Coalition formation during turbulence

A large-n study examining the effects of economic and political instability on government-coalition formation

Stina Lindgren

Bachelor Thesis, Fall 2020 Department of Government, Uppsala University Political Science C Supervisor: Pär Nyman Word Count: 13,822 Author Stina Lindgren

Title Coalition formation during turbulence: A large-n study examining the effects of economic and political instability on government-coalition formation

Abstract This thesis examines the effects of economic and political turbulence on coalition-formation across 37 EU and OECD democracies. Utilizing the existing potential-coalitions research, it analyzes how increases in turbulence affect common variables predicted to determine which coalitions are chosen of all potential cabinets following an election. These variables drawn from the coalition-formation field are examined using a conditional logit regression model with interaction effects, and results indicate that both political and economic turbulence highly affect the way coalition formation is carried out, although the effects of the two turbulence types vary. During economic turbulence larger coalitions appear to be warranted, although results simultaneously suggest that ideological cohesion is hard to achieve during turbulent times. During political turbulence, instead, results suggest ideologically wide coalitions are more common but that minority cabinets are still more likely to appear. Despite the varying results, this analysis finds support that coalition formation is greatly affected by both economic and political turbulence. While the effects of some coalition-formation variables utilized by previous researchers appear to withstand the addition of turbulence, other effects change greatly when levels of instability are considered.

2 Table of contents

Introduction ...... 5 Assumptions and limitations ...... 6 Previous coalition-formation research ...... 8 Research on cabinet size ...... 8 Research on cabinet unity and strategies ...... 9 Previous research insufficiencies ...... 10 Coalition formation during turbulence ...... 12 Understanding turbulence ...... 12 Theoretical implications of turbulence ...... 13 Considerations regarding cabinet-size ...... 13 Considerations regarding unity and strategies ...... 14 Research design and methods ...... 17 Operationalization ...... 17 The dependent variable ...... 17 The independent variables ...... 18 Data ...... 20 Method of analysis ...... 22 Conditional logit regression model ...... 22 Interaction variables ...... 24 Model descriptions ...... 25 Validity and reliability of variables ...... 25 Results and analysis ...... 28 Descriptive statistics ...... 28 Regression results ...... 29 Concluding remarks on models...... 34 Conclusions ...... 36 References ...... 38

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4 Introduction

The formation of party coalitions is a topic at the heart of all representative government. A coalition can be defined as two or more parties that agree to cooperate to achieve a mutually desired outcome, and the formation of these determines how electoral outcomes produce governments and, in extension, how they generate policy outcomes (Komorita and Kravitz, 1983). Since a single party rarely controls enough seats in parliament to alone determine politics in parliamentary democracies, the coalition-formation process is vital (Bäck, 2003a). It is not surprising, therefore, that coalition formation has long been of great interest to both researchers and the public. Originally, researchers believed that the primary motivator for politicians was to gain the power and prestige associated with being in office, as no considerations were initially made of policy preferences (Martin and Stevenson, 2001). Over time, refinements have been made to this theory to also consider the impact of such policy preferences and, for instance, institutional contexts on coalition formation. This has generated a vast research field with a significant number of coalition-formation theories. Despite this vast scope of the coalition-formation field, no researchers have to the best of my knowledge ever quantitatively examined whether the practice of coalition-formation remains the same across different economic and political circumstances. To specify, no researchers have examined whether coalition formation is carried out differently during, for instance, political instability or economic crises, even though there are reasons to believe negotiations are naturally affected by the political contexts that are being discussed. In fact, there are many different types of turbulence that have historically proven to affect the way parties cooperate and discuss in parliaments, for instance through ‘party truces’ or ideological consensus during turbulence (see Andersson, 2012; Bremer, 2017; Sennerö, 2015). Despite this, all previous quantitative research has assumed coalition-variable effects remain constant across different stability contexts. I argue that this neglect compromises the strength of the coalition-formation theories since the effects of common determinants cannot be expected to remain constant throughout all circumstances. Consequently, this thesis can contribute to the research by enabling a more thorough understanding of how determinants of coalition formation behave in previously neglected contexts. Specifically, the aim of this study is to examine

5 how coalition-formation processes are affected when economic and political circumstances are turbulent. I examine this by utilizing a large-n approach with data covering approximately 990 elections to determine which common variable effects vary as turbulence increases. Conditional logistic regression with interaction variables is used, and all potential coalitions that may form following a national election are modelled to enable examination of which coalition actually forms. Specifically, the following research question is addressed:

How does economic and political turbulence affect government-coalition formation?

Results show several interaction effects that suggest both economic and political turbulence indeed does impact the effects standard coalition variables have on coalition formation. While the two types of turbulence appear to affect mainly the size and ideological cohesion of coalitions differently, it becomes clear that both types of turbulence impact the way parties form coalitions in some way. This expands the existing knowledge on a fundamental aspect of representative government, not only for researchers but for people affected by it. Coalition- formation ultimately determines the outcomes of national elections, and it is therefore of great importance that information on how these coalitions form is available not only to scholars but to voters.

Assumptions and limitations To enable the realization of this analysis, some limiting assumptions are made. First, I assume party politics are conducted in a one-dimensional space across a left-right scale. Naturally, the degree to which a one-dimensional space captures all aspects of party competition may be subject to debate, but substantial research suggests the left-right scale is a useful tool when conducting research (Martin and Vanberg, 2003). Furthermore, multi-dimensional political scales are hard to consider since estimates of party positions are scarce and the number and relevance of dimensions are likely to vary between parties (Blom-Hansen, Serritz- lew & Skjæveland, 2010). Second, I assume that parties are unitary actors. While some researchers have argued that intraparty politics may impact coalition formation, most coalition theories still assume parties can be treated as unitary negotiators (Bäck, 2003b). A main reason for this is that bargaining takes place between parties, not by individual legislators. Also, since individual legislators are unlikely to succeed in bargaining when acting alone, individual legislators should aim to be bound together in unitary parties (Hix and Høyland, 2011). To enable interpretation of how parties form coalitions, I assume this is the case.

6 Lastly, a limitation is made to not take into consideration the institutional factors that may impact coalition formation. There exists a coalition-formation theory that emphasises the importance of institutional factors (Martin and Stevenson, 2001). Not only was finding and including such control variables outside the scope of this analysis since the potential number of institutional variables are many, but since this thesis utilizes interaction variables to examine the effects of turbulence, any institutional considerations would warrant the use of triple interactions. As will be illustrated, interpretations of interaction terms in logistic regressions are difficult enough when only including two variables.

7 Previous coalition-formation research

The study of coalition formation displays a large set of arguments regarding why some cabinet coalitions form over others, and the field’s vast relevance to multi-party systems has led to an overwhelming number of theories (Blom- Hansen, Serritzlew and Skjaeveland, 2007). In fact, there has historically been little understanding of which theories are superior and which variables are necessary to include in coalition models (Bäck, 2003a). To include an as varied consideration of theories as possible, I give a broad overview of some of the most prominent coalition theories below.

Research on cabinet size When researchers in the coalition-formation field first set out to develop theoretical models of cabinet formation in the 1960’s and the 1970’s, a fundamental distinction was made between office-seeking theories and policy- seeking theories (Martin and Stevenson, 2001). Initially, one group of scholars argued that parties are office-seeking agents in the pursuit of cabinet control, meaning primarily that government formation can be modelled as a zero-sum game where the only payoff is control of the government. As such, no considerations were initially made of policy preferences. These office-seeking ideas had fundamental implications for the expected size of coalitions. Ultimately, the theory implies that only majority cabinets can form, as a parliament majority would never allow a minority cabinet to form given the presumption that cabinet control is all that motivates politicians. Put differently, any minority cabinet naturally faces a majority opposition and therefore should not be expected to be chosen (Bäck, 2003b). A refinement to this logic was the specification of minimal-winning coalitions (Martin and Stevenson, 2001). Minimal-winning coalitions are characterized by the fact that they hold a majority of the seats but would no longer do so should one member leave the coalition (Bäck, 2003b). Thus, office-seeking theorists believed majority coalitions which carry no unnecessary member are most likely to form. Such coalitions were expected on the premise that parties pursue cabinet

8 control only to achieve power and prestige, and any coalition so large the potential benefits of being in office are not maximized would therefore be rejected.

Research on cabinet unity and strategies Following the emergence of the office-seeking theories, another group of scholars then stepped forward which instead predicted that coalition-forming parties in fact are policy-seeking agents (Döring and Hellström, 2013). These ideas acknowledge that potential cabinet unity and political strategies also impact coalition formation, as parties are assumed to try to maximize the policy coherence of the cabinet members. More specifically, they claimed, parties attempt to find coalition partners which will allow unity on policy-implementation ideas, so that their own policy output can be maximized. One theorist who expanded these policy-seeking ideas was Axelrod (1970) who, with the presentation of his minimal-connected-winning theory, maintained that coalitions are more likely to form the closer adjoined the parties are in an ideological space. While still maintaining that coalitions include no more parties than necessary to be winning, this theory emphasizes that ideological connection is in fact also of relevance (Crombez, 1996). This idea was then developed further by de Swaan in 1973, who argued that the coalition most likely to form will not only be a minimal-connected-winning coalition as Axelrod (1970) predicted, but it will be the minimal-winning coalition with the smallest ideological range (Martin and Stevenson, 2001). Ultimately, ideologically divided cabinets are less likely to survive since their parties are forced to make larger policy compromises. This highlights the idea that some minimal-winning coalitions are strategically and politically reasonable whereas others are absurd (Keman, Kleinnijenhuis and Pennings, 2006). Another policy-seeking theory which considers the role of ideology in cabinet formation processes commenced from the median voter theorem (Bäck, 2003b). The theory implies that, given a one-dimensional ideological space, the party which controls the median legislator will control the only point in the ideological dimension which is preferred by a majority of voters. Because of this, the party will have increased bargaining power, making it strategically important to include in cabinets. These ideas were presented by Laver and Schofield (1990, 111) who suggested that since the median party will be a policy dictator it will naturally also have a greater chance of getting into government. Furthermore, since parties aim to minimize the ideological range to its coalition partners, parties positioned closer to the median party are more likely to become cabinet members than those distanced far from it (Döring and Hellström, 2013). Ideology, however, is not only significant when considering the potential cabinets themselves. It is also a significant factor when examining the opposition

9 parties (Martin and Stevenson, 2001). Researchers have contended that ideological divisions among the opposition, for example, are as important to the viability of a minority cabinet as the ideological approach of the cabinet itself. Theoretically, the more ideologically divided an opposition is, the lower the chance is of it organizing to block a coalition. Strøm (1990), for example, argued that one reason minority governments can form and survive in the first place is that the opposition is too ideologically divided to exploit its minority position. The prediction that only majority cabinets will form thus becomes less valid when attention is given to ideology and cabinet strategies. Another debate through which cabinet strategies have been given more attention is the one regarding how many parties should be included in a cabinet. Leiserson (1968), for example, proposed that since negotiations and bargaining are made easier in coalitions with fewer parties, coalitions with fewer members will be preferred over coalitions with more members. Such coalitions, he argued, will also be easier to hold together and are therefore more likely to be chosen in the first place. Lastly, another area of theory which has developed over time is that of incumbency advantages. Scholars have previously found advantages in government-formation processes for incumbent coalition parties (Martin and Stevenson, 2010). Primarily, it has been shown that a coalition is more likely to form if its parties have recent experience of working with each other and if the parties, when working together, avoided exposed conflict. Similarly, transaction costs have been argued to be higher when changing coalition partners than when remaining with previous ones, making it strategically preferred by coalition members to remain with previous partners (Bäck, 2003a). Ultimately, it is believed that the incumbent advantages may be explained by many reasons. It is, for example, possible that the incumbent coalition may represent a ‘reversion point’ should other parties fail to create a new government. It is also possible that the advantages appear because incumbent parties naturally will have more governmental experience than other parties, which is likely to give incumbents a bargaining advantage in the coalition-formation process.

Previous research insufficiencies As illustrated, many theoretical refinements have been made to the coalition- formation theory over the past fifty years. However, all previous coalition research has assumed coalition-variable effects remain constant across varying stability contexts. I argue that there are few reasons to expect the effects of variables to remain the same in all circumstances, making the neglect damaging to the reliability of the field. Put differently, the research insufficiency has resulted in a perplexing and extensive number of theories all practically concerned with

10 the same matters. Cronert and Nyman (2020), for example, included as many as 34 different variables when measuring electoral competitiveness, none of which assessed the stability of their results during varying circumstances. And Bäck (2003b), when examining the appropriateness of different models, included over 20 different coalition variables in her models, but still did not incorporate stability-related controls. Conclusively, the research field has generated a vast number of ways to measure very similar coalition-formation effects, all failing to address variations in, for instance, economic stability or temporary political contexts. This implies that a significant area of the analysis has been ignored. Furthermore, any research which indeed has been concerned with different contexts and backgrounds has mostly examined geographical contexts. For example, there have been studies conducted using data from local rather than national governments (see Blom-Hansen, Serritzlew, and Skjaeveland, 2007; Bäck, 2003a), as well as systematic comparisons between the coalition determinants in Western and Central-Eastern European democracies (Döring and Hellström, 2013). Additionally, several country-specific case studies have been conducted (see, Bergman, 1995; Altman, 2000; Deschouwer, 2009). Conclusively, there is no lack of empirical research in the coalition-formation field. Still, researchers have failed to acknowledge that contexts and backgrounds can vary in many other ways. For instance, there has to the best of my knowledge been no examinations of variable effects during conflicts, different political movements, economic crises or political instability. This leaves questions regarding the strength of the theories unanswered. This thesis aims to expand the coalition- formation research to further consider stability backgrounds and varying contexts when examining coalition-variable effects.

11 Coalition formation during turbulence

I have chosen to examine the impacts of economic and political turbulence on coalition formation. The reason for this is that economic and political turbulence may capture wide effects of instability, generating a comprehensive discussion regarding the strength of coalition variables during uncertainties in general. Furthermore, as mentioned there does not exist any previous research considering economic or political stability contexts, even though there certainly are reasons to suspect coalition-formation processes may be influenced by such turbulence. Before a discussion of why this might be is carried out, an interpretation of the meaning of the two types of turbulence is warranted.

Understanding turbulence Economic turbulence can be explained through a collection of different components. To name a few, events such as high rates of job creation and job destruction as well as firms opening and closing have been attributed to turbulent times (Brown, Haltiwanger and Lane, 2006). Bora et al. (2019) described economic turbulence as a large range of adverse economic conditions, ranging from a disruption of regular commerce caused by hyperinflation to economically caused export changes. Goulielmos (2018) instead, when addressing such turbulence characterized by business failures, highlighted the occurrence of financial turbulence. A proper model of turbulence, he argued, will also allow for financial events such as price fluctuations, windfall profits and volatility clustering. To include such characterizations I will, when addressing economic turbulence, therefore refer to what is in the literature often known as financial turbulence (see, for example, Berger, 2013; Kritzman and Li, 2010). It remains clear that economic turbulence is a widely debated concept. Admittedly any range of adverse economic circumstances applies to it, as Bora et al. (2019) contended. Political turbulence, on the other hand, has been subject to far less definition attempts by researchers. Griswold (1999), when explaining turbulence in the Unites States in the 1990’s, chose to highlight ideological conflicts as a component of political turbulence. More specifically, she addressed ideological confrontations which had occurred over a wide range of federal

12 policies as part of a turbulent time period. She also highlighted that control over the governing institutions had changed hands, suggesting that large changes in mandate distributions may imply turbulence. Hilmersson, Purmand Hilmersson and Sandberg (2015) instead commenced from a more general definition of turbulence, which declares that turbulence can be described as any environment of political confusion and disorganized change. Of course, certain other country-specific attributes may also be characteristics of political turbulence. For example, Ibrahim (1991) previously explained political turbulence in Nigeria by a political battle for control of the theological space, while May (2003) finds non-democratic pressures cause political turbulence in Papua New Guinea. However, more general expressions of political turbulence seem to be explainable as certain abrupt political changes, in areas such as mandate distributions, which cause conflict or confusion.

Theoretical implications of turbulence

Now that there is a better understanding of the two concepts of turbulence, a discussion of why turbulence may impact coalition formation must be carried out. There are, in fact, multiple aspects of coalition formation which may be impacted by stability contexts. I will spell out predicted implications regarding such areas below, with regards to both previous coalition research and theories of turbulence.

Considerations regarding cabinet-size One area which may be affected by particularly economic turbulence is the size of the cabinet. More specifically, I consider the possibility that minority governments are less likely to occur in turbulent times, since turbulence may make governing increasingly difficult, making larger coalitions that enable shared responsibility and accountability desirable for parties. This means coalitions with larger seat shares should be warranted. A fundamental reason behind this is that shared responsibility will deter voter punishment which is otherwise often generated by economic dissatisfaction. To specify, Powell and Whitten (1993) explain that economic factors such as inflation, unemployment and growth affect government support. In other words, dissatisfaction with the economy tend to lead to voter punishment of incumbent governments. Governing through turbulent time periods might therefore make parties worse off in terms of future voter support than they would have been remaining in opposition. However, such voter punishment, the authors explain, depends on the transparency of the political responsibility since punishment is harder to distribute when accountability is low. Broad coalition governments

13 diffuse such perceived responsibility of the economy, making punishment harder (LeDuc and Pammett, 2013). Ultimately, assuming turbulence increases the risk of voters being dissatisfied with the economy, shared responsibility should diffuse the accountability of the individual members of the cabinet and therefore deter voter punishment. Because of this, larger majority cabinets should be increasingly expected in turbulent times. Perhaps a suggestion against these types of predictions may be found in work by Dostal (2020), who finds that more general types of turbulence (in this case, the corona virus) has concentrated the power among a narrow executive rather than generated shared responsibility. However, I argue that a power concentration among officials does not dispute the idea of broader coalitions since these can be found simultaneously. Furthermore, the argument that turbulence should generate broader coalitions is additionally strengthened by Sutherland’s (1991) findings that smaller minority governments are less able to protect their ministers against critique and demands of resignations than larger coalitions. This too highlights the importance of wide-ranging coalition formations in turbulent times. Lastly, Powell and Whitten (1993) find that voters seem to distribute less punishment to incumbent governments which are made up of a larger number of parties, since responsibility is then shared. The implications of this is that not only should the occurrence of minority coalitions be expected to decrease in turbulent times, as previously mentioned, but the preference to have coalitions containing fewer parties which Leiserson (1968) predicted should also be less important.

Considerations regarding unity and strategies Another implication which the necessity for broader coalitions in turbulent times may have is that ideology concerns are likely to become less significant. For example, not only will broader coalitions automatically imply that the ideological range cannot be kept as narrow, but I also argue that the turbulence itself is likely to initiate a ‘party truce’ in parliaments when ideological discussions become secondary to turbulence management. To elaborate, such tendencies have been apparent during more general turbulence historically. For instance, in Norway a civil peace (borgfred) was declared following the aftermath of the 2011 terrorist acts, leaving the then ongoing election campaigns postponed (Andersson, 2012). Similarly, during the outbreak of the Corona virus, a similar party truce was present in Sweden, generating lower degrees of disagreement within domestic policy (Sennerö, 2020). Also, when Denmark in 2009 were faced with the less turbulent but nonetheless significant task of arranging a United Nations climate change conference, all political disagreements and discussions were paused until after the event (Almlund, 2019). These party truces imply parties occasionally put policy disagreement on hold when external tasks or turbulences are present. And not surprisingly, this can also be applied to particularly economic turbulence.

14 Gemenis and Nezi (2014), for example, find that the economic turbulence in Greece between 2010 and 2015, although it created conflict about bailout agreements, decreased the importance of the usual left-right dimension. Furthermore, Bremer (2017), when examining the response of social democratic parties to the economic crisis in 2008, found that social democratic parties joined centre-right and liberal parties in emphasizing economic issues and promoting austerity. This suggests parties find consensus in parliament when external issues force them to do so. To further strengthen this point, English et al. (2016) show that Britain’s two largest parties narrated very similar strategies on how to deal with the great recession back in 2008 and onwards, implying that there existed a broad consensus in British politics. I argue that all of this suggests that wider political consent may be strived for and achieved during turbulence. For similar reasons, a short distance to the median party is no longer expected to be an as significant indicator of cabinet success either. Wider coalitions naturally generate wider ideological ranges and distances. I still, however, expect the advantages of including the median party to remain, as this party still controls the median legislator. These advantages should remain the same once other ideological variables are controlled for. However, it is important to point out that the image of ideology in turbulent times is not quite so one-dimensional. Bremer (2017) did find that while social democratic parties supported liberal economic measures, in other policy areas they instead shifted their positions further toward the left. And while Gemenis and Nezi (2014) found the left-right dimension less significant than previously, an entirely new conflict dimension was created instead. Therefore, while I anticipate wider ideological ranges during turbulent times, I remain aware that previous research is divided on the issue. Furthermore, I also acknowledge that this ideological truce may not extend to the opposition. For instance, De Giorgi, Moury and Ruivo (2015) explain that political and economic crises make the choice between the need for cooperation and the desire to weaken chosen cabinets more difficult for the opposition. They, in accordance with my predictions, found that while mainstream parties were less adversarial during crises than in normal times, the opposite could be said for radical parties. Cooperation thus does not always prevail over party desires to rule. Lastly, I do not expect turbulence to impact the effect incumbency has on the coalition-formation process. As described above, incumbent parties are generally expected to benefit from an incumbency advantage in coalition formation. There are no reasons to expect this to change during turbulence since the incumbent cabinet of course remains the reversion point and maintains bargaining advantages in turbulent times too. However, while this incumbency advantage is an acknowledged phenomenon in stable times, it is important to acknowledge two

15 reservations to these benefits during economic turbulence. For instance, since it can be expected that the voter punishment apparent in economically turbulent times may highly affect the incumbent cabinet, the incumbent parties may be subject to largely reduced vote shares if the economic turbulence has been felt by voters before the election. Evidence of such effects have been observed by, for example, Hernández and Kriesi (2015) who find large losses for incumbent parties during the great recession. This is connected to the second reservation which is that parties that lose many seats often struggle to remain in power due to lost momentum. Precisely so, in 2011, all current incumbents in Greece, Italy, Spain, and Portugal were driven out of office (Bosco and Verney, 2012). These effects must be controlled for.

16 Research design and methods

To answer the research question of this thesis, I will apply the potential- coalitions framework presented above on coalition data, using conditional logistic regression. In these logistic regression models, I include interaction variables to examine the effect turbulence has on coalition-formation variables. In the first section below, to enable statistical computing of the theory presented, all the theoretical definitions mentioned above will be operationalized. This means that the independent and dependent variables will be converted into actual, materialized indicators that are empirically measurable (Teorell and Svensson, 2016). Following the operationalization, the data collection is addressed, specifying sources and datasets. Also, temporal limitations to the data are disclosed. Lastly, the regression models are further presented and limitations to the methodology are considered.

Operationalization To enable examination of the effect that independent variables have on the choice of cabinets, all potential cabinet formations emanating from an election result will be modelled. This means all potential formations of parties which may govern together following an election are examined. This number is often very large and depends on the number of parties apparent in a party system (Bäck, 2003a). For instance, in a three-party system with parties A, B and C there are a total of seven potential cabinets. All parties can decide to govern alone, two parties can govern together (resulting in AB, BC or AC) or all three parties can govern together in a large coalition. The total number of potential coalitions is expressed as 2n-1, where n is the number of parties present in a party system.

The dependent variable The dependent variable of this analysis is a dichotomous variable revealing whether a potential cabinet formation is chosen or not. Specifically, coalition formation is modelled as a discrete-choice variable where a choice is made between all potential combinations of parties that may form a government.

17 The independent variables The dependent variable is expected to depend on several independent variables. To consider the large number of coalition-formation theories present in previous research, I include independent variables which adhere to beliefs in regular coalition-formation theory as well as theories regarding turbulence.

Turbulence The two independent variables that are particularly significant to this analysis are economic and political turbulence. Economic turbulence is, as previously explained, often referred to in terms of financial turbulence in the literature, and the perhaps most used indicator of such turbulence is implied volatility (Kritzman and Li, 2010). Implied volatility is known as the market’s assessment of the volatility of an asset, as reflected in the option price (Mayhew, 1995). Indices of such volatility are often considered among economists to be indicators of future economic conditions (Kliger and Kudryavtsev, 2013). This means these measurements are frequently used to examine economic stability and turmoil. Because of this universality, I will use the concept of volatility as a turbulence indicator. More specifically, I will apply a very commonly used measurement of implied volatility known as the VIX index as the indicator of economic turbulence. The VIX index has been cited in hundreds of articles in financial publications (ibid.). It is formally known as the CBOE (Chicago Board of Options) Volatility Index, and it provides a benchmark of the expected future volatility during the next 30 days (Baitinger and Flegel, 2019). It measures implied volatility using the S&P 500 options and quantifies the market volatility that is expected by market participants. This makes it commonly used as a turbulence detector in economic research (Kliger and Kudryavtsev, 2013). Put differently, because of its ability to measure investor fear the index is used as a signal of upcoming economic circumstances. It also allows for a continuous measurement of turbulence, rather than absolute classifications of time periods as either turbulent or not. Political turbulence on the other hand can, as mentioned above, instead be understood as abrupt political changes that cause conflict or confusion. Abrupt political changes may be caused by many country-specific events and circumstances, but one way it may be materialized on a more general level is through large changes in mandate shares. Recall, for instance, that Griswold (1999) highlighted ideological conflicts and changed control over governing institutions as components of political turbulence. This, I argue, suggests that certain abrupt changes in parliament mandates may be an indicator of political turbulence. Furthermore, Hilmersson, Purmand Hilmersson and Sandberg (2015) explained turbulence as disorganized political changes. I argue that large shifts in

18 vote shares can act as an indicator of such disorganized political change and confusion, since large changes in vote shares imply large political fluctuations. Put differently, large, unexpected changes in election results may imply turbulent events have caused political shifts in parliament, while smaller changes in election results suggest little political change has taken place. Because of this, political turbulence is here operationalized as the total change in vote shares between two following elections.

Cabinet size Following the operationalization of the two turbulence variables, the remaining independent variables must also be addressed briefly. One such set of variables is the one which adheres to ideas regarding office-seeking motivations of politicians and the implications these have on the size of cabinets. To test whether majority cabinets are most likely to form, I include both a variable which measures the seat shares of potential coalitions and an indicator of whether a potential cabinet is a minority. By doing so, the size aspect of coalition formation is thoroughly measured. I expect minority cabinets to be less likely to form during turbulent times, and I expect coalitions with larger seat shares. I also aim to test whether governments that form are likely to be minimal- winning coalitions. I therefore include a variable that indicates whether a potential coalition still maintains a majority should the smallest party decide to withdraw from it. I expect little change in the effects of this variable during turbulence. While politicians’ desire to form broader coalitions during turbulent times certainly may increase the likelihood of majority coalitions forming, naturally any majority government should suffice.

Cabinet unity and strategies The last independent variables that need to be addressed are the policy- seeking ones which consider the importance of cabinet unity and strategies. One such variable is the ideological range of potential cabinets. To test whether coalitions closely adjoined ideologically are more likely to form, I include a variable that measures the ideological range of all potential cabinet formations. Specifically, this variable measures the range between the two outer-edge parties in a potential cabinet to determine its ideological width, and it is based on party positions calculated on a scale from 0 to 10 using several party-expert surveys (Castles and Mair, 1984; Huber and Inglehart, 1995; Benoit and Laver, 2006; Bakker, et al. 2015). This 0-to-10 classification is widely used in the coalition- research field (see, for example, Döring and Manow, 2015; Arnold and Williams, 2014; Ginn, et al. 2020). Therefore, while any classification of ideological party preferences on a one-dimensional scale may struggle to exhaustively include all ideological information available, the popularity of this classification should be

19 suggestive of its analytical value. Ultimately, I expect coalitions to be kept less ideologically narrow during particularly economic turbulence, due to a desire for broader coalitions and shared responsibility. I also include a variable which measures the ideological range of the opposition, using the same party-position scale of 0 to 10. Again, ideologically divided oppositions are predicted to be less likely to hinder the formation and functioning of minority cabinets. I expect this to hold during turbulent times too and, as mentioned above, it is even possible that turbulence increases the political divisions among oppositions, since party disagreements may be highlighted by instability. While cabinet members must work together to achieve wide support during turbulence, it has been shown that oppositions may struggle to do so. Using the same ideological scale from 0 to 10, I also include a binary variable indicating whether the median party is included in potential cabinets. I expect the median party to be equally included during turbulent times, since there are few reasons to assume their bargaining advantages diminish during instability. Using this median-party position, I will also include a measurement of the average ideological distance of each potential cabinet to the median party. I expect the need for small ideological distances to the median party to decrease during turbulence, for the same reasons wider coalitions are now expected. Another variable which I include that I expect to be affected by turbulence and which is usually included in coalition-formation models is the number of parties included in each potential government. I suggest that the desire for broader coalitions during turbulence may generate coalitions containing more parties, since shared responsibility is warranted. Lastly, a potential incumbency advantage should also be considered. As mentioned, incumbents usually have bargaining advantages due to more experience. Additionally, an incumbency variable can also indicate whether a party has historically been considered fit to govern. For instance, there exist parties that due to unpopularity among other parties are never included in any chosen cabinets, making potential cabinets that include these discounted automatically (Bäck, 2003b). An incumbency variable is therefore an indicator of whether included parties are considered capable of governing. I expect the effects of incumbency to hold during turbulence, and this is tested using a variable that shows the share of incumbents in each potential government.

Data All data regarding elections and government coalitions has been collected from the data infrastructure ParlGov, containing information on 37 democracies throughout a total of 990 national elections (Döring and Manow, 2019). The database includes all elections and succeeding cabinets from 1945 (or country

20 democratization) to 2020. ParlGov is advocated as a reliable data source by Princeton University (2020), the European University Institute (2020) and the European Data Journalism Network (2019), among others, and all its methodological approaches of data collection as well as operationalizations are publicly declared (Döring, 2013; Döring and Manow, 2013; Döring, 2016). This enables data control and oversight for other researchers. In other words, the ParlGov database is a widely used and reliable source within coalition, election and party research. It has also previously been advocated for its inclusivity of countries from both the East and the West, namely including countries from Central-Eastern Europe which many other datasets have failed to do (Döring and Hellström, 2013). Using ParlGov, I specifically utilized the dataset on cabinet results which consists of information regarding cabinet parties and opposition parties across all EU countries and most OECD democracies, resulting in data covering approximately 1600 chosen cabinets. From this dataset, I collected the data that generated my independent variables regarding coalition formation. I used information on party and cabinet ID’s, as well as ideological positions and seat shares to develop the variables. This was done using the statistical software Stata. Because the number of potential cabinets increases exponentially with the number of parliament parties, I restricted the data to only include parliaments with a maximum of 15 parties. The political-turbulence data I used was also collected from the ParlGov database, but I instead utilized the dataset that presents information on election results. This dataset contained information on party vote shares over all available elections, which enables examination of the total change in vote shares in parliaments since the last elections. One source of concern apparent when using this measurement is the treatment of parties that are missing in previous elections. For instance, when parties enter parliament for the first time, examination of their previous vote share is prevented as no such data is available. In cases where the parties indeed did not take place in parliament previously, it is suitable to set their previous vote shares to 0, as there will in fact have occurred big changes in seat distributions. In some cases, however, the ParlGov data will treat old parties as new entities if the parties have previously participated in election alliances with other parties. Put differently, a party-identification code may differ between elections depending on whether a party runs an election campaign as a stand-alone party or a party alliance. In such cases, turbulence will be overestimated if the previous vote share is set to 0, since the party may in fact have taken place in parliament previously. Unfortunately, the dataset is too substantial for manual control of such cases. Because of this, I have opted to treat new party identification codes as new parties to enable proper examination of the entrance of new parties into parliament. While this may risk

21 overestimating the political turbulence in parliament, data management is made more manageable when setting all missing values to 0. Furthermore, because the disintegration of previous party alliances may also illustrate political changes, this approach may still measure political changes in a sufficient manner, making it preferred over turbulence underestimation. The political-turbulence list showing the highest turbulence periods generated from this method suggests that the measurement picks up important turbulence aspects, but also that temporary election alliances indeed are interpreted as turbulence. Lastly, the VIX data indicating economic turbulence was instead collected from Yahoo Finance. This data was collected on a daily basis, between January 1990 and November 2020 due to temporal restrictions. Because of a changed methodology in calculations, VIX data is not available before January 1990 (Zhang and Zhu, 2006). Therefore, the regression models which include economic turbulence will have a more restricted time period than the other models. Furthermore, there were missing values in the VIX dataset between 1990 and 2020 which were replaced by the previous value available. The reason missing values occurred is that the index lacks data on bank holidays. Because VIX still has very substantial coverage, the previously available notation is likely to be close in time and accurate. Daily VIX data was chosen to maximize information and variation, and the data was collected from Yahoo Finance since the source provided index notations in a cohesive dataset. CBOE, for instance, divided VIX data into two datasets between 1990–2003 and 2004–2020 due to revised methodology in the early 2000’s (CBOE, 2020). Therefore, to enable simpler data management, I chose the source that distributed a full data set. Data should remain unaffected and equal.

Method of analysis

Conditional logit regression model This thesis utilizes a logistic regression model to examine how turbulence af- fects coalition formation, due to the dichotomous nature of the dependent varia- ble. Logistic regression, or logit regression, is a non-linear regression model which allows dependent variables to be of qualitative rather than continuous na- ture (Walsh, 1987). Logit regression has long been used as the standard method within the coali- tion-research field (see, for instance, Bäck, 2003b; Döring and Hellström, 2013; Martin and Stevensson, 2001). It utilizes the cumulative standard logistic distribu- tion function, below denoted by F, and has the regressors X1, X2, through to Xn (Stock and Watson, 2012). Using multiple regressors, the model can be described as:

22 Pr(푌 = 1|푋 , 푋 , … , 푋 ) = 퐹(훽 + 훽 푋 + 훽 푋 + ⋯ + 훽 푋 ) [1] 1 2 푛 0 1 1 2 2 푛 푛

… where the probability of Y=1, or a cabinet being chosen, depends on the independent variables all the way to Xn, where n is the total number of independ- ent variables. 0 is the intercept and 1 through to n are the regression coeffi- cients. Naturally, any binary outcome expressed as a probability must be between 0 and 1 (Stoltzfus, 2011). Therefore, where linear models sometimes yield probabilities outside of the logical range, logit regression always maps the regression line withing the correct dimensions (Peng et al. 2002). Within these dimensions, the logit regression demonstrates a nonlinear relationship between the predictors and the probability of the Y-variable. This is illustrated in Figure 1.

Figure 1

Source: Cramer, 2003

More specifically, logit mathematically transforms the otherwise insufficient linear regression equation found in ordinary least squares (OLS) regression into the log of the odds of yielding a certain outcome, Y (Stoltzfus, 2011). The odds are simply the ratio of the probabilities of Y happening to the probabilities of Y not happening, and thus links how likely one outcome is to the other (Ingersoll, Lee and Peng, 2002). The odds are then logarithmized to enable easier comparisons between the odds for or against something happening. Put differently, logit generates the logged odds of being in one category over the other and therefore estimates the effect our variables have on the odds ratio. This generates:

푃 ln = 훽 + 훽 푋 + 훽 푋 + ⋯ + 훽 푋 [2] 1−푃 0 1 1 2 2 푛 푛

23 … where the coefficients explain how much the variables affect the logged odds ratio. One highlighted issue with logistic regression is that the coefficients are quite hard to interpret (Keating and Cherry, 2004). The effect an independent variable has on y is interpreted as a change in the logged odds of y occurring given a one- unit change in the independent variable. These interpretation difficulties are considered the greatest downside to the logistic regression model. However, in my case the coefficients used in regular linear models would be equally hard to interpret due to the very small baseline probabilities. These are caused by the fact that the probability of any cabinet forming in a parliament with thousands of potential cabinets is very small. I therefore recognize that the advantages involved in using logistic regression are greater than the disadvantages. Another highlighted issue with logistic regression is that all potential cabinet alternatives enter the regression as separate cases (Bäck, 2003b). For instance, potential coalitions involved in parliaments with many parties may have smaller probabilities than even highly unlikely coalitions in parliaments with fewer parties. To solve this issue, conditional logit regression is used. Conditional logit models coalition formation as a discrete choice problem, where each formation opportunity represents one case in a set of all potential combinations of parties which may form a government. By utilizing this methodology, general effects generated by aspects like the number of parties in parliaments are held constant.

Interaction variables To examine the effect of turbulence on coalition formation, I also include interaction variables in my regression models. Interaction variables are useful when the relationship between the dependent variable and at least one of the independent variables is expected to depend on another independent variable (Bowerman, Koehler and O’Connell, 2005). Put differently, the effect on y that a change in one independent variable generates, is conditioned at least partially by the presence of another independent variable. Mathematically, interaction variables are created by including the product of two independent variables in the regression model (Stock and Watson, 2012). The coefficient of this product then tells us how the effect of, for instance, X1 on the logged odds changes when X2 increases by 1. This is illustrated by 3 in Equation 3.

푃 ln = 훽 + 훽 푋 + 훽 푋 + 훽 푋 푋 + ⋯ + 훽 푋 [3] 1−푃 0 1 1 2 2 3 1 2 푛 푛

In this case, the effects of turbulence on the other independent variables will be explained by the size and significance of these interaction-term coefficients (for

24 more information on interaction variables, see Aiken and West, 1991; Bowerman, Koehler and O’Connell, 2005; Jaccard, Turrisi and Wan, 1990).

Model descriptions I begin by examining the effects that coalition-formation variables have on the chosen coalition without interaction terms. I create two versions of this model that excludes turbulence, one limited to the number of observations with economic- turbulence data and one limited to observations where there exists political- turbulence data. The reason for this is that since economic turbulence showcases significantly fewer observations than other variables, any model which includes the VIX index will likely have lower coefficients of determination (here, pseudo R2). This is thus controlled for. I then conduct two models which include interaction variables: one with economic turbulence (Model 2) and one with political turbulence (Model 3). A limited example of how such a regression model can be written is shown in Equation 4. The example uses the variables measuring the number of parties in a potential coalition, the seat share of potential coalitions, and economic turbulence.

푃 ln = 훽 + 훽 푝푎푟푡𝑖푒푠 + 훽 푠푒푎푡푠ℎ푎푟푒 + 훽 푒푐표푛. 푡푢푟푏푢푙푒푛푐푒 4 1 − 푃 0 1 2 3 + 훽4푝푎푟푡𝑖푒푠 푥 푒푐표푛. 푡푢푟푏푢푙푒푛푐푒 + 훽5푠푒푎푡푠ℎ푎푟푒 푥 푒푐표푛. 푡푢푟푏푢푙푒푛푐푒

Using interactions for all coalition variables, examining economic and political turbulence respectively, I will analyse whether the effects that regular coalition- formation variables have on coalition formation are, in fact, affected by turbulence.

Validity and reliability of variables The operationalization process of turning theoretical concepts into measurable indicators is an often problematic aspect of political analysis (Eriksson and Wiedersheim-Paul, 2014, 62). Indicators sometimes fail to demonstrate an exhaustive picture of what is meant by a theoretical definition, which may generate measurement errors (Teorell and Svensson, 2016). Therefore, potential measurement errors must be considered and addressed. The VIX index and the vote-share measurement applied as indicators of turbulence were thoroughly motivated above. However, some issues can be identified. First, a change in vote shares in parliament of course does not explain whether these changes were in fact unexpected and confusing, as theory suggests they should be. Granted, any large changes in vote shares should suggest that

25 some abrupt political changes have taken place, but one cannot necessarily be sure this change was turbulent. This may compromise the validity of the political- turbulence measurement, as the operational definition may not perfectly represent the theoretical one. For instance, it is possible that the political turbulence is overestimated if the vote-share changes that are observed were not turbulent per se. It is also possible that the turbulence is underestimated, as a country can experience political turbulence without it generating large changes in vote-shares. The implications of this issue remain unknown as little previous research has been concerned with political-turbulence operationalization. I expect the measurement to be effective and generate variations in stability contexts, but these potential validity issues must still be acknowledged. Second, a similar discussion can of course be had regarding the VIX index. Economic turbulence, like political turbulence, is a very expansive subject, making theories regarding turbulence measurements fragmented. The VIX index is only one way of measuring economic turbulence, but by no means can it acknowledge all aspects of economic turbulence. For instance, the S&P 500 options that the VIX index utilizes are based on American companies, which should, but may not, instantly translate into turbulence in other countries. This too may generate validity concerns. However, the index’s ability to measure expectations of the general market does make it very comprehensive and commonly used. Other researchers too have deemed it suitable as an indicator of economic conditions (Kliger and Kudryavtsev, 2013). Because of this, I still expect VIX to be a sufficient measurement, although I remain aware that economic turbulence is a multifaceted concept. Another potentially larger issue apparent in the operationalizations of this thesis is that the available dates that ParlGov present for its coalition data are the dates when coalitions enter government, not the date when negotiations take place. Because of this, the used economic-turbulence measurement matches the date a coalition takes over office, meaning it may not fully represent the turbulence present during negotiations. I include several coalition-formation variables that are based on ideas regarding negotiations and bargaining which thus may be affected by this. Consider, for instance, that economic turbulence increases after a coalition is chosen. This analysis will then apply turbulence to the coalition-formation process simply because there occurred turbulence during office entrance, regardless of whether it was present during negotiations. Similarly, it is possible that turbulence passes before the chosen coalition enters office, which instead generates underestimations of turbulence. This may lower the validity of the analysis. However, assuming the results of these systematic errors, i.e., the overestimations or underestimations of turbulence, occur on a random basis, analysis results should remain relatively accurate.

26 Lastly, the appropriateness of the other independent variables should also be considered. Several variables are easily quantifiable and should yield few faults in the operationalization, such as whether a cabinet is a minority cabinet, whether it is minimal winning, the number of parties and the incumbency share. The variables which are based on ideological classifications may, however, warrant a more substantial discussion of reliability. As mentioned above, the variables indicating the ideological range of both the cabinet and the opposition, as well as the indicator of median-party inclusion and the cabinet distance to the median party all depend on an expert-survey based ideological scale from 0 to 10. Again, this classification is widely used in the coalition-research field which should be suggestive of its appropriateness. However, any expert-based surveys risk not receiving the same results if experts were to reassess the information. Put differently, a methodology should be independent of individual assessors to enable high reliability (Eriksson and Wiedersheim-Paul, 2014). This may not be the case with expert-based classifications. Naturally, few other options are available due to the complex nature of party positions that make most ideological categorizations subject to arbitrariness. Still, the 0-to-10 scale remains a popular systematization and it is based on not one but several expert surveys, which should be suggestive of good methodology. Nevertheless, potential problems involved with such classifications are worth acknowledging.

27 Results and analysis

The interpretation of conditional logit models can be challenging since results are presented as the logged odds of an outcome occurring. The addition of interaction variables, where effects depend not only on one, but two variables makes interpretation even more difficult. To enable easier understanding, this presentation of results will focus mainly on the signs of the effects, which is also beneficial when the baseline probabilities of any coalition forming are very small. Furthermore, all effects need to be interpreted under the condition that all other variables are held constant. This may generate less intuitive results when, for instance, the seat-share variable is held constant even when the minority or party- number variables may change. In reality, these likely change together. Ultimately, I will attempt to present the results as straightforwardly as possible.

Descriptive statistics

Table 1. Descriptive statistics Variable Observations Mean Std. deviation Min Max Chosen Coalition 1,201,058 0.0012 0.0347 0 1 Economic turbulence (VIX) 579,034 20.0743 9.9703 9.46 80.74 Political turbulence 1,119,721 34.6781 23.0210 0.3699 157.21 Minority 1,193,220 0.5431 0.4981 0 1 Minimal winning 1,201,058 0.0207 0.1424 0 1 Seat share 1,201,058 47.7126 23.6537 0 1 Parties 1,201,058 5.5977 1.8153 1 12 Median party 1,201,058 0.5153 0.4998 0 1 Ideological distance 1,198,701 1.8319 0.9799 0 6.8772 Ideological range cabinet 1,198,701 5.3568 2.0739 0 9.2091 Ideological range opposition 1,198,135 5.6013 2.0295 0 9.2091 Incumbent share 1,164,688 0.4656 0.3267 0 1

Table 1 presents an overview of all variables included in this analysis, along with relevant descriptive statistics. The mean probability of any potential cabinet forming is 0.0012, which confirms the expectation of low baseline probabilities.

28 Economic turbulence has approximately half the observations of other variables, which was to be expected due to temporal limitations in the VIX index. The index has a mean value of 20.1, which can act as an indicator of when the market is stable, with the maximum value of 80.7 indicating spectacularly high turbulence. Political turbulence on the other hand has a mean value of 34.7, with the lowest change in vote share being approximately 0.4 and the highest change being 157.2.

Regression results Model 1.1 and Model 1.2 are the models that do not include turbulence or interaction terms, and thus only examines the stand-alone variables suggested by previous research. These results are shown in Table 2. Model 1.1 illustrates the regression based on the more limited number of observations defined by the temporal restrictions in the VIX data. Model 1.2 shows the regression that uses the wider set of observations present in the political-turbulence measurement.

Table 2. Logit regressions excluding interaction terms Chosen coalition Model 1.1 Std. deviation 1.1 Model 1.2 Std. deviation 1.2 Minority -0.4623** 0.2263 -0.2417* 0.1432 Minimal winning 1.2035*** 0.1495 1.0615*** 0.0965 Seat share 0.0595*** 0.0050 0.0629*** 0.0031 Parties -1.2013*** 0.0688 -1.1911*** 0.0460 Median party 0.7851*** 0.1619 0.8882*** 0.1046 Ideological distance -0.1521* 0.0852 -0.0939* 0.0513 Ideological range cabinet -0.4046*** 0.0398 -0.4528*** 0.0273 Ideological range opposition -0.0148 0.0411 -0.0178 0.0255 Incumbent share 2.0516*** 0.2030 2.0730*** 0.1303 Observations 487,118 924,876 Pseudo R2 0.3600 0.3622 Note: Standard deviations are shown in the parentheses. ***, **, and * represent 1 percent, 5 per- cent, and 10 percent significance levels.

As shown, almost all variables in Model 1.1 and Model 1.2 affect coalition formation in the anticipated way. The only variable which contradicts predictions is the ideological range of the opposition, which displays negative signs in the two models. While predictions are thus not supported here, the variable is statistically insignificant in both models. Of the variables which indeed did affect coalition choice in the anticipated way, almost all are statistically significant at the one percent level of significance in both models, except the minority variable and the ideological distance of the cabinet. The minority variable is instead statistically significant at the five percent

29 level of significance in Model 1.1 and at the ten percent level of significance in Model 1.2. The ideological distance is statistically significant at the ten percent level of significance in both models.

Table 3. Logit regressions including interactions Chosen coalition Model 2 Std. deviation 2 Model 3 Std. deviation 3 Minority -0.4628** 0.2272 -0.2223 0.1441 Minority x economic turbulence -0.3799* 0.2281 Minority x political turbulence 0.3233** 0.1399 Minimal winning 1.1943*** 0.1508 1.0453*** 0.0978 Minimal winning x econ. turbulence -0.4116*** 0.1575 Minimal winning x polit. turbulence 0.0523 0.0975 Seat share 0.0603*** 0.0050 0.0643*** 0.0032 Seat share x economic turbulence -0.0013 0.0055 Seat share x political turbulence 0.0032 0.0032 Parties -1.2034*** 0.0689 -1.1968*** 0.0463 Parties x economic turbulence 0.0237 0.0650 Parties x political turbulence 0.0468 0.0450 Median party 0.7860*** 0.1623 0.8669*** 0.1055 Median party x economic turbulence 0.0106 0.1625 Median party x political turbulence -0.1068 0.1004 Ideological distance -0.1515* 0.0854 -0.0937 0.0518 Ideo. distance x economic turbulence -0.0871 0.0886 Ideo. distance x political turbulence -0.0175 0.0508 Ideological range cabinet -0.4125*** 0.0402 -0.4544*** 0.0275 Ideo. range cab. x econ. turbulence -0.0967** 0.0449 Ideo. range cab. x polit. turbulence 0.0759*** 0.0260 Ideological range opposition -0.0148 0.0412 -0.0128 0.0263 Ideo. range opp. x econ. turbulence -0.0242 0.0408 Ideo. range opp. x polit. turbulence 0.0585** 0.0273 Incumbent share 2.0676*** 0.2038 2.0789*** 0.1319 Incumbent share x econ. turbulence 0.0077 0.1909 Incumbent share x polit. turbulence -0.01498 0.1287 Observations 487,118 924,876 Pseudo R2 0.3630 0.3642 Note: The turbulence measures have been standardized to have a mean of 0 since, for instance, a turbulence measure of 0 is not realistic. Standard deviations are still shown in the parentheses. ***, **, and * represent 1 percent, 5 percent, and 10 percent significance levels.

Model 2 and Model 3 instead include the interaction terms that examine the effect turbulence has on coalition formation, and this is therefore where the main contribution of this thesis can be found. Model 2 examines the effect that

30 economic turbulence has on coalition variables while Model 3 examines the effects of political turbulence. In both cases, the respective stand-alone turbulence variables were omitted when included as non-interaction variables, since these effects were held constant by the conditional logit regression to exclude effects that vary across coalition-formation occasions. These are therefore not illustrated in Table 3. Model 2 illustrates some significant effects of economic turbulence on coalition formation. One such effect is that turbulence changes the effect that being a minority has on the chances of a cabinet being chosen. More specifically, the negative effect of the minority variable that was illustrated originally is further enhanced, as the effect of being a minority decreases the logged odds of a cabinet forming with approximately 0.38 when economic turbulence increases by one standard deviation. Put more simply, the negative effect associated with being a minority increases by over 80 percent. This effect is statistically significant at the ten percent level of significance and the result is in accordance with predictions. Economic turbulence also affects the effect that the minimal-winning variable has on coalition formation. An increase by one standard deviation in economic turbulence decreases the previously illustrated positive effect of the variable by 0.41, and this result is statistically significant at the one percent level of significance. This means that the positive effect that being minimal winning usually has on formation chances is weakened by approximately 35 percent when economic turbulence increases. Put differently, in the cases that majority cabinets do form, it is less important that these do not contain unnecessary members if turbulence is high. Since I expected the effect of the minimal-winning variable to remain the same in turbulent, these results are not in accordance with predictions. There are a few possible explanations to this. One explanation may simply be that the minimal-winning theory generates unsatisfactory explanations of real coalition formations. In fact, the minimal-winning theory has previously received critique for failing to predict the coalitions that form in real life (Bäck, 2003b). Another possible explanation, I argue, is that the office-seeking motivations behind the minimal-winning theory are negatively affected when turbulence increases. To clarify, a fundamental explanation as to why minimal-winning coalitions are expected in the first place is that they maximize the fixed amount of office benefits involved in being in government for cabinet members (Bäck, 2003b). If turbulence increases, these motivations may diminish since incumbency during turbulence is often accompanied by voter punishment anyway, making the benefits of being in government less tempting when weighed up by its downsides. Of course, the changed effects in vote shares are controlled for in the models, but I argue that simply the threat of future punishment could steer potential cabinet members away from the task. This is further supported by Strøm (1990), who explain that parties often anticipate that incumbency is sometimes followed by

31 reduced votes, which may result in parties opting to stay out of government (Bäck, 2003b). Therefore, while the effect that the minimal-winning variable has on coalition formation still appears to be positive, it is not strange that office benefits nevertheless become a weaker motivator for parties during economically turbulent times. Another interaction variable that indicates a change in the effect that a variable has during economic turbulence is the interaction variable that examines the ideological range of the cabinet. The negative effect that a potential cabinet’s ideological range has on the chances of it forming is highlighted by economic turbulence. When economic turbulence increases by one standard deviation, the negative effect that the ideological range has on the logged odds of a coalition forming is increased by approximately 0.097. This means the negative effect increases by approximately 23 percent, and this result is statistically significant at the five percent level of significance. However, these results are not in accordance with predictions. The weakened negative effect that was anticipated is not illustrated, meaning that even though the minority and the minimal-winning interaction variables suggest larger cabinets are warranted during economic turbulence, there is no support that ideological consensus is easier to achieve. Since previous researchers appeared divided on the issue, however, these results are not very surprising. For instance, Bremer (2017) explained that while the parties he examined supported other parties’ economic measures, in other policy areas they instead shifted their positions further toward their original ideologies. Another researcher, Indridason (2008), who examined turbulence in the form of terrorist incidents, found that while cabinets are indeed more likely to be surplus coalitions following attacks, their ideological polarization tends to be low. Conclusively, I find somewhat contradictory indications that economic turbulence may generate wider coalitions, as illustrated by the minimal-winning and minority interaction variables, but that ideological differences are still more highlighted. A natural explanation for this is that while parties may aim to diffuse accountability and responsibility by searching wide support in excessive majority governments, disagreements still complicate cooperation. The remaining interaction terms in Model 2 were statistically insignificant. Many of them still showed the expected signs, as only the seat-share interaction variable and the interaction variable including the ideological distance of cabinets showed results which contradicted predictions. These effects were very small and suggest little change occurs due to economic turbulence. Furthermore, the stand- alone variable for ideological range of the opposition has not shown significant results in any of the models, so while this interaction term too did not show anticipated results, this will not be interpreted further. All other stand-alone variables maintain similar effects as in Model 1.1 and Model 1.2.

32 Model 3 instead illustrates the effects that political turbulence has on coalition formation, and quite a few interesting results are observed. Beginning with the statistically significant variables that are in accordance with predictions, the negative effect that the ideological range of the cabinet has on coalition choice is weakened. When political turbulence increases with one standard deviation, the negative effect of the cabinet’s ideological range is weakened by 0.08, which is in accordance with previous predictions since broader coalitions require a wider ideological range. This effect is statistically significant at the one percent level of significance. This means that the negative effect of the variable weakens by approximately 17 percent when political turbulence increases. Another variable which is now more in line with predictions is the ideological range of the opposition. This variable unexpectedly previously indicated that a wide ideological range of the opposition may worsen a coalition’s odds of forming, although not statistically significant in Model 1.1 or Model 1.2. This unexpected effect is now weakened significantly, which implies oppositions indeed struggle more to cooperate during turbulence. Ultimately, a one standard deviation increase in political turbulence diminishes the negative effect of the variable by 0.06, and these results are statistically significant at the five percent level of significance. This effect is in fact even larger than the original coefficient of -0.013, suggesting there may be entirely different effects of this variable depending on whether political turbulence is low or high. High turbulence indeed appears to generate the originally expected effects, while low turbulence does not. Proceeding to the remaining variables, results are not quite as consistent with predictions. Most notably, the negative effect associated with being a minority cabinet greatly diminishes in Model 3, with a change in effects of 0.32 when political turbulence increases by one standard deviation. This effect is statistically significant at the five percent level of significance. Consequently, these results imply that minority coalitions are more common during political turbulence rather than vice versa. This was not in accordance with predictions and suggests that some variable results support the prediction that broader cabinets may form during turbulence while others do not. A possible explanation is warranted. It is not entirely strange, firstly, that variables indicate opposing results when there is likely to exist correlation between variables. The more variables that are included that measure similar effects, the more likely it is that one or a few go against the main results of the analysis. Second, it must be acknowledged that these results need not necessarily be contradictory either. For instance, since the ideological range of the opposition now indicates that ideological divisions make it hard to block minority cabinets, it appears natural that minority cabinets may be more common during political turbulence since oppositions find it increasingly difficult to stop them. Therefore, while the interaction including the ideological range of the cabinet indicated that cabinets may have wider ideological ranges

33 during turbulence, minority cabinets are still allowed to a greater extent than in stable times. Put differently, ideologically wider coalitions may be warranted during turbulence, but these coalitions do not necessarily need to be majority cabinets. A possible explanation for this may be that political turbulence may coincide with the disintegration of alliances, warranting new, less ideologically cohesive minorities. In fact, researchers have previously even considered minority cabinets somewhat of a crisis phenomenon (Eppner, et al. 2019). Therefore, it is possible that while a party truce may be present among the chosen cabinets, failure to achieve such cooperation across large coalitions and in the oppositions still allow an increased number of minority cabinets to form. The remaining variables in Model 3 remained insignificant and approximately the same as previously. These include the minimal-winning variable, the median party indicator, and the incumbent share, which were all expected to remain the same during turbulence and are as such in line with expectations. Furthermore, the seat-share variable and the number of parties do show the anticipated signs but are not statistically significant, while the ideological-distance variable generates the opposite effect of what was expected but is statistically insignificant.

Concluding remarks on models My analysis illustrates several interaction effects in both Model 2 and Model 3 that suggest turbulence indeed does impact the effects standard coalition variables have on coalition formation, and these results are more than what would be expected purely by multi comparisons, or coincidence. However, some eminent differences between the effects of economic and political turbulence can be observed. Most notably, the expected implications of turbulence on coalition size are only prominent during economic turbulence. During economic turbulence, minority cabinets are noticeably less likely to form and, although somewhat unexpectedly, it is less necessary that majority coalitions are minimal winning. During political turbulence, instead, minority cabinets are increasingly popular. While not anticipated, I argue that the results regarding cabinet size are not as surprising as may seem. I originally predicted that the variables regarding cabinet size would be more likely to be affected by economic turbulence than political turbulence, since the threat facing a country and its parliament during economic turbulence is external. As in all the above-mentioned examples of party truces, wide cooperation is found when politicians are faced with a joint challenge. During political turbulence, this threat is not external but rather caused within the political sphere. As such, agreement on a solution is less likely to be consensual since no external threats force an agreement. This explains why larger coalitions may primarily be warranted during economic turbulence, and why minority cabinets may instead be a necessity when the turbulence is of a political nature.

34 Another varying result is that during economic turbulence, instead, no ideological consensus appears to occur. When the turbulence is of a political nature a consensus seems to take place as predicted, since the ideological range of the cabinet appears to have less of a negative effect during political turbulence than otherwise. This effect is not apparent when the turbulence is economical. These varying results must also be considered. A possible explanation is of course that economic turbulence highlights the ideological divisions along the economically based left-right scale in a way which political turbulence does not. Economic crises may, for instance, emphasize conflicting views on how to deal with such turmoil, making consensus more difficult to achieve during economic turbulence. Another possible explanation is that the change in vote share used as a measurement of political turbulence may capture other political effects that are not directly associated with turbulence, such as the dissolution of previous coalitions and alliances caused by drastic changes in vote shares. Such dissolutions of previous alliances may force new, less cohesive coalitions to form. It also, naturally, often coincides with new parties entering parliament. These parties are likely to not be included in existing bloc divisions, and to be less reliable due to their lower degree of experience. This creates fewer possible coalition partners for existing parliament members, which may allow coalitions with wider ideological ranges and fewer seat shares to form as new parties are excluded from government. These effects are not captured by the previously presented theory and perhaps imply that the VIX-index measurement is a better suited measurement of turbulence than the vote share is. It is possible, for instance, that more determinants need to be controlled for in Model 3. Conclusively, multiple variable effects are illustrated which suggest turbulence affect coalition formation. While these effects vary between the models, it remains clear that variables predicted by other researchers to affect coalition formation insufficiently explain the real coalition formation illustrated here. Specifically, while previous theorists appear to have developed theories that explain formation well during higher stability, several effects diminish during turbulence. For instance, the effect of the minimal-winning variable diminishes during economic turbulence and the predicted effects of the minority variable and the ideological range of the cabinet diminish during political turbulence. The results thus indicate that turbulence indeed should be considered when conducting coalition-formation research. This is further highlighted by the coefficients of determination, or pseudo R2, which are slightly higher in Model 2 and Model 3 than in the original models that excluded turbulence. Model 2 is compared to Model 1.1, where the pseudo R2 increased from 0.3600 to 0.3630, and Model 3 is compared to Model 1.2, where the pseudo R2 increased from 0.3622 to 0.3642. This suggests the turbulence models have a slightly better goodness of fit than those only including the stand-alone variables presented by other researchers.

35 Conclusions

This thesis has aimed to examine the effects of economic and political turbulence on coalition formation, using conditional logit regression with interaction variables. I have primarily utilized data from the ParlGov database, examining the effects of common coalition-formation variables during instability. The results illustrate several interaction effects that suggest both economic and political turbulence indeed does impact the effects standard coalition variables have on coalition formation. However, the exact nature of these effects is not entirely clear since the effects vary between the two models. During economic turbulence, an enhanced negative effect of being a minority and a diminished positive effect of being minimal winning both support the prediction that larger coalitions are warranted. However, a highlighted negative effect of having a wide ideological range in a potential cabinet instead suggests that ideological cohesion is hard to achieve during turbulent times. I have argued that these effects certainly can exist simultaneously, and the results thus shed light on some of the discrepancies in previous research regarding the importance of ideology during crises and instability. During political turbulence, instead, a weakened negative effect of the ideological range of the cabinet suggests ideologically wide coalitions are more common when the turbulence is of a political nature, but since oppositions find it increasingly hard to block minorities during such circumstances these are still more likely to appear. This is also apparent by the diminishing negative effect of the minority variable. Ultimately, my results show that coalition formation is greatly affected by both economic and political turbulence. While the two types of turbulence appear to affect the coalition-formation process slightly differently, it becomes clear that both types of turbulence impact the way parties form coalitions in some way. And furthermore, since this analysis utilizes data on all EU countries and most OECD democracies, results are likely to be highly generalizable. Specifically, since the ParlGov data includes countries from both the East and the West, the database can be said to include a wide range of differing analysis units which should make the results widely applicable. Large-n studies are also beneficial for the generalizability of results since the use of many observations minimizes the risk of random results and help isolate effects. Although the utilization of large-n studies is less suited for finding explanations of why these effects occur, I have

36 attempted to give explanations of why these results might emerge by utilizing existing theories regarding coalition formation and economic and political turbulence in general. Conclusively, this thesis has contributed to the existing research field by providing insight into the previously neglected area of coalition formation during instability. The analysis specifically adds to previous research by providing explanations of how variable effects vary with turbulence. It becomes clear that while some coalition-formation variables utilized by previous researchers appear to withstand the addition of turbulence, other effects as mentioned change greatly when levels of instability are considered. These results have great implications for the strength of previous coalition-formation theories since they suggest turbulence considerations in fact should not be neglected when conducting research. This should be considered by future researchers when examining the forming of governments. To further develop the research field, I suggest that other researchers test different operationalizations of turbulence to eliminate any possible effects that, for instance, may be associated with changes in vote shares rather than political turbulence as such. By doing so, any remaining uncertainties regarding the accuracy of the turbulence measurements utilized here can be discarded. It is, for instance, possible that some effects of political turbulence may be caused by institutional rather than turbulent aspects, as explained above. To examine this, future researcher may also attempt to consider other determinants of coalition formation during turbulence in their research. It needs to be considered, for instance, how the effects of institutional aspects change during turbulence. Unfortunately, examining such institutional variables was outside of the scope of this thesis, but by adding other potential determinants of coalition formation other researchers can examine the explanatory value of turbulence on coalition formation further. For instance, considerations of multi-dimensional ideological spaces may also be considered in future research, as well as the prospects of entirely different types of turbulence, such as wars or political movements. In whichever way future researchers decide to examine the effects of turbulence on coalition-formation, the results of this analysis show that it is important to do just that.

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