Political Cycles in Bank Lending to the Government∗

Michael Koetter Alexander Popov† IWH, University of Magdeburg, Deutsche Bundesbank ECB

Abstract

We study how political party turnover after German state elections affects banks’lending to the regional government. We find that between 1992 and 2018, party turnover at the state level leads to a sharp and substantial increase in lending by local savings banks to their home- state government. This effect is accompanied by an equivalent reduction in private lending. A statistical association between political party turnover and government lending is absent for comparable cooperative banks that exhibit a similar regional organization and business model. Our results suggest that political frictions may interfere with government-owned banks’ local development objectives. JEL classification: G21, D72, P16. Keywords: Government lending; Savings banks.

∗Without implicating them, we thank Philip Strahan (the editor), three anonymous referees, Viral Acharya, Claudia Buch, Serdar Dinc, Victoria Ivashina, Eeva Kerola (discussant), Olga Kuzmina (discussant), Elena Loutskina, Alberto Martin, Filippo de Marco (discussant), Felix Noth, Jana Ohls, Steven Ongena, Jean-Charles Rochet, Javier Suarez, Nikola Tarashev (discussant), Ernst-Ludwig von Thadden, Benjamin Weigert, Laurent Weill, Luigi Zingales, seminar participants at the Deutsche Bundesbank, ECB, LSE, Osnabrueck University, University of Groningen, and University of Zurich, and conference participants at the EBC-DNB-CEPR Conference "Avoiding and Resolving Financial Crises", the 6th MoFiR Workshop on Banking, the 2017 EEA annual congress, the 2017 EFA annual congress, and the 2018 University of Strasbourg "Workshop on Finance and Politics" for valuable comments. Niklas Grimm and Tim van Ark provided outstanding research assistance. The opinions expressed herein are those of the authors and do not necessarily reﬂect those of the Deutsche Bundesbank, the ECB, or the Eurosystem. †Corresponding author. European Central Bank, Financial Research Division, Sonnemannstrasse 20, D-60314 Frankfurt, email: [email protected] 1 Introduction

There are two broad opposing views on the economic implications of the government ownership of banks. The "development" view holds that government-owned banks support economic development by lending to borrowers who cannot obtain credit otherwise (Gerschenkron, 1962). According to the proponents of this view, governments can use their ownership stake in banks to develop strategic industries (Lewis, 1950), or to ﬁnance long-term development projects where private ﬁ- nance fails (Myrdal, 1968), especially in countries with weak institutions (Shleifer, 1998). The

"political" view holds that government ownership of financial institutions results in a politicized and ineffi cient resource allocation, a softening of budget constraints, and a reduction in economic effi ciency (Kornai, 1979; Shleifer and Vishny, 1994). This argument is supported by growing empirical evidence that government ownership of banks reduces bank profitability and slows down

ﬁnancial and economic development.1

Because government involvement in the ownership and management of banks around the globe is still pervasive,2 understanding whether lending by government-owned banks serves primarily a development, or rather a political objective is of ﬁrst-order importance. We go to the heart of this question by studying the political cycle of German banks’government lending. We focus on savings banks– local credit institutions established with the explicit mandate to support regional economic development. At the same time, they are governed and operated by a regional administrative entity

(typically, a city council). Local politicians may therefore be tempted to pressure savings banks to serve strictly political goals. One such goal, potentially inconsistent with a local-development objective, is funding government entities, especially if it comes at the expense of lending to the real economy. The contribution of our paper is to study how bank lending to the government– an object overlooked in empirical work– varies with election-driven political party turnover.

To trace the political cycle of bank lending to the government, we take advantage of the unique institutional setting of the German electoral and banking systems. Germany is a federation of

16 states that hold regular elections at diﬀerent points in time, to determine the composition of state parliaments and the resulting governing coalitions. In turn, the German banking system

1 See La Porta, Lopez-de-Silanes, and Shleifer (2002) for an early review, and Cull, Martinez Peria, and Verrier (2018) for a more recent one. 2 In more than a quarter of the countries in the world, the government owns more than a quarter of the total assets in the domestic banking sector (see Cull, Martinez Peria, and Verrier, 2018).

1 comprises private banks, cooperatives, and government-owned (mostly local) savings banks. Savings banks and cooperatives are similar in that– unlike private banks– they have no profit maximizing objective. Their mandate is to provide access to financial services to the community where they are domiciled. At the same time, cooperatives are free from direct political influence arising from government ownership and supervision, while savings banks are supervised by the state ministry of

finance, and local politicians sit on their board. Our identification strategy thus exploits both the staggered timing of state elections and the nature of local banking markets in Germany. We study how local savings banks adjust their government lending portfolios after state elections, depending on whether the incumbent party retains or loses power, relative to cooperative banks in the same county. With this identification strategy in hand, we study the evolution over the electoral cycle of government lending by 726 government-owned banks and 1,037 cooperatives, taking advantage of a total of 94 state elections between January 1992 and May 2018.

Our main finding is that an election that brings a new party to power at the state level is associated with a substantial increase in savings banks’lending to the home-state government. In the year after such elections, local savings banks’ lending to the state government increases by about 41.5 percent, relative to similar cooperative banks in the same county. This corresponds to an increase in lending of around 4.1 mln. euro for the average savings bank. Whereas the per-bank numerical effect is moderate, the associated aggregate effect is economically sizeable because of the large number of savings banks in Germany. For example, our estimates imply that the combined increase in government lending by the 108 savings banks in the state of Nordrhein-Westfalen in the 12 months after the 2010 state elections amounts to almost 443 mln. euro, or around 15% of the overall increase in spending by the new centre-left government during the same period.

Furthermore, once we add Landesbanken to the sample, we ﬁnd that an election that brings a new party to power at the state level is associated with an increase of around 42 mln. euro in home-state government lending by the average government-owned bank. To put this number into perspective, if a new party was to come to power in every German state today, government-owned banks would extend around 16 bln. euro in lending to state governments in the next year.

At the same time, we ﬁnd that local savings banks in states where a new party comes to power reduce private lending to the local economy by around 4.3 mln. euro in the next year, relative to local cooperative banks. This fully explains the increase in lending to the state government. Our

2 results thus suggest that a substantial share of the increase in spending by new state governments is funded by local in-state savings banks, at the expense of lending to the local private sector. We conclude that political party turnover can activate savings banks’political objectives, and it can interfere with their local development objectives.

There are two principal threats to our identification strategy. The first one is that the control group– cooperative banks– may be inappropriate. To address this concern, we first employ a matching procedure to select a sub-sample of cooperative banks that are as similar as possible– on observable bank balance sheet characteristics– to the sample of savings banks. We then base our analysis on the sub-sample of cooperative banks thus chosen. The main result of the paper also remains intact when we drop cooperative banks altogether, and only compare the behavior of local savings banks across state borders. Second, while election dates are predetermined by law, election outcomes may be endogenous to time-varying local economic conditions which also drive bank lending. We show that the main results of the paper still obtain when we look at close elections where the final result approaches a random outcome. In addition to addressing these two concerns, we also show that our results are not driven by banks’unobservable time-invariant motives to hold a particular asset class, or by unobservable shocks to lending propensity that are common to all banks in the same county at the same point in time. Finally, the statistical association between political party turnover and government lending disappears in placebo tests where we look at changes in the composition of the governing coalition and in the person of the prime minister, when the ruling party remains the same.

Our paper adds to the empirical literature on financial institutions as vehicles for assisting politicians’goals. La Porta, Lopes-de-Silanez, and Shleifer (2002) find that government ownership of banks is most prominent in low-income countries with underdeveloped financial systems and poor protection of property rights, and that it is associated with lower growth of per-capita income.

Sapienza (2004) shows that, controlling for firm characteristics, state-owned banks in Italy charge lower interest rates than private banks, especially if the political party affi liated with a given firm is stronger in the area where the firm borrows. In the same vein, Khwaja and Mian (2005) find that politically connected firms in Pakistan have easier access to credit from government banks. Brown and Dinc (2005) document evidence from emerging markets consistent with the hypothesis that governments tend to minimize the costs of political intervention before elections and, therefore,

3 intervene with bailouts after elections. Cull and Xu (2005) find that Chinese firms engaging in informal payments to government offi cials borrow more from banks. Micco, Panizza, and Yanez

(2007) find that state-owned banks located in developing countries tend to have lower profitability and higher costs than their private counterparts, and that this effect is stronger during election years. Li, Meng, Wang, and Zhou (2008) find that membership in the communist party helps private entrepreneurs in China to obtain loans from banks or other state institutions. Shen and

Lin (2012) demonstrate that political interference depresses the performance of government-owned banks in developing countries. Schoors and Weill (2015) document that Sberbank loans granted before the Russian presidential elections in 2000 to large ﬁrms nudged their employees to vote in favor of the regime, which was partly responsible for the re-election of Vladimir Putin. Related, a number of recent papers have documented "moral suasion" underpinning the bank-sovereign nexus, whereby banks are under pressure by governments to purchase sovereign bonds even when this is not necessarily in their proﬁt-maximizing interest.3 Our paper contributes to this literature by studying the link between political turnover and bank lending in an industrialized country with low corruption and good corporate governance.

Another strand of the literature has documented an electoral cycle in bank lending in a number of countries. Dinc (2005) shows that the lending behavior of government-owned banks in developing countries depends on the timing of elections. Claessens, Feijen, and Laeven (2008) show that

Brazilian firms that provide contributions to elected federal offi cials substantially increase their bank financing relative to a control group after each election, indicating that access to bank credit is an important channel through which political connections operate. Cole (2009) finds that agricultural lending by government-owned banks in India moves with the electoral cycle, and that the largest increases in lending occur right before elections. Carvalho (2014) documents that Brazilian

ﬁrms that are eligible to borrow from government-owned banks expand employment in politically contested regions prior to elections. The contribution of our paper to this literature lies in docu- menting the evolution of lending to the government by publicly owned banks around elections, an issue that has received no attention in the literature so far due to the general absence of granular data on bank lending to government entities. Our evidence is novel in another sense, too: while the

3 See Ohls (2017) and Ongena, Popov, and van Horen (2019) for evidence, and Horvitz and Ward (1987) and Romans (1966), for a discussion of the rationale.

4 extant literature has found strong bank-lending eﬀects of the electoral cycle right before elections, we show that a class of banks adjust their lending behavior after a particular kind of elections.

Closest to our analysis is the paper by Englmaier and Stowasser (2017). They show that German savings banks increase overall lending in the run-up to county elections, ostensibly to boost economic conditions, the mood of the electorate, and, ultimately, the re-election prospects of local incumbent politicians. While contaminated by political objectives, this result is not inconsistent with the development function of government-owned banks if such an increase in lending mostly targets the real sector, which Englmaier and Stowasser (2017) do not observe. In contrast, we show that after elections that bring a new party to power at the state level, local savings banks increase lending to the state government, at the expense of lending to local households. Because we observe how diﬀerent classes of loans behave along the political cycle, we are able to document a phenomenon that is diﬃ cult to square with a local development objective.

Our work is also related to the literature on the political determinants of bank behavior.

Kroszner and Strahan (1999) document that special interests aﬀected the timing of the removal of barriers to entry in the U.S. banking industry. Agarwal, Amromin, Ben-David, and Dinc (2012) show that during the recent ﬁnancial crisis, banks delayed foreclosures on mortgages located in districts whose representatives in the U.S. Congress were members of the Financial Services Com- mittee. In addition, a number of papers provide evidence that politicians in power routinely delay bad news about problems in the banking sector, both in developing and in industrialized countries

(e.g., Brown and Dinc, 2005; Imai, 2009; Liu and Ngo, 2014). We add to this literature by demon- strating that in the wake of elections in which the incumbent party is defeated, savings banks in

Germany appear to ﬁnance part of the election promises of the new government.

2 Institutional background

2.1 Elections and political parties in Germany

Germany is a federal republic which comprises 16 states (Bundesländer) and 438 counties (Kreise and kreisfreie Städte). States hold regular elections, in 4- or 5-year intervals, to determine the political composition of state parliaments and the resulting governing coalitions. In addition, county elections take place in 4-, 5-, or 6-year intervals, to determine the composition of the county

5 administration.

In our analysis, we focus on state elections and ensuing changes in bank lending to state authorities, rather than on county elections and local government lending. We do so because we want to study whether the political cycle interferes with savings banks’ local-development objectives.

Lending to the county or city administration could be part of a broader portfolio of local development objectives. Consequently, post-election changes in bank lending to the regional government could be perfectly in line with local development objectives if the new local administration uses the borrowed funds to promote regional development. In contrast, state authorities typically promote projects of state-wide importance that are farther removed from local-development objectives.

There were a total of 94 state elections between January 1992 and May 2018 in the 16 German states. State elections typically take place at diﬀerent points in time, thus leading to staggered changes in the composition of governing coalitions in German states, including neighboring ones.

This makes it possible to compare bank behavior across state borders in small and homogenous economic areas. Moreover, state elections take place at a predetermined frequency, i.e., it is very rare that snap elections are called in the middle of a term, the way it can happen at the federal level.4 Therefore, the timing of state and county elections is by deﬁnition exogenous to the local economic cycle.

Turning to the political spectrum, there are several parties in Germany that are active at all levels in all states, and that have been part of a governing coalition in at least one. The two main parties are the Christian Democratic Union (CDU) and the Social Democratic Party (SPD). The three smaller ones are the Green Party, the Free Democratic Party, and Die Linke (or the Left

Party). During the three decades since the uniﬁcation of Germany in 1991, the prime minister of each German state has always come from one of the two main parties, typically in a coalition with one or more of the remaining four parties. The only notable exceptions are the state of Baden-

Württemberg whose prime minister has been from the Green Party since 2011, and the state of Thüringen whose prime minister has been from Die Linke since 2014. While state governing coalitions are typically stable, 11 states experienced at least one change of the guard during our sample period, and 5 states experienced at least two.

4 Only two of the 94 state elections in our sample were snap elections.

6 2.2 The banking sector in Germany

The German banking system comprises private banks, cooperative banks, and government-owned banks. Private banks diﬀer from the other two types in that they are classic proﬁt-maximizing institutions. In contrast, cooperatives and government-owned banks are established with the explicit mandate to serve the public interest and to support local economic growth in the region where they are situated. As of 2018, there are 215 private banks, 910 cooperative banks, and 390 government- owned banks in Germany, including 384 savings banks and six Landesbanken. We focus in this paper on savings banks and cooperative banks.

Cooperative banks resemble credit unions in the United States and account for 14% of lending in Germany. Mutually owned cooperatives have no proﬁt maximizing objectives; instead, their mandate is to provide their member-owners with access to ﬁnancial services. Cooperatives adhere to regionally delineated markets to serve their local member communities. They are locally organized, with one to three cooperative banks in every county. Their main customers are private households and local businesses. The federal association of cooperative banks runs a national deposit insurance fund, in addition to the legal minimum insurance provided to all depository institutions in Germany.

Cooperative banks are legally independent from governmental institutions and politicians have no formal way to inﬂuence the business policies of cooperative banks.

There are two types of government-owned banks: local savings banks (Sparkassen)– typically one per county– and six head institutions (Landesbanken) as of 2018, which operate at the state level. Head institutions serve as clearing houses and capital market gateways for the local savings banks associated with them. In turn, local savings banks are set with the objective to collect savings from local depositors and use them to lend to local customers. Local savings banks are thus similar to cooperatives in that their mandate is to promote regional economic development rather than to maximize ﬁnancial proﬁt. Due to their local structure, and imposed by law, savings bank operations have always had a strong focus on the region where they operate. Already in the 18th century, local communities started establishing Sparkassen as municipal banks, collecting savings from local citizens with low income and using them to support local business start-ups. At present, the German savings banks operate a network of over 15,000 branches, employing over 250,000 people and presiding over 1 trillion euro in assets. Similar to cooperatives, their main customers

7 are local households and regional businesses. In particular, savings banks are the main creditor for the German small and medium enterprises that are traditionally considered the backbone of the

German economy. Savings banks account for around a quarter of total lending to both corporates and households, and are therefore an important player in the banking system.

Table 1 shows the number of banks by year and type over the sample period. The overall number of banks declined substantially since the reuniﬁcation of Germany. Out of 734 savings banks in

1992, only 384 savings banks remained in 2018. The decline in the total number of cooperatives is even more dramatic, from 3,132 in 1992 to just 910 in 2018. Nevertheless, the numbers reported in Table 1 suggest that during each year over our sample period, there is on average at least one savings bank and at least three cooperative banks per county, ensuring a valid comparison in bank behavior across the two diﬀerent classes of banks.

Table 2 reports the number of banks across the 16 states in the sample, for the ﬁrst (1992) and the last (2008) year of our sample period. The table makes clear that the consolidation process documented in Table 1 is more or less evenly spread across states. It also shows, not surprisingly, that the number of banks diﬀers widely across larger and smaller states, with 154 and 113 savings banks in the two most populous states (Nordrhein-Westfalen and Bayern, respectively), but only 2 in the city-state of Hamburg, and not a single one in Berlin. On the cooperative banks side, there were six states with more than 100 cooperative in 1992, and four in 2018.

2.3 Politicians and savings banks

While cooperatives and savings banks are similar in their mandate and scope of operation, they differ critically in the degree of influence that local politicians can have over their decision. For a start, each German state’s savings bank law (Sparkassengesetz) mandates that the local (county or city) senior politicians are appointed as chairmen of the supervisory boards (Verwaltungsrat) of the local savings banks, giving them considerable influence over bank managers.5 The county administration also has the formal right to send representatives to the board of directors and the central credit committee of the savings bank domiciled in the respective county. In these capacities,

5 It is true that sometimes, members of the German federal– not state– parliament (Bundestag) serve on supervisory boards of local cooperatives banks, usually those from their home county. However, in contrast to the state laws on savings banks (Sparkassengesetze) that require in all 16 states that regional politicians serve on boards ex oﬃ cio, local cooperative banks’members are not subject to any legally binding rules determining whom to appoint as the chair of this supervisory body.

8 senior local politicians have substantial inﬂuence over credit decisions that exceed the authority of the savings bank’s management, as the board of directors or the central credit committee have to vote on very large and risky loans.

Furthermore, savings bank laws provide the statutory source that the legal supervision of each state’s entire savings bank sector rests with a ministry at the state level, usually the ministry of

ﬁnance or economic aﬀairs. The political orientation of the legal supervisor thus changes once a new cabinet is formed after state parliament elections, which normally entails a change of guard at the ministry in charge of supervision.

This institutional set-up gives rise to state-local political connections that do not exist for cooperative banks. Unlike cooperative banks, Sparkassen are under the direct influence of home- state government authorities, suggesting the possibility that the political cycle at the state level can have a bearing for the decision making of local savings banks. In particular, the combination of the savings banks’ ownership structure and regional development mandate means that local governments can exert significant influence on their lending or profit disbursement decisions. This applies to local governments, but also to state ones who can apply "moral suasion" to affect the lending decisions of local banks. This channel can take two forms. Banks can be– and often are– directly pressured by the government, through explicit and implicit supervisory threats to those banks that decide not to cooperate (Horwitz and Ward, 1987; Reinhart and Sbrancia, 2015;

Romans, 1966).6 Alternatively, local politicians may use the lending capacity of the banks they are involved with to get on "good terms" with the state government.7

To summarize, cooperative banks and savings banks are well comparable because they have a similar regional structure, cater to a comparable clientele, and have an almost identical business model (Englmaier and Stowasser, 2017). At the same time, cooperatives are exempt from the direct

6 See Altavilla, Pagano, and Simonelli (2017), Becker and Ivashina (2018), and Ongena, Popov, and van Horen (2019) for recent empirical evidence. 7 The case of Stadtsparkasse Düsseldorf is an instructive piece of anecdotal evidence that county and state politics do interact– and at times interfere– with the management conduct of savings banks. This savings bank with total assets of 11 billion euro realized a substantial profit of 140 million euro in 2014. The annual financial report proposed by the CEO Arndt Hallmann to the supervisory board for approval retained almost the entire profit as reserves for general banking risks according to §340g of the German Commercial Code (HGB) although the bank was very well capitalized. The majority of the supervisory board agreed and approved the annual report, in particular the representatives of the conservative political spectrum. The chair of the supervisory board, the mayor of Düsseldorf, Thomas Geisel from SPD, however, challenged the vote and demanded a dividend for the city. The confrontation escalated and was ultimately resolved by the ruling of the State Ministry of Finance in its capacity as legal supervisor to revoke the approval of the annual financial accounts. At the time, the ministry was headed by Norbert-Walter Borjans from SPD.

9 control that local politicians exert over the lending policies of savings banks. This ensures that cooperatives are a suitable control group for an analysis looking into the impact of the political cycle on savings banks’lending.

Importantly for our purposes, political ties from either channel are determined exogenously from the perspective of the local savings bank. The political orientation of the legal supervisor changes after state elections which bring about a change of guard at the ministry in charge of supervision.

Thus, the staggered timing of state elections ensures that the potential need to pressure local banks into adjusting their lending emerges exogenously from the point of view of both the State government and the individual bank.

3 Data: Sources and patterns

3.1 State elections

Data on the outcomes of elections for state parliaments are readily available from the German

Federal Statistical Oﬃ ce. Figure 1 illustrates the staggered timing of elections, per Bundesland.

To conserve on space, we show here the years when elections were held. Note, however, that we observe the exact date of elections and match it to monthly lending and bank balance sheet data presented below.

Figure 2 indicates color-wise the lead party of the ruling coalition forming the respective state’s government. The composition of the ruling coalition can change after an election, but it doesn’t have to. For each state, the top band depicts the color of the party that became the senior partner in the governing coalition at each point in time. The lower band(s) shows the junior partner (or partners) in the said coalition. A black color stands for CDU (Christian Democratic Union of

Germany), the main center-right party, or its sister party CSU (Christian Social Union).8 A red color stands for SPD (Social Democratic Party of Germany), the main center-left party. A yellow color stands for FDP (Free Democratic Party), a liberal center-right party. A green color stands for the Bündnis ’90/Die Grünen (Alliance ’90/The Greens), an ecological center-left party. A purple color stands for Die Linke, a democratic-socialist party. After 18 of the 94 state elections in the

8 The CSU is only active in the state of Bavaria. Together, CDU and CSU form one (conservative) faction in the federal parliament (Bundestag).

10 16 German states during our sample period 1992—2018, the governing coalition changed such that there was a new senior party in the ruling coalition at the state level.

There are also instances when the composition of the governing coalition changed, but the senior party in the coalition did not. There are 58 such instances during our sample period, suggesting that more often than not, a change in the composition of the governing coalition is driven by a rotation of the junior member(s), rather than by a change of the senior partner. Such changes often happen after elections when the previous senior partner in the coalition once again garners the most votes, but it chooses to form a coalition with a diﬀerent party than the previous incumbent coalition partner.

Finally, there are also cases when the head of the state government, the prime minister, is replaced by another person. There are 49 such instances during our sample period. This pattern indicates that a change in the person at the head of the governing coalition is typically not accompanied by a change in the senior partner in the coalition. Such changes often happen outside of election periods, when the prime minister retires one or two years prior to an election to give time to the new prime minister (from the same party) to build a reputation, or assumes new political tasks, for instance at the Federal or the European level.

3.2 Bank data

To gauge the propensity of local savings banks to adjust their lending portfolios around state elections, we ﬁrst need to observe detailed information on lending to diﬀerent types of customers at the individual bank level. To that end, we use data from the monthly balance sheet statistics of

Deutsche Bundesbank. This dataset reports the stock of outstanding loans to county governments, state governments, the federal government, corporates, and households, on a monthly basis. We collect and harmonize these data for the period January 1992—May 2018, for 734 government- owned banks and 3,132 cooperatives. We deﬂate all lending data using a state-speciﬁc consumer price index, converting all aggregates into 2016 euros.

In Table 3, Panel "Full sample", we report summary statistics on the main variables of interest, for the starting sample of savings and cooperative banks. The top-left panel shows summary statistics for the various types of total lending, in thousands of real euros. The data suggest that lending to state governments at all levels is non-negligible in the case of savings banks, at 69.3 mln.

11 euro, or 3.5% of total assets. Around four-ﬁfths of that (3% of total assets) is lending to the local county government, and one-seventh (9.9 mln. euro) is lending to the home-state government. At the same time, comparable cooperative banks lend much less on average to the state government, at around 0.02% of total assets. Lending to the federal government is negligible, at between 0.9%

(in the case of savings banks) and 3.4% (in the case of cooperative banks) of combined lending to all levels of government.

The data also suggest that savings banks’lending to the state government is dwarfed by their lending to corporates and to households, at 399 mln. euro and 507 mln. euro, respectively, for the average Sparkasse, or around one-ﬁfth and one-quarter of total assets, respectively. Cooperative banks allocate a comparable share of their total assets to loans to the real sector. The only diﬀerence is that savings banks lend relatively more to local corporates, and cooperative banks lend marginally more to households in the form of mortgage loans.9

We also include information on a wide range of standard bank-speciﬁc characteristics– namely the natural logarithm of total assets, the ratio of stocks to total assets, the ratio of liquid assets

(cash and central bank reserves) to total assets, and capital ratio– at the annual level. The data are obtained from the monthly balance sheet statistics. The bottom-left panel in Table 3 provide descriptive statistics of these controls. In the empirical tests, all covariates are lagged by one year.10

4 Empirical methodology and identiﬁcation

4.1 Empirical model

Our identification strategy is based on a differences-in-differences estimation whereby we compare the propensity of local government-owned banks to adjust their loan portfolio in response to changes in political leadership at the state level, relative to comparable cooperative banks. Exploiting this identification mechanism, we model lending by bank b during electoral cycle c as follows:

Lending = αb + βkc + γsy + δElection Changesc Savings bankb Assets bc × (1) +θP ost Electionsc Savings bankb + ςXbc 1 + εbc − × − 9 We ignore consumer lending which is around twenty-ﬁve times lower than mortgage and corporate lending, which constitue the bulk of the business of both local savings and local cooperative banks. 10 Appendix Table 1 provides information on all variable deﬁnitions and sources.

12 Lending denotes the stock of loans to a particular set of customers (home-state government Assets bc in the main tests; county government, federal government, households, and corporates in robustness tests) divided by total assets, by bank b during election cycle c. Savings bankb is a dummy variable equal to one if bank b is a savings bank, and to zero if it is a cooperative bank.

Election Changesc is a dummy variable which is equal to one during the 12 months after, and to zero during the 12 months before, a state election after which a new party becomes the senior partner in the governing coalition. P ost Electionsc is a dummy variable which is equal to one − during the 12 months after, and to zero during the 12 months before, any state election. We are thus comparing changes in the lending behavior of savings banks, relative to cooperative banks in the same county, after election-driven turnover at the state-level, relative to elections after which the same party continued to govern the state.

To address concerns about autocorrelation, and following Bertrand, Duﬂo, and Mullainathan

(2004), we collapse the underlying monthly data into one observations per bank-period. The estimation of Equation (1) is thus based on one observation before an election (with data averaged over months -12 to -1), and one observation after an election (with data averaged over months

+1 to +12), per bank. In robustness tests, we employ alternative sample approaches, looking at year-end data and collapsing all information into two pre- and two post- observations.11

Elections are typically spaced at 4-year or 5-year intervals, and only exceptionally take place at

2-year or 3-year periods (see Figure 1). Therefore, by looking at banks’balance sheets during 12 months before and after elections, we make sure that the same pre- observation for one election is not a post- observation for another. Moreover, by including a P ost Electionsc dummy alongside − an Election Changesc dummy, we diﬀerentiate between elections with a change in government from all other elections. This allows us to rule out the possibility that changes in lending patterns observed after power-changing elections are simply driven by elections themselves, regardless of the outcome.

Xbc is a vector of time-varying bank-speciﬁc control variables: size (proxied by the natural logarithm of total assets); capitalization (proxied by the equity-to-assets ratio); liquidity position

11 Using end-year data is consistent with much of the previous literature (e.g., Brown and Dinc, 2005). This choice is typically prompted by data imitations: in traditional bank-level datasets, data are only reported end-year. The downside of this approach is that it blurs the distinction between elections that took place in the beginning versus the end of the year.

13 (proxied by the ratio of cash and central bank reserves to total assets); and stock holdings (proxied by the ratio of stocks to total assets). We lag these variables by one year in all regressions, to make sure that we capture their predetermined eﬀect on government lending.

Crucially, in all regressions we include a vector of bank fixed effects αb, a matrix of county electoral × cycle fixed effects β , and a matrix of state year fixed effects γ . The inclusion of αb allow us to kc × sy net out the effect of unobservable bank-level characteristics that might be fixed over a long period of time and can thus explain a large part of the cross-sectional variation in lending behavior across banks. Including these fixed effects is enormously important because any permanent differences across banks (degree of corporate control, managerial quality or taste for risk, etc.) can influence the estimates without any panel variation existing.12

βkc captures any variation in banks’lending patterns that is common to all banks in the same county at the same point in the electoral cycle, where the cycle is deﬁned as the period between

-12 and +12 months around a state election. For example, local economic conditions or investment opportunities can co-move with the electoral cycle. Other determinants of banks’ propensity to focus on a certain class of loans, like credit risk, can be correlated with election outcomes. It is therefore important to account for this possibility by holding constant background forces at the local level that aﬀect the investment incentives of both savings and cooperative banks.

Finally, the inclusion of γsy nets out the variation in banks’ propensity to extend different classes of loans that is induced by state-specific shocks that are common to all banks in the same state during the same year. By doing so, we allow for the possibility that the same banks in the same county are subject to different shocks following, e.g., an election in 1994 and an election in

2014.

We estimate the parameters of the speciﬁcation in Equation (1) using OLS. We cluster standard errors at the county level (Petersen, 2009).

The main coeffi cient of interest is δ. In a classical differences-in-differences sense, it captures the difference in lending between local savings banks (the treatment group) and local cooperative banks

(the control group) in the same county, in states with power-changing elections, versus states where elections were won by the incumbent party. For example, in the specification where Lending Assets bc 12 When two savings banks or two cooperative banks merge, we assign the post-merger fixed effect to the acquiring bank in the pre-merger period. The fixed effect of the acquired bank disappears after the merger.

14 stands for lending to the state government, a positive coeffi cient would imply that– all else equal, and relative to cooperatives in the same county– local savings banks increased total lending to the state government, as a share of total assets, in states that experienced election-diven political turnover in state government. The coeffi cient δ thus captures the difference in the overall bank lending to the state government induced by switching from the control group to the treatment group. Numerically, an election that leads to a change in the senior partner in the governing coalition at the state level leads a savings bank in this state to increase its lending to the state government by δ percent of total assets, relative to a similar cooperative bank in the same county.13

4.2 Matched sample

Our identification strategy is based on comparing the behavior of savings banks and cooperative banks in the same locality around state elections, while isolating local shocks that are common to both types of banks. Our assumption is that cooperative banks are a valid comparison group because– unlike private banks, and similar to savings banks– they have a non-profit-maximizing local-development motive, yet their governance structure insulates them from direct political pressure. At the same time, the left Panel "Full sample" of Table 3 suggests that cooperative banks are on average very different from savings banks: they lend less to state and local government, extend fewer corporate loans, are on average smaller, hold fewer stocks as a share of total assets, and are better capitalized. All of these differences are significant as indicated by normalized differences as defined by Imbens and Wooldridge (2009).14

To address this issue, and following Abadie and Imbens (2006; 2016), we conduct a propensity score matching procedure to match treated (savings) banks to non-treated (cooperative) banks. To choose a sub-sample of comparable cooperative banks, we match banks based on their observable traits 12 months prior to elections, corresponding to t = 0 in our panel setting. We use a probit

13 One caveat is in order. We do not presume to claim that projects funded by the state government reduce local welfare, rather that they increase local welfare by less than projects funded by the local government. In other words, even though the purpose of the loan can be an investment in a highway or in a new airport– possibly a perfectly useful investment– such lending is still largely inconsistent with a local-development objective as it promotes projects that are important for the state, but only marginally useful to a specific locality. From a local-development objective, the same euro will be associated with a higher marginal increase in local welfare when lent out to local businesses, local households, or the local government, than when lent out to the state government. 14 Normalized differences are the difference in means of two sub-groups, divided by the square root of the sum of the variances of the respective distributions. Imbens and Wooldridge (2009) consider these differences as statistically significant for absolute values larger than 0.25.

15 model to estimate propensity scores conditional on the following bank-level variables: the natural logarithm of total assets; the ratio of stocks to total assets; the ratio of liquid assets (cash and central bank reserves) to total assets; and Tier 1 capital ratio. For each savings bank we identify one unique cooperative bank match from the same county, and we require that the absolute difference in predicted propensity scores is not larger than 0.05. This procedure reduces the number of cooperative banks specified in estimations from 3,132 to 1,037. We also disregard 8 savings banks without a match that fulfills the caliper criterion.15

While the left-hand box plots of Figure 3 demonstrates that predicted propensity scores differ considerably between treated and non-treated banks in the unmatched sample, the right-hand box plots of predicted scores after matching exhibit virtually identical median propensities and distributions. This similarity underpins the favorable balancing properties of the employed matching procedure.16 In addition to visual inspection, the right-hand panel of Table 3 demonstrates that matched cooperative banks are also statistically not discernible from savings banks in terms of most balance sheet traits. The normalized differences are for the largest part well below the threshold indication of 0.25. The only exception is size: even the largest cooperatives are on average significantly smaller than the savings banks. However, the difference is significantly smaller than in the full sample. Moreover, when matching on covariates, the difference across savings banks and cooperatives becomes insignificant in the case of the ratio of loans to the state government to total assets, and of the ratio of corporate lending to total assets.

5 Empirical results

5.1 Main result

The headline results are reported in Table 4. They show the impact of political turnover on lending by local savings banks to the state government. We progressively saturate the speciﬁcation in

Equation (1) with different combinations of fixed effects and time-varying, bank-specific controls.17

15 Choosing alternative caliper in the range 0.02—0.1 entails matched samples that give rise to very similar headline ﬁndings. Likewise, the choice of a logit model for the estimation of predicted propensity scores results in virtually identical matches between savings and cooperative banks. 16 The comparability of matched savings and cooperative banks is further corroborated by box plots of individual covariate matches, which are provided in Appendix Figure 1. 17 All regressions in Table 4 and on are based on the matched sample from Table 3.

16 We start with a simplified version of Equation (1) with bank fixed effects, but no bank-level control variables, county electoral cycle fixed effects, state year fixed effects, and the interaction × × P ost Electionsc Savings bankb. We include bank fixed effects from the start to account for the − × fact that any differences in government lending between savings and cooperative banks on average

(see Table 3) may simply reflect some other unobserved bank traits. Because we do not control for county- or state-specific cycles, we can include the variable Election Changesc on its own, in addition to the interaction thereof with the dummy Savings bankb. The estimates reported in column (1) show that on its own, a state-election-driven change in the senior party in the governing coalition is not associated with an increase in lending to the state government, as a share of total assets, by the average bank. However, that same event is associated with a significant increase in lending to the state government by savings banks. The coeffi cient of 0.1365 suggests that a state-election-driven change in the senior party in the governing coalition is followed by an increase in savings banks’lending to the state of around 27.5 percent in the next year.

Clearly, the point estimate on Election Changesc Savings bankb is also capturing changes × in lending to the government by savings banks– relative to cooperatives– after all elections, not only after those that are won by the opposition. To address this issue, in column (2) we include the interaction P ost Electionsc Savings bankb. Thereby we can estimate the impact of power- − × shifting elections relative to elections that preserve the status quo. We find that elections that do not lead to a power change are not followed by an increase in bank lending to the government, neither on average, nor by savings banks relative to cooperative banks. State-election-driven changes in the senior party in the governing coalition are still associated with a significant increase in lending to the state government by savings banks. The magnitude of this effect is very similar to the one reported in column (1).

In column (3), we include county electoral cycle fixed effects. In this way, we net out all × variation driven by changes in local economic conditions around elections that are common to all banks in a county at the same point in time. These fixed effects alone explain close to one-quarter of the variation in bank lending to the government. With these fixed effects on the right-hand side, we can no longer identify the independent effect of the Election Changesc dummy and of the P ost Electionsc dummy. The coeffi cient on Election Changesc Savings bankb is now − × significant at the one percent statistical level, and its magnitude is more than one-quarter larger

17 than in column (2).

In column (4), we exclude the county electoral cycle fixed effects, but we include state year × × fixed effects. The inclusion of these fixed effects is crucial because local conditions that affect banks propensity to lend to the government may change over time and even co-move with the state election cycle. By including this combination of fixed effects, we make sure that changes in growth opportunities that are common to both types of banks at the level of the state are not contaminating our results. These fixed effects explain a considerably smaller share of the variation in bank lending to the government than the county electoral cycle fixed effects. Once again, the × coeffi cient on Election Changesc Savings bankb is significant at the one percent statistical level. × In column (5), we include both county electoral cycle fixed effects and state year fixed effects × × (in addition to bank fixed effects). In this way, we achieve two objectives. First, we net out all variation driven by changes in local economic conditions around elections that are common to all banks in a county at the same point in time. Second, we also allow for state elections to have a different effect on average bank lending to the government at different points in time (i.e., we allow for an election in the state of, e.g., Thüringen in 1994 to be a different event than an election in the same state in 2014). The main result still obtains: an election-driven change in the ruling party at the state level is followed by a significant increase in lending to the state government by savings banks, relative to cooperatives and relative to elections that are not associated with a power change.18 The coeffi cient of 0.2058 suggests that a state-election-driven change in the senior party in the governing coalition is followed by an increase in savings banks’lending to the state of around

41.5 percent (given an average ratio of lending to the state government to total assets of 0.496, for savings banks) in the next year.19

18 Appendix Table 2 demonstrates that the main result in this paper is robust to employing non-linear regression models to account for the structure of the government-lending data. It also demonstrates that the main result in the paper is robust to looking at changes in lending behavior for banks that always have a non-zero stock of home-state government lending. Appendix Table 3 shows that our results are not driven by the choice of a panel structure, and they still obtain when we use end-year data, and when we collapse the data into two pre- and two post- observation, averaged over 9 months each. 19 Figure 4 plots point estimates and 95-percent confidence bands for months -12 to +12, relative to the election month, for the group of savings banks and cooperatives separately, using the underlying monthly data on lending in regressions with month fixed effects. It does so for the two types of elections under consideration: regular and power-changing ones. The figure makes it clear that in the case of cooperative banks, there is no adjustment in lending relative to total assets after any type of election (left panel). For savings banks, there is no change in lending after elections that do not produce a power change (top right panel), but there is a significant increase in lending to the state government after power-changing elections (bottom right panel). This suggests that the positive sign on the main interaction term is driven by an increase in lending by savings banks, and not by a decline in lending by cooperatives. Appendix Figure 2 further presents a test of parallel trends before the election. The figure makes it

18 Finally, in column (6), we add bank-speciﬁc controls averaged over -12 to -1 months and +1 to

+12 months around state elections, and then lagged by one year. We find that shocks to none of these variables are significantly associated with changes in lending to the government, as a share of total assets, by savings banks. Including these variables doesn’tincrease the explanatory power of the regression, either. Moreover, even when lagged, these variables are on average likely to be jointly determined with bank lending. For this reason, we settle on the specification in column (5) as the preferred specification in the rest of the paper.

The estimates reported in Table 4 strongly suggest that local savings banks increase– relative to local cooperative banks in the same county– their lending to the state government when the incumbent party loses power at the state level, and a government dominated by another party takes over. What is the monetary equivalent of an increase in lending to the state government by 41.5 percent? The average savings bank has on average loans outstanding to the state government of

9.9 mln. euro. Holding assets constant, the realized increase implied by the point estimate in the preferred speciﬁcation in column (5) corresponds to an increase of around 4.1 mln. euro, for the average savings bank. This eﬀect is economically sizeable; for example, our estimates imply that the combined increase in government lending by the 108 savings banks in the state of Nordrhein-

Westfalen in the ﬁrst year after the 2010 state elections amounts to 442.8 mln. euro. Given that spending by the new centre-left government increased by about 2.9 bln. euro in the next year, the increase in local savings banks’lending to the government accounts for around 15.2% of the overall increase in government spending.

We note that this behavior mirrors an ostensible election-driven cycle of overall state borrowing, reﬂecting an election-driven cycle of public spending. We downloaded public data on the evolution of the stock of state-level credit taken by the state government from credit institutions, for all

German states, starting in 2010 when these data become available. We then matched them with the election patterns described in Figures 1 and 2. There are 6 state-level elections which result in a change of guard, and 10 state-level elections that were won by the incumbent party. As Appendix

Table 4 reports, borrowing by the state government increases on average by 5.32% over the course of 2 years in states where a new party comes to power. This compares with a decline of 8.41% over the same period after state elections won by the incumbent party. These statistical patterns suggest clear that the trend we document in Table 4 does not predate election events.

19 that a new ruling party tends to assume power with an agenda of increased public spending, partly

ﬁnanced by an increase in borrowing. Remarkably, this runs contrary to a Germany-wide trend towards reducing government indebtedness (a policy known as Schwarze Null, or Black Zero): after

9 out of 10 elections where the incumbent party was reelected for another term in oﬃ ce, borrowing declined. The evidence documented in Table 4 furthermore implies that part of this new debt ends up on the balance sheet of local savings banks.20

5.2 Crowding out of local and private lending

A number of recent papers have studied how the propensity of banks to fund the government aﬀects their capacity to support the real economy. Because of data limitations, researchers have typically looked at changes in banks’sovereign bond holdings, not in government lending.21 We now ask the natural question, does the increased propensity of local savings banks to lend to the home-state government after particular types of election outcomes have an impact on lending to local ﬁrms and households? For completeness, we also study how lending to the local county administration reacts to election outcomes, under the assumption that such lending is also consistent with a local- development mandate. We do note, however, that county lending is less than one-tenth of both household and corporate lending.

In Table 5, we study whether in the year following state elections won by the opposition, savings banks grant less credit to local private and public entities. We employ our preferred specification of Equation (1), with bank fixed effects, county electoral cycle fixed effects, and state year fixed × × effects, but without time-varying bank controls. The dependent variable is now total mortgage lending, total corporate lending, and total lending to the local government, all divided by total assets. To account for the possibility that these types of lending are mostly driven by a local electoral cycle (as in Englmaier and Stowasser, 2017), we also include an interaction between the

Savings bankbks dummy and a dummy equal to one during a year which features local (county) elections. 20 The case of Germany is representative of a general trend whereby election-driven changes in the ruling party are associated with significant changes in expenditure composition, especially in mature democracies (Brenden and Drazen, 2013). 21 Gennaioli, Martin, and Rossi (2014; 2018), Popov and Van Horen (2015), Altavilla, Pagano, and Simonelli (2017), Acharya, Eisert, Eufinger, and Hirsch (2018), and Becker and Ivashina (2018) all provide evidence that higher sovereign bond holdings by banks are associated with crowding out of lending to the non-financial sector.

20 We ﬁnd that in states where a new party comes to power following a state election, local savings banks reduce mortgage lending by approximately 2.66 percent in the next year and a half (column

(1)). This result is significant at the 5-percent statistical level. At the same time, corporate lending increases by around 2.3% (column (2)), however, this effect is not significant at any acceptable statistical level. The fact that we do not find a reduction in lending to local corporates is important as it does not support the idea that local economic conditions are deteriorating around elections in which the incumbent party lost.

Why does lending to firms and lending to households respond differently? The most natural interpretations is that lending to households, especially in the case of mortgage lending, is trans- actional in nature. Banks do not acquire proprietary information about the borrowing households, and so there is no learning involved regarding the quality of the borrowers. At the same time, banks acquire valuable proprietary information on the quality of a firm over the course of a lending relationship (e.g., Berger and Udell, 2002). This may allow banks to continue to lend to firms when credit needs to be reduced or reallocated (Rajan, 1992; von Thadden, 1995). Furthermore, as banks tend to engage in long-term lending relationships with firms– which often involves pro- viding a variety of products– they are likely inclined to continue lending to them in order to take advantage of being able to provide auxiliary business now and in the future. In the case of lending to households, which often is a one-off loan, this mechanism is not relevant.

The overall reallocation away from local lending implied by the point estimates reported in

Table 5 is substantial. Overall outstanding mortgage loans and corporate loans by the average savings bank over the sample period stands at 507 mln. euro and 399 mln. euro, respectively, in the matched sample. The point estimates in columns (1) and (2) imply a reduction in mortgage lending by 13.5 mln. euro, and an increase in corporate lending by 9.2 mln. euro. Our results therefore suggest that an election which brings a new party to power at the state level is followed by a reduction of private lending to the local economy by 4.3 mln. euro in the next year and a half, for the average savings bank. This reduction fully explains the increase in lending to the state government. Using once again the example of Germany’smost populous state, our estimates imply that the combined decline in local private lending by the 108 savings banks in the state of

Nordrhein-Westfalen in the ﬁrst 12 months after the 2010 state elections amounts to around 464.4 mln. euro.

21 Finally, we look at the effect of elections that bring a new party to power on loans by savings banks to the local government (column (3)). Higher lending to the local government would not be inconsistent with the savings banks’primary mandate, assuming that the local government spends these funds on local development. Under this assumption, there is a clear difference between the state and the local government: while the state government promotes projects that are important for the state as a whole, and are therefore only marginally useful to a specific locality (e.g., highways or airports), the local government promotes strictly local projects (e.g., parks or swimming pools).

Therefore, from a local-development objective, the same euro will be associated with a higher marginal increase in local welfare when lent out to the local government. Column (3) of Table 9, however, suggests that while power transition at the state level is associated with an increase in lending to the state government, it is not followed by an increase in lending to the local government.

Combined, the results reported in Table 4 and in Table 5 suggest that a substantial part of the increase in spending by new state governments is funded by local in-state savings banks, at the expense of lending to some segments of the real economy. We conclude therefore that government turnover can activate savings banks’political objectives, possibly interfering with their ability to fulﬁll their local development mandate.

5.3 Robustness

5.3.1 Falsiﬁcation tests

We now subject this main result to a number of falsiﬁcation tests, to make sure that it is indeed driven by a particular type of government turnover. For a start, we take advantage of changes in state government other than one party losing its status as a senior partner in the governing coalition.

We identify two such changes. The ﬁrst one includes cases when the head of the state government– the prime minister– is replaced by another person, but the governing coalition remained intact.

There are 49 such instances during our sample period, meaning that a change in the person at the head of the governing coalition is typically not accompanied by a change in the senior party itself.

Such changes often happen outside of election periods, when the prime minister retires one or two years prior to an election to give time to the new prime minister (from the same party) to build a reputation or when (s)he takes oﬃ ce in a new political function. An example of the latter is the

22 prime minister of Nordrhein-Westfalen, Johannes Rau, who stepped down in June 1998 and was subsequently elected Federal President by the Federal Assembly of Germany. In his home state, he was succeeded by Wolfgang Clement, who continued presiding over the same SPD-Green Party coalition that Rau had led until then.

The second such change includes instances when the composition of the governing coalition changes, but the senior party in the coalition remains the same. Such changes are typically election- driven, and the same prime minister continues in the function. There are 58 such instances during our sample period, suggesting that more often than not, a change in the composition of the governing coalition is driven by a rotation of the junior partner(s), rather than by a change in the senior partner. Such changes often happen after elections when the previous senior partner in the coalition once again garners the most votes, but it chooses to form a coalition with a new junior partner.

For example, after the state elections in Mecklenburg-Vorpommern in September 2006, the winner of the most votes, SPD, chose to form a coalition with CDU, ending an 8-year coalition with its previous junior partner, Die Linke. In this way, the senior partner in the coalition government

(SPD) did not change as a result of the election, and neither did the state’sprime minister of eight years, Harald Ringstorﬀ, who continued in this function for two more years.

We now re-estimate our main speciﬁcation in Equation (1) with these alternative changes in place of the Election Changest dummy. As before, the control group are elections that did not lead to a change in the governing coalition. We report the estimates from these falsiﬁcation tests in Table

6. We ﬁnd that a change in the person of the prime minister has no signiﬁcant impact on lending to the state government by savings banks, relative to cooperatives operating in the same county

(column (1)). We also find that a change in the junior partner in the governing coalition is not followed by a significant increase in savings banks’lending to the government (column (2)). This is despite the fact that a rotation in the junior partner can have a material effect on government policy (e.g., the state Ministry of Finance is often in the hands of the junior party in the coalition).

Finally, in column (3), we look at the impact of power-changing state elections on lending to the federal government. We find that this type of lending is not affected by an election-driven party turnover at the state level. Whereas the coeffi cient on Election Changest Savings bankbks is × positive, it is very small and insignificant.

23 5.3.2 "Random" election outcomes

One other concern with our approach is that while election dates are predetermined, election outcomes may not be exogenous. Banks may already adjust their behavior ex-ante, if the change in government was anticipated. Alternatively, banks may react to economic conditions which are correlated with election outcomes. For example, deteriorating economic conditions can lead banks to adjust lending away from the real sector and towards a safer asset class (e.g., government loans), and at the same time spell doom for the reelection prospects of the ruling party.

In Table 7, we put these considerations to the test. In columns (1)—(3),we repeat our main test on the sample of close elections only. We deﬁne "close elections" as elections where the diﬀerence in vote share between the largest and the second-largest party is less than the 5th, the 10th, and the

20th percentile of the sample distribution, respectively. These correspond to diﬀerences of 1.1%,

3.3%, and 4.4% of the vote share. We thereby exclude all elections where the election outcome was not "close", regardless of whether the incumbent party won or lost. The idea is that in close elections, the outcome could plausibly go either way. Therefore, whether party A or party B in the end emerges with the larger vote share approximates a "random" assignment.

In all cases, we find that when looking at close elections where the end result was not clear ex-ante, elections which lead to turnover in government are associated with a significant increase in lending to the state government by savings banks, relative to cooperatives. In two of the three cases (columns (1) and (3)), the point estimate is significant at the 1-percent statistical level, and numerically larger than the one in Table 4, column (5). In the third case (column (2)), the effect is significant at the 10-percent level.

We broadly conﬁrm the main result of the paper in an alternative sample of elections with a

"random" outcome: elections that are either close, or the outcome is "surprising", i.e., the party that emerges with the most votes is not the one that was projected to win in the run-up to the election by local forecasting companies (columns (4)—(6)). The estimates reported in Table 7 should serve to assuage any remaining concerns that power-changing elections are a poor instrument for exogenously activated political frictions aﬀecting savings banks’lending behavior.

24 5.3.3 Alternative channels

In Table 8, we address the concern that the eﬀect we observe is driven by other bank shocks that coincide with particular election outcomes. It is possible that at the same time when state elections bring a new party to power at the state level, government-owned banks are facing concurrent shocks to their government lending propensity– unrelated to political distortions– that similar cooperative banks in the same region(s) are not experiencing.

We ﬁrst note that in 2007 and 2008, ﬁve Landesbanken (Sachsen LB, West LB, Bayern LB,

Landesbank Baden-Württemberg, and HSH Nordbank) that had invested substantially in the U.S. subprime mortgage market before the ﬁnancial crisis, declared signiﬁcant losses. Because savings banks in the respective federal states were required by law to provide support to their respective

Landesbank (for details, see Puri, Rocholl, and Steﬀen, 2011), they became at the time less likely to engage in other activities, such as making loans or purchasing securities. In column (1), we account for this shock by including an interaction between the Savings bankbks dummy and a dummy equal to one if the bank operates in a state whose Landesbank required public assistance. We ﬁnd that our main result still obtains after controlling for this concurrent shock, even though its numerical magnitude declines somewhat.

In column (2), we augment Equation (1) with an interaction between the Savings bankbks dummy and the bank’sequity ratio. In this way, we account for the possibility that the propensity to increase lending to the government in the wake of power-changing elections coincides with a loan portfolio reallocation due to shocks to savings banks’regulatory capital. The point estimates suggest that this particular concurrent shock does not explain away the propensity of local savings banks to increase lending to the state government after power-shifting elections. At the same time, lower regulatory capital on its own is associated with a signiﬁcant decline in lending to the government, for savings banks relative to cooperatives.

In column (3), we find that local savings banks are marginally more likely to lend to state governments that are higher-rated.22 This fact suggests that lending to state governments is inconsistent with government-owned banks’objectives to lend to less credit worthy customers who face diffi culties obtaining credit from private banks. This effect is not statistically significant, and the

22 German state governments are on average very highly rated (see Appendix Table 5).

25 eﬀect which captures the mechanism related to political frictions continues to obtain.

5.3.4 Alternative samples

Our identification strategy throughout the paper is based on using cooperative banks in the same locality as a control group, and including county electoral cycle fixed effects to account for shocks × that are common to both treatment and control banks in the same locality. Furthermore, all our regressions so far use a comparison group that is chosen to be similar to the treatment group in terms of observables, such as size, liquid assets, and regulatory capital.

Yet, cooperative banks could in theory have objectives unobservable to the econometrician that can correlate with election results and can thus bias the estimates of δ. In column (1) of Table

9, we address this concern parametrically by excluding cooperative banks from the sample. We estimate a version of Equation (1) whereby we simply compare local savings banks at the same point in time across counties and states. Because there is on average one savings bank per county, we can no longer include county electoral cycle fixed effects. However, we can still include bank × fixed effects and state year fixed effects. In this way, we are comparing savings banks in localities × with an election-driven power change to savings banks in states where the ruling party is not voted out of power. The point estimate from this test is significant at the 1-percent statistical level. The magnitude of the coeffi cient suggests that savings banks in states that experienced an election- driven power change at the state level increased lending to the government by 26.1 percent, relative to savings banks in states where elections confirmed the status quo.

We next study the role of Landesbanken. These are a special case of government-owned banks: they are very large, they are not local, and they are close to the state government by default. For this reason, we have excluded them from the analysis so far. At the same time, the bulk of lending to the state government after a new party comes to power may come from them. The exclusion of

Landesbanken would then underestimate the true magnitude of election-driven changes in public banks’lending to the government.

To address this question, we now account for the behavior of Landesbanken in the analysis.

In column (2), we estimate Equation (1) after including Landesbanken in the treatment group.

The point estimate in this regression is at par with the one reported in Table 4, column (5).

With Landesbanken in the sample, the average government lending by savings banks– local and

26 central– is around 106 mln. euro. When Landesbanken are included in the sample, the holdings of state loans by the average public banks are 0.57 percent of total assets. The coeﬃ cient of 0.2143 implies that lending is higher by 37.6 percent, which corresponds to an increase of around 39.9 mln. euro. The elasticity of government lending to electoral change once we include Landesbanken in the analysis suggests that if a new party was to come to power in every German state today, government-owned banks would extent around 15.6 bln. euro in lending to state governments in the next year.

One final concern is that an empirical strategy based on comparing changes in bank behavior across counties and states can produce biased estimates in the presence of unobservable trends which differ across counties and which affect different banks in different ways. Economic conditions can vary across counties at the time of elections. For example, the creditworthiness of retail customers that borrow from savings banks in such localities may be deteriorating. This would make savings banks less willing to extend loans to the real sector, and more willing to lend to the government instead. The specification in Equation (1) allows us to estimate the average effect of political frictions net of the impact of individual bank characteristics. However, our results can still be contaminated by a host of unobservable factors that render the population of a county in a state without electoral change a poor control group.

To assuage such concerns, we adopt a version of the empirical strategy used by Card and Krueger

(1994) and Huang (2008), among others. We compare individual banks in adjacent counties across neighboring German states, one of which experienced a post-election power change, while the other did not. The assumption is that two neighboring counties are really one economic area when it comes to observable factors, such as economic growth and industry structure, and to unobservable factors such as growth opportunities or labor supply. Hence, any discernible diﬀerences in the portfolio composition between certain types of banks can be attributed to changes in political alignment in one county but not in the other.

Columns (3) and (4) report the estimates from this test. By focusing on neighboring counties across state borders, we lose about 56% of all observations, but we still have plenty of variation left.

We adopt two diﬀerent procedures. We ﬁrst use all savings banks and all propensity-score-matched cooperative banks in a county (column (3)), and we also add Landesbanken to the analysis (column

(4)). Our main result continues to obtain in these considerably more restrictive speciﬁcations.

27 However, in column (3), the estimated coeffi cient is significantly different from zero at the 5- percent, rather than at the 1-percent, statistical level, and is 40-percent lower than the headline estimate. This suggests that when comparing banks across all counties, our results may be upward- biased by different unobservable economic circumstances which correlate positively with savings banks’propensity to lend to the home-state government. Even so, the analysis continues to point to a genuine political effect distorting banks’ incentives to engage in government lending. This effect appears to be uncontaminated by concurrent unobservable adjustments– at the level of the county– in local market conditions that affect savings banks and cooperative banks differently.

5.3.5 Election issues

The result we document in this paper can be sensitive to a number of election issues. For example, the eﬀect might depend on whether the new governing coalition is dominated by a more or less ideologically conservative party. For example, it is natural to conjecture that a left-wing government would be more likely to increase (social) spending, and therefore be in higher need of bank funding.

The response of a local savings bank may also depend on whether the local government is dominated by the same party as the new government at the state level. In this case, it might be easier for the state government to apply "moral suasion" on the local government, and thus on the local savings bank, to fund its new spending priorities. Finally, the eﬀect we observe may be contaminated by the local electoral cycle. Englmaier and Stowasser (2017) show that total lending by local savings banks is substantially higher during an election year, and declines afterwards. If a state election takes place shortly after county elections in the state, then part of the increase in lending to the state government can be due to local savings banks’reshuﬄ ing their lending portfolio away from local lending.

In Table 10, we augment Equation (1) with a variable that captures one of the above considerations. None of these election events matter in a material way. In column (1), we find that a government led by a centre-right party is less likely to be funded by local savings banks. This effect is intuitive and economically meaningful, but it is not significant in the statistical sense. Next, to address the issue of local elections, we collect information on 3,064 county election events over the sample period to determine the strongest party to emerge following a local election. This allows us to control for whether the change of guard at the state level is associated with an emerging

28 political alignment or misalignment between state and local government. We ﬁnd that the extent of this political (mis)alignment has no impact on the propensity of local savings bank to support the state government with loans (column (2)). Finally, in column (3)) we control for whether the post-state-election reshuﬄ ing of bank portfolios is taking place during a county election year. We

ﬁnd that the main result in the paper is not contaminated by a concurrent readjustment of banks’ lending portfolios around county elections. In fact, we ﬁnd that county elections themselves have no bearing on the propensity of local savings banks to extend loans to the state government. At the same time, in all three cases the main result of the paper continues to obtain: local savings banks are substantially more likely to fund the state government after an election in which the incumbent party is voted out of power.23

6 Conclusion

Using detailed monthly balance sheets for a matched sample of 726 savings banks and 1,037 cooperative banks in Germany between January 1992 and May 2018, we investigate how political party turnover at the state level aﬀects local banks’lending to the state government. We exploit changes in the composition of governing coalitions at the state level resulting from 94 staggered elections in 16 states. Savings and cooperative banks have similar local-development objectives, yet local politicians are involved in the management, and state politicians in the supervision, of savings banks, but not of cooperatives. The similarity in their mandate, combined with the diﬀerence in political control, makes cooperative banks an appropriate control group for studying how savings banks respond to changes in government.

We show a robust new fact: following an election that brings a new party to power at the state level, local savings banks increase strongly and signiﬁcantly their lending to the home-state government, and reduce overall lending to the local economy by an equivalent amount. Comparable cooperative banks in the same county do not adjust their behavior either way, which is consistent with the absence of local politicians from their governance and management structure. We also ﬁnd that the same election event does not lead the same savings banks to adjust their stock of lending to

23 A ﬁnal robustness test addresses the fact that the three city-states in our sample– Berlin, Bremen, and Hamburg– comprise only one county (two in Bremen), hence there is little-to-no within state variation. Appendix Table 6 conﬁrms the baseline results when we drop these three city-states individually and jointly.

29 the federal or to the local government, and it is absent when we look at changes in the composition of the governing coalition which do not involve a change in the senior party. Our paper is thus the

ﬁrst to document a robust link between the electoral cycle and bank lending to the government.

Our evidence suggests that political turnover at the government level can activate savings banks’political objectives, and by doing so can have a negative impact on their local development objectives. Given that comparable savings banks– government-owned and committed to similar development objectives– exist in many other countries (e.g., Cajas in Spain, Casse di Risparmio in

Italy, Sparebank in Norway, and Sberbank in Russia), the question we attempt to answer in this paper potentially has implications for the link between politics and bank lending that go beyond the borders of Germany.

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34 Figure 1. Election patterns in Germany between 1991 and 2018, by state

Note: This figure shows the year of regular state parliament elections, between 1991 and 2018, for the 16 German states.

Figure 2. Coalition composition changes between 1992 and 2018, by state and year

Note: This figure shows changes in the governing coalition at the state level, by year, between 1992 and 2018, starting with the status quo in 1991. The senior coalition partner is shown in the first row. The junior coalition partner is shown in the second row. If the ruling coalition consists of more than two parties, the third coalition partner is shown in the third row. A black color stands for CDU (Christian Democratic Union of Germany), the main center-right party, or its sister party CSU (Christian Social Union), which is only active in the state of Bayern. Together, CDU and CSU form one common parliamentary group in the federal parliament. A red color stands for SPD (Social Democratic Party of Germany), the main center-left party. A yellow color stands for FDP (Free Democratic Party), a liberal center-right party. A green color stands for the Bündnis 90/Die Grünen (Alliance ’90/The Greens), an ecological center-left party. A purple color stands for Die Linke, a democratic-socialist party. An orange color stands for a local party that is active in one state only (such as the Schill Party in Hamburg, or the South Schleswig Voters’ Assoication (SSW) that represents the Danish minority in Schleswig-Holstein).

Figure 3. Propensity scores before and after matching conditional on bank observables

Balance plot propensity score Raw Matched 1 .8 .6 .4 .2 0

Cooperatives Savings

Note: This figure depicts propensity scores estimated using a probit model to match (treated) savings banks to (untreated) cooperative banks conditional on natural logarithm of total assets, the ratio of stocks to total assets, the ratio of liquid assets (cash and central bank reserves) to total assets, and Tier 1 capital ratio. We match savings and cooperative banks based on observable traits 12 months before an election. For each savings bank we identify exactly one matching cooperative bank from the same county and require that the difference in predicted propensity score is smaller than 0.05. Box plots of covariate matches are shown in Appendix Figure 1.

Figure 4. Regression coefficients and confidence bands for lending to the state government, monthly data: Savings banks versus cooperatives, for power-changing versus regular elections

Note: The figure shows coefficient plots obtained from regressing indicator variables for each of the twelve months preceding and succeeding two different types of elections for savings banks and matched cooperative banks, respectively, on the share of lending to the state government relative to gross total assets. “No change” denotes election events without a turning of the leading party. “Change” indicates elections, after which the incumbent leading party lost office. The reference period is the month when either the incumbent or the new state government is inaugurated. Regressions are estimated in event time including bank fixed effects. Standard errors are clustered at the county level.

Table 1. Savings banks and cooperative banks: Evolution over time

Year Savings banks Cooperative banks 1992 734 3,132 1993 715 2,909 1994 691 2,770 1995 638 2,661 1996 623 2,586 1997 606 2,506 1998 597 2,418 1999 589 2,253 2000 567 2,036 2001 547 1,793 2002 528 1,620 2003 510 1,490 2004 491 1,389 2005 472 1,330 2006 457 1,286 2007 447 1,249 2008 442 1,225 2009 432 1,190 2010 429 1,151 2011 427 1,132 2012 424 1,115 2013 421 1,096 2014 415 1,072 2015 414 1,041 2016 411 1,017 2017 397 969 2018 384 910 Note: The table shows the number of banks per year separated according to ownership (savings banks vs. cooperatives). For both groups, we exclude head institutions (“Landesbanken” and “genossenschaftliche Zentralbanken”). The sample is based on those banks that report at least total assets to the monthly balance sheet statistics of Deutsche Bundesbank between January 1992 and May 2018.

Table 2. Savings banks and cooperative banks: Evolution across states

Savings banks Cooperative banks State 1992 2018 1992 2018

Baden-Württemberg 87 51 658 183 Bayern 113 65 841 247 Berlin N/A N/A 6 3 Brandenburg 35 11 51 11 Bremen 3 2 4 2 Hamburg 2 2 9 6 Hessen 42 32 256 73 Mecklenburg-Vorpommern 28 9 42 9 Niedersachsen 67 40 334 103 Nordrhein-Westfalen 154 92 401 138 Rheinland-Pfalz 38 23 211 46 Saarland 10 6 39 8 Sachsen 50 12 74 18 Sachsen-Anhalt 36 13 59 15 Schleswig-Holstein 34 10 90 34 Thüringen 35 16 57 14 Note: The table shows the number of banks by state, in 1992 (the starting point of the sample) and in 2018 (the end-point of the sample), separated according to ownership (savings banks vs. cooperative banks). For both groups, we exclude head institutions (“Landesbanken” and “genossenschaftliche Zentralbanken”). Commensurate with de jure and de facto delineation of regional markets, banks are allocated to states on the basis of their headquarter location.

Table 3. Savings banks and cooperative banks: Summary statistics

Full sample Matched sample Savings banks Co-operative banks Savings banks Co-operative banks Normalized Normalized Mean St. dev. Mean St. dev. difference Mean St. dev. Mean St. dev. difference

Dependant variables

0.586 1.790 0.091 0.933 0.245 0.496 1.652 0.197 0.930 0.158 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑙𝑙

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 0.059 0.372 0.028 1.015 0.028 0.031 0.199 0.024 0.174 0.026 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 3.005 2.614 0.618 1.132 0.838 2.959 2.557 0.479 0.890 0.916 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑔𝑔𝑔𝑔𝑔𝑔 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 19.932 6.194 17.252 7.012 0.286 20.055 6.107 18.815 7.401 0.129 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 25.000 8.260 25.869 9.479 -0.069 25.478 7.612 25.796 10.190 -0.025 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Bank𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇-𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎level controls

Ln (Total assets) 14.101 1.139 11.975 1.159 1.309 13.999 0.961 13.253 0.812 0.593

4.814 4.828 1.802 4.051 0.478 4.869 4.843 3.826 5.850 0.137 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 2.081 0.824 2.003 0.968 0.062 2.105 0.746 2.178 0.825 -0.066 𝐿𝐿𝐿𝐿𝐿𝐿Tier𝐿𝐿𝐿𝐿 𝐿𝐿1 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎capital ratio 4.379 1.190 5.126 1.566 -0.380 4.392 1.141 4.691 1.058 -0.192 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 Observations 6,992 26,135 6,993 7,141 Banks 734 3,132 726 1,037 Note: The table shows summary statistics of the main and alternative dependent variables, events, and covariates for all (Panel “Full sample”) and for propensity-score matched (Panel “Matched sample”) local savings banks and local cooperative banks. The sample is based on monthly data between January 1992 and May 2018. Nominal variables are converted to 2016 euros, using a state-specific consumer price index. With the exception of Ln (Total assets), all variables are in percentage points. The columns labelled ‘Normalized difference’ provide the difference in means of the respective sub-samples of savings banks and co-operative banks, divided by the squared root of the sum of the variances of each sample. For variable definitions and sources, see Appendix Table 1.

Table 4. Political cycles in banks’ lending to the government: Main result

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (1) (2) (3) (4) (5) (6) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

Election change × Savings bank 0.1365** 0.1400** 0.2058*** 0.1686*** 0.2058*** 0.2065*** (0.0536) (0.0594) (0.0699) (0.0606) (0.0709) (0.0721) Post-election × Savings bank -0.0091 -0.0339 -0.0404 -0.0339 -0.0318 (0.0339) (0.0367) (0.0303) (0.0373) (0.0370) Election change -0.0127 -0.0145 (0.0372) (0.0391) Post-Election 0.0060 (0.0174) Ln (Total assets) 0.0284 (0.1078) Stocks / Total assets -0.0076 (0.0087) Liquid assets / Total assets 0.0001 (0.0216) Tier 1 capital ratio 0.0624 (0.0470) Observations 14,134 14,134 14,134 14,134 14,134 14,134 R-squared 0.576 0.576 0.800 0.617 0.800 0.800 Bank FE Yes Yes Yes Yes Yes Yes County × Electoral cycle FE No No Yes No Yes Yes State × Year FE No No No Yes Yes Yes Standard errors clustering County County County County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party

43 becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity-score matched local cooperative banks. Ln (Total assets) denotes the natural logarithm of the bank’s total assets, 1-year lagged. Stocks / Total assets denotes the bank’s total stocks divided by total assets, 1-year lagged. Liquid assets / Total assets denotes the bank’s total cash and Central Bank reserves divided by total assets, 1-year lagged. Tier 1 capital ratio denotes the bank’s total equity divided by total assets, 1-year lagged. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Table 5. Political cycles in banks’ lending to the government: Crowding out of local lending

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (1) (2) (3) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 Election change × Savings bank -0.6789** 0.4555 0.0435 (0.3265) (0.2801) (0.0817) Post -election × Savings bank 0.0972 0.0916 0.0655 (0.1373) (0.1095) (0.0402) County election × Savings bank -0.9834*** -0.3926 -0.4196*** (0.3399) ( 0.3066) (0.1017) Observations 14,134 14,134 14,134 R-squared 0.940 0.922 0.907 Bank FE Yes Yes Yes County × Electoral cycle FE Yes Yes Yes State × Year FE Yes Yes Yes Standard errors clustering County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable is the bank’s total residential real estate lending, divided by total assets (column (1)); the bank’s total corporate lending, divided by total assets (column (2)); and the bank’s total lending to the local government, divided by total assets (column (3)). Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. County election is an indicator variable equal to one during a year when a local (country) election takes place. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity- score matched local cooperative banks. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Table 6. Political cycles in banks’ lending to the government: Falsification tests

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (1) (2) (3) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

Election change × Savings bank 0.0073 (0.0119) Prime minister change × Savings bank 0.0894 (0.0553) Junior coalition partner change × Savings bank 0.0628 (0.0523) Post-election × Savings bank 0.0070 -0.0236 -0.0052 (0.0454) (0.0420) (0.0046) Observations 14,134 14,134 14,134 R-squared 0.800 0.800 0.721 Bank FE Yes Yes Yes County × Electoral cycle FE Yes Yes Yes State × Year FE Yes Yes Yes Standard errors clustering County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable is the bank’s total lending to the home-state government, divided by total assets (columns (1) and (2)), and the bank’s total lending to the federal government, divided by total assets (column (3)). Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Prime minister change is an indicator variable equal to one after the State Prime minister is replaced without a change in the governing coalition. Junior coalition partner change is an indicator variable equal to one after a change in the junior coalition partner. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity- score matched local cooperative banks. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Table 7. Political cycles in banks’ lending to the government: “Random” election outcomes

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Close elections Close or surprise elections 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 5th percentile 10th percentile 20th percentile 5th percentile 10th percentile 20th percentile (1) (2) (3) (4) (5) (6)

Election change × Savings bank 0.2353*** 0.1445* 0.2763*** 0.2253** 0.1548 0.2765*** (0.0868) (0.0780) (0.1009) (0.1094) (0.1052) (0.1007) Post-election × Savings bank -0.0729 -0.0761 0.0393 0.0750 -0.0364 0.0356 (0.0555) (0.1220) (0.0743) (0.2077) (0.1611) (0.0738) Observations 928 2,134 3,599 1,821 2,574 3,755 R-squared 0.955 0.870 0.882 0.904 0.837 0.872 Bank FE Yes Yes Yes Yes Yes Yes County × Electoral cycle FE Yes Yes Yes Yes Yes Yes State × Year FE Yes Yes Yes Yes Yes Yes Standard errors clustering County County County County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity-score matched local cooperative banks. In columns (1)–(3), only those elections are included where the difference in the vote shares between the largest and the second largest company was less than the 5th, the 10th, and the 20th percentile of the distribution of sample election outcomes, respectively. In columns (4)–(6), only those elections are included where: 1) the difference in the vote shares between the largest and the second largest company was less than the 5th, the 10th, and the 20th percentile of the distribution of sample election outcomes, respectively; or 2) the party that won the most vote was not forecast to do so by the official forecasting institutes. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Table 8. Political cycles in banks’ lending to the government: Alternative channels

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Landesbank shock Tier 1 equity State ratings 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 (1) (2) (3)

Election change × Savings bank 0.1775*** 0.2061*** 0.1726** (0.0650) (0.0712) (0.0770) Post -election × Savings bank -0.0516 -0.0533 -0.0256 (0.0401) (0.0378) (0.0521) Shock × Savings bank 0.0001 -0.1486* 0.1452 (0.0001) (0.0878) (0.0903) Observations 14,134 14,134 14,134 R-squared 0.800 0.801 0.798 Bank FE Yes Yes Yes County × Electoral cycle FE Yes Yes Yes State × Year FE Yes Yes Yes Standard errors clustering County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Shock corresponds to an indicator equal to one if a local savings bank was tied to a Landesbank affected by the fallout of the US subprime mortgage crisis (column (1)); to the bank’s Tier 1 equity (column (2)); and to the rating—by Moodys and/or Standard & Poors—of the respective German state in which the bank is domiciled (column (3)). Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity-score matched local cooperative banks. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Table 9. Political cycles in banks’ lending to the government: Alternative treatment and control groups

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Contiguous Excluding co- With 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎Contiguous counties, with operative banks Landesbanken counties Landesbanken (1) (2) (3) (4)

Election change × Savings bank 0.2143*** 0.1250** 0.1263** (0.0655) (0.0622) (0.0604) Post -election × Savings bank -0.0379 -0.0051 -0.0152 (0.0369) (0.0809) (0.0814) Election change 0.1295*** (0.0458) Post-election -0.0030 (0.0267) Observations 7,009 14,291 6,243 6,294 R-squared 0.593 0.804 0.863 0.869 Bank FE Yes Yes Yes Yes Electoral cycle Yes No No No County × Electoral cycle FE No Yes Yes Yes State × Year FE Yes Yes Yes Yes Standard errors clustering County County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity-score matched local cooperative banks. In column (1), we exclude co-operative banks. Column (2) shows results for the full sample of local government-owned and co-operative banks, and also includes Landesbanken in the treatment group. Column (3) shows results for the full sample of local government-owned and co-operative banks, for counties that border each other across state borders where one state experienced a change in the senior partner in the governing coalition, and the other one did not. Column (4) shows results for the full sample of local government-owned and co-operative banks, also including Landesbanken, for counties that border each other across state borders where one state experienced a change in the senior partner in the governing coalition, and the other one did not. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Table 10. Political cycles in banks’ lending to the government: Election issues

Centre-right vs.𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 Party𝑔𝑔𝑔𝑔𝑔𝑔 𝑔𝑔alignment𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑙𝑙Controlling for centre-left county𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎-state county elections (1) (2) (3)

Election change × Savings bank 0.3059** 0.2143** 0.2086*** (0.1303) (0.0980) (0.0706) Post-election × Savings bank -0.0282 -0.0339 -0.0317 (0.0374) (0.0373) (0.0365) Election change × Savings bank × Center-right -0.1752 (0.2071) Savings bank × Center-right -0.1752 (0.2071) Election change × Savings bank × Party alignment -0.0183 (0.1451) Savings bank × Party alignment 0.0233 (0.1004) County election × Savings bank 0.0418 (0.0690) Observations 14,134 14,134 14,134 R-squared 0.800 0.800 0.800 Bank FE Yes Yes Yes County × Electoral cycle FE Yes Yes Yes State × Year FE Yes Yes Yes Standard errors clustering County County County Notes: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity-score matched local cooperative banks. Center-right is an indicator variable equal to one if the senior partner in the post-election coalition at the state level is the CDU, or its Bavarian sister party CSU. Party alignment is an indicator variable equal to one if the senior partner in the post-election coalition at the state level is the same as the senior partner at the county level. County election is an indicator variable equal to one during a year when a local (country) election takes place. Savings bank is an indicator variable equal to one for local government- owned savings banks, and to zero for local cooperative banks. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Appendix Figure 1. Balancing properties of bank-specific covariates after propensity score matching

Box plots before and after matching on observables Cash Equity Raw Matched Raw Matched 15 10 8 10 6 4 5 2 0 0

Size Stocks Raw Matched Raw Matched 18 50 40 16 30 14 20 12 10 0 10

Cooperatives Savings

Note: Box plots on Size, equity, stock share, and cash

Appendix Figure 2. Parallel trend test, power-changing elections

Note: The figure shows coefficient plots obtained from interacting indicator variables for each of the twelve months before and after an election, respectively, with an indicator equal to one for savings banks. This indicator equals zero for a sample of matched cooperative banks. We also specify monthly pre- and post-election dummies interacted with an indicator equal to zero for election spells where the incumbent state government was re-elected into office versus one for election spells during which the incumbent leading party lost office. The reference period is the month when the new state government is inaugurated. Regressions are estimated in event time including bank fixed effects, event time fixed effects, and county-by-calendar time fixed effects to account for regional business cycles. Standard errors are clustered at the county level.

Appendix Table 1. Variable definitions and sources

Variable Unit Definition Source Dependent variables

P.p. The ratio of the stock of loans extended to the home-state government to Bista

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 the bank’s total assets, in percentage points. 𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 P.p. The ratio of the stock of loans extended to the local county administration Bista

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 to the bank’s total assets, in percentage points. 𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 P.p. The ratio of the stock of loans extended to the federal government to the Bista

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 bank’s total assets, in percentage points. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 P.p. The ratio of the stock of residential mortgage loans to the bank’s total Bista

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 assets, in percentage points. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 P.p. The ratio of the stock of loans to non-financial corporations to the bank’s Bista

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 total assets, in percentage points. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 Control variables

Ln (Total assets) € The natural logarithm of total assets, measured in ‘000 of 2016 euro. Bista

P.p. The ratio of traded stocks to the bank’s total assets, in percentage points. Bista

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 P.p. The ratio of the stock of liquid assets (cash and reserves with central Bista

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 banks) to the bank’s total assets, in percentage points. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 Tier 1 capital ratio P.p. The ratio of core capital to risk-weighted assets. BBK

Political variables

Election change 0/1 A dummy variable equal to during the 12 months after a state election SSO which resulted in a change in the senior partner in the governing coalition, and to zero otherwise.

Post-election 0/1 A dummy variable equal to one during the 12 months after a state election, SSO and to zero otherwise.

Junior partner change 0/1 A dummy variable equal to one during the 12 months after a state SSO election which resulted in a change in the junior partner in the governing coalition, but no change in the senior partner, and to zero otherwise.

Prime minister change 0/1 A dummy variable equal to one during the 12 months after the prime SSO minister was changed, with no change in the senior or in the junior partner in the governing coalition, and to zero otherwise.

Close election 0/1 A dummy variable equal to one during the 12 months after a state Cantow election in which the vote share difference between the largest and the second-largest party is less than the 5th, 10th, or 20th percentile of the sample distribution.

Surprise election 0/1 A dummy variable equal to one during the 12 months after a state Cantow election in which the party that won the most vote was not forecast to do so by the official forecasting institutes, and to zero otherwise.

Party alignment 0/1 A dummy variable equal to one during the 12 months after a state SSO election where the party that won the most votes was the same as the part in power at the county level, and to zero otherwise.

Center-right 0/1 A dummy variable equal to one during the 12 months after a state SSO election in which CDU or CSU became the senior party in the governing coalition, and to zero otherwise.

County election 0/1 A dummy variable equal to one during a year when a county election is SSO taking place, and to zero otherwise.

Treatment variable

Savings bank 0/1 An indicator equal to one if the bank is a savings bank directly or indirectly BBK held by the local government, and to zero if the bank is a cooperative banks.

Notes: BBK is the acronym for Deutsche Bundesbank, the German Central Bank. Bista abbreviates the monthly balance sheet statistic of BBK. SSO abbreviates State Statistical Offices. Cantow information is extracted from www.walrecht.de

Appendix Table 2. Political cycles in banks’ lending to the government: Non-linear regression models

Probability (Government

lending=1) 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (1) (2) (3) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 LPM Probit Lenders only

Election change × Savings bank 0.0794*** 0.3299*** 1.0299** (0.0241) (0.1339) (0.4135) Post-election × Savings bank -0.0104 -0.1208 -0.5122** (0.0140) (0.0759) (0.2300) Observations 14,134 13,896 3,640 R-squared 0.758 0.947 Bank FE Yes Yes Yes County × Electoral cycle FE Yes Yes Yes State × Year FE Yes Yes Yes Standard errors clustering County County County Note: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable is a dummy variable equal to 1 if the bank is lending to the state government (columns (1) and (2)), and the bank’s total lending to the home-state government, divided by total assets (column (3)). Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government-owned savings banks, and to zero for propensity-score matched local cooperative banks. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS (columns (1) and (3)) and Probit (column (2)). Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.

Appendix Table 3. Political cycles in banks’ lending to the government: Alternative sample set-up

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (1) (2) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 End-year data 9-month frequency

Election change × Savings bank 0.1327* 0.1822*** (0.0708) (0.0601) Post-election × Savings bank -0.0284 -0.0189 (0.0207) (0.0218) Observations 21,426 29,249 R-squared 0.791 0.798 Bank FE Yes Yes County × Electoral cycle FE Yes Yes State × Year FE Yes Yes Standard errors clustering County County Notes: In column (1), two post- and two pre-election end-year observations for each bank are used. In column (2), two post-election and two pre-election observations, averaged over 9 months each, are used. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government- owned savings banks, and to zero for propensity-score matched local cooperative banks. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For variable definitions and sources, see Appendix Table 1.

Appendix Table 4. Changes in the stock of State debt around elections

State 2-year percentage change in public debt

States where incumbent party lost Baden-Württemberg 2.23% Hamburg 6.10% Niedersachsen 12.34% Nordrhein-Westfalen 18.49% Schleswig-Holstein -6.58% Thüringen -0.65% Average 5.32%

States where incumbent party won Bayern -18.52% Berlin -1.79% Brandeburg -4.06% Bremen 5.58% Hessen -2.09% Mecklenburg-Vorpommern -5.11% Rheinland-Pfalz -5.28% Saarland -0.80% Sachsen -43.10% Sachsen-Anhalt -8.98% Average -8.41% This table summarizes average changes in the stock of State public debt during the 2 years after State elections, for those elections in our sample for which data on State public debt are available. The elections in question are: Baden-Württemberg (2011); Bayern (2013); Berlin (2011); Brandenburg (2014); Bremen (2011); Hamburg (2011); Hessen (2013); Mecklenburg-Vorpommern (2011); Niedersachsen (2013); Nordrhein-Westfalen (2010); Rheinland-Pfalz (2011); Saarland (2012); Sachsen (2014); Sachsen-Anhalt (2011); Schleswig-Holstein (2012); and Thüringen (2014). Data on State debt (Staatsverschuldung) are obtained from https://www.haushaltssteuerung.de/staatsverschuldung- deutschland.html.

Appendix Table 5. Timing and existence of State ratings

Moodys Standard and Poors Rating Date Rating Date

Baden-Württemberg Aaa 14.12.1999 AAA 06.03.2012 Bayern Aaa 20.01.2000 AAA 19.01.2012 Berlin Aa1 15.12.2006 Brandenburg Aa1 15.12.2006 Bremen Hamburg Hessen AA 29.11.2005 Mecklenburg-Vorpommern Niedersachsen Nordrhein-Westfalen Aa1 04.03.2014 AA- 20.12.2004 Rheinland-Pfalz Saarland Sachsen AAA 19.01.2012 Sachsen-Anhalt Aa1 15.03.2007 AA+ 16.12.2010 Schleswig-Holstein Thüringen Notes: This table reproduces ratings reported in "Issuer Guide Deutsche Bundesländer 2015" (Nord LB). Only State ratings as opposed to occasional bond issues are considered. Quarters before the reported time of rating are considered not rated. Ratings are converted into 16 categories in ascending order of quality, which corresponds to the number of prime ratings by both rating agencies.

Appendix Table 6. Political cycles in banks’ lending to the government: Excluding single-county States and Bremen

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (1) (2) (3) (4) (5) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 Excluding Excluding Excluding Excluding all Baseline Berlin Bremen Hamburg three

Election change × Savings bank 0.2058*** 0.2058*** 0.2045*** 0.2145*** 0.2132*** (0.0709) (0.0709) (0.0709) (0.0711) (0.0710) Post-election × Savings bank -0.0339 -0.0339 -0.0306 -0.0378 -0.0345 (0.0373) (0.0372) (0.0373) (0.0373) (0.0374) Observations 14,134 14,083 14,068 14,052 13,935 R-squared 0.800 0.801 0.800 0.801 0.801 Bank FE Yes Yes Yes Yes Yes County × Electoral cycle FE Yes Yes Yes Yes Yes State × Year FE Yes Yes Yes Yes Yes Standard errors clustering County County County County County Note: The level of observation is bank-period, with one pre-election and one post-election observation per bank-election, averaged over 12 months before and 12 months after an election, respectively. The dependent variable in all columns is the bank’s total lending to the home-state government, divided by total assets. Election change is an indicator variable equal to one after a state election which results in a new party becoming the senior partner in the governing coalition. Post-election is an indicator variable equal to one after a state election. Savings bank is an indicator variable equal to one for local government- owned savings banks, and to zero for propensity-score matched local cooperative banks. Column (1) replicates the baseline result from column (5) in Table 4. The three subsequent columns exclude one by one those states with only one county where no within-state variation of the Election change indicator is possible (Berlin, Bremen, and Hamburg). Column (5) excludes all three city-states. The sample period is January 1992 until May 2018. All regressions are based on the matched sample from Table 3. The model is estimated using OLS. Robust standard errors are clustered at the county level. */**/*** denote significance at the 10%/5%/1% levels, respectively. For precise variable definitions and sources, see Appendix Table 1.