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Beyond party: ideological convictions and foreign policy conflicts in the US congress

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Recent work finds evidence that partisan calculations, not ideological preferences, drive congressional decisions on foreign policy. While legislators support the wars launched by their party’s presidents, they often oppose those by the other party’s presidents. However, it is unclear whether such partisan calculations are limited to a narrow set of security votes or whether they are part of a broad pattern of foreign policymaking in Congress. To examine the importance of partisan versus ideological motivations, we examine the substantive contributions legislators make to, and the votes they cast on, foreign policy measures. Specifically, we collect sponsorship and voting data on amendments that allow Congress to restrict presidential spending on defense programs and foreign aid. In analyzing data from 1971 to 2016, we find that ideology is the most consistent factor that determines whether legislators propose and support spending limits on security-related bills.

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  1. We use the term partisan warfare here rather than the term partisan polarization, because in this project we examine the conflict between the two congressional parties—not the ideological distance between them. See Theriault (2013).

  2. For additional studies on the importance of partisanship in foreign policymaking, see Meernik (1993), Prins and Marshall (2001), Kupchan and Trubowitz (2007), Souva and Rohde (2007), Peake et al. (2012).

  3. Of course, legislators follow partisan cues in most policy areas, since no member has sufficient expertise to develop independent views on the vast range of issues that Congress considers. But partisan cues are especially powerful in the realm of foreign policy. Because foreign policy often concerns classified matters, alternative cue-givers—such as interest groups—cannot provide the kind of informed guidance to legislators that they do on most domestic issues. Bendix and Quirk (2020) make a similar point about congressional decisions on intelligence policy.

  4. Some observers argue that Donald Trump’s presidency challenges this point, since he flip-flopped often on foreign policy without apparent political cost (see McDonald et al., 2019). But his low approval ratings across four years suggest that he suffered some political blowback for his unpredictability and sudden policy reversals.

  5. In considering foreign policy ideologies, we focus on the hawk-dove dimension and ignore the internationalist-isolationist dimension. We do so because existing studies show that the hawk-dove dimension captures more than 90% of foreign policy voting after WWII (Jeong, 2018; Jeong and Quirk, 2019). Furthermore, our data do not cover the Trump presidency, the period during which the internationalist-isolationist dimension is likely to be again salient.

  6. Both the House and the Senate follow a two-step process for funding government operations. First, the authorizing committees—such as the House and Senate Armed Services committees—draft authorization bills that create, modify, or continue federal policies and programs. Second, after the authorization bills have passed, the Appropriations committees review the authorized funding levels for the policies and programs, and then develop appropriations bills that either approve or modify those levels (Oleszek, 2014, pp. 47–60).

  7. Although the most ambitious riders against the Iraq War failed to pass, ones that dealt with the treatment of detainees did become law.

  8. The House restricted the ability of members to offer limitation riders in 1983 and, again, in the mid-1990s, but our data do not show obvious declines in riders offered because of those rule changes. See Fig. 1.

  9. However, in a separate analysis, we add the foreign aid riders that relate to military support in our defense rider sample, since both likely reflect dovish policy efforts. This analysis produces similar substantive results to the findings reported in Table 1 of this paper. See the online appendix for a full discussion of this robustness check.

  10. We exclude amendments that increase or restore spending for specific or overall programs from the analysis. Only amendments that prohibit, reduce, or attach conditions for spending are included in our data.

  11. Jeong (2018) has generated hawk-dove estimates for all members of Congress using only votes that concern defense and foreign policy. Although these hawk-dove estimates are generally appropriate for studies on Congress and foreign policy (see, e.g., Bendix and Jeong, 2020; Jeong and Quirk, 2019), they do not serve our purposes here for two reasons. First, in order to explore the relative influence of ideology versus partisanship among members, we need to explore how legislators respond to presidents based on their partisan affiliation. To do so, we need to include control variables that account for a president’s ideology to see the extent to which party drives sponsorship decisions by legislators. Only NOMINATE provides the estimated ideological locations of presidents that are directly comparable with those of Congress members. Second, Jeong’s hawk-dove estimates are partly derived from the same roll call data that we examine in this study, raising concerns of built-in correlation between our independent and dependent variables.

  12. The average correlation coefficient between the estimates of foreign policy positions by Jeong (2018) and the first-dimension estimates of the NOMINATE model is 0.91 for the period covered in our data.

  13. The eight panels are the Senate Foreign Relations Committee, the Senate Armed Services Committee, the Senate Appropriations subcommittees on defense and foreign operations, the House Foreign Affairs Committee, the House Armed Services Committee, and the House Appropriations subcommittees on defense and foreign operations.

  14. We use the number of terms in the House and the number of years in the Senate to track the seniority of members. Member-level data, such as committee assignment and veteran status, are from Bendix and Jeong (2020); much of these data are also available at Charles Stewart’s website:

  15. Admittedly, the Washington consensus over Cold War containment policy may have already been breaking down during the Ronald Reagan presidency (see, e.g., Kupchan and Trubowitz, 2007, 2010; compare Chaudoin et al., 2010). But, by current standards, party conflict over security questions was rather muted back then.

  16. To implement Bayesian estimation, we assigned non-informative priors. We used WinBUGS, and we checked the convergence using the Gelman-Rubin statistics and traceplots. All the Gelman–Rubin statistics were close to 1, suggesting convergence.

  17. The significance of the Cold War variable, coupled with the insignificance of all coefficients at the Poisson process, provides further corroboration.

  18. This model is also estimated using Bayesian estimation. We assigned non-informative priors, used WinBUGS, and checked the convergence using the Gelman–Rubin statistics and traceplots. All the Gelman–Rubin statistics were close to 1, suggesting convergence.

  19. Note that the measures of uncertainty (i.e., the credible intervals) are small because we have set all indicator variables and fixed effects to 0.

  20. For a Senate foreign aid rider, no change in ideology can achieve such a change in predicted probabilities because the ideology variable is not statistically different from 0.

  21. For a related discussion, see Holman and Lantis (2020) on the effects of intraparty factionalism in congressional foreign policymaking.


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Bendix, W., Jeong, GH. Beyond party: ideological convictions and foreign policy conflicts in the US congress. Int Polit 59, 827–850 (2022).

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