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

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American Political Science Review Vol.96,No.3 September 2002 Commerce,Coalitions,and Factor Mobility:Evidence from Congressional Votes on Trade Legislation MICHAEL J.HISCOX Harvard University The extent to which political conflict over U.S.trade policy has led to clashes between broad- based class coalitions has varied significantly over time during the past two centuries.I argue that much of this variation can be explained by changes in economywide levels of interindustry factor mobility.Class distinctions between voters are more economically and politically salient when interindustry mobility is high;when mobility is low,industry distinctions become more critical and tend to split apart broader political coalitions.I report evidence indicating large changes in levels of labor and capital mobility over the last two centuries.These changes coincide with significant shifts in the character of American trade politics.Analysis of congressional voting on 30 major pieces of trade legislation between 1824 and 1994 provides evidence of large swings in coalition patterns. istory has shown that international trade can create class antagonisms and yet also affect the relative generate intense class conflict,pitting capital fortunes of different industries.What is at issue is the against labor,or farmers against industry,and definition of the basic building blocks of political econ- making the tariff the central policy issue in electoral omy:the alignment of preferences that creates political competition between political parties.In the United coalitions. States,at the turn of the twentieth century,the trade is- I argue here that variation in coalition patterns can be sue did ignite a fierce political contest between protrade explained in large measure by changes in economywide farmers and protectionist urban interests,and the tariff levels of interindustry factor mobility:that is,the ease became the focal point for the parties in virtually every with which owners of factors of production(land,labor, election fought between 1888 and 1914.But this type and capital)can move between industries.Class distinc- of intense class warfare was not the norm in American tions between voters are more economically and polit- trade politics prior to the Civil War,when battles over ically salient when interindustry mobility is high;when policy were dominated by regionally specific,industry- mobility is low,industry distinctions become more crit- based groups (Pincus 1977),nor has it continued in ical and tend to split apart broader political coalitions. more recent times,when policies have been shaped in I report evidence indicating large changes in levels of large measure by the lobbying efforts of industry asso- labor and capital mobility over the last two centuries ciations,labor unions,and political action committees. These changes coincide with significant shifts in the and the trade issue has all but vanished at election time character of American trade politics.Analysis of con- (Destler 1992). gressional voting on 30 major pieces of trade legisla- In what circumstances does international trade tion between 1824 and 1994 provides evidence of large deepen class cleavages in politics?When do narrower, swings in coalition patterns.The findings carry impor- industry-based coalitions tend to flourish instead?The tant implications for political-economic studies of eco- existing scholarly literature is largely silent on the ques- nomic policymaking in general,for the future direction tion and strangely polarized.While Rogowski(1989) of U.S.trade policy,for future economic growth,and for presents evidence that trade can create class divisions arguments in favor of adjustment assistance programs that are so fundamental that they can reshape entire po- that would raise levels of interindustry factor mobility. litical systems,much of the recent analysis of American trade politics follows Schattschneider(1935)in placing industry-based lobbies at center stage (Baldwin 1985; TRADE THEORY,COALITIONS AND Grossman and Helpman 1995).This division mirrors FACTOR MOBILITY a more fundamental divide between Marxist politi- According to the Stolper-Samuelson (1941)theorem. cal economy,in which all politics is class politics,and trade increases real returns for owners of the factor of pluralist-style approaches to American politics that fo- production with which the economy is relatively abun- cus on the activities of interest groups.Bridging the dantly endowed,while real returns for owners of the gap is vital for understanding the political-economic scarce factor decline.This result depends critically on origins of not only trade policy,but a vast range of the assumption that factors of production,while im- regulatory,industrial,and monetary policies that can mobile internationally,are perfectly mobile within the domestic economy.The logic is simple enough:In- creased trade lowers the price of the imported good. Michael J.Hiscox is John L.Loeb Associate Professor of the Social leading to a reduction in its domestic production and Sciences,Harvard University,Cambridge,MA 02138 (hiscox@fas. harvard.edu). I thank Jim Alt,Lawrence Broz,Jeff Frieden,Mike Gilligan,Peter 1 Factors are identified as broad categories of productive inputs and Gourevitch,David Lake,Lisa Martin,Ron Rogowski,Verity Smith, include at least labor and capital.Traditional studies focus on land and seminar participants at Harvard University,MIT,and UCSD for labor,and capital,though the case has been for subdividing these comments and suggestions. into narrower categories(Leamer 1984). 593

American Political Science Review Vol. 96, No. 3 September 2002 Commerce, Coalitions, and Factor Mobility: Evidence from congressional Votes on Trade egisl la ti on- - MICHAEL J. HISCOX Harvard University he extent to which political conflict over U.S. trade policy has led to clashes between broad- Tbased class coalitions has varied significantly over time during the past two centuries. I argue that much of this variation can be explained by changes in economywide levels of interindustry factor mobility. Class distinctions between voters are more economically and politically salient when interindustry mobility is high; when mobility is low, industry distinctions become more critical and tend to split apart broader political coalitions. I report evidence indicating large changes in levels of labor and capital mobility over the last two centuries. These changes coincide with signiJicantshifts in the character of American trade politics. Analysis of congressional voting on 30 major pieces of trade legislation between 1824 and 1994 provides evidence of large swings in coalition patterns. History has shown that international trade can generate intense class conflict, pitting capital against labor, or farmers against industry, and making the tariff the central policy issue in electoral competition between political parties. In the United States, at the turn of the twentieth century, the trade issue did ignite a fierce political contest between protrade farmers and protectionist urban interests, and the tariff became the focal point for the parties in virtually every election fought between 1888 and 1914. But this type of intense class warfare was not the norm in American trade politics prior to the Civil War, when battles over policy were dominated by regionally specific, industrybased groups (Pincus 1977), nor has it continued in more recent times, when policies have been shaped in large measure by the lobbying efforts of industry associations, labor unions, and political action committees, and the trade issue has all but vanished at election time (Destler 1992). In what circumstances does international trade deepen class cleavages in politics? When do narrower, industry-based coalitions tend to flourish instead? The existing scholarly literature is largely silent on the question and strangely polarized. While Rogowski (1989) presents evidence that trade can create class divisions that are so fundamental that they can reshape entire political systems, much of the recent analysis of American trade politics follows Schattschneider (1935) in placing industry-based lobbies at center stage (Baldwin 1985; Grossman and Helpman 1995). This division mirrors a more fundamental divide between Marxist political economy, in which all politics is class politics, and pluralist-style approaches to American politics that focus on the activities of interest groups. Bridging the gap is vital for understanding the political-economic origins of not only trade policy, but a vast range of regulatory, industrial, and monetary policies that can Michael J. Hiscox is John L. Loeb Associate Professor of the Social Sciences, Harvard University, Cambridge, MA 02138 (hiscox@fas. harvard.edu). I thank Jim Alt, Lawrence Broz, Jeff Frieden, Mike Gilligan, Peter Gourevitch, David Lake, Lisa Martin, Ron Rogowski, Verity Smith, and seminar participants at Harvard University, MIT, and UCSD for comments and suggestions. create class antagonisms and yet also affect the relative fortunes of different industries. What is at issue is the definition of the basic building blocks of political economy: the alignment of preferences that creates political coalitions. I argue here that variation in coalition patterns can be explained in large measure by changes in economywide levels of interindustry factor mobility: that is, the ease with which owners of factors of production (land, labor, and capital) can move between industries. Class distinctions between voters are more economically and politically salient when interindustry mobility is high; when mobility is low, industry distinctions become more critical and tend to split apart broader political coalitions. I report evidence indicating large changes in levels of labor and capital mobility over the last two centuries. These changes coincide with significant shifts in the character of American trade politics. Analysis of congressional voting on 30 major pieces of trade legislation between 1824 and 1994 provides evidence of large swings in coalition patterns. The findings carry important implications for political-economic studies of economic policymaking in general, for the future direction of U.S. trade policy, for future economic growth, and for arguments in favor of adjustment assistance programs that would raise levels of interindustry factor mobility. TRADE THEORY, COALITIONS AND FACTOR MOBILITY According to the Stolper-Samuelson (1941) theorem, trade increases real returns for owners of the factor of production with which the economy is relatively abundantly endowed, while real returns for owners of the scarce factor decline. This result depends critically on the assumption that factors of production, while immobile internationally, are perfectly mobile within the domestic economy.' The logic is simple enough: Increased trade lowers the price of the imported good, leading to a reduction in its domestic production and Factors are identified as broad categories of productive inputs and include at least labor and capital. Traditional studies focus on land, labor, and capital, though the case has been for subdividing these into narrower categories (Leamer 1984)

Commerce,Coalitions,and Factor Mobility September 2002 freeing up more of the factor it uses relatively inten- issue that affects relative commodity prices-will di- sively (the scarce factor)than is demanded elsewhere vide an economy into very different types of coalitions in the economy at existing prices.When factor prices if there is substantial variation in levels of interindustry adjust,returns to the scarce factor fall even further than factor mobility (see Appendix A for a formal,general- the price of the imported good,while returns to the equilibrium treatment) abundant factor rise even further than the price of the exported good.The interindustry mobility of the fac- tors assures that trade affects owners of each factor in EVIDENCE OF TRENDS IN FACTOR MOBILITY IN THE U.S.ECONOMY the same way no matter where they are employed in the economy.This is the insight that encouraged Rogowski (1989)to anticipate broad-based conflict among owners Measuring Interindustry Factor Mobility of land,labor,and capital in trade politics.2 Given the obvious importance of interindustry factor Very different results are generated by alternative mobility in determining the income distribution effects types of models (often referred to as"Ricardo-Viner" of trade(and,hence,the politics of trade),it is vexing,as models)in which one or more factor of production is as- Grossman and Levinsohn(1989)have pointed out,that sumed to be immobile between industries (Jones 1971; very few attempts have actually been made to assess Mussa 1974.1982).3 In these models,the returns to levels of mobility empirically.The most direct evidence "specific"factors are tied closely to the fortunes of the has been provided in work on industry wage differen- industry in which they are employed.Factors specific to tials(e.g.,Krueger and Summers 1988),the response of export industries receive a real increase in returns due stock-market returns to import price shocks(Grossman to trade,while those employed in import-competing in- and Levinsohn 1989),and prices in secondary markets dustries lose in real terms.4 Factor specificity thus drives for capital equipment (Ramey and Shapiro 1998).6 All a wedge between members of the same class employed these studies suggest significant factor specificity and in different industries.The implication is that political sizable industry rents in U.S.manufacturing in recent coalitions form along industry lines,and this has guided years,but we do not have a historical standard of ref- much of the empirical analysis in the"endogenous pol- erence with which to compare these findings. icy"literature in economics that relates variation in To compare levels of factor mobility in the U.S.econ- import barriers across industries to the relative polit- omy in different periods,I have examined the variation ical strength of different industry-based groups (e.g., between rates of return for factors employed in differ- Anderson 1980:Lavergne 1983). ent industries.This is simply an application of the "law The Stolper-Samuelson and Ricardo-Viner models of one price."If factors are highly mobile (i.e.,mov- examine extreme,or polar,cases,in which productive able),return differentials should be arbitraged away factors are either perfectly mobile or specific.5 Fac- by (actual or potential)factor movement.Smaller dif- tor mobility is better regarded as a continuous vari- ferentials in wages and profits across industries are thus able,affected by a range of economic,technological, indicators of higher levels of mobility.The magnitude of and political conditions.Allowing that factors can have the differentials will reflect the costs of moving factors varying degrees of interindustry mobility,the simple between industries,which are influenced by a range of prediction is that broad class-based political coalitions economic and political variables,including the speci- are more likely where factor mobility is high,while ficity of human and physical capital to particular firms narrow industry-based coalitions are more likely where and industries,any factor market regulations that affect mobility is low.The trade issue-and,in fact,any policy firm entry and exit and hiring and firing,any policies that assist relocation and retraining,and the costs of transportation and communication.Different versions 2 Classes are defined here simply in terms of factor ownership:Each of this type of measure have been used previously in factor class comprises those individuals well endowed with a factor relative to the economy as a whole.This definition allows for the fact a wide range of studies of labor and capital mobility. that individuals often own a mix of factors(Mayer 1984). 3 The original model was introduced independently by Jones(1971) and Samuelson(1971):The former christened it the"specific-factors' 6 Magee (1980)examined the "revealed preferences"of industry model,while the latter named it the "Ricardo-Viner"model. groups to make inferences about mobility in his much-cited study Again,the logic is straightforward:A decrease in the domestic of testimony by labor unions and management groups before the production of an imported good releases any mobile factors for em- House Ways and Means Committee on the Trade Act of 1974. ployment elsewhere in the economy and thus renders factors specific In contrast,a great deal of empirical work has been done on the to the import-competing industry less productive,driving down their interregional mobility of labor and capital in the American economy real returns.Returns on the mobile factor rise relative to the price of aimed explicitly at uncovering historical trends,with much of the the imported good but fall relative to the price of exports,so that the attention focused on the geographic integration of the markets for income effects of trade for owners of this factor depend on patterns labor and capital during the nineteenth century (e.g.,Coelho and of consumption. Shepherd 1976;Davis 1965;Lebergott 1964:Odell 1989;Rosenbloom S The bifurcation is generally considered unproblematic in the eco 1990). nomics literature:Specific-factors effects are regarded as important 8 On industry wage variance in recent years,see Dickens and Katz in the short term but not the long term (Caves et al.1990,146-49; 1987,Gibbons and Katz 1992,Katz and Summers 1989,and Krueger Krugman and Obstfeld 1988,81;Mussa 1974).It is simply assumed and Summers 1987,1988.Almost all the work on the geographic that,over time,all factors are perfectly mobile.But this ignores integration of U.S.labor and financial markets has focused upon politics:Factor owners do not just choose between accepting their regional differences in wages and interest rates,and rate-of return returns in one industry and moving to another,they can also lobby differentials have also been used to gauge the level of international to influence policy (and hence returns). capital mobility (e.g.,Frankel 1991). 594

Commerce, Coalitions, and Factor Mobility freeing up more of the factor it uses relatively intensively (the scarce factor) than is demanded elsewhere in the economy at existing prices. When factor prices adiust. returns to the scarce factor fall even further than thi price of the imported good, while returns to the abundant factor rise even further than the price of the exported good. The interindustry mobility of the factors assures that trade affects owners of each factor in the same way no matter where they are employed in the economy. This is the insight that encouraged Rogowski (1989) to anticipate broad-based conflict among owners of land, labor, and capital in trade politics.* Very different results are generated by alternative types of models (often referred to as "Ricardo-Viner" models) in which one or more factor of production is assumed to be immobile between industries (Jones 1971; Mussa 1974. 198213 In these models. the returns to "specific" factors ire tied closely to the fortunes of the industry in which they are employed. Factors specific to export industries receive a real increase in returns due to trade, while those employed in import-competing industries lose in real terms.4 Factor specificity thus drives a wedge between members of the same class employed in different industries. The implication is that political coalitions form along industry lines, and this has guided much of the empirical analysis in the "endogenous policy" literature in economics that relates variation in import barriers across industries to the relative political strength of different industry-based groups (e.g., Anderson 1980; Lavergne 1983). The Stolper-Samuelson and Ricardo-Viner models examine extreme, or polar, cases, in which productive factors are either perfectly mobile or ~pecific.~ Factor mobility is better regarded as a continuous variable, affected by a range of economic, technological, and political conditions. Allowing that factors can have varying degrees of interindustry mobility, the simple prediction is that broad class-based political coalitions are more likely where factor mobility is high, while narrow industry-based coalitions are more likely where mobility is low. The trade issue-and, in fact, any policy Classes are defined here simply in terms of factor ownership: Each factor class comprises those individuals well endowed with a factor relative to the economy as a whole. This definition allows for the fact that individuals often own a mix of factors (Mayer 1984). The original model was introduced independently by Jones (1971) and Samuelson (1 971): The former christened it the "specific-factors" model. while the latter named it the "Ricardo-Viner" model. "gain, the logic is straightforward: A decrease in the domestic production of an imported good releases any mobile factors for employment elsewhere in the economy and thus renders factors specific to the import-competing industry less productive, driving down their real returns. Returns on the mobile factor rise relative to the price of the imported good but fall relative to the price of exports, so that the income effects of trade for owners of this factor depend on patterns of consumption. The bifurcation is generally considered unproblematic in the economics literature: Specific-factors effects are regarded as important in the short term but not the long term (Caves et al. 1990. 14649; Krugman and Obstfeld 1988, 81; Mussa 1974). It is simply assumed that, over time, all factors are perfectly mobile. But this ignores politics: Factor owners do not just choose between accepting their returns in one industry and moving to another, they can also lobby to influence policy (and hence returns). September 2002 issue that affects relative commodity prices-will divide an economy into very different types of coalitions if there is substantial variation in levels of interindustry factor mobility (see Appendix A for a formal, generalequilibrium treatment). EVIDENCE OF TRENDS IN FACTOR MOBILITY IN THE U.S. ECONOMY Measuring Interindustry Factor Mobility Given the obvious importance of interindustry factor mobility in determining the income distribution effects of trade (and, hence, the politics of trade), it is vexing, as Grossman and Levinsohn (1989) have pointed out, that very few attempts have actually been made to assess levels of mobility empirically. The most direct evidence has been provided in work on industry wage differentials (e.g., Krueger and Summers 1988), the response of stock-market returns to import price shocks (Grossman and Levinsohn 1989), and prices in secondary markets for capital equipment (Ramey and Shapiro 1998)~ All these studies suggest significant factor specificity and sizable industry rents in U.S. manufacturing in recent years, but we do not have a historical standard of reference with which to compare these findings7 To compare levels of factor mobility in the U.S. economy in different periods, I have examined the variation between rates of return for factors employed in different industries. This is simply an application of the "law of one price." If factors are highly mobile (i.e., movable), return differentials should be arbitraged away by (actual or potential) factor movement. Smaller differentials in wages and profits across industries are thus indicators of higher levels of mobility. The magnitude of the differentials will reflect the costs of moving factors between industries, which are influenced by a range of economic and political variables, including the specificity of human and physical capital to particular firms and industries, any factor market regulations that affect firm entry and exit and hiring and firing, any policies that assist relocation and retraining, and the costs of transportation and communication. Different versions of this type of measure have been used previously in a wide range of studies of labor and capital m~bility.~ Magee (1980) examined the "revealed preferences" of industry groups to make inferences about mobility in his much-cited study of testimony by labor unions and management groups before the House Ways and Means Committee on the Trade Act of 1974. 'In contrast. a great deal of empirical work has been done on the interregional mobility of labor and capital in the American economy aimed explicitly at uncovering historical trends, with much of the attention focused on the geographic integration of the markets for labor and capital during the nineteenth century (e.g., Coelho and Shepherd 1976; Davis 1965: Lebergott 1964; Odell1989; Rosenbloom 1990). 'On industry wage variance in recent years, see Dickens and Katz 1987, Gibbons and Katz 1992, Katz and Summers 1989, and Krueger and Summers 1987, 1988. Almost all the work on the geographic integration of U.S. labor and financial markets has focused upon regional differences in wages and interest rates, and rate-of return differentials have also been used to gauge the level of international capital mobility (e.g.. Frankel 1991)

American Political Science Review Vol.96,No.3 FIGURE 1.Interindustry Variation in Wages 40 35 20 15 8 10 1800 1820 1840 1880188019001920194019601980 2000 Year -Annual eamings in 15 industries (Census)-Annual eamings in 20 industries(Census) Hourty wage rates for unskilled workers (NICB)Hourty eamings of prod.workers (BLS) -Annual eamings of prod.workers (Census)-Daity wages of common labor(Weeks) There are good reasons for exercising caution when on annual wages of production workers in two-digit SIC examining wage and profit differentials,since they may industries is readily available from the Department of partly reflect other features of factor markets besides Commerce,12 hourly earnings for production workers mobility (these issues are discussed further below).It are calculated after 1947 by the Bureau of Labor Statis- is the size of industry rents that is key for the political tics(BLS),13 and separate data on hourly wages for un- story here,however,and wage and profit differentials skilled workers between 1920 and 1937 were compiled are the clearest measure we have of whether such rents by the National Industrial Conference Board(Glasser actually exist.9 1940,36). Using each of the data series to calculate coefficients Interindustry Variation in Wages and Profits of variation across industries yields an interesting set of results.The data,shown in Figure 1,indicate two broad Following Long(1960)I use data on wage payments trends:a general decline in interindustry variation in reported in the decennial census to calculate annual wages over the course of the nineteenth century,con- wages for workers in major manufacturing industries sistent with a marked rise in interindustry labor mobil- (approximates of the modern two-digit Standard In- ity,and a general increase in wage variation beginning dustrial Classification [SIC]categories)for each census sometime between the 1910s and the 1930s,indicating year beginning in 1820.10 I also calculated average daily a steep decline in interindustry mobility more recently. wages of"common laborers"in each of these industries These different trends have been noted separately from the payroll records of firms compiled in the Weeks by analysts focusing on particular eras (e.g.,Atack report of 1886.11 After the turn of the century,evidence Bateman,and Margo 2000;Bell and Freeman 1991), and the evidence of sizable differences in wages across industries in recent years is also consistent with much Hiscox(2002)providesa detailed discussion and treatment of these recent work by labor economists using more detailed measurement issues and a more detailed analysis of all the available evidence on historical trends in U.S.factor mobility. survey data on individual workers (e.g.,Dickens and 10 I began with the 17 industries examined by Long(1960,72-73) for the period 1860 to 1890,amending the list to extend the series and Pennsylvania only(since all data were entered manually for each for 15 of these industries for which data are available over the period hrm). 1820 to 1910.I then created a separate series for 20 industries,adding 12 See the U.S.Department of Commerce's,Census of Manufactures five categories that were excluded from Long's study but for which and Annual Survey of Manufactures (various years).Beginning in data exist over the full span of years.All lists,and original data,are 1900,earnings data are reported for 15 two-digit SICindustries;from available from the author. 1 The Weeks report was published as U.S.Congress,House(1886).I Employmen an amin calculated simple averages across firms in Massachusetts,New York, (various years). 595

American Political Science Review Vol. 96, No. 3 Year +Annual earnings in 15industries (Census) +Annual earnings in 20 industries (Census) 4Houriy wage rates for unskilled workers (NICB) tHouriy earnings of prod. workers (BLS) I+Annual earnings of prod. workers (Census) tDaily wages of cwnmon labor (Weeks) There are good reasons for exercising caution when examining wage and profit differentials, since they may partly reflect other features of factor markets besides mobility (these issues are discussed further below). It is the size of industry rents that is key for the political story here, however, and wage and profit differentials are the clearest measure we have of whether such rents actually exist.9 Interindustry Variation in Wages and Profits Following Long (1960) I use data on wage payments reported in the decennial census to calculate annual wages for workers in major manufacturing industries (approximates of the modern two-digit Standard Industrial Classification [SIC]categories) for each census year beginning in 1820." I also calculated average daily wages of "common laborers" in each of these industries from the payroll records of firms compiled in the Weeks report of 1886." After the turn of the century, evidence Hiscox (2002) provides a detailed discussion and treatment of these measurement issues and a more detailed analysis of all the available evidence on historical trends in U.S. factor mobility. lo I began with the 17 industries examined by Long (1960, 72-73) for the period 1860 to 1890, amending the list to extend the series for 15 of these industries for which data are available over the period 1820 to 1910. I then created a separate series for 20 industries, adding five categories that were excluded from Long's study but for which data exist over the full span of years. All lists, and original data, are available from the author. l1 The Weeks report was published as U.S. Congress, House (1886). I calculated simple averages across firms in Massachusetts, New York, on annual wages of production workers in two-digit SIC industries is readily available from the Department of commerce,12 hourly earnings for production workers are calculated after 1947 by the Bureau of Labor Statistics (BLS),'~ and separate data on hourly wages for unskilled workers between 1920 and 1937 were compiled by the National Industrial Conference Board (Glasser 1940,36). Using each of the data series to calculate coefficients of variation across industries yields an interesting set of results. The data, shown in Figure 1, indicate two broad trends: a general decline in interindustry variation in wages over the course of the nineteenth century, consistent with a marked rise in interindustry labor mobility, and a general increase in wage variation beginning sometime between the 1910s and the 1930s, indicating a steep decline in interindustry mobility more recently. These different trends have been noted separately by analysts focusing on particular eras (e.g., Atack, Bateman, and Margo 2000; Bell and Freeman 1991), and the evidence of sizable differences in wages across industries in recent years is also consistent with much recent work by labor economists using more detailed survey data on individual workers (e.g., Dickens and and Pennsylvania only (since all data were entered manually for each firm). l2 See the U.S. Department of Commerce's, Census of Manufactures and Annual Survey of Manufactures (various years). Beginning in 1900, earnings data are reported for 15 two-digit SIC industries; from 1947, they are reported for 19 industries. l3 See the U.S. Bureau of Labor Statistics' Employment and Earnings (various years)

Commerce,Coalitions,and Factor Mobility September 2002 FIGURE 2.Interindustry Variation in Profits 140 120 100 60 40 20 0 18001820 184018601880 19001920194019601980 2000 Year --Annual profits,of capital in 15 industries (Census)--Annual profits,of capital in 20 industries (Census) Annual profits per man-hour (Census) --Corporation after-tax profits,of net worth (SEC) Katz 1987;Krueger and Summers 1988).14 As Figure 1 according to their main activities into two-digit SIC indicates,the size of these much-discussed "indus- industries.16 For earlier years,following Bateman and try rents"trended downward markedly during ear- Weiss (1981),I used census manuscripts to calculate lier stages of industrialization and upward only more profits(value-added minus wage costs)as a percentage recently.15 of the capital invested for firms in each of the major There is very little direct evidence on firm profits in manufacturing industries in each census year.After different industries prior to 1909,when federal taxes 1919,the Department of Commerce ceased reporting were first imposed on corporate incomes(Epstein and data on capital invested,but from 1947 reports total Gordon 1939,122).Beginning in 1933,data from an- man-hours consumed per year for each industry,and nual reports on corporation profits (as percentages of these can be used as a proxy for total investments.17 net worth and equity)are available from the U.S.Secu- Figure 2 charts coefficients of variation in profits rities and Exchange Commission (SEC),categorized across manufacturing industries using these different data series.The results generally match the pattern ex- 14 Only very basic controls can be applied in the aggregate data to hibited in the wages data.There was a general decline account for heterogeneity in skill levels across industries.There is in interindustry variation in profits over most of the strong evidence,however,that interindustry differences in skill mixes nineteenth century,indicating a sharp rise in capital are quite stable over time and controlling for a greater range of indi- mobility,but then a long-term increase in profit differ- vidual skill variables is not important for estimating the relative size entials beginning some time between the 1880s and the of differentialsover time.See Hiscox 2002 and Krueger and Summers 1987. 1910s,indicating a significant decline in interindustry is Note too that the latter trend fits with evidence of a long-term de- capital mobility since then.18 The evidence suggesting cline in quit rates among manufacturing workers since 1919(Hiscox high levels of capital specificity in recent years matches 2002:Ragan 1984)and with survey data on job tenure that show that the number of years spent on the same job by the average worker rose substantially between 1950 and 1990.Workers aged 55 to 64 1 The data are reported by the U.S.Securities and Exchange Com- were at their jobs an average of 16.0 years in 1991,compared with mission,in Survey of American Listed Corporations:Corporation 9.5 years in 1951;those aged 45 to 54 had been at their jobs an av- Profits (various years),and the U.S.Department of Commerce,in erage of 12.2 years in 1991,up from 7.9 years in 1951;and for those Statistical Abstract of the United States (various years). in the 35 to 44 age bracket the average tenure rose to 7.9 years in This follows Alt et al.1999.Note that the industry lists used for cal- 1991 from 4.3 years in 1951.Data are from the Employee Benefits culations of profit variation are identical to those used in the analysis Research Institute:see The Economist,January 28,1995.Economists of wages. have noted that these data clash violently with the widely held per- 18 There are no controls here for cross-industry differences in risk ception that the U.S.workforce has become increasingly mobile in or demand shocks,but Hiscox (2002)reports matching results us- response to globalization and technological change;see reports in ing measures of profits disaggregated to the four-digit SIC level to The Economist,January 28.1995,and in The New York Times,April estimate equations and control for industry-specific risk and demand- 1993. side variables. 596

Commerce, Coalitions, and Factor Mobility September 2002 FIGURE 2. Interindustry Variation in Profits 0 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 Year Ib~nnual profits, % of capital in 15 industries (Census) +Annual profls, % of capital in 20 industries (Census) +Annual profits per man-hour (Census) Katz 1987: Krueger and Summers 1988).14 As Figure 1 indicates, the size of these much-discussed "industry rents" trended downward markedly during earlier stages of industrialization and upward only more recently.'' There is very little direct evidence on firm profits in different industries prior to 1909, when federal taxes were first imposed on corporate incomes (Epstein and Gordon 1939, 122). Beginning in 1933, data from annual reports on corporation profits (as percentages of net worth and equity) are available from the U.S. Securities and Exchange Commission (SEC), categorized Only very basic controls can be applied in the aggregate data to account for heterogeneity in skill levels across industries. There is strong evidence, however, that interindustry differences in skill mixes are quite stable over time and controlling for a greater range of individual skill variables is not important for estimating the relative size of differentials over time. See Hiscox 2002 and Krueger and Summers 1987. l5 Note too that the latter trend fits with evidence of a long-term decline in quit rates among manufacturing workers since 1919 (Hiscox 2002; ~agan 1984) and with survey data-on job tenure that show that the number of years spent on the same job by the average worker rose substantially between 1950 and 1990. Workers aged 55 to 64 were at their jobs an average of 16.0 years in 1991, compared with 9.5 years in 1951; those aged 45 to 54 had been at their jobs an average of 12.2 years in 1991. up from 7.9 years in 1951; and for those in the 35 to 44 age bracket the average tenure rose to 7.9 years in 1991 from 4.3 years in 1951. Data are from the Employee Benefits Research Institute; see The Economist, January 28.1995. Economists have noted that these data clash violently with the widely held perception that the U.S. workforce has become increasingly mobile in response to globalization and technological change: see reports in The Economist, January 28. 1995, and in The New York Times. April 1993. +Corporation after-tax profits, % of net worth (SEC) according to their main activities into two-digit SIC industries.16 For earlier years, following Bateman and Weiss (1981), I used census manuscripts to calculate profits (value-added minus wage costs) as a percentage of the capital invested for firms in each of the major manufacturing industries in each census year. After 1919, the Department of Commerce ceased reporting data on capital invested, but from 1947 reports total man-hours consumed per year for each industry, and these can be used as a proxy for total investments." Figure 2 charts coefficients of variation in profits across manufacturing industries using these different data series. The results generally match the pattern exhibited in the wages data. There was a general decline in interindustry variation in profits over most of the nineteenth century, indicating a sharp rise in capital mobility, but then a long-term increase in profit differentials beginning some time between the 1880s and the 1910s, indicating a significant decline in interindustry capital mobility since then.18 The evidence suggesting high levels of capital specificity in recent years matches '"e data are reported by the U.S. Securities and Exchange Commission, in Survey of American Listed Corporations: Corporation Profits (various years), and the U.S. Department of Commerce. in Statistical Abstract of the United States (various years). l7 This follows Alt et al. 1999. Note that the industry lists used for calculations of profit variation are identical to those used in the analysis of wages. l8 There are no controls here for cross-industry differences in risk or demand shocks, but Hiscox (2002) reports matching results using measures of profits disaggregated to the four-digit SIC level to estimate equations and control for industry-specific risk and demandside variables

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 capital equipment.19 Industrialization and Factor Mobility The evidence indicates that there have been substantial changes over time in general levels of interindustry labor and capital mobility in the U.S. economy. The pattern that emerges-rising mobility during most of the nineteenth century, falling mobility in recent decadescan 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 industrialization 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 importance of geography to economy (Davis, Hughes, -and McDougall 1961, 27&96). Labor migration and capital 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 manufacturing 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 technology was readily adaptable to use in alternative industries (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, technological changes in manufacturing began to reverse these trends. Most important was the growing complementarity between labor skills and the newest technology (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 management and operation of highly-complex tasks (Cain and Paterson 1986; Goldin and Katz 1996). Growth in the demand for specialized human capital has been concomitant with continued technological improvements lY ~otetoo that increasing capital specificity in recent decades is evidenced by growing rates of investment in research and development 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 specialized technologies (Ramey and Shapiro 1998) and as a function of the higher start-up costs and increased investments in physical capital associated with the general 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 factor 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 periods 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 nineteenth century. The emerging political parties were split over the tariff issue along regional lines and trade legislation 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, however, trade became the partisan issue in American politics, as Republicans, drawing broad support mostly from business and labor, supported high protectionist 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 factors 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 consequences 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 legislation" 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, however, 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 patterns (and the relative utility of class and group-based models) by examining congressional votes on major pieces of trade legislation in different historical periods. The presumption here is that legislators' voting decisions reflect their response to pressures from societal coalitions. If the theory is correct, voting decisions should more clearly reflect legislators' responses to demands by broad factor classes when levels of interindustry 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 automobile 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 activity among both groups opposed to and groups supporting productspecific trade protection in the 1970s and 1980s. September 2002 Uslaner 1998). Conybeare (1991) looks at votes in earlier 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 significant, positive effects on the likelihood that legislators vote in favor of protection. Dependence on export industries in electoral districts, on the other hand, tends to raise the likelihood that legislators vote for liberalizing 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. Evidence 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 industry composition of contributing labor groups-laborintensive 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 industrygroup models, I take a simple approach here, relating 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 protectionist 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 production 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 population. Measuring the importance of capital poses somewhat 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 Manufactures, 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, Statistical 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. Using 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 capital 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 exporting 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 total production in the 10 leading exporting and importcompeting 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 particular 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 locations of members of Congress, even though previous work indicates that both types of variables have been good predictors of voting patterns on trade at different times. I exclude them here to provide the clearest imaginable test between the class and the industrygroup 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 classbased constituencies over the years and to supporters in different geographical regions, and those regions themselves 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 capital 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 identical 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 Commerce 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 authority 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 importcompeting concerns. My main concern here is not to muddy the water when comparing the performance of the class and group-based models by inadvertently including class effects in the group-based model, or vice versa. I have divided the main analysis into five parts, pooling 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 performed 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 manufacturing, and profits earned by capital in manufacturing. According to the basic class-based approach, we should expect that the value of farm production is negatively 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 protection, 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, finally, 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 policy 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 coincidentally) 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 estimation 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 endowments, 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 protection 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 probability 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 production (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 associated 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 farming 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 negative. Since employment and profits are highly collinear, however, this may simply indicate that highly capitalintensive 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 industrygroup model, we anticipate that the importance of 36 I have tried variants of the basic class model for the recent periods 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

American Political Science Review Vol.96,No.3 TABLE 2.Probit Estimations for Senate Votes on Trade Bills-Industry Group Model Estimation Result Effect of Individual Variables (Dependent Variable =Vote for Protection)a on Probability of Vote for Protection 1824-601875-19131922-371945621970-941824-601875-19131922-371945621970-94 Exporting industries -2.30** -1.09* -3.55*-2.80* -4.79* -0.73 -0.48 -0.50 -0.26 -0.54 (0.31) (0.25) (0.43) (1.06) (1.41) (0.05) (0.07 (0.04) (0.05) (0.10) mport-competing 1.27 1.27* 1.70 1.24 3.45* 0.65 0.52 0.47 0.46 0.73 industries (1.03) (0.56) (1.06) (0.92) (0.79) (0.04) (0.06) (0.09) (0.26) (0.06) N 372 532 367 280 382 log-likelihood -199.80-347.00 -226.24-129.36-229.52 Pseudo-R2 .2249 .0578 .1041.0768.0750 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). TABLE 3.Probit Estimations for House Votes on Trade Bills-Class Model Estimation Result Effect of Individual Variables on (Dependent Variable Vote for Protection)a Probability of Vote for Protection 1824601875-19131922-371945-621970-941824601875-19131922-371945621970-94 Value of farm -1.36* -0.53* -0.032.69* -1.72* -0.40 -0.68 -0.52 0.28 -0.49 production (0.26) (0.11) (0.31) (0.43) (0.43) (0.03) (0.03) (0.04) (0.12) (0.04) Employment in 6.47* 8.46* 15.57* 8.95* 4.04* 0.64 0.73 0.81 0.81 0.69 manufacturing (1.07 (1.17 (2.06) (1.53) (1.73) (0.02) (0.02) (0.02) (0.04 (0.03) Profits in -2.25* -1.32 -2.46* -0.31 0.19 0.45 0.80 0.71 0.68 0.45 manufacturing (0.99) (1.00) (0.72) (0.87) (0.68) (0.10) (0.02) (0.05) (0.12) (0.11) N 1,584 2.656 1.5651.2622.480 log--likelihood-985.28-1,658.12-909.46-754.86-1,605.73 Pseudo-R2 .1001 .0992.1552 .0504 .0638 Estimations include constant and dummy variables for individual bills(not shown).Standard errors in parentheses.*p<.05:"p<.01 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). exporting industries should be negatively related to Overall,the results of the analysis of the Senate votes for protection,since individuals employed or votes are quite consistent with expectations based upon invested in those industries benefit from trade liber- changes in factor mobility over time.Voting decisions alization,while the importance of import-competing more closely reflect Senator's consideration of the in- industries should be positively related to votes for terests of broad factor classes when levels of mobil- protection ity were higher(in the years between 1875 and 1937) As expected,in each period the estimated coeffi- than when mobility levels were lower (in the periods cients for the exporting industries variable are negative, between 1824 and 1860 and from 1945 to the 1990s). and the coefficients for import-competing industries The pattern works just the other way when we exam- are positive.Again,we must exercise care here in inter- ine the responsiveness of Senate voting to demands preting the size and significance of the separate effects, from free-trade and protectionist industries within each since these two variables appear quite collinear across state. states in early periods.Again(on the right),Isimply cal- Tables 3 and 4 report the results of the analysis of culated the first difference effects on the probability of House votes for each model.These must be treated voting for protection for a change in each variable from with a little more caution since the measures of the its theoretical minimum to its theoretical maximum importance of classes and industries are available only (Oto 1)when excluding the other industry variable from at the state level,rather than the district level,and so we the estimation.Here the pattern in the size of effects are relying on an assumption that the class and industry over time is the reverse of that for the class variables: composition of districts within states are similar. Both industry variables have larger effects on voting in The results are very similar to those obtained from the first and last period and smaller effects on votes in the analysis of Senate votes.The estimated coefficients between. are comparable for each class and industry variable 601

American Political Science Review Vol. 96, No. 3 TABLE 2. Probit Estimations for Senate Votes on Trade Bills-Industry Group Model Estimation Result Effect of Individual Variables (Dependent Variable = Vote for Pr~tection)~ on Probability of Vote for Protectionb 1824-60 1875-1913 1922-37 1945-62 1970-94 1824-60 1875-1913 1922-37 1945-62 1970-94 Exporting industries -2.30** -1.09** -3.55** -2.80** -4.79** -0.73 -0.48 -0.50 -0.26 -0.54 (0.31) (0.25) (0.43) (1.06) (1.41) (0.05) (0.07) (0.04) (0.05) (0.10) Import-competing 1.27 1.27 1.70 1.24 3.45* 0.65 0.52 0.47 0.46 0.73 industries (1.03) (0.56) (1.06) (0.92) (0.79) (0.04) (0.06) (0.09) (0.26) (0.06) N 372 532 367 280 382 log-likelihood -1 99.80 -347.00 -226.24 -1 29.36 -229.52 Pseudo- R2 .2249 ,0578 ,1041 .0768 ,0750 aEstimations include constant and dummy variables for individual bills (not shown). Standard errors in parentheses. *p< .05; **p< .01. b~ffectsestimated 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). - TABLE 3. Probit Estimations for House Votes on Trade Bills--Class Model Estimation Result Effect of Individual Variables on (Dependent Variable = Vote for Pr~tection)~ Probability of Vote for Protectionb 1824-60 1875-1913 1922-37 1945-62 1970-94 1824-60 1875-1913 1922-37 1945-62 1970-94 Value of farm -1.36** -0.53** -0.03 2.69** -1.72** -0.40 -0.68 -0.52 0.28 -0.49 production (0.26) (0.11) (0.31) (0.43) (0.43) (0.03) (0.03) (0.04) (0.1 2) (0.04) Employment in 6.47** 8.46** 15.57** 8.95** 4.04** 0.64 0.73 0.81 0.81 0.69 manufacturing (1.07) (1.1 7) (2.06) (1.53) (1.73) (0.02) (0.02) (0.02) (0.04) (0.03) Profits in -2.25* -1.32 -2.46" -0.31 0.19 0.45 0.80 0.71 0.68 0.45 manufacturing (0.99) (1.00) (0.72) (0.87) (0.68) (0.1 0) (0.02) (0.05) (0.1 2) (0.11) N 1,584 2,656 1,565 1,262 2,480 log-likelihood -985.28 -1,658.1 2 -909.46 -754.86 -1,605.73 Pseudo-R2 .I001 .0992 .I552 ,0504 ,0638 aEstimations include constant and dummy variables for individual bills (not shown). Standard errors in parentheses. *pi .05; **p< .01. 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). exporting industries should be negatively related to Overall, the results of the analysis of the Senate votes for protection, since individuals employed or votes are quite consistent with expectations based upon invested in those industries benefit from trade liber- changes in factor mobility over time. Voting decisions alization, while the importance of import-competing more closely reflect Senator's consideration of the inindustries should be positively related to votes for terests of broad factor classes when levels of mobilprotection. ity were higher (in the years between 1875 and 1937) As expected, in each period the estimated coeffi- than when mobility levels were lower (in the periods cients for the exporting industries variable are negative, between 1824 and 1860 and from 1945 to the 1990s). and the coefficients for import-competing industries The pattern works just the other way when we examare positive. Again, we must exercise care here in inter- ine the responsiveness of Senate voting to demands preting the size and significance of the separate effects, from free-trade and protectionist industries within each since these two variables appear quite collinear across state. states in early periods. Again (on the right), I simply cal- Tables 3 and 4 report the results of the analysis of culated the first difference effects on the probability of House votes for each model. These must be treated voting for protection for a change in each variable from with a little more caution since the measures of the its theoretical minimum to its theoretical maximum importance of classes and industries are available only (0 to 1) when excluding the other industry variable from at the state level, rather than the district level, and so we the estimation. Here the pattern in the size of effects are relying on an assumption that the class and industry over time is the reverse of that for the class variables: composition of districts within states are similar. Both industry variables have larger effects on voting in The results are very similar to those obtained from the first and last period and smaller effects on votes in the analysis of Senate votes. The estimated coefficients between. are comparable for each class and industry variable

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