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Foreign policy, bipartisanship and the paradox of post-September 11 America

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Abstract

The attacks of September 11 and the resulting war on terrorism present a puzzle to conventional explanations of foreign policy bipartisanship. Public anxiety about the international environment increased sharply after the attacks in 2001, but this did not translate into greater foreign policy consensus despite the initial predictions of many analysts. In this article, we advance a theory of foreign policy bipartisanship that emphasizes its domestic underpinnings to explain the absence of consensus in Washington. We argue that bipartisanship over foreign policy depends as much on domestic economic and electoral conditions as on the international security environment. Using multivariate analysis of roll call voting in the House of Representatives from 1889 to 2008, we show that bipartisanship over foreign policy is most likely not only when the country faces a foreign threat but also when the national economy is strong and when party coalitions are regionally diverse. This was the case during the Cold War. Despite concern about terrorism in recent years, economic volatility and regional polarization have made bipartisan cooperation over foreign policy elusive.

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Notes

  1. Following common practice, bipartisanship is defined here as the extent to which majorities or near majorities of both parties in Congress vote together. Operationally, we classified each foreign policy roll call vote cast in Congress as receiving ‘bipartisan’ support if either (a) a majority of both parties voted together on the measure or (b) majorities of each party were opposed, but the difference between them was less than or equal to 20 per cent. As an example of this latter criterion, if 65 per cent of Democrats supported a measure and 45 per cent of Republicans supported that measure, the vote was considered bipartisan. (Cooper and Young (1997) refer to this as ‘cross-partisanship.’) Our measure of bipartisanship is similar to other, commonly used measures of partisan ideological similarity in Congress. For example, when using all foreign and domestic policy votes, our measure of bipartisanship is inversely correlated with a Poole and Rosenthal party difference index that relies on DW-NOMINATE scores (r=−0.584, P=0.000).

  2. Bipartisanship on domestic policy issues (from 1889-2004) occurs about 45 per cent of the time on average. Votes were labeled as foreign policy or domestic policy using Clausen's (1973) policy issue schema at Voteview.com, a widely used source of congressional votes. Clausen's foreign policy category covers a broad range of issues and importantly, includes roll call votes on defense policy matters. To construct a single measure of domestic policy we combined roll call votes that Clausen labelled as government management, social welfare, agriculture, or civil liberties. Roll call votes that Clausen categorized as ‘miscellaneous’ issues were dropped from the analysis.

  3. The domestic trade-off explanation of bipartisanship thus assumes some ‘stickiness’ in foreign policy preferences. This strikes us as plausible, especially for the party-in-power because presidents who invest heavily in foreign policy assume domestic ‘audience costs’, for themselves and for their parties (Fearon, 1994).

  4. The difficulty with this aggregation is that it includes significant legislation with less significant votes. A standard procedure to weed out insignificant votes from analysis is to exclude universal votes, those on which more than 90 per cent of both parties agree. As our interest is in bipartisanship and this includes universal voting, this is not an option.

  5. Although changes in military size are the result of federal-level decisions, votes in Congress on this issue make up a very small portion of all foreign policy votes. For example, during the 1970s when manpower issues (for example, selective service; volunteer army) were especially prominent, they averaged just 3 per cent of the foreign policy votes per Congress. Thus, there is little risk of endogeneity using this variable.

  6. One of the challenges in constructing the data set for this model was to find data that were consistent across the entire (century-plus) time period under study. In particular, it would be preferable to use public opinion data to directly test our hypotheses about the domestic economy. Unfortunately, these public opinion measures are unavailable to us because reliable survey data do not exist for the first five decades of our time series. Instead, we relied on aggregate indicators of the economy to indirectly measure public anxiety and its effect on congressional bipartisanship. The assumption here is that changes in these aggregate indicators ultimately find expression in public sentiment (Page and Shapiro,1992; Nardulli, 2005).

  7. The difference from one Congress to the next was calculated and this number was then divided by the per capita GNP in the first Congress (that is, (t2t1)/t1). This produces the rate of growth and allows us to control for exponential growth in per capita GNP over time. One difficulty with this measure is that current dollars are used, because chained dollars, annual amounts that are standardized to one selected year, were not available in a consistent fashion for the entire time period. While taking the rate of growth is a useful corrective, periods of rampant inflation may drive the number up in ways that do not necessarily indicate positive economic news.

  8. It could be argued that change in the unemployment rate is a better measure, as it controls for changes in the absolute level of unemployment and historical differences in how Americans have perceived and responded to unemployment. However, as we use unemployment as an indicator of pocketbook and class pressures on lawmakers, the actual rate of how many people (constituents) are out of jobs at any given time is the most useful measure.

  9. The states that make up the ‘core’ include: CA, CT, DE, IL, IN, ME, MD, MA, MI, NH, NJ, NY, OH, OR, PA, RI, VT, WA and WI. The ‘periphery’ consists of AL, AZ, AK, CO, FL, GA, ID, IO, KA, KY, LA, MN, MS, MO, MT, NE, NV, NM, NC, ND, OK, SC, SD, TN, TX, UT, VA and WY.

  10. Some researchers (Sanders, 1999) refer to the Pacific Coast states as ‘mixed’, given the timing of their industrial development and their combination of core and periphery activities.

  11. The formula as it applies to Republicans (R) in the core is: LQ R_core=((# of R in core/# of R in US)/(# of representatives in core/# of representatives in US)). When the number is greater than 1, it indicates over representation in the region (relative to the national average); a number less than 1 indicates under-representation. This same formula was used to produce a location quotient for Republicans in the periphery and for Democrats in core and periphery.

  12. There is large literature on the effects of casualties on American public support for the use of military force. Some scholars argue that public support declines consistently and inexorably in response to combat operation casualties. Other studies suggest that relationship is more nuanced and contingent on mediating factors, most notably, the level of elite consensus over the policy, international support for the military mission, and public expectations about the likelihood of military success. What is not in dispute among opinion experts is that public attention to foreign policy increases as US combat losses mount (for a survey of the literature see Aldrich et al, 2006).

  13. The data for more recent congresses were calculated by the authors, following Stanley and Niemi's logic.

  14. An alternative method would be to count the number of instances of force used per Congress, but this is potentially problematic because Congresses with the same number of deployments but very different types of conflicts would be treated equally. For example, a Congress during the Vietnam conflict would be seen as equivalent to a Congress during which the United States intervened in Somalia: both actions counted as one instance. In contrast, the decade number treats the Somalia intervention as one instance because the duration was limited to one congressional cycle, but treats the Vietnam conflict as many instances because it endured over successive congressional cycles.

  15. On the chance that the smoothness of the data was problematic, we tried an alternative measure, also using the Stanley and Niemi data, that more closely resembles a moving average, but this produced no appreciable differences in the results. We also considered a measure of US relative power, the Composite Index of National Capability, that is part of the Correlates of War data set. This variable, too, produced similar results, but it was highly correlated with the ‘Foreign Threat’ variable.

  16. In the case of 1980, the election gave Reagan's Republican Party control of the Senate, the first time in 25 years that Republicans controlled one of the two chambers of Congress. Given talk of the ‘Reagan Revolution’, it is not unreasonable to speculate that the electoral success of 1984, and the anticipation of more to come, emboldened Republicans across the board while stiffening Democratic resistance.

  17. Data used to create the variables in the model are from the following sources: Bipartisanship and Regional Polarization: Voteview.com and New York Times; Unemployment and GNP growth rate: Kurian (2001) and US Statistical Abstracts 2004–2005 and 2010; Power Projection: Stanley and Niemi (2001); and Foreign Threat: Correlates of War (National Material Capabilities, v3.02) and Singer (1987).

  18. To ensure that our results were robust, and not simply an artifact of how we measured regional polarization, we ran the same model using a ‘sectional stress’ variable, a measure developed by Bensel (1984) that is designed to capture the degree to which the votes of core and periphery congressional delegates are in opposition. As we do not have sectional stress data after 1984, we ran the regression model on Houses 51 (1889–1890) through 98 (1983–1984). As with our regional polarization measure, the variable for sectional stress was also statistically significant, with a high degree of sectional stress resulting in less bipartisanship.

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Table A1

Table A1 Mean and standard deviation of dependent and independent variables

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Trubowitz, P., Mellow, N. Foreign policy, bipartisanship and the paradox of post-September 11 America. Int Polit 48, 164–187 (2011). https://doi.org/10.1057/ip.2011.12

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