Trade protection and the role of campaign contributions in U.S. food and tobacco industries.

Lopez, Rigoberto A. ; Pagoulatos, Emilio

To exert political pressure, an organization must be capable of obtaining contributions from its members for lobbying activities. For this reason, past studies analyzing the determinants of political outcomes such as tariffs (such as Caves [1976]; Lavergne [1983]; Saunders [1980]; Salamon and Siegfried [1977]; Ray [1981a; 1981b]) have used an industry's structural characteristics, such as the number of firms and the sales concentration ratio, to account for political activity. More recently, Baldwin [1989], among other economists, has advocated the study of more direct indicators of lobbying, such as political funding levels and the number of employees supported by private interests.

The importance of lobbying activity in determining the levels of trade protection depends upon the responsiveness of government to such activity. Bhagwati [1989] distinguishes two types of government behavior, passive behavior and self-willed behavior. Under the first type, policymakers maximize political support and will be more likely to respond to lobbying activity.(1) However, if behavior is driven by self-willed principles, then policymakers will make choices that reflect the needs of their constituents and will be less susceptible to special-interest group pressures.

Viewed from the perspective of an industry that is willing to spend resources in order to influence political outcomes, contributions to political candidates can have a facilitative and/or a persuasive role.(2) Under a facilitative role, campaign contributions are merely a transaction cost for granting access and enabling the industry to inform politicians about the industry's needs. In this case, the industry structural characteristics are what lend political weight. Under a persuasive role, campaign contributions are independently effective in influencing political outcomes, regardless of the industry's structural attributes. Combining the latter role with the passive government behavior discussed above gives rise to models of exchange and political investment, as in the work of Peltzman [1976].

Concern over identifying the determinants of trade barriers and the role of campaign contributions in particular follow from three considerations. The first consideration is efficiency. If institutional arrangements encourage lobbying as a means of influencing policy choices, then a significant waste of resources may occur through competition in the political arena.(3) Lobbying, therefore, may result in a decline of overall economic efficiency. The second consideration is the dominance of special interests over social concerns. The arguments for protection often include protection of jobs and unfair competition, which may differ from the factors that drive the policy decisions, such as the strength of special interest lobbying. The third consideration is redistributional justice. A significant redistribution of wealth may occur as trade barriers increase prices and domestic industry profits at the expense of domestic consumers.

The objectives of this article are twofold. The first objective is to explain the variation of the level of trade barriers (tariffs and import-quota tariff equivalents). These will be explained in terms of industry structure characteristics (number of firms, seller concentration, and geographic dispersion), comparative disadvantage, and lobbying activities, using data from the U.S. food and tobacco manufacturing industries for the empirical analysis. The second objective is to ascertain the role and importance of lobbying activities in determining the levels of trade barriers. A novel feature of this article is that it incorporates more direct measures of lobbying activity, such as the contributions of Political Action Committees (PACs) associated with the industries affected by the trade barriers. It also emphasizes the need to consider contributions and protection together in order to capture the exchange between political investments and income transfers as shown in Snyder [1990].

The first section of this paper presents the conceptual framework for analyzing the economics and politics of trade barrier formation. The second section discusses the data and econometric specification, and the third section analyzes the empirical results. Brief concluding remarks appear in the final section.

I. CONCEPTUAL FRAMEWORK

A framework widely accepted and used by economists and political scientists to analyze the determinants of trade barriers is one that conceptualizes them within a political market for protection.(4) In this framework, therefore, domestic producers can be viewed as the "demanders" of trade protection, and government legislators and bureaucrats as the "suppliers." As argued by Magee, Brock, and Young [1989], a complete model of policy determination should have both policy decisions and lobbying activity as choice variables. By combining both sides of the political market for protection, the model presented draws from the work of Peltzman [1976], in which political investments and income transfers are exchanged.

The demand side of protection relies on the theory of rent-seeking (surveyed by Tollison [1982]; and Brooks and Heijdra [1989]) and focuses on the determinants of campaign contributions. Tariffs and import quotas artificially increase the price of commodities while restricting foreign entry, thus generating rents, or slowing or preventing the demise of a domestic industry. These rents provide the incentives for demanding trade protection through lobbying activities. Since industrial trade policy can be viewed as a public good, a common-interest group advocating such a policy needs to overcome the free rider problem typically present in raising lobbying funds. Olson [1965] provides the key theoretical insights for overcoming the free rider problem by emphasizing the role of industry structure.

The supply side of trade protection is based on the theory of endogenous protection developed by Baldwin [1989]; Bhagwati [1982]; Hillman [1989]; Magee, Brock, and Young [1989]. Past empirical studies, such as Caves [1976]; Saunders [1980]; Ray [1981a; 1981b] have used several industry characteristics as proxies for political activity that results in political outcomes such as tariffs. Esty and Caves [1983] point out that the exclusion of political contributions would not be serious if all PAC contributions do is to facilitate the communication of the industry's needs to policymakers. However, the omission of political contributions will undermine the analysis to the extent that these contributions have an independent role in influencing policy choices. Besides the issue of whether PAC contributions have a facilitative or persuasive role (or both), PAC contributions are not the only component of the lobbying activity of interest groups. Also, not all PAC contributions are aimed at trade protection alone. It is, therefore, important to continue using measures like industry characteristics as explanatory variables, in addition to PAC contributions.

Two broad categories of factors determining both the level of campaign contributions and the level of protection are suggested by the literature: industry structure and campaign contributions by those supporting and opposing trade protection. In addition, comparative advantage factors also determine the need for protection.

Industry Characteristics

Industry characteristics are represented by market concentration, the number of firms, the size of the industry, and geographic dispersion. The focus of previous work has been on the impact of market concentration on campaign contributions and protection.

From the standpoint of a demand for protection, Olson's [1965] conclusion is that concentrated industries are better able to curtail free riding and incur lower organization costs; they are, therefore, more successful in raising funds for a protectionist lobby. However, much of the empirical evidence is mixed in this regard. Esty and Caves [1983], Zardkoohi [1985], Munger [1988], and Zaleski [1992] find the impact of concentration to be statistically insignificant or negatively associated with the level of campaign contributions. The typical explanation is that highly concentrated industries might exhibit either market power or political efficiency, which results in less favorable public policies or a need to rely less on political contributions. On the other hand, Pittman [1976] and Grier, Munger and Roberts [1991] have found a positive association between industrial concentration and political contributions that support Olson's argument.

From a policymaker's perspective, the more deconcentrated the industry is (i.e., more competitive), the greater the political support will be in terms of the number of votes.(5) By properly controlling for PAC contributions, one should establish a negative relationship between industry concentration and the level of trade barriers. In terms of the size of the industry, increases in the size of the industry could result in either greater political power or an increased likelihood of adverse policy reactions as shown by Esty and Caves [1983], Cahan and Kaempfer [1992], and Salamon and Siegfried [1977].

We adopt the conceptual framework suggested by Magee, Brock, and Young [1989] for assessing the impact of industry structure and the size of the industry on campaign contributions and the level of protection. Magee, Brock, and Young [1989, ch. 6] advocate the use of a lobbying "power function" measure, where what matters is the interaction between industry concentration and sales. More specifically, the "power function" defines the degree of incentives for campaign contributions and is equal to the Herfindahl index times total industry sales. The first component captures the disincentives for free riding; the second is a proxy for the size of the jackpot benefits expected for a given tariff or tariff equivalent. The implication is that concentration and industry sales do not have independent, but rather, interactive effects upon the level of lobbying expenditures.

The impact of the number of firms on the level of trade protection is ambiguous. For instance, Godek [1985] argues that the number of firms should be negatively related to trade protection. On the other hand, Peltzman [1976] argues that granting trade protection to an industry with a large number of firms could increase the political pay-off by increasing the number of political votes.

Geographic dispersion affects both organizational cost for lobbying purposes and its geopolitical representation. A high geographic concentration, which characterizes regional or localized industries, implies lower lobbying costs but lack of representation in Congress. If the lobbying cost effect dominates, then the more spatially concentrated the industry is, the higher the level of campaign contributions. The opposite should be expected if the representation effect dominates, whereby geographic representation acts as a substitute for campaign contributions because it lowers the need to rely on campaign contributions to influence policy. The empirical results have been mixed in terms of whether geographic concentration or dispersion lead to more campaign contributions.(6)

Campaign Contributions

An industry's campaign contributions are likely to be affected by the lobbying efforts of other industries that may benefit or be harmed by trade protection. Domestic industries selling to the sector in question will support protection in that sector as demand for their products expands, while industries buying from the sector will oppose protection as the price for their input increases. Thus, one expects to observe supporting PAC contributions from the former and opposing PAC contributions from the latter. It is expected that supporting PAC contributions are negatively related to a sector's PAC contributions (a complementary effect) while opposing PACs raise the stakes and positively relate to those contributions (a substitute effect). Esty and Caves [1983] empirically confirmed the latter effect, suggesting that the opposition raises the amount of lobbying resources employed by a given industry. Also, as shown theoretically by Grossman and Helpman [1994], each lobby pays according to the political strength of its rival.

The significance of PAC contributions as a determinant of trade protection, once industry structure is taken into account, depends on whether PACs have a facilitative or independent role, as Esty and Caves [1983] observe. Many authors subscribe to the notion that PAC contributions simply have a surrogate impact or facilitating/access role in the legislative process (Welch [1982], Grenske [1989], Wilhite and Theilmann [1987], and Chappell [1982]) or other political outcomes (Esty and Caves [1983]). Others conclude that contributions by PACs are important determinants of political outcomes (Stratmann [1991]).

Following Becker [1983], the political influence of interest groups is a relative concept, in that the impact of one interest group depends on the strengths of other interest groups, which either oppose or support the same political outcomes. Supporting PAC contributions are expected to have a positive impact on trade barriers of a given sector, while opposing PAC's contributions will tend to lower those barriers. This argument is theoretically supported by Grossman and Helpman [1994] in that lobbies that face less opposition are more successful in extracting surplus from its political relationship with the government.

Although the United States has no explicit policy programs toward food manufacturing as it does for the farm sector (e.g., the multi-year Farm Bill), protection at the food processing level may be needed in order to support farm policies, like the sugar and milk policy programs. In addition, the farm lobby is likely to seek food processing trade barriers, because the farm industry is often forward-integrated into processing and/or because food manufacturing is an important outlet for farm products. On the output side, an important determinant of countervailing lobbying is the importance of the commodity in question as an input into other industries. Since industries are likely to be more organized than consumers, trade protection on finished or consumer products is likely to face less opposition and face higher levels of protection than intermediate goods as a result. In the framework of this article, these factors are captured by opposing and supporting PAC contributions.

Comparative Advantage

In general, comparative advantage is negatively associated with trade barriers.(7) For internationally competitive industries, Lavergne [1983] shows that import trade barriers are likely to be redundant or ineffective if an industry is export-oriented to start. In sum, political behavior is often characterized by concern for re-election, i.e., the maximization of votes (Becker [1983] and Peltzman [1976]), or concern for social welfare (principled behavior). If behavior is characterized by vote-seeking, then policymakers or bureaucrats will make choices to balance political support from conflicting interest groups within their geopolitical arena. Thus, to some extent, they will take into account the interests of consumers and will be concerned with public opinion regarding protection through trade barriers. However, Baldwin [1989] notes that if behavior is driven by societal principles, then policy-makers will make choices that reflect the social consensus on income distribution, employment, and social goals in general.

II. EMPIRICAL MODEL SPECIFICATION

The objective of our data analysis is to determine the influence of market structure characteristics, comparative advantage, and lobbying on the inter-industry pattern of campaign contributions and trade barriers within U.S. food and tobacco manufacturing. The dependent variable representing an industry's PAC contributions (PAC) was measured by the total dollars contributed to the 1987-88 congressional elections from the PACs associated with that industry.(8) The PAC spending data came from Makinson [1990]. The dependent variable representing the degree of trade protection (T) used in this study was the simple average U.S. nominal tariff rate based on the c.i.f. value of imports at the four-digit SIC level for 1987. The tariff rates were obtained from a computer tape supplied by the U.S. International Trade Commission. In those industries where import quotas were the main instrument of protection, their tariff equivalents were used and obtained from recent International Trade Commission reports (U.S. International Trade Commission [1990a; 1990b]).(9)

Market structure is represented by three variables: the lobbying power measure (Herfindahl index (H) times value of shipments (Vs) suggested by Magee, Brock, and Young [1989], the number of firms (N), and geographic dispersion (Geo). Following Peltzman [1976], the number of beneficiaries is expected to have a nonlinear impact on trade barriers. Thus ([N.sup.2]) was included to capture any nonlinearities of the impact of the number of firms on trade protection decisions and on campaign contributions. All market structure variables came from the 1987 Census of Manufacturers (U.S. Department of Commerce [1990; 1991a]). Finally, to capture the geo-political distribution of support for a particular industry, we included a variable (Geo) that was computed as a Herfindahl index of spatial concentration of production by adding the squared shares of value of shipments of each state obtained from the U.S. Department of Commerce [1991a].

Lobbying by other organizations affected by trade protection in a specific food manufacturing industry was measured by the total contributions of supporting PACs (industries selling to the sector) and opposing PACs (industries buying from the sector). The total dollars contributed by those industries to the 1987-88 congressional candidates were weighed by the input-output coefficients. Given that there is a set of well-classified industries for which the tariff equivalents for import quotas have been used, and since there are political and economic differences between the use of tariffs and quotas, a slope quota-dummy (Quota) on PAC contributors is introduced.(10) This variable measures the effectiveness of rent-seeking toward the end of obtaining quantitative restrictions. As before, all PAC contributions from supporting PACs (Spac) and opposing PACs (Opac) came from Makinson [1990], while the input-output coefficients came from U.S. Department of Commerce [1991b].

To control for comparative advantage, we included three variables: growth in employment (Ge), value-added per employee (Vae), and export share (Xs). The growth in employment variable was defined as the change in employment from 1982 to 1987 (1987 Census of Manufactures). This variable should be inversely related to trade barriers, as trade protection is expected to be higher in declining industries. The value-added variable Vae, a proxy for total (human and physical) capital per person, was computed from the 1987 Census of Manufactures. This variable should be negatively related to trade protection to the extent that it measures comparative advantage (Godek [1985]; Ray [1981a; 1981b]). The export share variable Xs, a proxy for trade performance, was computed as the ratio of F.A.S. value of exports to apparent domestic consumption and should be negatively related to trade protection.

Although campaign contributions are certainly not independent regressors in the trade barrier equation, it seems plausible that the contributions and tariff rates are joined in a recursive system. The equations which summarize the empirical determinants of the level of campaign contributions and trade barriers in food processing industries discussed above are

[Mathematical Expression Omitted]

[Mathematical Expression Omitted]

The sign below a variable indicates the hypothesized sign of its coefficient; a (+/-) indicates that no a priori sign prediction is warranted. The empirical equations are assumed to be linear in the coefficients. The two equations constitute a recursive system, given that PAC contributions is the dependent variable in the first one and a regressor in the second one. After a test for correlation between error terms in both equations, the ordinary least squares technique was applied to estimate the model coefficients.(11) The data consisted of observations for forty-four four-digit SIC food and tobacco manufacturing industries for the year 1987. The results are presented below.

III. EMPIRICAL RESULTS

The parameter estimates and selected statistics are presented in Table I. To investigate the relevance of the PAC contributions and tariff rate models, we conducted F-tests for the hypothesis that all the coefficients in each model are zero. This hypothesis was rejected for both models as the resultant F-statistics (reported in Table I) exceed the critical F-values at the 1 percent level. Thus, the explanatory variables are jointly relevant in explaining campaign contributions and tariff rates. To assess the relative importance of campaign contributions, we used two approaches: beta coefficients and F-tests for restricted models of tariff rates.

To determine which variable contributes most to the regression, the beta coefficient attempts to use the effect of a typically or "equally likely" change in the variable, using sample standard deviations as a measure of a typical change.(12) The beta coefficients in Table I suggest that both industry characteristics and PAC contributions variables are important in determining tariff rates. Noticeably, the comparative advantage variables were the least important.

To further assess the importance of PAC contributions, an F-test was conducted to test whether the tariff rate model in Table I adds a significant amount of information over a restricted model, where the group of PAC contribution variables is excluded. The resultant F-statistic was 25.5 with (4,32) degrees of freedom, a result which exceeds the critical F-value at the 5 percent (critical F = 2.58) and at the 1 percent (critical F = 3.95) levels. This result indicates that PAC contributions add a significant amount of information in explaining trade protection. It should be noted that by excluding PAC contributions, the [R.sup.2] drops from 0.785 to 0.101. The F-statistics of the restricted model are 0.578, which is below the critical F-value of F = 2.37 at the [TABULAR DATA FOR TABLE I OMITTED] 5 percent level and (7.36) degrees of freedom. In other words, without PAC contributions, we failed to reject the hypothesis that coefficients associated with industry structure and comparative advantage are all zero. This finding implies that the sole use of comparative disadvantage, industry structure, and indirect lobbying variables may be unsatisfactory in modeling the determinants of trade protection; it also supports the use of direct political variables such as political contributions.

The same F-test was conducted by excluding the group of industry characteristics, and the [R.sup.2] dropped from 0.785 to 0.661. The resultant F-value was 4.63, which exceeded the critical F-tests (same values as before) at the 5 and 1 percent levels. Finally, the exclusion of the three comparative advantage variables yielded an F-test which was insignificant at the 5 percent level. The [R.sup.2] dropped only from 0.785 to 0.735. In sum, the F-tests of the restricted models suggest that among the three groups of variables, PAC contributions are jointly the most important.

We turn next to the statistical significance of the individual coefficients and their implications. All the coefficients in the PAC contributions equation were significant at the 5 percent levels, except for the one associated with supporting PACs, and all satisfied a priori expectations. The most striking result is that market concentration is quite significant when incorporated in Magee, Brock, and Young's [1989] power index (Herfindahl index times sales). Another important finding is that opposing PACs generate a greater amount of contributions in the industry in question, suggesting strategic behavior. That is, rent-seeking activity by an industry will largely depend on the rent-seeking of other organizations.

Except for growth in employment and value added per employee, all explanatory variables in the tariff rate equation were significant at the 10 percent level; and most had the expected sign. The results suggest that as the number of companies increases, there is an upward pressure on trade protection. They also support the nonlinearity of the impact of the number of firms in the industry. Through the lobbying power variable (H x Vs), the results imply that the higher the level of industry concentration or the size of the industry (sales), the lower the level of trade protection. This finding implies that more perfectly competitive industries are more effective in raising trade protection. The conventional wisdom states that because highly concentrated industries are more effective in organizing lobbying activities, they are more likely to face favorable policy decisions. The first result was confirmed in the PAC contributions equation, but the latter result was rejected in the tariff rate equation. As argued by Zardkoohi [1988] and others, industry concentration may reflect efficiency. If highly concentrated industries are more efficient and more likely, therefore, to experience comparative advantage, then trade protection will be lower or unnecessary.

Among the comparative advantage variables, only export share (Xs) was significantly different from zero at the 5 percent level. Both value added per employee (Vae) and employment growth (Ge) fail to show discernable impact on trade protection. The model and the data fail to support the hypothesis that trade protection responds to losses in employment. The weak results obtained on the comparative advantage variables are not totally surprising. Twenty-one industry categories (about half of our sample) were net exporters in 1987. For net exporters, therefore, competitiveness variables do not explain protection. The relationship between comparative advantage measures and protection holds primarily for net importers.

The empirical results also confirm the independent role of PAC contributions in influencing trade protection. That is, other factors remaining constant, PAC contributions are instrumental and significant in influencing favorable trade protection decisions. Furthermore, the significance and the power of PAC contributions increase when industries seek quantitative restrictions. The latter finding is consistent with the conclusions of Godek [1985], Ray [1981a; 1981b] and Kaempfer and Willett [1989].

Finally, the results indicate that PAC contributions by other industries connected to the sector being protected are important. In particular, PAC contributions by industries that sell to the protected sector are also successful in raising the level of trade protection in that sector. By the same token, the special interests of those industries hurt by trade protection are also significant in lobbying to remove protection, as their PAC contributions tend to lower the level of trade barriers.

IV. CONCLUDING REMARKS

This study examined the determinants of the inter-industry pattern of trade protection in forty-four U.S. food and tobacco manufacturing industries in 1987. In terms of our objectives, the main findings are the following. First, both industry structure characteristics and PAC contributions are important determinants for the level of protection, but less so are the comparative advantage variables. The findings confirm both the facilitative and independent role of PAC contributions. Furthermore, as PAC contributions themselves depend partly upon industry characteristics, they influence trade protection decisions independently of the industry making the contribution.

The second major point of the article is the analysis of the relative importance of PAC contributions (i.e., special interests) versus industry characteristics. The results indicate that PAC contributions are vital in driving trade protection decisions. In fact, the results lean toward recognizing that they are apparently the most important factors, relative to industry structure and comparative disadvantage. From a modeling standpoint, the exclusion of PAC contributions makes the whole model of trade protection determinants insignificant. This is not so if the industry characteristics or the comparative disadvantage variables are excluded. Furthermore, trade protection in the food and tobacco processing industries appears to be significantly insensitive to the arguments often used in trade policy debates over the need for protection, such as employment losses.

Finally, a third finding of this article is that the political pressures of related industries affected by trade protection in one sector are a significant ingredient in the political market for protection, as reflected by the impact of their PAC contributions. From a research standpoint, the ultimate implication of this finding is that a more comprehensive framework, which includes the characteristics of those industries as well, is needed for a more conclusive assessment of the impact of rent-seeking activities in trade protection decisions or other political outcomes. In particular, a general equilibrium political market framework might be useful in providing further insight into the dynamics of campaign contributions in political markets. As it stands now, this article is focused on one type of political outcome (trade protection), one type of rent-seeking activity (campaign contributions), and one major manufacturing sector (food and tobacco processing). Extensions in any or all of those dimensions would provide further insights into the relationship of campaign contributions and changes in public policy.

1. See Becker [1983] and Peltzman [1976].

2. Esty and Caves [1983], Wilhite and Theilmann [1987], Havrilesky [1990], and Mueller and Stratman [1994] develop this distinction.

3. See Tullock [1967] and Lopez and Pagoulatos [1994].

4. See Anderson and Baldwin [1981]; Baldwin [1985; 1989]; Nelson [1988]; Ray [1981a; 1981b] for examples.

5. See Caves [1976].

6. See Esty and Caves [1983] on this point.

7. See, e.g., Ray [1981a, 1981b].

8. In some cases it was necessary to allocate the contributions of a specific PAC over several industries. In those cases, PAC contributions were allocated by sales of the parent organization sponsoring the PAC across four-digit SIC industries. The 1987 S.I.C. numbers of the forty-four industries included in this study are: 2011, 2013, 2016-7, 2021, 2022, 2023, 2024, 2026, 2032, 2033, 2034, 2035, 2037-8, 2041-5, 2043, 2044, 2046, 2047, 2048, 2050, 2061-2-3, 2065, 2066, 2067, 2074, 2075, 2076, 2077, 2079, 2082, 2084, 2085, 2086, 2087, 2091, 2092, 2095, 2097, and 2098, 2099, 2111, 2121, 2131, and 2141.

9. Food industries protected by quotas rather than tariffs include meat packing (S.I.C. = 2011), creamery butter (2021), cheese (2022), condensed and evaporated milk (2023), fluid milk (2026), and refined sugar (2061-3).

10. The efficiency equivalence between tariffs and tariff equivalents of import quotas (those tariffs that result in the same level of imports as the quota) has been questioned due to such dynamic considerations as market power (Bhagwati [1965]). In terms of political inequivalence, much of the empirical evidence supports the notion that quotas are used to pursue higher levels of protection because of their informational advantages (Godek [1985]), their transparency in trade negotiations (Ray [1981a; 1981b]), their ability to overcome free riding and be less visible (Kaempfer and Willet [1989]) or their expediency in terms of hidden consumer cost and direct tax or treasury expenditure avoidance (Lopez [1989]).

11. Empirically, a necessary but not sufficient condition for a recursive system is that the equation-error covariance matrix diagonal elements are zero. This property was tested with the covariance matrix obtained from using Zellner's seemingly unrelated regressions technique. The Lagrangian multiplier test of Breusch and Pagan failed to reject the zero equation-error covariance hypothesis at the five percent level.

12. The beta coefficient was calculated by multiplying the estimated coefficients by the standard error of each regressor and dividing by the standard error of the dependent variable. They can also be obtained via a regression where all variables have been standardized (divided by their own standard errors).

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RIGOBERTO A. LOPEZ and EMILIO PAGOULATOS, respectively, Associate Professor, and Professor and Head, in the Department of Agricultural and Resource Economics, University of Connecticut, Storrs, Conn. 06269-4021. We are grateful for the helpful comments of two anonymous referees.