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Erickson and Way, “Doves <strong>Dilemma</strong>” Statistical Results Our findings reveal a strong, positive relationship between pursuit of nuclear weapons and major conventional weapons imports. We start our analysis by looking at total major conventional arms transfers received by each importing state. Here our unit of observation is <strong>the</strong> importer country-year. We <strong>the</strong>n move to <strong>the</strong> exporter-importer dyad in order to explore <strong>the</strong> patterns of transfers that are providing more weapons to states pursuing nuclear weapons states. This allows us to explore possible differences in behavior across <strong>the</strong> major exporting states. For <strong>the</strong> dyadic analysis, we have data pairing <strong>the</strong> top arms exporters with 182 potential arms importers for years spanning 1960 to 2001. Let us start by comparing <strong>the</strong> average total major conventional arms imports received per country-year by countries pursuing nuclear weapons with <strong>the</strong> amount of arms received by o<strong>the</strong>r countries. Figure 1 shows that <strong>the</strong> average total value of arms received per year by states pursing nuclear weapons is much greater than that received by o<strong>the</strong>r states. On average, states pursuing nuclear weapons received just over $694 million TIV of arms imports per year, whereas states not pursuing nuclear weapons received a little less than $103 million TIV on average. 15 [Figure 1 about here] However, potential confounding relationships between pursuit of nuclear arms and access to conventional arms, as described above, provide compelling reasons to take <strong>the</strong> strong relationship depicted in Figure 2 with more than a few grains of salt. 16 Most importantly, 15 All dollar values are for 1990 US dollars. 16 We point out that <strong>the</strong> variables we now introduce are ―good controls,‖ in <strong>the</strong> language of Angrist and Pischke (2009), since <strong>the</strong>y almost surely influence pursuit of nuclear weapons but are not <strong>the</strong>mselves plausibly caused by <strong>the</strong> regressor of interest. For example, it is implausible to think of pursuit of nuclear weapons as causing more rapid increases in GDP per capita. More plausibly, pursuit of nuclear weapons might harm <strong>the</strong> security environment by 15
Erickson and Way, “Doves <strong>Dilemma</strong>” countries interested in nuclear weapons are likely to face challenging security environments, which are also likely to generate a strong demand for conventional weapons. To explore <strong>the</strong> relationship between access to conventional arms and pursuit of nuclear weapons, we thus need to estimate arms transfers conditional on <strong>the</strong>se potentially confounding variables. Countries’ security and economic situations are likely to affect both <strong>the</strong>ir access to conventional weapons and <strong>the</strong>ir likelihood of pursuing nuclear weapons. Consequently, variables to account for <strong>the</strong>se circumstances need to be included if we are to gauge properly <strong>the</strong> size and direction of <strong>the</strong> relationship between pursuit of nuclear weapons and conventional arms transfers. To this end, we estimate pooled time series models of arms imports by 182 o<strong>the</strong>r states from 1960 to 2001, limiting our models to include only potential confounding variables as controls. This enables us to explore <strong>the</strong> relationship of interest between pursuit of nuclear weapons and conventional arms transfers (as opposed to understanding all <strong>the</strong> determinants of arms transfers). As is well known, pooled models of this sort are vulnerable to violations of <strong>the</strong> assumptions underpinning OLS regression models. Specifically, errors are likely to be heteroskedastic by panel and auto-correlated over time within panel. Diagnostic tests indicate that we can reject both <strong>the</strong> assumptions of panel-homoskedasticity and non-correlated errors over time in our data. 17 Following <strong>the</strong> recommendations of Beck and Katz (1995, 1996), we use panel-corrected standard errors to control for possible bias in <strong>the</strong> standard errors and Prais- Winsten correction for first-order autocorrelation. 18 The dependent variable, major conventional exacerbating tensions with o<strong>the</strong>r countries, but even here we find <strong>the</strong> existence of a strong causal effect to be unlikely. None<strong>the</strong>less, we explore <strong>the</strong>se possibilities later in <strong>the</strong> paper. 17 Following <strong>the</strong> advice of Wilson and Butler (2007), we do not implement <strong>the</strong> ―cure‖ unless we have reason to think we have <strong>the</strong> ―disease.‖ A test for auto-correlation in panel models (Wooldridge 2009) rejects <strong>the</strong> null of zero correlation at better than <strong>the</strong> 1% level, and a likelihood ratio test for panel-level heteroskedasticity does <strong>the</strong> same for <strong>the</strong> null of panel-homoskedasticity. 18 Beck and Katz's recommendation of lagged dependent variables has been more controversial than <strong>the</strong>ir advocacy of panel-corrected errors; we choose to use <strong>the</strong> Prais-Winsten correction for auto-correlation instead of lags, but this choice does not influence our findings. 16
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