Читать книгу Toppling Foreign Governments - Melissa Willard-Foster - Страница 10

CHAPTER 3

Testing the Logic of Foreign-Imposed Regime Change

Scholars since Plato have posited that domestic political turmoil in a state inspires other states to intervene.¹ But despite this broad consensus, the literature has yet to establish a definitive link between domestic opposition in the target state and the foreign overthrow of its government. Many statistical studies, for example, focus on the broader concept of foreign intervention, which includes cases other than regime change. In contrast, studies that focus explicitly on FIRC often exclude a number of cases that arguably qualify. One study, for example, focuses on regime change imposed after war and, therefore, excludes cases of covert and indirect FIRC.² Others examine a broader range of FIRC cases but neglect to consider the conditions under which regime change is not pursued.³ Without testing nonevents, we cannot know whether the conditions argued to cause FIRC are just as likely to occur when states negotiate.

The statistical tests presented in this chapter serve three purposes. First, they help fill a gap in the literature by analyzing whether the long-hypothesized link between domestic opposition in the target state and FIRC does indeed exist. Second, they test my theory’s core causal claim while controlling for other possible explanations. If my theory is empirically valid, at a minimum, we should find that variables measuring domestic opposition in the target state are substantively and statistically significant. Failure to find such a relationship would seriously challenge the theory. If the variables associated with alternative arguments have a stronger effect, this would also cast doubt on my theory’s explanatory value. Finally, the tests in this chapter can also indicate whether my argument is generalizable across a broad range of cases.

The results show that the target’s domestic opposition levels are positively correlated with the probability of FIRC. These results hold throughout a variety of model specifications and robustness checks. In contrast, variables representing arguments based on psychological bias, bureaucratic or interest-group pressure, credible commitment, and incomplete information are either insignificant or lose their significance when subjected to additional tests.

Statistical tests, however, have their limitations. The existence of a correlation between domestic opposition in the target state and FIRC does not imply a causal relationship. Certain types of leaders may be prone to domestic opposition, and it could be attributes of these leaders, and not their opposition, that explain their overthrow. Domestic opposition may also lead to FIRC for reasons other than those proposed by my theory. States plagued by domestic instability, for instance, may be subject to FIRC because other states fear the trajectory of their policies. The proxies I use to measure domestic opposition might also capture other effects that explain FIRC. Lastly, coding decisions can lead to the exclusion or inclusion of certain cases and, therefore, could influence the results.

These limitations can be diminished in some instances. To reduce the potential influence of coding decisions, I test my model on FIRC data compiled by other scholars, in addition to my own. I also test two proxies for domestic opposition to reduce the chance that my variable captures effects other than domestic opposition. I also run a variety of robustness checks to ameliorate such problems as omitted-variable bias and measurement error. Finally, to better test whether a causal relationship exists, I evaluate my theory’s causal logic in a series of case studies in Chapters 4, 5, and 6. In all, the statistical results in this chapter constitute an important first step in identifying whether domestically unstable states are prone to FIRC.

In the sections ahead, I explain how I code the 133 cases of FIRC that appear in my data set (see Appendix 1).⁴ Then, I describe the structure of the data set and the model I use to test my theory’s primary prediction that domestic opposition in the target state leads to FIRC. Next, I discuss the various strengths and shortcomings of the most common approaches to measuring domestic opposition. I then explain how the two proxies I use avoid some of the pitfalls associated with these other approaches. I follow this with a discussion of the covariates in the model, including the controls used to test alternative arguments. The last section of this chapter presents my findings.

The Dependent Variable

I use a dichotomous dependent variable (RC) that is coded 1 for each year that an attempt at regime change occurred. To identify these occurrences, I drew from a variety of existing data sets on foreign intervention, imposed democratization, and FIRC.⁵ I also consulted historical sources to identify cases that fit my definition.⁶ In assessing the historical record, I looked for evidence that the policymakers of one state (1) chose to abandon negotiations with the leader or regime of a sovereign state, (2) implemented an explicit plan to depose that leader or regime, and (3) did not intend to annex, colonize, or otherwise permanently rule that state.⁷ I explain these components of my definition of FIRC more fully in the introduction. In this section, I explain coding decisions that affect how the data appear in the data set.

In contrast to many existing FIRC data sets, my data include instances of attempted FIRC. I include such cases because the circumstances that motivate FIRC should apply whether it succeeds or fails. I code only the first year of an attempt as subsequent years are not independent events. Including them could also bias the statistical results in favor of my argument. When states attempt to overthrow foreign leaders, the aid they provide the opposition typically strengthens it further. Even when their efforts at FIRC ultimately fail, if the intervention empowers the opposition for some period of time, then coding each year of an attempt would increase the chances of finding that higher levels of opposition lead to regime change.

My definition of FIRC also requires that the state seeking regime change implement a plan of action aimed at toppling the targeted leader or regime. When it takes several years for such a plan to come to fruition, I use the first year in which the imposing state takes either covert or overt action toward removing the targeted leader. For example, I code the year in which the Allies initiated their attempt to overthrow the governments of Germany and Japan as 1943. Although hostilities preceded this year, President Roosevelt declared the goal of unconditional surrender in 1943, thereby indicating that the Allies would fight until they achieved the victory necessary to impose regime change. The action undertaken by the imposing state must be more than merely symbolic or ad hoc.⁸ States, for example, frequently impose sanctions that they claim are aimed at regime change, though it is well understood that the sanctions are too mild to do more than satisfy a domestic demand for action. In examining such cases, I use evidence that sanctions were part of a larger campaign that included either overt or covert measures to topple the targeted leader.⁹ The Nixon administration’s sanctions on Chile, for example, were paired with covert operations explicitly designed to foment a coup against President Salvador Allende.

Although my definition excludes cases in which the foreign power annexes the target state or rules it as a colonial possession, I do include cases in which states restore the target’s sovereignty but later establish formal control. In 1882, for example, the United Kingdom deposed Ahmad Urabi Pasha al-Misri, commander in chief of the Egyptian army, who had challenged the khedive, Muḥammad Tawfīq Pasha, for power. Although the period following the khedive’s restoration is often referred to as a “veiled protectorate,” the United Kingdom did not formalize its control until 1914.¹⁰ Nevertheless, some cases fall short of the criteria for statehood as defined by the Correlates of War data set, which I use to construct several of the variables in the model.¹¹ Because these cases are dropped when running the model, I list them separately in Table 15 of Appendix 1.

Finally, 24 of the 133 cases of FIRC I identify involve more than one intervening state. I include each state that played a major role in the operation if the primary goal of that state was regime change. For example, although I include the US invasion of North Korea, I exclude the South Korean joint invasion, because the South Korean government’s goal was to annex the North.¹² I enter these multilateral FIRC events separately into the data, which brings the total number of FIRC events to 148. In the case of Germany following World War II, for example, the Soviet, American, and British decisions to impose regime change are counted as separate observations. Although these events are clearly related, each state’s decision to participate (or not) informs our understanding of when states pursue FIRC. To control for the nonindependence between multilateral cases, I use robust standard errors clustered on the target-state year.

The Data Structure and Model

I employ a logit model to test the probability of FIRC. The unit of analysis is the directed-dyad year, which means each observation contains a potential imposing state (Side 1) and target state (Side 2) for every year, starting in 1816.¹³ This produces an enormously large data set with many dyads for which the probability of conflict is almost zero. The large number of potentially irrelevant observations poses two problems. First, large data sets can be sensitive to very minor effects, which can overstate the importance of variables that have little explanatory power. Second, because my theory explains how states reconcile conflicts of interests, it assumes a population of dyads with conflicting interests. Testing the model on data that include many dyads with almost no potential for conflict introduces inefficiency and possible bias.

I correct for these problems by limiting the scope of the data in three ways. First, I test the model using politically relevant dyads, commonly used to exclude dyads that have almost no probability of conflict. Politically relevant dyads are dyads in which Side 1 or 2 is a major power or the two states are contiguous.¹⁴ Twelve FIRC events fall short of these criteria. To include these cases, I perform a robustness check using an expanded definition of politically relevant dyads, which includes dyads located in the same region rather than only ones that are contiguous.

Second, I test the model on interstate rivals. Given that rivals engage in repeated disputes, we know that they are likely to have conflicting interests. I use James Klein, Gary Goertz, and Paul Diehl’s rivalry data set, which defines rivals as states that have had at least three or more militarized interstate disputes (MIDs) concerning the same issue(s). Rivalries are coded as ending ten to fifteen years after the last dispute.¹⁵ The disadvantage of restricting the population of cases to rivals is that the data exclude dyads that reconcile their differences through negotiation without the use of force. These data also leave out FIRC events that involve nonrivals, such as the United States and Guatemala in 1954.¹⁶ Lastly, to account for these cases, I use a third approach in which I limit the population to dyads that have experienced at least one MID in their shared history. These dyads should have a higher probability of conflict than merely politically relevant ones, though they do not necessarily meet the criteria to be considered interstate rivals.¹⁷

Measuring Domestic Political Opposition

If my theory is correct, FIRC should become more likely as the targeted leader’s external or internal domestic opposition grows in power. Quantitative measures of domestic opposition can be either direct or indirect. Frequently used direct measures include counts of political-unrest events (e.g., riots, insurgencies, coups) or the number of disadvantaged sub-state groups. Direct measures are helpful because they are less likely to pick up effects other than domestic opposition. However, missing data can be a problem. In one of the most commonly used domestic unrest datasets, 75 percent of politically relevant dyads are missing.¹⁸ High levels of “missingness” can introduce bias.¹⁹ Likewise, data on disadvantaged sub-state groups or ethnic-religious heterogeneity is frequently only available for recent time periods. Such figures could produce misleading results if used to proxy for societal divisions further back in time.²⁰

Direct measures of opposition also undercount internal and latent opposition. Leaders facing the threat of an internal coup will not necessarily encounter popular protests, insurgencies, or other forms of observable political unrest. Yet the fear of losing supporters to an internal rival could drive a leader to resist a foreign power, while the presence of internal opposition may tempt the foreign power to try regime change. Latent threats from groups that oppose the leader but are unwilling to fight under the status quo can also precipitate FIRC. Leaders may resist a foreign demand for fear of transforming this latent opposition into active opposition. At the same time, the foreign power may attempt to organize, equip, and train members of the latent opposition to carry out FIRC. By ignoring latent or internal opposition, we risk overlooking some of the conditions most likely to lead to FIRC.

Indirect measures of opposition better account for internal and latent opposition. One such approach relies on the assumption that certain regime types are less adept at managing domestic opposition and, therefore, face greater domestic threats. H. E. Goemans, for example, argues that mixed regimes, which are neither fully democratic nor autocratic, are sensitive to domestic challenges because they can rely on neither repression nor democratic institutions to manage opposition.²¹ Abel Escribà-Folch and Joseph Wright take a similar approach, arguing, for example, that personalist authoritarian leaders, who exercise personal control over the political system, are more likely to be brought down by sanctions, which undercut their ability to buy off and deter domestic opponents.²² Indirect measures, however, can be prone to measurement error. For example, one problem with using regime type as a proxy for a leader’s domestic vulnerability is that this method overlooks potential differences within regime type. Older, well-established regimes may find ways to compensate for their institutional weaknesses that enable them to fend off foreign meddling. Newer regimes, in contrast, may be more prone to FIRC because they lack the experience, routines, and relationships that can help ensure their power.²³

Another commonly used indirect measure captures this difference within regime type by using the amount of time that has passed since a major political transition to account for regime stability.²⁴ Political change, as Edward Mansfield and Jack Snyder argue, can set off struggles between new and old elites, making periods of change and their aftermath prone to political turmoil.²⁵ Regime transitions, liberal or otherwise, have also been shown to increase the risk of both civil and diversionary wars.²⁶ Yet, a problem with using regime transitions as a proxy for domestic opposition is that leaders may attempt to counter domestic threats, particularly latent or internal ones, by making minor institutional changes. Though the leader may succeed in the short term, these changes will not necessarily eradicate the domestic threat the leader faces. Indeed, these attempts to alter the political system could backfire and inspire greater opposition that later leads to a major transition. In short, leaders that institute minor political change may be doing so because they face significant domestic political pressure.