as an instrument for the lagged dependent variable in estimating the model to generate a consistent estimate of the serial correlation coefficient that is used to quasi-difference the data. The final model contains all of the quasi-differenced variables, including the lagged dependent variable, plus the lagged error term from the estimation that generated the serial correlation coefficient. Thus, we can account for persistence effects and the effect of a shock last period on exports or imports in the current period. (22) Results from applying the Hatanaka 2-Step correction for serial correlation and persistence are presented in Table 2. U.S. income and population are not significantly different from zero once persistence and lagged shocks are accounted for in the model. Foreign income and population have the same effect as in Table 1. That is, foreign income has a positive effect on U.S. exports, and the United States tends to import more from countries with higher income. The negative coefficient on foreign population indicates that, holding income constant, an increase in population results in lower U.S. exports and imports. Price deflators and export and import unit value ratios have the same effect on the volume of trade as in Table 1. Immigrant stock has a positive and significant effect on both exports and imports, which is slightly larger than these coefficients were in Table 1. Linguistic distance has a smaller, but statistically significant negative effect on both exports and imports. The effect on imports is significantly larger than the effect on exports, indicating that language difficulty was more important for U.S. imports.

Lagged exports and imports have significant negative coefficients, which indicates that the United States tended to trade less with countries that it had traded more with in the past or a negative persistence effect. The lagged error term generated a negative and significant effect for trade. That is, larger than expected exports from the United States to a country in one year resulted in a reduction in exports to that country relative to what was expected in the subsequent year. The effect was analogous for imports.

Controlling for all of the standard gravity model variables, including the presence of a stock of immigrants, we have shown that trade will be lower with countries in which the language is more distant from English (i.e., more difficult for an English speaker to learn). It is frequently the case that different types of products are differentially affected by gravity model variables. (23) We shall examine the differential effect of linguistic distance on consumer manufactures and producer manufactures.

Product Variety

Information requirements for exporting or importing consumer goods can be quite different from the information requirements for exporting and importing producer goods. Evidence of different information requirements for exports versus imports was observed in Table 2, where the coefficients on both immigrant stock and linguistic distance were larger for imports than exports. (24) If different types of goods, for example, consumer goods versus producer goods, required differential amounts of information, then linguistic distance would be expected to have a differential effect on trade in these goods. Both producer goods imports and consumer goods imports into the United States tend to require the transfer of more information than producer and consumer goods exports from the United States, especially if one believes that import goods are less standardized than export goods. If U.S. goods are better known through brand name recognition or experience, then language differences create less of a barrier for exports and imports of these goods. One might also expect that consumer goods are more complex than producer goods, resulting in imports of consumer goods requiring greater information than imports of producer goods. (25)

Table 3 contains the regression results for consumer goods exports and imports and producer goods exports and imports. Coefficients for the standard gravity variables have the expected signs and magnitudes, except for distance in the consumer goods import regression. (26) Linguistic distance and migrant stock are both significantly more important for imports than for exports of either type of good. Linguistic distance is significantly more important for imports of consumer goods than it is for producer goods. (27) The results for linguistic distance are consistent with the discussion above regarding the relatively greater complexity of consumer goods and the larger amount of information necessary for imports. Although there is no significant difference between consumer goods and producer goods with respect to the stock of immigrants, the immigrant stock effect is significantly larger for imports than it is for exports.

The ratio of skilled to unskilled immigrants in the host country has a significant negative effect on imports of consumer goods and a significant positive effect on imports of producer goods. One could argue that higher skilled immigrants possess greater knowledge of products produced in their home country, while being more likely to start businesses in their host country. (28) If the businesses started by these higher skilled immigrants were competing with imports of consumer goods from their home country, then one would expect a higher ratio of skilled to unskilled immigrants from a country to result in lower imports of consumer goods. Moreover, if higher skilled immigrants also possess greater knowledge about products from their home country, one would expect imports of producer goods to be greater, especially for producer goods that are inputs to the consumer goods businesses these higher skilled immigrants are more likely to start.

5. Final Comments

Nearly every gravity model of bilateral trade has employed dummy variables to account for a common language between bilateral trading partners. Gould (1994) showed that immigrants from non-English-speaking countries had a larger pro-trade effect than did immigrants from English-speaking countries when examining trade between the United States and other countries. With the use of a gravity model for trade, which in addition to the traditional variables, accounts for transactions costs, we find that linguistic distance reduces the volume of U.S. trade with a country even in the presence of a stock of immigrants from that country. Although immigrants might provide information that enhances trade, linguistic distance diminishes the volume of trade. (29) We show that, among non-English-speaking countries, one observes lower trade with countries whose language is more distant from English.

We show that trade with Japan and South Korea is different from trade with the other countries in the sample; that is, trade is much greater relative to GNP than it is for the other countries. Controlling for this special trade relationship results in a much larger and significantly negative effect of linguistic distance on trade. The Hatanaka 2-Step process is employed to correct for serial correlation and to capture any persistence effects in trade between the United States and these 36 countries. (30)

The linguistic distance results hold for total exports and total imports as well as consumer manufactures and producer manufactures exports and imports. We find that information costs are more important for imports than exports and especially for imports of consumer manufactures.

Appendix A

Country List

Austria (2.25)
Denmark (2.25)
France (2.5)
Israel (2.0)
Malaysia (2.75)
Norway (3.(1)
Singapore (2.75)
Switzerland (2.25)
Tunisia (1.5)

Brazil (2.5)
El Salvador (2.25)
Greece (1.75)
Italy (2.5)
Morocco (1.5)
Pakistan (1.75)
Spain (2.25)
Syria (1.5)
Turkey (2.0)

Colombia (2.25)
Ethiopia (2.0)
Hungary (2.0)
Japan (1.0)
Netherlands (2.75)
Philippines (2.25)
Sri Lanka (1.75)
Thailand (2.0)
West Germany (2.25)

Cyprus (1.75)
Finland (2.0)
Iceland (2.25)
Jordan (1.5)
Nicaragua (2.25)
South Korea (1.0)
Sweden (3.0)
Trinidad (2.25)
Yugoslavia (2.0)

Numbers in parentheses are the linguistic distance
indexes, which were inverted in the gravity regressions.

Immigrant Stock

With the 1980 Census as a benchmark, the Immigration and Naturalization Service public use data were used to generate the stock of immigrants from each of the 36 countries in the data set.

Appendix B
Product Lists

Consumer manufactured products
  Preserved meat products                Dairy products
  Canned fruits and vegetables           Canned and preserved fish
  Bakery products                        Cocoa and sugar confectionery
  Wine products                          Malt liquors
  Soft drinks and carbonated waters      Tobacco manufactures
  Textiles excluding wearing apparel     Wearing apparel
  Leather products excluding footwear    Leather footwear
  Cane containers and small caneware     Wood furniture and fixtures
  Soap and cosmetics                     Pottery, china, and earthware
  Glass products                         Cutlery and general hardware
  Metal furniture and fixtures           Metal products excluding
                                           machinery and equipment
  Radio and TV equipment and apparatus   Electric appliances and
                                           housewares
  Motor vehicles                         Motorcycles and bicycles
  Photographic and optical goods         Watches and clocks
  Jewelry                                Musical instruments
  Sporting goods

Producer manufactured goods
  Prepared animal feeds                  Weaving and finishing textiles
  Knitting mills                         Rope and twine industries
  Fur dressing and dyeing industries     Sawmills, planing, and other
                                           wood mills
  Pulp and paper                         Paper boxes and paperboard
  Basic industrial chemicals             Fertilizers and pesticides
  Synthetic resins and plastics          Paints, varnishes, and
                                           lacquers
  Drug and medicines                     Petroleum products
  Miscellaneous petroleum and            Tire and tube industries
    coal products
  Cement, lime, and plaster              Nonmetallic mineral products
  Iron and steel basic industries        Metal scrap
  Structural metal products              Engines and turbines
  Agricultural machinery                 Metal and woodworking
                                           machinery
  Aircraft                               Industry machinery excluding
                                           metal and woodworking
                                           machinery
  Electric industrial machinery          Office, computing, and
                                           acounting machinery
  Shipbuilding and repairing             Transport equipment
  Scientific equipment

Appendix C.1
Descriptive Statistics

Variable                       Mean       Standard Deviation

Imports                       2389.859          7044.509
Exports                       1777.019          3298.803
Consumer imports              1166.246          4405.579
Consumer exports               251.528           440.060
Producer imports               700.426          1943.972
Producer exports               724.1815         1284.865
Distance                      8116.519          3272.206
U.S. GNP price deflator          0.6923            0.210
Foreign price deflator           0.5681            0.344
U.S. export unit value          73.055            23.970
U.S. import unit value          72.593            27.971
Foreign export unit value       68.387            31.430
Foreign import unit value       66.348            30.708
U.S. population                223.6527           10.369
U.S. GNP                     2,417,316       983,188.1
Foreign population              26.1271           30.643
Foreign GNP                 91,275.4         191,858.4
Stay period                      7.2694            3.500
Immigrant stock             93,427.37        153,404.2
Skilled                       8433.821        13,982.59
Unskilled                   11,286.52         16,930.98
Language distance                0.5108            0.1521

Appendix C.2: Data Descriptions

Data Provided by David Gould

Aggregate Exports and Imports

Constructed from the International Monetary Fund Directions of Trade statistics (IMF 1990a). Trade data for consumer and producer goods were derived from the OECD (1990) Statistics on Trade in Manufactured Goods. All data are in millions of U.S. dollars. Consumer and producer goods were determined using the four-digit International Standard Industrial Codes (ISIC). For example, jewelry and bicycles are consumer goods, and scrap metal and engines are producer goods. An attempt was made to exclude ambiguous goods, which might have added some degree of error to the analysis.

Skilled Workers

Defined as immigrants whose occupation is listed as "'professional. technical, and kindred workers."

Unskilled Workers

Defined as immigrants listed as "general machine operators," "laborers," "farm workers." or "service workers."

Average Stay

Constructed from dates of entry into the United States during the period 1970-1986. Thus, it is the average length of stay for immigrants who arrived between 1970 and 1986. Changes in immigrant stock from return migration and death could not be estimated, which might have biased the average stay upward for some groups. Europeans tended to stay longer and Africans, Asians, and Latin Americans shorter periods.

Income, Price Deflators, and Population

All taken from the IMF (1990b) International Financial Statistics. Income is in millions of U.S. dollars and prices are export and import unit value indexes scaled to 1985 100.

Distance

Great circle routes between national capitals. Data are available at the website for international trade resources maintained by Raymond Robertson: http://www.macalester.edu/research/ economics/PAGE/HAVEMAN/Trad.Resources/Trade. html#Graviry.

Language Distance

Data were taken from Chiswick and Miller (1998) in which they used the language scores reported in Hart-Gonzalez and Linderman (1993) from the U.S. Department of State School of Language Studies. The difficulty associated with learning a foreign language is rated on a scale of 1.0 to 3.0 in increments of 0.25, with 1.0 being the most difficult and 3.0 being the easiest languages for a native English speaker to learn. Index values were assigned according to the dominant language spoken in a country. Chiswick and Miller described the construction of this index in greater detail in the appendix to their paper.

Table 1. Results for Total Bilateral Exports and Total Bilateral
Imports: The United States and 36 Trading Partners, 1971-1986

Variable                   Exports       Imports

Constant                 1097.07 ***   1304.70 ***
                           (5.38)        (4.32)
U.S. income                 2.55 *        5.07 **
                           (1.77)        (2.35)
Foreign income              0.87 ***      1.12 ***
                          (21.00)       (18.96)
U.S. population          -211.49 ***   -258.19 ***
                           (5.63)        (4.65)
Foreign population         -0.11 ***     -0.19 ***
                           (2.91)        (3.58)
U.S. price deflator         7.16 ***      3.30
                           (4.54)        (1.41)
Foreign price deflator     -0.16 ***     -0.14 ***
                           (7.66)        (5.75)
U.S. export/foreign        -0.15 **
  import unit value        (2.59)
U.S. import/foreign                       0.27 ***
  export unit value                      (3.83)
Distance                   -0.09         -0.2
                           (0.81)        (1.17)
Immigrant stock             0.09 ***      0.12 ***
                           (2.82)        (2.67)
Skilled/unskilled          -0.02         -0.16 *
  immigrants               (0.33)        (1.67)
Period stayed              -1.75 ***     -2.44 ***
                           (8.84)        (8.92)
Linguistic distance         0.13         -1.18 ***
                           (0.85)        (3.86)
Year                        1.64 ***      2.23
                           (4.52)        (4.12)
Number of observations    547           547
[R.sup.2]                   0.79          0.72

Variable                 Exports(D)    Imports(D)

Constant                  865.3 ***     625.24 ***
                           (4.48)        (3.03)
U.S. income                 2.15          3.90 ***
                           (1.59)        (2.61)
Foreign income              0.67 ***      0.51 ***
                          (15.09)       (11.97)
U.S. population          -167.01 ***   -127.78 ***
                           (4.68)        (3.36)
Foreign population         -0.01          0.12 ***
                           (0.02)        (3.05)
U.S. price deflator         0.14 ***      0.91
                           (7.12)        (0.57)
Foreign price deflator     -0.16 ***     -0.13 ***
                           (7.48)        (5.58)
U.S. export/foreign        -0.18 ***
  import unit value        (3.30)
U.S. import/foreign                       0.19 ***
  export unit value                      (3.85)
Distance                   -0.13         -0.30 ***
                           (1.33)        (2.91)
Immigrant stock             0.08 ***      0.10 ***
                           (2.72)        (3.38)
Skilled/unskilled           0.01         -0.07
  immigrants               (0.13)        (1.15)
Period stayed              -1.24 ***     -0.96 ***
                           (6.20)        (4.99)
Linguistic distance        -1.37 ***     -5.60 ***
                           (5.99)       (20.37)
Year                        1.25 ***      1.07 ***
                           (3.62)        (2.90)
Number of observations    547           547
[R.sup.2]                   0.82          0.86

The t statistics are in parentheses. The t values are based
on the robust standard errors correction procedure for
heteroscedasticity of the STATA software package. The
coefficients for the South Korea and Japan dummy variable
in Export(D) and Import(D) were 2.11 (10.39) and 6.18
(24.16), respectively, with t statistics in parentheses.

* p < 0.01.

** p < 0.05.

*** p < 0.10.

Table 2. Results for Total Exports and Total Imports
With Hatanaka 2-Step Correction: The U.S. and 36
Non-English-speaking Trading Partners, 1972-1986

Variable                                       Exports

Constant                                  21.63
U.S. income                               -1.76
Foreign income                             0.86 *** (20.43)
U.S. population                          -17.73
Foreign population                        -0.14 *** (4.37)
U.S. price deflator                        5.68
Foreign price deflator                    -0.14 *** (7.27)
U.S. export/foreign import unit values    -0.16 *** (3.05)
U.S. import/foreign export unit values
Distance                                  -0.16 *   (1.91)
Immigrant stock                            0.09 *** (3.53)
Skilled/unskilled immigrants              -0.02
Period stayed                             -2.53 *** (12.02)
Linguistic distance                       -0.44 *** (2.68)
Lagged exports                            -0.07 *** (3.71)
Lagged imports
Lagged error term                         -0.82 *** (16.25)
Number of observations                   476
[R.sup.2]                                  0.88

Variable                                       Imports

Constant                                  67.60     (0.89)
U.S. income                               -5.17
Foreign income                             0.96 *** (15.32)
U.S. population                          -47.51
Foreign population                        -0.16 *** (3.11)
U.S. price deflator                        7.85
Foreign price deflator                    -0.15 *** (5.80)
U.S. export/foreign import unit values
U.S. import/foreign export unit values     0.28 *** (3.81)
Distance                                  -0.33 **  (2.55)
Immigrant stock                            0.14 *** (3.84)
Skilled/unskilled immigrants              -0.06
Period stayed                             -2.66 *** (8.89)
Linguistic distance                       -2.59 *** (8.89)
Lagged exports
Lagged imports                            -0.07 *** (3.01)
Lagged error term                         -0.80 *** (11.73)
Number of observations                   476
[R.sup.2]                                  0.82

The t statistics are in parentheses. The t values are
based on the robust standard errors correction procedure for
heteroscedasticity of the STATA software package. The coefficients
for the South Korea and Japan dummy variable were not included
but were 1.12 (5.23) and 3.28 (10.20), respectively, with t
statistics in parentheses. The time trend coefficient is not
reported in the table, and it was only marginally positive
for imports.

* p < 0.01.

** p < 0.05.

*** p < 0.10.

Table 3. Consumer Goods (C) and Producer Goods (P) Exports and
Imports with Hatanaka 2-Step Correction: The US and 36
Non-English-speaking Trading Partners, 1972-1986

Variable                       Exports(C)          Imports(c)

Constant                   5.66     (0.16)    -68.56     (1.51)
U.S. income               -2.91     (0.51)     -5.01     (0.48)
Foreign income             0.97 *** (21.37)     1.24 *** (19.32)
U.S. population            2.74     (0.06)    115.56     (1.39)
Foreign population        -0.34 *** (8.76)     -0.33 *** (5.58)
U.S. price deflator        7.06     (1.13)     -6.09     (0.56)
Foreign price deflator    -0.13 *** (6.54)     -0.22 *** (7.41)
U.S. export/foreign
  import unit values      -0.22     (3.21)
U.S. import/foreign
  export unit values                           -0.07     (0.74)
Distance                  -0.33 *** (3.31)      1.16 *** (8.26)
Immigrant stock            0.12 *** (4.22)      0.24 *** (6.02)
Skilled/unskilled
  immigrants               0.06     (1.02)     -0.25 **  (2.52)
Period stayed             -3.48 *** (14.81)    -2.71 *** (8.27)
Linguistic distance       -0.52 *** (2.96)     -2.26 *** (7.54)
Lagged exports            -0.03     (1.40)
Lagged imports                                 -0.09 *** (4.22)
Lagged error              -0.79 *** (12.70)    -0.91 *** (13.23)
Number of
  observations           476                  476
[R.sup.2]                  0.85                 0.86

Variable                      Exports(P)           Imports(P)

Constant                 -44.51     (0.88)     -7.17     (0.13)
U.S. income               -1.43     (0.34)      2.04     (0.30)
Foreign income             0.94 *** (22.64)     1.46 *** (27.58)
U.S. population           41.48     (0.93)     -0.44     (0.01)
Foreign population        -0.13 *** (3.60)    -0.60  *** (12.05)
U.S. price deflator        5.85     (1.21)     -3.75     (0.57)
Foreign price deflator    -0.15 *** (8.30)     -0.05 *   (1.96)
U.S. export/foreign
  import unit values      -0.26 *** (4.93)
U.S. import/foreign
  export unit values                          -0.30  *** (2.70)
Distance                  -0.34 *** (3.52)     -0.69 *** (4.56)
Immigrant stock            0.08 *** (2.87)     0.20  *** (5.12)
Skilled/unskilled
  immigrants              -0.03     (0.58)      0.28 *** (3.16)
Period stayed             -2.66 *** (12.91)    -2.62 *** (9.25)
Linguistic distance       -0.94 *** (5.44)    -1.50  *** (5.30)
Lagged exports            -0.03     (1.58)
Lagged imports                                 -0.05     (2.84)
Lagged error              -0.74 *** (14.28)   -0.70  *** (12.43)
Number of
  observations           476                  476
[R.sup.2]                  0.88                 0.88

The t statistics are in parentheses. The t values are based
on the robust standard errors correction procedure for
heteroscedasticity of the STATA software package. The
coefficients for the South Korea and Japan dummy variable were 1.22
(5.53), 1.13 (5.47), 3.23 (10.32), and 2.46 (7.30), respectively,
for Exports(C), Imports(C), Exports(P), and Imports(P), with t
statistics in parentheses. The time trend variable is not reported
in the table because it was insignificant for all but exports of
producer goods, which was marginally significant.

* P < 0.01.

** P < 0.05.

*** P < 0.10.

The author thanks Barry Chiswick, William Collins, James Dunlevy, Andre Souza, and members of the Brown Bag Seminar at Vanderbilt University for helpful comments on an earlier draft. Special thanks to Yanqin Fan for helpful econometric advice. As usual, the author takes responsibility for remaining errors.

Received October 2003; accepted November 2004.

(1) Bergstrand (1985) provides a review of the earlier papers as does Frankel (1997).

(2) See Head and Ries (1998), Dunlevy and Hutchinson (1999), Rauch (1999), Hutchinson and Dunlevy (2001), and Wagner, Head, and Ries (2002), who consider the effects on bilateral trade resulting from the formation of information linkages due to immigration and other network formation. Rauch and Trindade (2002) find that the larger the stock of Chinese immigrants in two countries, the larger the volume of trade between the two countries.

(3) For example, see Frankel and Rose (2002) and many of the papers cited therein.

(4) Jacques Melitz (2002) examines various issues related to language and communication: importance of particular languages and whether negative effects might result from groups of similar languages.

(5) See the data descriptions for an explanation of this variable.

(6) We do not consider tariffs in this paper; however, because of the limited availability of data on bilateral tariffs for many of the countries in the sample. If one believes that language commonality is correlated with tariffs and other trade barriers, then omitting tariffs could bias our results. However. we believe that the effects of successive GATT rounds have reduced the variation of tariffs for the United States among countries, which will minimize the possible bias from omitting tariff data.

(7) We have already made the substitutions of price indexes for the theoretically derived price indexes which would have excluded the price of the commodity under consideration. Such indexes are not available. Goods in [P.sub.ij] and [P.sub.jj] are the nontraded goods for each country.

(8) The particular form used by Gould forces him to use a specific estimation procedure because of the nonlinearities that result.

(9) Alternative measures of distance were tried (e.g., nearest port instead of capitals), but the results for the variable of interest were not qualitatively different. Thus. the more standard great circle route distances between capitals were used.

(10) See Anderson (1979), Bergstrand (1985, 1989). and Baier and Bergstrand (2001) for theoretical derivations of gravity models that result in estimating models similar to Gould t 1994) and the one we employ.

(11) See Frankel (1997) for a discussion of how these variables account for size in gravity models.

(12) Classification was based on data from the website for international trade resources maintained by Raymond Robertson (2001). Index values only relate to learning a foreign language if one is a native English speaker, which is why only non English-speaking countries are in the sample.

(13) Gould (1994) indicates that an attempt was made to exclude ambiguous categories.

(14) The reader is referred to Gould (1994) for a more detailed description of data collection, or one is available from this author on request.

(15) Chiswick and Miller (1998) use the language scores reported in Hart-Gonzalez and Linderman (1993) from the U.S. Department of State School of Language Studies. As they do, we assume that the difficulty encountered for a non English speaker learning English is symmetric with the difficulty for an English speaker to learn a foreign language.

(16) For information on the capacity pressure hypothesis see Dunlevy (1980) and Raynold and Dunlevy (1998).

(17) One might be concerned regarding the potential endogeneity of immigrant stock. An optimal instrumental variable was created for immigrant stock. Equation 4 was estimated instrumenting for immigrant stock, and the results were qualitatively unchanged. Similar results were obtained by Dunlevy and Hutchinson (1999), who addressed the endogeneity issue differently.

(18) As discussed extensively in Bergstrand (1989), coefficients on GNP deflators affect the elasticity of substitution between foreign and domestic goods in the model for trading partners. Trade in this paper is between the United States and a sample of trading partners, which does not allow for interpreting this coefficient.

(19) The ratio of skilled to unskilled immigrants reflects the stock of immigrants and is uncorrelated with the per capita income of the country of origin.

(20) South Korea lies above Japan in both figures at the LD value of 1.0. Singapore is an additional outlier for exports and Trinidad is an additional outlier for imports. One might ask about the special relationship between the United States and European countries. Germany has been the United States' second or third largest trading partner, but its trade is not inordinately large relative to its GNP.

(21) If one controls for Singapore in exports and Trinidad in imports with South Korea and Japan, the LD coefficient is marginally increased. The relationship between the United States and these two additional countries is not as unique as it is for South Korea and Japan, which is why Trinidad and Singapore are not treated as special. The unique trading relationship between the United States and Japan and South Korea results from particularly close ties that evolved after World War II and the Korean War, respectively.

(22) See Greene (1993) for a more detailed explanation of this process.

(23) Gould (1994), Dunlevy and Hutchinson (1999), and Hutchinson and Dunlevy (2001) have found that the presence of a stock of immigrants differentially affects particular product categories.

(24) The linguistic distance coefficient for imports was significantly large than the coefficient for exports.

(25) Commodities included in each group are listed in Appendix B.

(26) It would appear that the United States imports more consumer goods from countries that are farther away. If one replaces the distance between capitals with the distance to San Francisco for the Asian and Pacific countries, the distance coefficient is negative but not significant. All other coefficients remain unchanged.

(27) F statistics indicate that these differences are significant at the 0.01 level or better.

(28) This argument is made by Gould (1994).

(29) In the regression not reported, linguistic distance was interacted with immigrant stock, but the results were insignificant. The coefficients on immigrant stock and linguistic distance remained the same as reported in this paper.

(30) Regressions including time fixed effects were also run for Equation 4, and the results were unchanged.

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William K. Hutchinson, Department of Economics, Station B #351819, Vanderbilt University, Nashville, TN 37235-1819, USA; E-mail [e-mail].