《国际政治经济学》文献资料（Political Economy of International Trade：Domestic Politics）Commerce, Coalitions, and Factor Mobility

American Political Science Review Vol.96,No.3 the findings of Grossman and Levinsohn(1989),based since that time (Mincer 1984).Job tenure rose along upon a study of stock-market returns in the 1970s and with training in firm-specific skills(Carter and Savocca 1980s,and conclusions reached by Ramey and Shapiro 1990:Sundstrom 1988).Meanwhile.barriers to exit and (1998),based upon prices in secondary markets for cap- entry for manufacturing firms appear to have risen ital equipment.19 markedly along with the growing importance of spe- cialized technologies(Ramey and Shapiro 1998)and Industrialization and Factor Mobility as a function of the higher start-up costs and increased investments in physical capital associated with the gen- The evidence indicates that there have been substan- eral growth in the scale of production (Caves and Porter tial changes over time in general levels of interindustry 1979).21 labor and capital mobility in the U.S.economy.The pat- tern that emerges-rising mobility during most of the COALITION PATTERNS IN U.S.TRADE nineteenth century,falling mobility in recent decades- POLITICS:CONGRESSIONAL VOTES, can be explained by the technological transformations 1824-1994 associated with industrialization.Historical accounts of American economic development have emphasized a Expectations and Evidence range of technological changes that combined to make the economy more fluid during the early stages of in- In light of the evidence that levels of interindustry fac- dustrialization in the nineteenth century (e.g.,Sokoloff tor mobility have varied substantially in the american and Villaflor 1992).Major innovations in systems of economy over time,the question remains as to whether water,rail,and road transportation drastically lowered these changes have produced the expected changes the costs of factor movement and lessened the impor- political coalitions.If the argument advanced above tance of geography to economy(Davis,Hughes,and is correct,the formation of broad factor-owning class McDougall 1961,276-96).Labor migration and cap- coalitions should have been most likely during peri- ital flows grew markedly (Perloff 1965).Agricultural ods when interindustry factor mobility was relatively producers were affected too,as distance from markets high (between the 1880s and the 1920s),while narrow and resources became less important for the location industry-based coalitions should have been most likely of production.At the same time innovations in man- in periods when interindustry mobility was relatively ufacturing technology had profound implications for low(earlier in the nineteenth and later in the twentieth interindustry mobility.New mills and factories replaced centuries).22 craft shops and home manufacture,and the old skills These expectations do fit with some of the stylized of the artisan class were rendered obsolete (Sokoloff historical facts of American trade politics.According to and Villaflor 1992).Much of the new factory technol- standard accounts,trade politics was a predominantly ogy was readily adaptable to use in alternative indus- local,group-based affair at the beginning of the nine- tries (Landes 1969,293-94)and created a vast demand teenth century.The emerging political parties were split for unskilled labor,making it far easier for industrial over the tariff issue along regional lines and trade leg- workers to shift between jobs in different industries islation reflected the competing pressures placed on (Sokoloff 1986).20 Congress by a vast array of locally organized groups Around the turn of the century,however,techno- (Pincus 1977;Stanwood 1903,240-43;Taussig 1931, logical changes in manufacturing began to reverse 25-36).In the years following the Civil War,how- these trends.Most important was the growing comple- ever,trade became the partisan issue in American pol- mentarity between labor skills and the newest tech- itics,as Republicans,drawing broad support mostly nology (Bartel and Lichtenberg 1987;Griliches 1969; from business and labor,supported high protection- Hamermesh 1993).The key change appears to have ist tariffs over the vehement opposition of Democrats taken place in the 1910s and 1920s with the move from and their largely rural constituency (Stewart 1991, assembly-line to continuous-process technology-the 218;Taussig 1931,chaps.5-8;Verdier 1994,108-15). latter requiring more skilled workers in the manage- ment and operation of highly-complex tasks(Cain and Paterson 1986:Goldin and Katz 1996).Growth in the 2 While the evidence that scale economies alone act as powerful demand for specialized human capital has been con- barriers to entry in practice is not strong(Scherer 1980),there is more evidence that larger capital requirements mean that fewer individuals comitant with continued technological improvements or groups can secure the funding needed for entry (Geroski and Jacquemin 1985).Strategic considerations also tend to inhibit exit 1 Note too that increasing capital specificity in recent decades is when scale economies are large (Ghemawat and Nalebuff 1990). evidenced by growing rates of investment in research and develop- 22 For simplicity,levels of mobility are treated as general to all fac- ment by firms-a popular indicator of specificity since it captures tors here.One might prefer to differentiate measures of mobility for the emphasis placed by firms on developing their own technologies each factor,but the evidence indicates that technological forces have (Acs and Isberg 1991).Spending by U.S.manufacturing companies affected levels of mobility in a very similar fashion for all factors. on R&D rose from about 0.5%of sales in 1950 to over 3%in 1990 From Figures 1 and 2 it does seem that levels of interindustry capital (see U.S.Department of Commerce,Statistical Abstract of the United mobility may have peaked earlier than levels of labor mobility,and States,various years). one might thus anticipate that industry-based schisms among owners 20 Goldin(1990.115)has argued that,by the turn of the century,the of capital would predate similar divisions among workers late in the market for labor in the manufacturing sector was essentially a spot nineteenth century.For an extended formal treatment of the con- market,with most jobs easily handled by the average worker.See sequences of allowing different rates of change in capital and labor also Gordon,Edwards,and Reich (1982,112-28). mobility,see Hiscox 1997 597

American Political Science Review the findings of Grossman and Levinsohn (1989), based upon a study of stock-market returns in the 1970s and 1980s, and conclusions reached by Ramey and Shapiro (1998), based upon prices in secondary markets for cap￾ital equipment.19 Industrialization and Factor Mobility The evidence indicates that there have been substan￾tial changes over time in general levels of interindustry labor and capital mobility in the U.S. economy. The pat￾tern that emerges-rising mobility during most of the nineteenth century, falling mobility in recent decades￾can be explained by the technological transformations associated with industrialization. Historical accounts of American economic development have emphasized a range of technological changes that combined to make the economy more fluid during the early stages of in￾dustrialization in the nineteenth century (e.g., Sokoloff and Villaflor 1992). Major innovations in systems of water, rail, and road transportation drastically lowered the costs of factor movement and lessened the impor￾tance of geography to economy (Davis, Hughes, -and McDougall 1961, 27&96). Labor migration and cap￾ital flows grew markedly (Perloff 1965). Agricultural producers were affected too, as distance from markets and resources became less im~ortant for the location of production. At the same time innovations in man￾ufacturing technology had profound implications for interindustry mobility. New mills and factories replaced craft shops and home manufacture, and the old skills of the artisan class were rendered obsolete (Sokoloff and Villaflor 1992). Much of the new factory technol￾ogy was readily adaptable to use in alternative indus￾tries (Landes 1969,293-94) and created a vast demand for unskilled labor, making it far easier for industrial workers to shift between jobs in different industries (Sokoloff 1986).~(' Around the turn of the century, however, techno￾logical changes in manufacturing began to reverse these trends. Most important was the growing comple￾mentarity between labor skills and the newest tech￾nology (Bartel and Lichtenberg 1987; Griliches 1969; Hamermesh 1993). The key change appears to have taken place in the 1910s and 1920s with the move from assembly-line to continuous-process technology-the latter requiring more skilled workers in the manage￾ment and operation of highly-complex tasks (Cain and Paterson 1986; Goldin and Katz 1996). Growth in the demand for specialized human capital has been con￾comitant with continued technological improvements lY ~otetoo that increasing capital specificity in recent decades is evidenced by growing rates of investment in research and develop￾ment by firms-a popular indicator of specificity since it captures the emphasis placed by firms on developing their own technologies (Acs and Isberg 1991). Spending by U.S. manufacturing companies on R&D rose from about 0.5% of sales in 1950 to over 3% in 1990 (see U.S. Department of Commerce, Statistical Abstract of the United States. various years). 20 Goldin (1990,115) has argued that, by the turn of the century, the market for labor in the manufacturing sector was essentially a spot market, with most jobs easily handled by the average worker. See also Gordon, Edwards, and Reich (1982,112-28). Vol. 96, No. 3 since that time (Mincer 1984). Job tenure rose along with training in firm-specific skills (Carter and Savocca 1990; Sundstrom 1988). Meanwhile, barriers to exit and entry for manufacturing firms appear to have risen markedly along with the growing importance of spe￾cialized technologies (Ramey and Shapiro 1998) and as a function of the higher start-up costs and increased investments in physical capital associated with the gen￾eral growth in the scale of production (Caves and Porter 1979).2l COALITION PAlTERNS IN U.S. TRADE POLITICS: CONGRESSIONAL VOTES, 1824-1994 Expectations and Evidence In light of the evidence that levels of interindustry fac￾tor mobility have varied substantially in the American economy over time, the question remains as to whether these changes have produced the expected changes political coalitions. If the argument advanced above is correct, the formation of broad factor-owning class coalitions should have been most likely during peri￾ods when interindustry factor mobility was relatively high (between the 1880s and the 1920s), while narrow industry-based coalitions should have been most likely in periods when interindustry mobility was relatively low (earlier in the nineteenth and later in the twentieth ~enturies).~~ These expectations do fit with some of the stylized historical facts of American trade politics. According to standard accounts, trade politics was a predominantly local, group-based affair at the beginning of the nine￾teenth century. The emerging political parties were split over the tariff issue along regional lines and trade leg￾islation reflected the competing pressures placed on Congress by a vast array of locally organized groups (Pincus 1977; Stanwood 1903, 240-43; Taussig 1931, 25-36). In the years following the Civil War, how￾ever, trade became the partisan issue in American pol￾itics, as Republicans, drawing broad support mostly from business and labor, supported high protection￾ist tariffs over the vehement opposition of Democrats and their largely rural constituency (Stewart 1991, 218; Taussig 1931, chaps. 5-8; Verdier 1994, 108-15). 21 While the evidence that scale economies alone act as powerful barriers to entry in practice is not strong (Scherer 1980), there is more evidence that larger capital requirements mean that fewer individuals or groups can secure the funding needed for entry (Geroski and Jacquemin 1985). Strategic considerations also tend to inhibit exit when scale economies are large (Ghemawat and Nalebuff 1990). 22 For simplicity, levels of mobility are treated as general to all fac￾tors here. One might prefer to differentiate measures of mobility for each factor, but the evidence indicates that technological forces have affected levels of mobility in a very similar fashion for all factors. From Figures 1 and 2 it does seem that levels of interindustry capital mobility may have peaked earlier than levels of labor mobility, and one might thus anticipate that industry-based schisms among owners of capital would predate similar divisions among workers late in the nineteenth century. For an extended formal treatment of the con￾sequences of allowing different rates of change in capital and labor mobility, see Hiscox 1997

Commerce,Coalitions,and Factor Mobility September 2002 Regional divisions began to yield to a growing class Uslaner 1998).Conybeare(1991)looks at votes in ear- cleavage that separated landowners (especially in the lier times,and Gilligan (1997)provides an excellent South and West)from urban interests and helped to analysis that covers 12 bills in Congress between 1890 generate the Granger and Populist movements.23 At and1988. the height of the conflict,the Republican tariff of 1890 The findings from these studies shed some light on was denounced as the"culminating atrocity of class leg- the coalitions issue,but only indirectly.In analyses islation"in the Democratic party platform,and the two of recent trade votes,measures of the importance of parties squared off on the trade issue at each election. import-competing industries in districts have signifi- Growing rifts over the trade issue within the parties cant,positive effects on the likelihood that legislators became more apparent in the 1920s and 1930s,how- vote in favor of protection.Dependence on export in- ever.and by the 1960s there were deep divisions in dustries in electoral districts,on the other hand,tends both parties and in the peak associations representing to raise the likelihood that legislators vote for liberal- labor,business,and rural classes(Destler 1992,176-77; izing bills.These relationships,which fit well with the Turner and Schneier 1970,71).24 Meanwhile,lobbying industry-based approach to trade politics,appear much by industry groups appeared to intensify (on both sides less clear in the studies of earlier votes:Conybeare of the trade issue)and played a key role in shaping (1991)finds evidence of industry effects,but Gilligan policy outcomes (Baldwin 1985;Destler 1992,189-96; (1997)indicates that such effects are quite weak.Evi- Lavergne 1983).25 dence on the importance of factoral or class variables is even less clear.Recent studies have indicated that votes Congressional Voting against NAFTA in 1993 were positively associated with the degree to which legislators relied upon campaign We can better assess temporal changes in coalition pat- contributions from labor political action committees terns (and the relative utility of class and group-based (Baldwin and Magee 2000;Steagall and Jennings 1996). models)by examining congressional votes on major But it is difficult to draw clear inferences from this pieces of trade legislation in different historical peri- without knowing the extent of the bias in the indus- ods.The presumption here is that legislators'voting try composition of contributing labor groups-labor- decisions reflect their response to pressures from soci- intensive import-competing industries tend to be more etal coalitions.If the theory is correct,voting decisions unionized and,thus,are likely to be the primary source should more clearly reflect legislators'responses to de- of contributions. mands by broad factor classes when levels ofinterindus- To compare the relative utility of class and industry- try factor mobility are relatively high and demands group models,I take a simple approach here,relat- from protectionist and free-trade industries within their ing voting patterns among members of the Senate and districts when mobility levels are relatively low. House over time to measures of the class and industry A number of studies of congressional votes on trade makeup of their constituencies.The dependent variable policy have appeared in the literature to date.Most of is the legislator's vote for protection(1 for a protec- these have been limited to examining a specific piece tionist bill or against a liberalizing bill,0=against a of legislation,usually in recent years(see Baldwin and protectionist bill or for a liberalizing bill).26 Votes on Magee 2000).They include studies of votes on auto- 30 major pieces of trade legislation between 1824 and mobile domestic content legislation in 1982(Coughlin 1994 are examined (Appendix B provides a detailed 1985;McArthur and Marks 1988),the Trade Act of list of these bills). 1974(Baldwin 1985),textile quota legislation in 1985 The explanatory variables are measures of the class (Tosini and Tower 1987),the Export Facilitation Act or industry characteristics of each state in each year in of 1987(Uri and Mixon 1992),and the omnibus trade which a vote was taken.For factor classes,I derived legislation of 1987 (Marks 1993).The votes on the several measures from the available census data.27 As NAFTA have been given special attention in recent a basic measure of the importance of farmers in each work (Baldwin and Magee 2000;Holian,Krebs,and state,I have used the total value of agricultural pro- Walsh 1997;Kahane 1996;Steagall and Jennings 1996; duction as a fraction of state income.As a measure of the importance of labor,I used total employment in 23 As one simple indicator of the trend,the proportion of states in manufacturing as a proportion of each state's popula- which two senators split their votes on trade legislation rose from tion.Measuring the importance of capital poses some- 0.09 in the final votes on the Tariff Act of 1824 to 0.22 in votes on the what greater problems,since the census data on capital Tariff Act of 1842 and 0.32 for the Trade Act of 1875.Meanwhile the average party cohesion(Rice indexes)for votes on major trade bills in the House rose from 2.8%in 1824,to 44.1%in 1842,and to 66.1% 26 All models are estimated using probit in STATA 7.0. in 1875.Later votes became even more polarized along partisan lines 27 The state data on factors are drawn from decennial censuses(prior as Republicans and Democrats went head to head:average cohesion to 1919)and the U.S.Department of Commerce's Census of Man registered98.7(in1890).90.2(1894).98.0(1897),97.4(1909).and ufactures,Census of Agriculture,and Census of Mining (afterward) 94.3(1913).See Appendix B for the full list of tariff bills. for years closest to the years in which each vote was taken.For years 24 Average party cohesion indexes for House votes on major trade prior to 1840 the state data are extrapolated from the time series on bills were only43.9(in1955).43.3(1962),36.3(1974),33.0(1993) later observations.State income data are from the U.S.Department and 33.0(1994).See Appendix B for the full list of tariff bills. of Commerce,Bureau of the Census (1989),State Personal Income 25 Destler and Odell(1987)document a marked rise in political ac- (various years),and Kuznets et al.(1960).State population data are tivity among both groups opposed to and groups supporting product- from the U.S.Department of Commerce,Bureau of the Census,Sta- specific trade protection in the 1970s and 1980s. tistical Abstract of the United States. 598

Commerce, Coalitions, and Factor Mobility Regional divisions began to yield to a growing class cleavage that separated landowners (especially in the South and West) from urban interests and helped to generate the Granger and Populist movements.23 At the height of the conflict, the Republican tariff of 1890 was denounced as the "culminating atrocity of class leg￾islation" in the Democratic party platform, and the two parties squared off on the trade issue at each election. Growing rifts over the trade issue within the parties became more apparent in the 1920s and 1930s, how￾ever, and by the 1960s there were deep divisions in both parties and in the peak associations representing labor, business, and rural classes (Destler 1992,17&77; Turner and Schneier 1970,71).~~ Meanwhile, lobbying by industry groups appeared to intensify (on both sides of the trade issue) and played a key role in shaping policy outcomes Baldwin 1985; Destler 1992, 189-96; Lavergne 1983).2 $ Congressional Voting We can better assess temporal changes in coalition pat￾terns (and the relative utility of class and group-based models) by examining congressional votes on major pieces of trade legislation in different historical peri￾ods. The presumption here is that legislators' voting decisions reflect their response to pressures from soci￾etal coalitions. If the theory is correct, voting decisions should more clearly reflect legislators' responses to de￾mands by broad factor classes when levels of interindus￾try factor mobility are relatively high and demands from protectionist and free-trade industries within their districts when mobility levels are relatively low. A number of studies of congressional votes on trade policy have appeared in the literature to date. Most of these have been limited to examining a specific piece of legislation, usually in recent years (see Baldwin and Magee 2000). They include studies of votes on auto￾mobile domestic content legislation in 1982 (Coughlin 1985; McArthur and Marks 1988), the Trade Act of 1974 (Baldwin 1985), textile quota legislation in 1985 (Tosini and Tower 1987), the Export Facilitation Act of 1987 (Uri and Mixon 1992), and the omnibus trade legislation of 1987 (Marks 1993). The votes on the NAFTA have been given special attention in recent work (Baldwin and Magee 2000; Holian, Krebs, and Walsh 1997; Kahane 1996; Steagall and Jennings 1996; 23 AS one simple indicator of the trend, the proportion of states in which two senators split their votes on trade legislation rose from 0.09 in the final votes on the Tariff Act of 1824 to 0.22 in votes on the Tariff Act of 1842 and 0.32 for the Trade Act of 1875. Meanwhile the average party cohesion (Rice indexes) for votes on major trade bills in the House rose from 2.8% in 1824, to 44.1 % in 1842, and to 66.1 % in 1875. Later votes became even more polarized along partisan lines as Republicans and Democrats went head to head: average cohesion registered 98.7 (in 1890), 90.2 (1894), 98.0 (1897), 97.4 (1909), and 94.3 (1913). See Appendix B for the full list of tariff bills. 24 Average party cohesion indexes for House votes on major trade bills were only 43.9 (in 1955), 43.3 (1962), 36.3 (1974), 33.0 (1993). and 33.0 (1994). See Appendix B for the full list of tariff bills. 25 Destler and Odell (1987) document a marked rise in political ac￾tivity among both groups opposed to and groups supporting product￾specific trade protection in the 1970s and 1980s. September 2002 Uslaner 1998). Conybeare (1991) looks at votes in ear￾lier times, and Gilligan (1997) provides an excellent analysis that covers 12 bills in Congress between 1890 and 1988. The findings from these studies shed some light on the coalitions issue, but only indirectly. In analyses of recent trade votes, measures of the importance of import-competing industries in districts have signifi￾cant, positive effects on the likelihood that legislators vote in favor of protection. Dependence on export in￾dustries in electoral districts, on the other hand, tends to raise the likelihood that legislators vote for liberal￾izing bills. These relationships, which fit well with the industry-based approach to trade politics, appear much less clear in the studies of earlier votes: Conybeare (1991) finds evidence of industry effects, but Gilligan (1997) indicates that such effects are quite weak. Evi￾dence on the importance of factoral or class variables is even less clear. Recent studies have indicated that votes against NAFTA in 1993 were positively associated with the degree to which legislators relied upon campaign contributions from labor political action committees (Baldwin and Magee 2000; Steagall and Jennings 1996). But it is difficult to draw clear inferences from this without knowing the extent of the bias in the indus￾try composition of contributing labor groups-labor￾intensive import-competing industries tend to be more unionized and, thus, are likely to be the primary source of contributions. To compare the relative utility of class and industry￾group models, I take a simple approach here, relat￾ing voting patterns among members of the Senate and House over time to measures of the class and industry makeup of their constituencies. The dependent variable is the legislator's vote for protection (1 = for a protec￾tionist bill or against a liberalizing bill, 0 = against a protectionist bill or for a liberalizing Votes on 30 major pieces of trade legislation between 1824 and 1994 are examined (Appendix B provides a detailed list of these bills). The explanatory variables are measures of the class or industry characteristics of each state in each year in which a vote was taken. For factor classes, I derived several measures from the available census data.27 As a basic measure of the importance of farmers in each state, I have used the total value of agricultural pro￾duction as a fraction of state income. As a measure of the importance of labor, I used total employment in manufacturing as a proportion of each state's popula￾tion. Measuring the importance of capital poses some￾what greater problems, since the census data on capital 26 All models are estimated using probit in STATA 7.0. 27 The state data on factors are drawn from decennial censuses (prior to 1919) and the U.S. Department of Commerce's Census of Man￾ufactures, Census of Agriculture, and Census of Mining (afterward) for years closest to the years in which each vote was taken. For years prior to 1840 the state data are extrapolated from the time series on later observations. State income data are from the U.S. Department of Commerce, Bureau of the Census (1989), State Personal Income (various years), and Kuznets et al. (1960). State population data are from the U.S. Department of Commerce, Bureau of the Census, Sta￾tistical Abstract of the United States

American Political Science Review Vol.96,No.3 invested in manufacturing industries ends in 1919.Us- in which farming outweighed manufacturing interests ing total manufacturing production in each state is one and exporting industries were far larger than import- possible approach,but this does not permit distinctions competing concerns.My main concern here is not to between the amounts of capital and labor engaged in muddy the water when comparing the performance of production.Instead I used profits earned by capi- the class and group-based models by inadvertently in- tal in manufacturing (measured as value-added minus cluding class effects in the group-based model,or vice wage payments)as a fraction of the state income,on versa. the assumption that these profits vary from state to I have divided the main analysis into five parts,pool- state largely as a function of the total magnitude of ing the votes taken in five historical periods:1824-60 investments.28 To measure the industry characteristics 1875-1913,1922-37,194562,and1970-94.The aim is of each state I examined the size of the leading export- simply to provide some clear comparisons over time.31 ing and import-competing industries in each state using The estimations of each model have also been per- data on trade from the Department of Commerce and formed on a bill-by-bill basis and the conclusions are census data on production in manufacturing,mining. substantively identical to those reported below.32 The and agricultural sectors.For each state I calculated to- class and industry models are estimated separately,and tal production in the 10 leading exporting and import- their performance in different periods is then compared competing industries in each year as a proportion of and evaluated using Davidson and MacKinnon's(1981) the state income.29 “test.33 The analysis includes dummy variables for each bill, The"class model"includes the three indicators of the to account for individual characteristics of particu- importance of different factor classes in each state:the lar bills(or years)when examining votes in favor of value of agricultural production,employment in manu- protection.30 On the other hand,I have not included facturing,and profits earned by capital in manufactur- controls for the party affiliations and regional loca- ing.According to the basic class-based approach,we tions of members of Congress,even though previous should expect that the value of farm production is neg- work indicates that both types of variables have been atively related to votes for protection over the entire good predictors of voting patterns on trade at differ- time span,since the U.S.economy has been relatively ent times.I exclude them here to provide the clearest well endowed with land,compared to other nations,and imaginable test between the class and the industry- owners of land should thus have favored freer trade (in group models.Party affiliations and regional locations accord with the Stolper-Samuelson model).34 Owners are both strongly correlated with the measures of the of labor,on the other hand,should have favored pro- class and industry characteristics of states at different tection,since the economy has been relatively poorly levels in different periods.This in unsurprising:The endowed with labor compared with its trading partners. competing parties have appealed to very different class- and thus employment in manufacturing in states should based constituencies over the years and to supporters in be positively related to votes for protection.And,fi- different geographical regions,and those regions them- nally,according to Rogowski (1989,29),the United selves have often displayed marked differences in their States is properly regarded as a capital-scarce economy economic composition in terms of both factor classes for most of the period prior to 1914,transforming into and trade-affected industries (see Kim 1998).In the a capital-abundant economy sometime before the First antebellum years,for instance,the Jackson Democrats World War.We should thus expect a change in the pol- in Congress were elected mainly from Southern states icy preferences of owners of capital sometime between the second and the third periods examined here (or perhaps even earlier),with a shift away from support 28 The measure is strongly correlated(at 0.92)with the total capi- for protection.In terms of the estimated effects,that tal invested as a fraction of the state income for the period (1840 means that total profits earned by capital in each state 1919)for which data on the latter are available.I have performed the analysis using a range of alternative measures of the class variables, including the total value of land in agriculture and total land area(for farmers),aggregate wages in manufacturing (for labor),and total 31 The division of the post-1945 period just recognizes that U.S.trade manufacturing production and production per worker(for capital). patterns were quite volatile in the immediate postwar period,as the The key results,discussed in the next section,are substantively iden- European and Japanese economies were rebuilding,and(not coin- tical regardless of which combination of measures is employed. cidentally)the two political parties switched sides on the trade issue 29 The 10 leading exporting and import-competing industries in each in the 1960s. year in which a vote occurred were identified using figures for exports and imports drawn from the U.S.Department of Commerce's Com- 32 Note that since some members of Congress vote on more than one bill in each of the pools considered,all observations are not merce and Navigation of the United States.This approach follows that independent and so the estimated standard errors are biased in a used by Gilligan (1997),though the set of votes/years differs in that downward direction in that analysis.I am grateful to an anonymous my analysis includes the antebellum period as well as many bills after reviewer for making this point.Results for the bill-by-bill analysis 1870 that have been excluded from previous studies.The full lists if the top 10 export and import-competing industries in each year are eo iry variables are colimearinand available from the author. 30 I have also examined specifications of each model that include degrees that differ over time,so including them all in one estima- tion would actually make it very difficult to interpret the size and variables such as dummies for bills with provisions delegating author- ity to the president to negotiate tariff reductions with other nations ee on US factor e and for bills that ratified trade treaties already negotiated.The key dowments,with deductions about class preferences on trade derived substantive results are identical to the ones reported below so the from the Stolper-Samuelson theorem.Rogowski's designations are simplest specifications have been presented. applied here. 599

American Political Science Review invested in manufacturing industries ends in 1919. Us￾ing total manufacturing production in each state is one possible approach, but this does not permit distinctions between the amounts of capital and labor engaged in production. Instead I used profits earned by capi￾tal in manufacturing (measured as value-added minus wage payments) as a fraction of the state income, on the assumption that these profits vary from state to state largely as a function of the total magnitude of investment^.^^ To measure the industry characteristics of each state I examined the size of the leading export￾ing and import-competing industries in each state using data on trade from the Department of Commerce and census data on production in manufacturing, mining, and agricultural sectors. For each state I calculated to￾tal production in the 10 leading exporting and import￾competing industries in each year as a proportion of the state income.29 The analysis includes dummy variables for each bill, to account for individual characteristics of particu￾lar bills (or years) when examining votes in favor of prote~tion.~~ On the other hand, I have not included controls for the party affiliations and regional loca￾tions of members of Congress, even though previous work indicates that both types of variables have been good predictors of voting patterns on trade at differ￾ent times. I exclude them here to provide the clearest imaginable test between the class and the industry￾group models. Party affiliations and regional locations are both strongly correlated with the measures of the class and industry characteristics of states at different levels in different periods. This in unsurprising: The competing parties have appealed to very different class￾based constituencies over the years and to supporters in different geographical regions, and those regions them￾selves have often displayed marked differences in their economic composition in terms of both factor classes and trade-affected industries (see Kim 1998). In the antebellum years, for instance, the Jackson Democrats in Congress were elected mainly from Southern states 28 The measure is strongly correlated (at 0.92) with the total capi￾tal invested as a fraction of the state income for the period (184CL 1919) for which data on the latter are available. I have performed the analysis using a range of alternative measures of the class variables, including the total value of land in agriculture and total land area (for farmers), aggregate wages in manufacturing (for labor), and total manufacturing production and production per worker (for capital). The key results, discussed in the next section, are substantively iden￾tical regardless of which combination of measures is employed. 29 The 10 leading exporting and import-competing industries in each year in which a vote occurred were identified using figures for exports and imports drawn from the U.S. Department of Commerce's Com￾merce and Navigation of the United States. This approach follows that used by Gilligan (1997), though the set of voteslyears differs in that my analysis includes the antebellum period as well as many bills after 1870 that have been excluded from previous studies. The full lists if the top 10 export and import-competing industries in each year are available from the author. 30 I have also examined specifications of each model that include variables such as dummies for bills with provisions delegating author￾ity to the president to negotiate tariff reductions with other nations and for bills that ratified trade treaties already negotiated. The key substantive results are identical to the ones reported below so the simplest specifications have been presented. Vol. 96, No. 3 in which farming outweighed manufacturing interests and exporting industries were far larger than import￾competing concerns. My main concern here is not to muddy the water when comparing the performance of the class and group-based models by inadvertently in￾cluding class effects in the group-based model, or vice versa. I have divided the main analysis into five parts, pool￾ing the votes taken in five historical periods: 1824-60, 1875-1913,1922-37,1945-62, and 1970-94. The aim is simply to provide some clear comparisons over time.31 The estimations of each model have also been per￾formed on a bill-by-bill basis and the conclusions are substantively identical to those reported below.32 The class and industry models are estimated separately, and their performance in different periods is then compared and evaluated using Davidson and MacKinnon's (1981) "J test."33 The "class model" includes the three indicators of the importance of different factor classes in each state: the value of agricultural production, employment in manu￾facturing, and profits earned by capital in manufactur￾ing. According to the basic class-based approach, we should expect that the value of farm production is neg￾atively related to votes for protection over the entire time span, since the U.S. economy has been relatively well endowed with land, compared to other nations, and owners of land should thus have favored freer trade (in accord with the Stolper-Samuelson Owners of labor, on the other hand, should have favored pro￾tection, since the economy has been relatively poorly endowed with labor compared with its trading partners, and thus employment in manufacturing in states should be positively related to votes for protection. And, fi￾nally, according to Rogowski (1989, 29), the United States is properly regarded as a capital-scarce economy for most of the period prior to 1914, transforming into a capital-abundant economy sometime before the First World War. We should thus expect a change in the pol￾icy preferences of owners of capital sometime between the second and the third periods examined here (or perhaps even earlier), with a shift away from support for protection. In terms of the estimated effects, that means that total profits earned by capital in each state 31 The division of the post-1945 period just recognizes that U.S. trade patterns were quite volatile in the immediate postwar period, as the European and Japanese economies were rebuilding, and (not coin￾cidentally) the two political parties switched sides on the trade issue in the 1960s. 32 Note that since some members of Congress vote on more than one bill in each of the pools considered, all observations are not independent and so the estimated standard errors are biased in a downward direction in that analysis. I am grateful to an anonymous reviewer for making this point. Results for the bill-by-bill analysis are available from the author. 33 The various class and industry variables are collinear in ways and degrees that differ over time, so including them all in one estima￾tion would actually make it very difficult to interpret the size and significance of their competing effects on voting. 34 See Rogowski (1989) for quantitative evidence on U.S. factor en￾dowments, with deductions about class preferences on trade derived from the Stolper-Samuelson theorem. Rogowski's designations are applied here

Commerce,Coalitions,and Factor Mobility September 2002 TABLE 1. Probit Estimations for Senate Votes on Trade Bills-Class Model Estimation Result Effect of Individual Variables (Dependent Variable=Vote for Protection) on Probability of Vote for Protection 1824601875-19131922-371945-621970-941824601875-19131922-371945621970-94 Value of farm -0.84 -0.82* -1.57*2.84* -1.26 -0.46 -0.62 -0.64 0.82 -0.31 production 0.50) (0.38) (0.53) (0.73) (0.77) (0.09) (0.07) (0.06) (0.10) (0.10) Employment in 9.32* 16.02* 9.38* 3.00 2.11 0.53 0.69 0.74 -0.32 0.42 manufacturing (2.21) (2.64) 3.27 (3.40) (4.12) (0.04) (0.03) (0.04 (0.07) (0.27) Profits in -6.04 -8.69* -2.89* -2.02 0.08 0.12 0.68 0.64 -0.38 0.34 manufacturing (2.10) (2.03) (1.43) (2.01) (1.59) (0.38) (0.04) (0.11) (0.08) (0.30) N 372 532 367 280 382 log-likelihood -225.25 -324.51-219.96-121.49-241.43 Pseudo-R2 .1246 .1189 .1288.1329.0270 Estimations include constant and dummy variables for individual bills(not shown).Standard errors in parentheses.'p<.05;"p<.01. PEffects estimated for change in each variable from minimum(0)to maximum(1)values for equations including only that variable and bill dummies using Clarify(King,Tomz,and Wittenberg 2000). should be positively associated with votes for protec- The value of farm production is negatively associ- tion in the first period and most of the second period ated with votes for protection,as anticipated,in all but and negatively thereafter. the fourth period.The votes taken in the immediate Table 1 reports two sets of results.On the left are the post-1945 years may be anomalous in this regard due estimated coefficients and pseudo-R2statistics from the to the new rural reliance on farm support programs probit estimations of the class model in each period, introduced in the 1930s.The estimated effects of farm- which can be compared (see Table 5 below)with the ing on votes (shown on the right)are smallest in the results from the alternative industry-group model.On first and last periods;the largest negative effects appear the right,to give some idea of the magnitude of the in the periods between 1875 and 1937.Manufacturing different effects,are the first differences in the prob- employment is positively associated with protectionist ability of voting for protection when each of the class votes,as expected,although the results are again less variables changes from its theoretical minimum to its clear between 1945 and 1962,the postwar boom period theoretical maximum value (from 0 to 1).Interpreting for all kinds of U.S.manufacturing exports.While the the estimated coefficients in the full model(on the left) class model anticipates that owners of capital favored is rather difficult here because employment and profits protection up until at least 1914,the coefficients for in manufacturing are so highly collinear across states the profits variable in the first three periods are nega- (they are correlated at about 0.7 in each period).Both tive.Since employment and profits are highly collinear, directly reflect the size of the manufacturing sector in however,this may simply indicate that highly capital- each state and the separate effects of the different class intensive producers were less supportive of protection variables are thus difficult to discern.35 An interesting than others.The effects of profits on votes,calculated part of the problem here is that when both employment with employment excluded from the estimation (on and profits are included in the one model,the estimated the right),are positive until 1937,and largest between coefficients will also measure the effects of variation 1875 and 1937,as are the effects of employment on in labor and capital intensities in manufacturing pro- votes.36 duction (using more labor with the same amount of Table 2 presents the results of estimations for the capital,and vice versa).As a partial corrective here same set of votes on trade legislation in the Senate,but I have simply calculated the first differences for each now using indicators of the importance of exporting variable (on the right)when other class variables are and import-competing industries in each state as the excluded from the model.The separate effects are less explanatory variables.In line with a simple industry- important,in the end,than the overall performance of group model,we anticipate that the importance of the class model in each period and how it compares with the industry-group model,so this is not a crucial issue. 36 I have tried variants of the basic class model for the recent peri- ods that include measures of the skill level of the workforce in each state assuming,in line with Midford (1993)and Scheve and Slaughter (1998,2000),that skilled workers,viewed as a separate class,oppose protection.Yet models that include measures of the proportion of the 35 For a discussion,see Gujarati 1995,327-35.The problem is not state's adult population with high school diplomas or higher levels of just inefficiency,though the standard errors for the estimates more education perform no better than the basic specification in Table 1. than double when all three variables are included in the model rather In none of the estimations are the coefficients on these variables than one alone.It is also a question of effective sample size:There significant,and often they take the wrong (positive)sign.Since such are hardly any observations,for instance,in which state employment data are unavailable for previous periods,I have reported only the in manufacturing is high while state profits in manufacturing are low simplest model here to provide straightforward comparisons over (or vice versa). time. 600

Commerce, Coalitions, and Factor Mobility September 2002 TABLE 1. Probit Estimations for Senate Votes on Trade Bills-Class Model Estimation Result (Dependent Variable = Vote for Pr~tection)~ 1824-60 1875-1913 1922-37 1945-62 1970-94 Value of farm -0.84 -0.82' -1.57" 2.84** -1.26 production (0.50) (0.38) (0.53) (0.73) (0.77) Employment in 9.32" 16.02" 9.38** 3.00 2.1 1 manufacturing (2.21) (2.64) (3.27) (3.40) (4.12) Profits in -6.04** -8.69" -2.89' -2.02 0.08 manufacturing (2.1 0) (2.03) (1.43) (2.01) (1.59) N 372 532 367 280 382 log-likelihood -225.25 -324.51 -21 9.96 -121.49 -241.43 ~ieudo-~~,1246 ,1189 ,1288 ,1329 ,0270 Effect of Individual Variables on Probability of Vote for Protectionb 1824-60 1875-1913 1922-37 1945-62 1970-94 -0.46 -0.62 -0.64 0.82 -0.31 (0.09) (0.07) (0.06) (0.10) (0.10) 0.53 0.69 0.74 -0.32 0.42 (0.04) (0.03) (0.04) (0.07) (0.27) 0.12 0.68 0.64 -0.38 0.34 (0.38) (0.04) (0.1 1) (0.08) (0.30) aEstimations include constant and dummy variables for individual bills (not shown). Standard errors in parentheses. *pi .05; **pi.Ol. bEffects estimated for change in each variable from minimum (0) to maximum (1) values for equations including only that variable and bill dummies using Clarify (King, Tomz, and Wittenberg 2000). should be positively associated with votes for protec￾tion in the first period and most of the second period and negatively thereafter. Table 1reports two sets of results. On the left are the estimated coefficients and pseudo- R~statistics from the probit estimations of the class model in each period, which can be compared (see Table 5 below) with the results from the alternative industry-group model. On the right, to give some idea of the magnitude of the different effects, are the first differences in the prob￾ability of voting for protection when each of the class variables changes from its theoretical minimum to its theoretical maximum value (from 0 to 1). Interpreting the estimated coefficients in the full model (on the left) is rather difficult here because employment and profits in manufacturing are so highly collinear across states (they are correlated at about 0.7 in each period). Both directly reflect the size of the manufacturing sector in each state and the separate effects of the different class variables are thus difficult to di~cern.~%n interesting part of the problem here is that when both employment and profits are included in the one model, the estimated coefficients will also measure the effects of variation in labor and capital intensities in manufacturing pro￾duction (using more labor with the same amount of capital, and vice versa). As a partial corrective here I have simply calculated the first differences for each variable (on the right) when other class variables are excluded from the model. The separate effects are less important, in the end, than the overall performance of the class model in each period and how it compares with the industry-group model, so this is not a crucial issue. 35 For a discussion. see Gujarati 1995, 327-35. The problem is not just inefficiency, though the standard errors for the estimates more than double when all three variables are included in the model rather than one alone. It is also a question of effective sample size: There are hardly any observations, for instance, in which state employment in manufacturing is high while state profits in manufacturing are low (or vice versa). The value of farm production is negatively associ￾ated with votes for protection, as anticipated, in all but the fourth period. The votes taken in the immediate post-1945 years may be anomalous in this regard due to the new rural reliance on farm support programs introduced in the 1930s. The estimated effects of farm￾ing on votes (shown on the right) are smallest in the first and last periods; the largest negative effects appear in the periods between 1875 and 1937. Manufacturing employment is positively associated with protectionist votes, as expected, although the results are again less clear between 1945 and 1962, the postwar boom period for all kinds of U.S. manufacturing exports. While the class model anticipates that owners of capital favored protection up until at least 1914, the coefficients for the profits variable in the first three periods are nega￾tive. Since employment and profits are highly collinear, however, this may simply indicate that highly capital￾intensive producers were less supportive of protection than others. The effects of profits on votes, calculated with employment excluded from the estimation (on the right), are positive until 1937, and largest between 1875 and 1937. as are the effects of employment on votes.36 Table 2 presents the results of estimations for the same set of votes on trade legislation in the Senate, but now using indicators of the importance of exporting and import-competing industries in each state as the explanatory variables. In line with a simple industry￾group model, we anticipate that the importance of 36 I have tried variants of the basic class model for the recent peri￾ods that include measures of the skill level of the workforce in each state assuming, in line with Midford (1993) and Scheve and Slaughter (1998,2000), that skilled workers, viewed as a separate class, oppose protection. Yet models that include measures of the proportion of the state's adult population with high school diplomas or higher levels of education perform no better than the basic specification in Table 1. In none of the estimations are the coefficients on these variables significant, and often they take the wrong (positive) sign. Since such data are unavailable for previous periods, I have reported only the simplest model here to provide straightforward comparisons over time