Fighting Inequality Or Buying Votes? the Political Economy of Redistributive Transfers. Evidence from the Italian Earned Income Tax Credit

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Fighting Inequality or Buying Votes? The Political Economy of Redistributive Transfers. Evidence from the Italian Earned Income Tax Credit.

Silvia Vannutelli∗

Boston University

January 31, 2019

Abstract

Can electoral motives explain welfare transfers’ design? Do programmatic welfare programs increase pro-incumbent voting? If present, how persistent are electoral rewards? Do rewards extend to different levels of government? I address these questions by examining the political economy of a large welfare transfer in Italy, exploiting quasi-exogenous variation in the share of beneficiaries at the local level. I start by investigating the targeting of the program. Places with higher treatment intensity exhibit higher historical vote shares for the incumbent but also higher vote shares for the main opposition parties in 2013 elections, and high electoral participation. Notably, however, places with higher income inequality received significantly less allocations. Turning to electoral consequences, the program yields significant electoral returns to the incumbent: a 1 standard deviation increase in the share of recipients leads to a 3 percentage points increase in the incumbent party’s vote share in subsequent elections. The effect persists and exhibits an inverted U-shape pattern over time. Electoral rewards are coming from mobilization of core-supporters rather than persuasion of swing voters in the short run, while the opposite is true in the long run. Electoral rewards also extend to mayors aligned with the incumbent party in local elections.

∗email: [email protected]. I am grateful to my advisors, Ray Fisman and Daniele Paserman, for useful comments and suggestions.Please don’t cite or circulate. 1 Introduction

”Politics is all about who gets what, when, how” (Harold Lasswell 1936)

The design of taxes and transfers crucially affect the level of redistribution. Ultimately, these decisions lie at the heart of politics. Understanding the link between redistributive policies and electoral outcomes is thus crucial for democratic accountability and welfare redistribution. Elections incentivize politicians to calibrate policy to voters’ preferences, but this incentivizing effect of electoral accountability crucially depends on voters’ retrospective evaluations. If politicians allocate transfers in order to please their voters, then understanding whether and how their voters respond is a crucial part of the accountability mechanism. From a political economy point of view, if politicians are electorally motivated when adopting redistributive policies, this may turn them away from optimal redistributive policy choices. If mostly needy voters are not the most responsive ones in terms of voting and political participation, redistribution may be shifted away from them and targeted to other groups, with substantial negative impacts on aggregate welfare (see, among others, Finan and Mazzocco 2017). This may help to explain why we observe little redistribution when inequality rises and why the most needy in a society don’t always receive the highest share of social spending. It is also crucial to understand how long the electoral rewards last. If electoral rewards for beneficial policy decay rapidly, then re-election pressures induce poli- cymakers to bias policy toward opportunistic short-term solutions and to under- invest in more sustainable long-term efforts aimed at improving overall welfare. Finally, it is important to ask whether electoral benefits extend to different levels of government. If local incumbents get rewarded for actions taken by the national level, this may pose democratic accountability at risk and impact negatively voters’ welfare. Local politicians may be less prone to adopt social policies at the local level, and local elections might favor candidates who are less competent but better at claiming credit. In this paper, I address these questions empirically. I investigate the electoral consequences of the introduction of the largest Italian welfare transfer program in April 2014, a month before national-level elections for the European Parliament. The program is very similar in its structure to the Earned Income Tax Credit (EITC), the most famous transfer program in the US. It entails a transfer of 80

2 euro per month for all payroll employees having a gross annual income between 8 and 26,000 euros. Program eligibility thus depends both on the type of occupation (only payroll employees could be eligible) and on the income level of the individual. Over 10 million workers, corresponding to 40% of Italian payroll employees and 20% of the eligible voters, benefited from the policy in 2014 and in the following years. The transfer indeed received a lot of attention from its introduction, as it can be clearly seen from Figure ??. The picture depicts data from Google Trends on the relative frequency of the search for the term “80 Euro” over 2014. Notably, the peak of searches is registered in the week of 25th May, when the European Elections occurred1. To evaluate the electoral effects of the policy, I exploit the quasi-random geo- graphic variation in program intensity at the local level, induced by pre-determined variation in the income and occupation distribution. I apply a panel difference-in- difference design comparing electoral performance over time in places with more vs. fewer beneficiaries among the voting eligible population. To conduct the analysis, I construct a panel of city-level data merging together voting data for both local and national elections and information on local income and occupation distribution, spanning the period from 2000 to 2018. In the first part of my analysis, I investigate the targeting of the program, evaluating if electoral motives might explain the structure of the policy. To do so, I exploit information about past electoral performance of the incumbent and estimate to what extent this predicts treatment intensity at the local level. I find evidence that places with higher treatment intensity exhibit higher historical vote shares for the incumbent but also higher vote shares for the main opposition parties in 2013 elections, and high electoral participation. Notably, however, places with higher income inequality received significantly less allocations. Furthermore, I exploit detailed information on local income distribution to estimate the coverage of over 150 simulated transfers. Results show that the actual transfer structure is the one maximizing the average share of recipients over the eligible population. Turning to evaluate electoral consequences, my results show that the program yielded significant electoral returns to the incumbent: a 1 standard deviation increase in the share of 1Google’s approach in constructing this index generates results that are strictly ordinal; no cardinal interpretation can be applied to the values. The index is created by determining the number of searches for a particular search term as a share of the total number of searches conducted in each time and place. Locations/places with higher index values are those where searches for a particular term relative to total searches are higher. This could occur because of more absolute searches for that term or fewer other searches.

3 recipients (around 5% of the electorate) leads to a 3 percentage points’ increase in the incumbent party’s vote share, from a pre-treatment mean of 28 %. When I look at the dynamics of the treatment effect, I found that the positive electoral rewards persist and exhibits an inverted U-shape pattern over time. These facts are particularly relevant and new to the literature. Previous studies finding pro-incumbent rewards for distributive allocations look at measures that are clientelistic in nature and/or from which recipients can be excluded discretionally. Furthermore, most theoretical models predict that, even if present, electoral rewards should last only in the short run and disappear once the policy becomes permanent. To the best of my knowledge, I am the first documenting significant and long-lasting electoral rewards for a purely programmatic and permanent policy. I then turn to inspect the mechanisms behind the estimated effects. I exploit detailed information about other parties’ vote shares and electoral turnout to disentangle between two potential channels, i.e. the mobilization of new supporters who were not showing up to vote - turnout channel - or the persuasion of existing voters who would otherwise have voted for other parties. I found suggestive evidence that the program acts mostly through mobilization in the short run, while persuasion becomes more relevant in the longer run. In the last part of the paper, I investigate down-ballot consequences of the program. I exploit the exogenous staggered structure of the Italian city-level mayoral elections, comparing cities that happened to go to elections before vs. after the introduction of the program. There are numerous reasons why we may see an electoral effect at the local level. First, voters may have difficulty distinguishing between levels of government. They might mistakenly attribute the program success to municipal governments. Second, voters might simply want to signal their favor for the national incumbent also in local elections, and thus support also local candidates aligned with the national party. I find evidence that electoral rewards also extend to mayors aligned with the incumbent party in local elections, while no effect can be detected for incumbent mayors. This leads to reject the hypothesis of voters’ attribution bias.

1.1 Literature

This paper speaks to diﬀerent strands of the literature:

4 1.1.1 Political economy of Conditional Cash Transfers

Scholars have long observed that the particularistic exchange of benefits for electoral support is more common in developing countries (e.g. Hicken 2011, Scott 1969). Therefore, most of the literature focuses on electoral returns to public programs in low and middle-income countries. The bulk of this literature focuses on the political returns to conditional cash transfer programs and examines the voting behavior of program beneficiaries, yielding mixed results. The cleanest evidence is the one of Manacorda et al. (2011), who finds large impacts on pro-incumbent voting among beneficiaries of a randomized conditional cash transfer program in Uruguay. Similarly, De la O (2013) shows large effects of Mexico’s Oportunidades program on national incumbent party performance; however, Imai et al. (2017) re-examine the paper and find null effects. Linos (2013) and Zucco (2013) find large electoral returns of cash transfer programs in Honduras and Brazil for the incumbent parties. Labonne (2013) provides evidence of similar pro-incumbent effects of a large randomized CCT in the Philippines, the effect though being present only in competitive, non-dynastic municipalities. On a slightly different line, Pop-Eleches and Pop-Eleches (2012) document the electoral effects of a transfer program to help poor families to purchase computers in Romania. They document a positive electoral premium for politicians holding office at the moment in which vouchers are distributed, even though the policy was originally adopted by a different party. My paper contributes to this literature by providing the first analysis of a large welfare program in the context of a developed country. Differently from other papers, I study the effects of an earned income tax credit which differs from standard conditional cash transfers in two salient aspects. First, it is entirely automatic, requiring no action on the side of individuals and leaving no scope for manipulation of eligibility conditions. Second, it is entirely programmatic, namely that, at the moment of the adoption, transfer receipt was not linked in any way to incumbent will. This makes the transfer very different and not susceptible of forms of clientelist exchange that might instead be in place for CCTs, particularly in developing countries. Furthermore, most of the

1.1.2 Electoral Accountability and Political Budget Cycles

My paper also speaks to a more general set of studies investigating the link between electoral cycles and public spending. Since the seminal work of Rogoﬀ and Sibert (1988) and Rogoﬀ (1990), a vast literature has documented the exis-

5 tence of political budget cycles (Alesina et al. 1997, Persson and Tabellini 2003, Brender and Drazen 2005). More specifically, some papers have documented the so-called phenomenon of “pork-barrel” cycles in the US, where congressmen try to influence voters by targeting federal money to their constituencies before the elections. (Levitt and Snyder 1997; Drazen and Eslava, 2006; Kriner and Reeves 2012). A similar phenomenon has been documented by Golden and Picci (2008) for Italy, with districts electing more influential politicians were awarded higher shares of infrastructure spending. Lastly, a number of related papers document the use of transfers as a way to influence elections at different government levels (Brollo and Nannicini 2012, Bracco et al. 2015). Most of these papers take the electoral response as an assumption motivating the spending cycles, but only few of them empirically test if and how voters respond to increased spending. My paper contributes to this literature by providing empirical evidence of the effects of increased spending, in the form of a welfare transfers, on subsequent election outcomes and for different levels of government.

Taking a whole look at the literature, there is currently no paper providing an estimate of the electoral effects of programmatic welfare transfers, both in the short run and in the long run, and across different levels of government. In addition to the primary empirical puzzle about the existence and magnitude of a government spending-driven change in voting behavior, my paper addresses a number of open questions about the mechanisms through which the effect operates: whether the electoral support gains of incumbents are driven primarily through the mobilization of citizens, who would have stayed away from the polls in the absence of government spending benefits, or whether the spending mat- ters because it persuades former supporters of the opposition parties/candidates to switch vote for the incumbents; and whether politicians at different levels of government are able to reap away some of the electoral benefits getting credit for policies they haven’t put in place in the first place. The remainder of the paper proceeds as follows. Section 2 outlines a simple conceptual framework discussing theoretical reasons behind voters’ electoral response to transfers and politicians’ distributive choices. Section 3 provides some background information about electoral competition and welfare system in Italy and details on the policy. In Section 4 I describe the main data source used in the paper. In Sections ?? and 6, I

6 discuss identiﬁcation strategy and analyze the main results. Section 7, concludes.

2 Conceptual Framework

2.1 Who Receives Distributive Allocations?

When dealing with distributive politics, the first question is how politicians determine transfers’ beneficiaries. Political economists and political scientists have developed two main models to describe determinants of politicians’ distributive allocation choices: the “core voter” model concludes that parties primarily target voters with whom they share common traits (Cox and McCubbins 1986); on the other side, the “swing voter” model suggests that parties target voters with no strong ideological attachment, which are easier to buy off with benefits (Lindbeck and Weibull 1987, Dixit and Londregan 1996 and 1998). Both of these models are referred to The empirical literature has reported conflicting findings regarding whether parties employ core or swing strategies (e.g. Ansolabehere and Snyder 2006, Dahlberg & Johansson 2002, Hicken 2011, Larcinese et al. 2006, Stokes et al. 2013, Diaz-Cayeros et al. 2016). Of course, politicians may employ multiple strategies concurrently (Kramon & Posner 2013, Magaloni et al. 2007, Niehaus 2006), or subsequently. Parties may turn to targeting swing voters when pandering their core supporters’ base is no longer sufficient to political victory. Independently of the group targeted, a puzzle remains: in presence of secret ballot, why politicians should offer transfers to buy off voters whose voting behavior they are unable to monitor? In presence of vote secrecy, limiting the possibility of politicians to monitor individuals’ voting, politicians would have an incentive to deliver transfers prior to elections only if they can’t make credible promises to their voters. As suggested by Nichter (2008), a plausible alternative answer could be that voters use transfers for “turnout buying”, rather than for vote-buying. While in presence of vote-buying parties are expected to target “swing” voter- s/marginal opposers, turnout buying instead predicts that parties would target their un-mobilized core supporters. Turnout buying requires less monitoring, as for parties might be sufficient to observe who goes to vote, rather than actual voting choices. While Nichter assume voters that are homogeneous in their vote costs, it is important to consider that different groups might differ in their mobilization ability and political participation. If this is the case, we can expect parties to target those who have the lowest voting costs among the set of un-mobilized sup-

7 porters. Previous empirical evidence in the turnout literature show that turnout increases with income and education, so the local income distribution may also be relevant in determining which voters are targeted because of their mobilization costs.

2.2 Why Should Voters Reward Politicians for Transfers?

A vast theoretical literature in political science and political economy has given different answers to why voters should reward incumbents for transfers. The standard political agency model views retrospective voters’ reward as a crucial part of the electoral accountability mechanism. (Barro 1973; Ferejohn 1986; Besley 2006). In this setting, voters reward politicians’ behavior retrospectively when choosing whether or not to re-elect them. General pocketbook voting theories, on the other hand, present mechanisms through which a voter would reward political leaders for receiving a cash transfer, even if that cash is a product of an objectively-targeted social program independent from incumbent’s activity. Ac- cording to both of these models, voters are rational actors, voting for the party that will provide the most benefits. Finally, behavioral economics explanations have been put forward in recent years. In this view, voters reward politicians who benefit them with transfers for a sense of reciprocity (Finan and Schechter, 2012; Lawson and Greene, 2014). Overall, theoretical predictions crucially differ according to the type of transfer. Two dimensions are relevant for the defini- tion of tranfers’ type: a) clientelistic vs. programmatic transfers, depending on whether individual’s eligibility depends on politicians’ discretion or on publicly stated rules that can’t be manipulated (see, among others, Hicken (2011) and Barhan and Mokerjee (2017)) ; b) temporary vs. permanent transfers, the dis- tinction being based on the expected duration of the transfer, and in particular whether it is expected to last independently of the electoral cycle (see Golden and Min (2013) for a review on this point). Standard theoretical models with rational voters and full information can easily explain only clientelistic, temporary transfers. On the other side, if voters have incomplete information about politicians’ type, then any type of transfers can be expected to have electoral effects, if voters use the transfer as a signal to infer politicians’ preferences. In presence of full information about politician types, only reciprocity models are able to rationalize electoral effects of programmatic transfers which are perceived as permanent by voters. In my setting, the transfer could be considered as both programmatic and

8 permanent.

2.3 Down-Ballot consequences

Are electoral rewards concentrated at the national level or should we also expect them to extend at the local level? On one hand, if voters accurately reward with vote for good performance to the appropriate levels of government there is no reason why local politicians aligned with the democratic party should benefit from a program they played no role in helping enact or implement. However, existing research show that voters may fail to do so (Burnett and Kogan 2017, Healy, Malhotra and Mo 2010, Rogers 2013, Sances 2017). On the other hand, if the pro-incumbent effect at the national level operates primarily through the mobilization of new supporters, these benefits might be shared broadly by local aligned candidates among the newly mobilized voters.

2.4 Testable Implications

Based on the above discussion, I can derive a number of testable implications:

1. If pursuing turnout-buying strategy, the incumbent will tend to target its un-mobilized core supporters. Places with historically higher level of support for the incumbent party are expected to receive higher transfers. These places are then expected to exhibit both higher turnout and higher support for the incumbent.

2. In the long-run, in presence of turnout-buying, the compliers (those who went to vote only as a consequence of the transfer) are less likely to show up to the polls again, compared to individuals who were already voting. On the other side, vote-buying of weak opposers among stable voters may have a stronger back-lash eﬀect than turnout buying: the latter keep on going to vote but voting for the opposition, while the former may simply not show up to the poll anymore.

3. assuming that all voters below median income would be potential core- supporters of the Democratic party, the latter would target the relative

9 richer ones as they have lower voting costs - easier to mobilize

4. Program beneﬁciaries are expected to reward incumbent with vote in the short run (independently of the underlying model).

5. Program beneﬁciaries are expected to reward incumbent with vote in the short run (independently of the underlying model).

6. Changes in policy type (from temporary/discretionary to programmatic/permanent) may induce changes in beneﬁciaries’ behavior.

7. If voters are reciprocal, they may still reward politicians also in the longer run, even when they cease to have any scope in transfers’ delivery.

8. In presence of attribution bias, all local-level incumbents running for re- election should experience an electoral reward from program beneﬁciaries.

9. Absent attribution bias, we could still observe some local-level reward, but only from mayors aligned with the national party (if beneﬁciaries want to express their gratitude/reciprocity towards the national incumbent, they could do so also in local elections by voting for an aligned local candidate).

3 Institutional Background

3.1 A brief on Italian Political History

Italy is a parliamentary republic with a multi-party system, historically characterized by frequent government turnovers 2. Parliamentary elections are held every 5 years, according to electoral systems that have been changing over time. In the period between 1946 and 1992, the so-called “First Republic”, a fully proportional electoral system was in place, leading to a fairly fragmented party system and the formation of multi-party coalition governments. Coalitions were always lead by the Christian Democratic party, a center-right moderate party, ensuring

2In the past 70 years, 65 diﬀerent governments were formed

10 continuity and stability. The main objective was to keep the Italian Communist Party (PCI), the strongest Communist Party in Western Europe, out of power in order to maintain Cold War equilibrium in the region. Gradually, the Socialist Party was also included in the governing coalitions. In 1994, after the eruption of a large corruption scandal called “Tangentopoli” (from “tangenti”, the Italian word for bribes), the whole political system went under a turmoil, thousands of politicians went under investigation, both the Cristian Democratic Party and the Socialist Party were dismantled while the Communist Party changed its name to become the Democratic Party. The change was so abrupt that the following years were branded as the “Second Republic”. Together with the corruption scandal, the shift from a pure proportional to a mostly majoritarian system also altered the political landscape. New parties emerged and the multi-party system slowly evolved towards a “bi-polar” system, with a set of parties gathered around a center-right and a center-left coalition. On the center-right, after the disruption of the Christian Democratic party, a new liberal movement, Forza Italia, gained wide support among moderate voters. The leader of this party was Silvio Berlusconi, a man coming from media business that seemed to have no ties with old parties and the old corruption system. On the center-left, after the end of the Cold war, the former Communist Party, now called Democratic Party adopted more moderate positions gaining votes and becoming the leading party of the center-right coalition. In 1994, Berlusconi surprisingly won the national elections, but his government lasted only a few months because the Northern League, an independentist party, quickly withdrew its support. A series of center-left governments lead the country between 1996 and 2001 and between 2006 and 2008, while Berlusconi was again in power from 2001 to 2006 and from 2008 to 2011. In 2011 the peak of the economic and sovereign debt crisis forced Berlusconi to resign and lead to the formation of the technical government with Mario Monti, an economics professor from Bocconi, as Prime Minister. In 2013, new parliamentary elections are held, giving chaotic results and producing a new earthquake in Italian politics, larger than the one of 1994. The two main coalitions lost almost 10 million votes (22 % of the voting-age population, 28% of the voters), while a new party, the 5 Star Movement (M5S) was able to get 25 % of the votes and entered the Parliament for the ﬁrst time with a third of the seat. The Democratic Party and the center-right coalition, led by Berlusconi, got respectively 29.55 % and 29.18 % of the votes. The Democratic Party is in charge of forming the government. In the meanwhile,

11 a new set of scandals and judiciary issues force Berlusconi out of politics. A large coalition government of both center-right and center-left is ﬁnally formed after two months, with a Prime Minister from the Democratic Party, Enrico Letta. In December 2013, the mayor of Florence Matteo Renzi won the primary election and became the new secretary of the Democratic Party. Three months later, on February 2014, Matteo Renzi forced Enrico Letta to resign and built a new government with the same parliamentary majority. Even before becoming prime minister, Renzi advocated the need to adopt some large institutional reforms of the electoral law, the welfare system and labor market regulations. In may 2014, European Elections are held all across Europe. While voters in other European countries expressed support for protest groups, Eurosceptic and extremists, the Italian electorate gave a huge endorsement to the pro-European Democratic Party (PD) guided by Matteo Renzi. The PD took 41% of the vote, the best showing by a party in a national election since 1958. Less than two years later, in a referendum vote on a large constitutional change3, aimed at changing the composition of the Senate and reshaping the structure of autonomy across diﬀerent levels of government, Matteo Renzi is defeated. This is also a very salient election, with very high turnout compared to previous ref- erenda. Contrary to 2014, this time the PD was defeated in in almost all cities (give exact numbers). After the defeat, Matteo Renzi resigns as prime minister but the PD remains the main party guiding the government. Finally, in 2018, new national political elections are held. Matteo Renzi is again the front-runner and the PD is again defeated, reaching a down-peak of 18%. Figure 2 gives a visual depiction of the timeline of relevant events. After the defeat, Mr. Renzi resigned as prime minister. In March 2018, the last national parliamentary elections are held, seeing a massive drop in votes for both the Democratic Party and the center-right party of Berlusconi, who got respectively 18 % and 16 % of votes, and the incredible, unexpected surge of both the Northern League and the 5 Star Movement, who are currently leading the government together.

The turbulent evolution of the Italian political system is crucial to understand electoral competition and its changing patterns over time. It also allows to under-

3As final part of the official review process outlined in the Constitution, a Constitutional Referendum is necessary to finalize the reform. The Referendum took place on December 4th 2016 and the “No” won with 60 % of votes, after a strong anti-reform campaign led jointly by both opposition parties, the Five Star Movement and the center-right party of Berlusconi.

12 stand why European Parliament Elections are as salient as national elections. As a founding member of the European Union, Italy always played a crucial role in European politics, and at the same time Italian politics always affected the EU. Italian citizens vote to elect the members of the European Parliament. European Elections are held every five years and, for the period of my analysis, spanning the years from 1999 to 2018, they are always held a year after national elections. For this reason, they represent a crucial moment to signal approval or discon- tent with the incumbent government without necessarily directly affecting the existence and survival of the government per se. Contrary to other countries, Eu- ropean Elections average turnout is no different than the one in national elections.

In the last part of the paper, I evaluate whether electoral rewards from national policies extend to different level of government. Therefore, it is crucial to have a basic understanding on the division of power below the national level. Italy is characterized by three sub-national levels of government: 23 regions (regioni), 110 provinces (province), and over 8000 municipalities (comuni). The structure of power is fairly decentralized, with both Regions and municipalities exert a large degree of autonomy and spending power on a number of issues, such as social services, education and health. Local elections are held every five years in municipalities to elect a mayor and a city council. The timing of election is staggered in a way that can be considered as good as exogenous, as it depends mainly on historical reasons related to the distant past. Figure 16 shows the share of cities going to election every year, giving a clear sense of the variation that I am going to exploit for identification. Mayors are subject to a two-term limit and are elected according to slightly different rules depending on population thresholds: for “small” cities (below 15000 inhabitants), a first-past-the-post rule is in place, while for “large” cities a runoff round will take place if no candidate reaches the absolute majority. The runoff rule creates strong incentives to form electoral coalitions. Notably, however, over 90 % of Italian municipalities have less than 15000 inhabitants. In the vast majority of municipalities, coalitions are rarely formed and local candidates are not usually aligned with national parties. This could limit the scope of my analysis, as only XX % of the mayors could be identified as being aligned with the incumbent party.

13 3.2 The 80 Euro Bonus, Italy’s EITC

Italy was one of the European Countries mostly severely affected by the recent economic crisis. While attention on the Euro crisis has been focusing primarily on Greece and Cyprus, the impacts of the great recession on the italian economy are comparable to the ones of the Great Depression. According to the bank of Italy, between 2009 and 2013, “total consumption of Italian households dropped from nearly 970 billion euro in 2010 to 909 in 2013. While foreign demand kept supporting exports, the recession was prolonged by the stagnation of internal demand” (Andini et al. 2018). It is thus not surprising that one of the first announcements of the new Democratic Party’s Prime Minister, Matteo Renzi, was the creation of a large welfare transfer that could support consumption and help recovering internal domand. The policy was officially announced as the “80 euro” bonus on March 12th. At the end of April 2014, exactly a month before the European Elections, the government formally adopts the Law Decree 66/2014, introducing the “80 Euro Bonus” 4. This is a tax-credit benefiting payroll employees with a total gross annual income between 8,145 and 26,000 euros The bonus was granted under the condition of having earnings subject to a positive tax. People with gross annual income below €8,145 do not pay any taxes and thus were excluded. The threshold of €8,145 applies to individuals who worked the entire year, but it might be lower for workers who have been employed for less than 365 days. Enrollment in the transfer is entirely automatic: the employer is required to assess eligibility based on individual’s annual total income under the employment contract and re- duce tax contributions automatically withheld, thus increasing the monthly wage bill by 80 euro. A small phase-out region was introduced for earnings between 24,000 and 26,000, where the amount of the bonus is reduced linearly with income. Figure 3 plots the structure of the policy, which is indeed very similar to the structure of the Earned Income Tax Credit in the US. Eligibility is determined based on the individual’s total income, with no family means-testing. It is worth noting that the assessment of eligibility was made based on 2014 gross income in May 2014. Thus, at the time the bonus was initially distributed the eligibility status of a given employee was not known with complete certainty. Precise 2014 gross income information became available only in 2015 when people filed their tax returns. As a consequence, some of the persons with income close to the thresholds may have been initially misclassified as eligible,

4The policy was publicly announced by Renzi on April 9th

14 but a year later had to reimburse the bonus received. About 1.5 million people had to return the bonus in 2015. According to Government estimates, the policy induced a transfer to households of 10 billion overall, equivalent 0.5 per cent of household disposable income, for around 10 million individuals. While initially announced as a temporary policy in place for 2014, in 2015 the policy became permanent and is still currently in place. Beginning January 2018, the maximum income threshold was slightly raised from 26000 to 26600 euro. Political and economic factors explain the structure of the policy and the eligibility criteria. First of all, the choice of giving the bonus only to payroll employees as recipients was taken so that the tax credit could be automatically acknowledged by the withholding agent (the employer), so as to make program implementation easier and faster. Any alternative structure of the measure, entailing some form of application and/or some direct transfer of money from the state would have required at least 3 months to be implemented, definitely not in time for the Eu- ropean Elections. On the other side, the lower threshold was set so as to exlude individuals who do not pay any taxes. This was done in order to avoid the withholding agent to pay the transfer out of pocket, which would have created both political and financial issues with firms.

4 Data and Descriptive Evidence

The analysis is developed using data on the universe of Italian municipalities (7959). I merge data coming from different sources. The first set of data contains information on the annual income distribution in the universe of Italian municipalities, based on individual Tax Returns, provided by the Ministry of Treasury. Data are grouped in 7 income categories and are available for all years from 2000 to 2016. In the last two years, the data contains information about the number of recipients of the Bonus and the average amount per capita. I merge this dataset to another dataset containing information on local, national and European elections, for each municipality, from 1994 to 2016, provided by the Italian Home Office. Furthermore, for the local-level analysis, I also include a dataset with information on the characteristics of local politicians, such as age, gender, level of education, previous job, tenure in politics. This dataset is also provided by the Home Office.

15 Finally, I collect a wide set of data on municipal characteristics to be used as controls, provided by the Italian Statistical Oﬃce (ISTAT). These includes the ed- ucational level of the municipal population, the percentage of children and elderly and municipal total population; economic variables like number of ﬁrms, average income and unemployment rate, and local production structure; geographical co- ordinates; information about the foreign population legally resident in Italy and registered at municipal level. All these factors have been included as controls as they are considered to be important determinants of voting behavior.

Figure 4 depicts the taxable income distribution of Italy in 2013 using data from official tax records, for both the universe of taxpayers (blue) and the sub- sample of payroll employees (red). The left panel shows a more granular version, while the right panel displays a more condensed version in 8 income brackets. The figure delivers two main messages. First, the transfer has a very wide coverage. Second, it should not be considered as a “pro-poor” redistributive policy, as it targets exactly the middle of the income distribution. Furthermore, the absence of household-level means-testing reduces the ability of the policy to target most-needy individuals. Due to the structure of within-family labor force participation dynamics in Italy, a large part of bonus recipients are likely to be married women who would not meet the income requirements in presence of household- level means-testing. More generally, those households with higher labor market participation and more payroll income recipients have benefited to a greater extent than those with fewer. To analyze the electoral response to the 80 euro bonus, I construct a measure of policy treatment intensity at the city level, measured as the number of bonus recipients in 2014 over the voting age population in 2013 5. Figure 5 and ?? display the variation of treatment intensity across cities, which is going to be crucial for my identification strategy. Descriptive statistics for all outcomes and relevant controls are displayed in table 1. Control variables are measured in 2013, prior to the introduction of the policy.

5In Italy, voting registration is automatic, so the voting age population is equivalent to the population eligible to vote

16 5 Empirical Strategy

In this section, I outline the approach used to estimate the impact of the 80 euro bonus introduction on subsequent electoral performance of the incumbent party. My empirical strategy exploits variation across municipalities in the ex-ante exposure to the policy, given by the share of low-income and payroll employees in the population, inducing variation in the actual share of recipients. I implement a difference-in-differences approach based on city-level treatment intensity. The thought experiment is as follows. Suppose there are two cities, one in which ev- eryone is eligible to receive the bonus and one in which no one is eligible. The experiment uses the latter city as a control to assess the counterfactual electoral performance of the democratic party in the absence of the program for the city with all eligible recipients. This allows for an estimate of the marginal impact of the policy on electoral outcomes. In reality, I do not observe cities with full and city with no recipients, so I am going to compare the electoral performance in cities with more vs fewer recipients. Under the identifying assumption that absent the policy, cities with high vs. low intensity of treatment would have behaved similarly in the period from 2014, this allows me to capture the effect of the policy.

I exploit the panel structure of my data and apply a panel Difference-in- Difference design including city FE to control for time-invariant town characteristics. The baseline specification is the following:

0 Ymt = βRecipientsm,2014POST + Xmtζ + γt + δpt + αm + mt (1)

where Ymt is Share of Votes for the Democratic Party, Recipientsm,2014 is my main treatment intensity variable, measuring the Share of Bonus Recipients in

2014 over the Voting Eligible population, Xmt is a matrix of time-varying controls,

αm are city fixed effects, δpt are province linear Trends and γt are year fixed effects. The coefficient of interest is β, capturing the differential impact of the 80 Euro bonus policy on incumbent’s vote share, by treatment intensity. All regressions are weighted by the size of electorate in 2013. Throughout the article, I cluster standard errors at the city level to allow for serial correlation in the error term. In this baseline specification, I do not distinguish between the various elections happening after the introduction of the policy. To inspect the dynamics of the

17 eﬀect, I run a dynamic version of the above speciﬁcation:

k=+T X 0 Ymt = βk ∗ [t = T − k]Recipientsm,2014 + γt + Xmtζ + δpt + αm + mt (2) k=0

As is standard in difference-in-difference estimation, the identification of my coefficient of interest relies on two assumptions. The first is the absence of con- temporary shocks that differentially affected cities with higher vs. lower bonus intensity. I am not aware of other policies targeting middle-income payroll employees that occurred concurrently with the 80 Euro policy. Furthermore, as discussed in the background Section, this was the very first policy announced and adopted by the newly formed government of Matteo Renzi, in a sudden and unexpected way. However, one could still be concerned that cities with high share of recipients may also differ in other important ways that independently influence their electoral outcomes, and thus characteristics unrelated to the policy are responsible for any differential electoral patterns in high versus low exposure cities. I address this concern by presenting alternative specifications where I include interactions between a comprehensive set of geographical, demographic and socioeconomic controls, all measured before the beginning of the program, and the post-program indicator. Notably, this include a set of variables proxying for the eligible and non-eligible population, such as the share of payroll employees, the share of self-employed, the share of individuals earning less than 10 thousand euro, between 10 thousand euro and 26000, and between 26000 and 55000. This allows me to compare cities that had differential exposure to the treatment because of the intersection between the two relevant dimensions for determining eligibility (income and payroll employment), but that were otherwise similar along the two dimensions itself. The second assumption is the presence of parallel trends in the outcome variable, i.e. that municipalities with different exposure to the bonus policy would have presented similar electoral behavior over time in the absence of this policy intervention. To assess the validity of this assumption, I run an event-study model that estimates separate effects for years both before and after the policy implementation. The underlying regression is the following:

k=+T X 0 Ymt = + βk ∗ [t = T − k]Recipientsm,2014 + γt + Xmtζ + δpt + αm + mt (3) k=−t

18 Results are displayed in the left panel of figure 10. As can be seen, no clear pre- trend can be detected. If anything, cities with higher treatment intensity were on a declining trend of votes for the Democratic Party, and there has been a clear break in the trend after the policy was introduced. The pattern in the figure seems instead to suggest that the measure was somewhat targeted to those places in which the Democratic Party was losing ground, in order to re-gain consensus. In what follows, I will address this question directly by looking at the determinants of bonus intensity at the city level. As a further test, I repeat the same specification above but replacing the share of recipients with the share of individuals earning between 10000 and 26000, a proxy for the share of potentially eligible individuals. The pattern is very similar to the other panel, except that the coefficients are all shrunk toward zero. In the last part of the paper, I test whether electoral rewards extend beyond the national level to different levels of government. To do so, I exploit both heterogeneity in treatment intensity at the local level and staggered timing of local elections. I estimate the following baseline specification:

Ymt = β0+β1POSTm∗Recipientsm,2014+δaPOSTm+γt+αm+ηaPOSTm+mt (4)

In this specification, the main dependent variable, Ymt, is either an indicator for incumbent mayor re-election or an indicator for the election of a mayor aligned with the incumbent party, as well as the turnout in the local elections. Notably, now, the dummy variable POST has a subscript m, as local elections happen at different times in different cities. As explained in section 3, the timing of local elections is due to historical reasons and uncorrelated with any other political or economic factors. The coefficients ηa capture post-program period fixed effects that are specific to the five Italian macro-areas where municipality m is located6. As for national elections, I also estimate a dynamic version of the above equation, where I estimate time-specific effects for each of the years after the introduction of the policy.

6Macro areas are North-East, North-West, Center, South, Islands.

19 6 Results

6.1 Targeting

The first part of my analysis provides an answer to the question on how politicians determine policy beneficiaries when designing welfare transfers. I evaluate whether the particular design of the 80 Euro policy could be driven by political motives and in particular if the incumbent party is targeting swing voters or pandering its core supporters. I proceed in two ways. First, I investigate determinants of treatment intensity at the local level. In particular, I assess whether treatment intensity is predicted by political variables. According to the theoretical predictions highlighted in Section ??, electoral performance of the incumbent party in both recent and distant elections, as well as turnout are expected to be key determinants of the treatment intensity. Consistent with previous literature in the field (Stromberg 2004; Larcinese, et al. 2006; Ansolabehere and Snyder 2006, Nupia 2012), I use the average of the incumbent party’s vote share in all races held from 1994 to 2009 to measure the concentration of core-voters in a municipality. This is a “long-run” measure of loyalty. Alternatively, I look at incumbent performance in 2013 election, the last election before the transfer was introduced, as a measure of “short-run” loyalty. To measure the concentration of swing voters in a municipality, I use the standard deviation of the incumbent vote share in all races held between 1994 and 2009. As an alternative to this measure, I also look at the share of votes for the 5 Star movement in 2013. Given that this party was a total novelty and never run before 2013, this represents a “short-run” measure of swing propensity of a given municipality. In figure 7, I present binned scatterplots of these four measures as predictors of treatment intensity, measured as the . All specifications include province FE and control for average income at the city level. Regressions are weighted by the size of electorate in 2013. From visual inspection, we see a pretty strong negative correlation between the democratic party performance in 2013 and treatment intensity, while no clear relationship can be detected with the average share of votes for the PD over the window 1994-2009. In the bottom part of the figure, we clearly see a strong, positive and significant relationship between the share of votes for the 5 star movement and the treatment intensity. On the other side, the correlation with the more long-run measure of swing propensity is negative and less significant. Overall, short run political factors seem to matter more than long run ones,

20 and results suggest that the incumbent party was targeting recent swing municipalities at the expense of core supporters. I then turn to regression analysis. I inspect which factors predict treatment intensity at the local level, distinguishing between political variables, demographic characteristics and economic factors, all measured in 2013. Results are presented in table ??. A graphical representation of the results is provided in figure 8. In the left panel, I display the coefficients of 3 separate regressions, in which I include political, demographic and income variables separately. In the right panel I display the coefficient from the full regression, including all the variables together. Results in the left panel confirm that the share of votes to the 5 Star movement is a strong positive and significant predictor of bonus intensity. Interestingly now also the coefficient on long-run loyalty (proxied by the variable Mean Share PD 1994-2009) is positive and significant, and of even larger magnitude than the former one. The relationship between incumbent performance and treatment intensity seem to be non-monotonic, given that the coefficients on measures of past performance (share PD 2008, share PD 2009 and share PD 2013) are either negative or zero. Finally, average turnout also is a positive and significant predictor of bonus intensity, a result that is consistent with voter persuasion rather Comparing the two panels, we see that once the economic variables are introduced, coefficients on the political variables are shrunk towards zero, suggesting both a positive correlation between economic and political variables and, most importantly, the fact that economic variables could be used as proxies for political ones for the scope of targeting. To further inspect whether electoral motives can explain the particular structure of the policy, I exploit detailed information on local income distribution to estimate the potential treatment intensity of 160 simulated transfers. Relative to the real bonus structure in figure 3, I keep the shape of the transfer constant (i.e. keeping a fixed window of 18000 euro) and shift it along the x-axis in intervals by 100 euros each. I create 160 simulated transfers, where the first one covers the interval from 0 to 18000 euro, and the last one covers the interval from 16000 to 34000 euro. I then exploit tax data available for 2014 on the local income distribution for the universe of Italian municipalities, containing detailed information on number of taxpayers in each of the 100 euro income brackets. This allows me to estimate simulated treatment intensity at the local level for each of the simulated transfers. Figure 9 reports the share of beneficiaries over the voting eligible population for each of the transfers. Results clearly show that the actual transfer

21 (the vertical red line) is the one maximizing the average share of recipients over the eligible population. This can be seen as further suggestive evidence of the fact that the incumbent party was aiming at maximizing the number of potential votes. Notably, however, all the transfers to the left of the existing one would have beneﬁtedmore individuals with lower incomes, thus having a larger impact on inequality and social welfare.

6.2 Electoral Rewards

In this section, I investigate the electoral consequences of the 80 Euro Bonus Pol- icy. Figure 11 provides a visual analysis of the relationship between treatment intensity at the city level and the change in the share of votes for the incumbent party (the PD) between 2013 and 2014. The binned scatterplot displays a clear positive relationship. Table 3 formalizes the analysis and presents results of the difference-in-differences estimation highlighted in equation (3). A graphical representation of the main DID coefficients is also provided in figure 12. Column (1) represents the DID coefficients from a regression including city and year FE only. The effect is positive and significant. A 1 standard deviation increase in the share of bonus recipients raises the vote share of the PD by 3.3 percentage points (from a sample mean of 28.41 % in 2013). The magnitude of the effect is only slightly attenuated by the inclusion of controls in column (2). The remain- ing columns turn to examine the specific mechanisms through which the 80-euro policy improved the electoral fortunes of the incumbent democratic party, testing some of the hypotheses highlighted in Section 2. One possibility is that the new welfare benefits induced some of the voters of concurring parties to switch their votes and reward incumbents for the benefits. Alternatively, the program may have worked to mobilize citizens who had previously not bothered to vote (see, e.g., Pop-Eleches and Pop-Eleches 2012, Clinton and Sances 2017). Although it is impossible to disentangle these effects completely without individual-level polling data, I provide some evidence of the two concurring mechanisms by looking at the impact on turnout vs. on the total votes for the democratic party. Columns (3) to (6) look at the effects on turnout. I take advantage of the fact that turnout data are available separately for both men and women to look at the heterogeneity on this margin. Given the fact that women are more likely to be Bonus beneficiaries, effects are expected to be stronger for female turnout. Results indeed point in this direction for the specification without controls, but the sign of the difference re-

22 verts in the full specification. On average, I detect a large positive and significant impact of the policy on turnout, suggesting that most of the positive electoral effect has been indeed driven by mobilization. A 1 standard deviation increase in the share of bonus recipients raises overall turnout by almost 1.6 percentage point (from a sample mean of 74.92), so an absolute increase of 0.7 % of the electorate. On the other side, the effect on the total number of votes received by the party is also striking (columns (7) and (8)). A 1 standard deviation increase leads to a 10 % increase of the sample mean of total votes. Coupled together, results suggest that the bulk of the electoral effect is coming through the mobilization of non- voting supporters. This is further confirmed by looking at the change in the share of votes to the other two main opponent parties, namely the 5 Star movement and the right party of Berlusconi, presented in columns (3) to (6) of table 5. Table 4 presents results of the dynamic version of the difference-in-differences, corresponding to equation (4). Here, I allow the treatment effect to vary over time since the implementation of the policy. Results indicate that treatment effects indeed vary a lot over time, in ways that differ depending on the outcomes. Look- ing at incumbent performance, the effect follows an inverted U-shaped pattern, reaching a peak in 2016. This is particularly interesting for two reasons. First, because the incumbent party was defeated in this election. Second, because most of the theoretical models would predict a decay of the effect over time. The effects on turnout and votes are even more striking. For turnout, the peak of the effect is in 2014 and then rapidly decays over time. A 1 standard deviation increase in bonus recipients raises turnout by roughly 3 percentage points in 2014. The effect is halved in 2016 and even becomes negative in 2018. On the other side, vote flows remain remarkably stable, with the coefficient in 2018 being only slightly smaller than the one in 2014. This suggests some important heterogeneous dynamics behind the overall positive electoral effect. While the incumbent’s electoral rewards in 2014 could be mostly explained by the mobilization of supporting non-voters, the converse is true for 2016 and 2018. This is further confirmed by looking at the dynamics of vote flows for the other parties in table 5. Here I find no significant change in votes for both the 5 Star and the right party in 2014. On the other side, in 2018, both parties experience a significant negative impact. A 1 standard deviation increase in the share of bonus recipients is associated with a 5 % reduction in the sample mean of votes for both parties in 2018. Unfortunately, I cannot recover the effect in 2016, due to the fact that

23 the 2016 election was a referendum and the two opposition parties were running together as an anti-referendum coalition7.

6.3 Down-Ballot Consequences

I conclude the analysis by examining the down-ballot electoral consequences of the 80 Euro policy. Theoretical predictions highlighted in Section 2.3 suggest that, in presence of voters’ attribution bias, we should observe positive pro-incumbent effects in local elections, independently of the incumbent mayor’s party. Absent attribution bias, instead, we could still observe positive electoral effects but only for mayors aligned with the democratic party. Table 6 present the results from equation (5). Column (1) reports the result from a regression where the dependent variable is an indicator equal to 1 if the incumbent mayor was re-elected, and zero otherwise. In Column (2), the dependent variable is an indicator equal to 1 if the elected mayor was aligned with the democratic party, and zero otherwise. Finally, in Column (3) I inspect the impact of the bonus on electoral participation at the local level, the dependent variable being turnout. All specifications include city and year FE as well as the full set of controls. The results show no real effect on incumbent re-election, the coefficient in column (1) is negative but insignificant. On the other side, there is a very strong, positive and significant impact of the program on election of left mayors. A 1 standard deviation increase in the share of recipients raise by 8 % the probability that a left mayor is elected. Finally, no significant effect on turnout can be detected. In table 7, I repeat the same analysis but using the dynamic specification of equation (5). Results are largely insignificant, mainly because of a lack of power, due to the limited number of elections in which I could detect an aligned mayor every year. Interestingly though, there is still a positive effect, significant at the 10 % level, in 2016, suggesting that down- ballot positive rewards are not concentrated in the short run but rather spread out. Overall, results provide evidence of a positive alignment effect, with mayors aligned with the democratic party enjoying a great electoral advantage following the introduction of the bonus. The absence of any effect on incumbent re-election per se leads to reject the voters’ attribution bias hypothesis.

7Note also that for 5 Star Movement the number of observations is signiﬁcantly smaller, due to the fact that the party entered the political arena only in 2013

24 7 Discussion and Conclusion

This paper investigates the political economy of the introduction of the largest programmatic welfare transfer in Italy, the 80 Euro Bonus policy. In the first part of my analysis, I investigate the targeting of the program, evaluating if electoral motives might explain the structure of the policy. To do so, I exploit information about past electoral performance of the incumbent and estimate to what extent this predicts treatment intensity at the local level. I find evidence that places with higher treatment intensity exhibit higher historical vote shares for the incumbent but also higher vote shares for the main opposition parties in 2013 elections, and high electoral participation. Notably, however, places with higher income inequality received significantly less allocations. Furthermore, I exploit detailed information on local income distribution to estimate the coverage of over 150 simulated transfers. Results show that the actual transfer structure is the one maximizing the average share of recipients over the eligible population. Turn- ing to evaluate electoral consequences, my results show that the program yielded significant electoral returns to the incumbent: a 1 standard deviation increase in the share of recipients (around 5% of the electorate) leads to a 3 percentage points’ increase in the incumbent party’s vote share, from a pre-treatment mean of 28 %. When I look at the dynamics of the treatment effect, I found that the positive electoral rewards persist and exhibits an inverted U-shape pattern over time. I then turn to inspect the mechanisms behind the estimated effects. I exploit detailed information about other parties’ vote shares and electoral turnout to disentangle between two potential channels, i.e. the mobilization of new supporters who were not showing up to vote - turnout channel - or the persuasion of existing voters who would otherwise have voted for other parties. I found suggestive evidence that the program acts mostly through mobilization in the short run, while persuasion become more relevant in the longer run. In the last part of the paper, I investigate down-ballot consequences of the program. I exploit the exogenous staggered structure of the Italian city-level mayoral elections, comparing cities that happened to go to elections before vs. after the introduction of the program. I provide evidence of positive electoral effects for mayors aligned with the incumbent party. My results have several important policy implications. Results on electoral rewards indicate that programmatic welfare policies can have significant and long-lasting impacts on voters’ preferences. These facts are particularly relevant and new to the literature. Previous studies finding pro-incumbent

25 rewards for distributive allocations look at measures that are clientelistic in nature and/or from which recipients can be excluded discretionally. Furthermore, most theoretical models would suggest that, even if present, electoral rewards should last only in the short run and disappear once the policy becomes permanent. To the best of my knowledge, I am the first documenting significant and long-lasting electoral rewards for a purely programmatic and permanent policy. I also provide evidence that places with higher treatment intensity display significantly higher turnout in subsequent elections. This is also a policy relevant result, showing a potential channel through which welfare policies can lead to enhance citizens’ participation. Last but not least, results on targeting help to understand some of the well-known puzzles of redistribution. If left parties tend to design transfers in order to maximize their re-election chances, this might turn them away from designing welfare transfers in a way that favor the poorest citizens. Further work is needed to evaluate how much the optimal policy from an electoral point of view (i.e. the one maximizing votes) deviates from the optimal policy in a social welfare sense.

26 Figure 1 Google Trends, Search Results for “80 Euro”

Google Trends. indexed values of relative frequency of search over 2014. Peak corresponds to the 25th of May, day of the European Elections

Figure 2 Timing of Elections and 80 Euro Policy Adoption

27 Figure 3 80 Euro Bonus Structure 1200 960 480 Total Bonus Annual

8150 26000 24000 Total Annual Income

Worked Full Year Worked 6 Months

28 Figure 4 Taxable Income Distribution 2013 (Oﬃcial Tax Records) 20 40 30 15 20 10 Share of Share Taxpayers Share of Share Taxpayers 10 5 0 0 0-10000 0 Below 0 15-26000 1000150020002500300035004000500060007500 -2000-1000 100001200015000200002600029000350004000050000550006000070000750008000090000 10000-15000 26000-55000 55000-75000 Over 120000 100000120000150000200000300000 75000-120000

Total Payroll Employees Total Payroll Employees

Figure 5 Variation in Policy Intensity .06 .04 Density .02 0 0 10 20 30 40 50 Bonus80 Recipients

29 Figure 6 Variation in Policy Intensity Across Cities

Note: Orange (Blue) indicates cities where the share of beneﬁciaries is above (below) the national average (22 % of the voting-eligible population).

30 Figure 7 Policy Targeting - Political Determinants of Policy Intensity

31 Figure 8 Policy Targeting - Regression Coeﬃcients from Table 2

Separate Full Mean Share Left 1994-2009 SD Share Left 1994-2009 shareLEFT2013 shareLEFT2009 shareLEFT2008 share5s_2013 Turnout 1994-2013 maintown University graduates High-School graduates Females public Sector Employees Pensioners lnavg_income_growth Income Ineq. (Gini) Avg. Income (2013) Ratio 50-10 Unemp. Rate Active Labor Force -2 -1 0 1 2 -2 -1 0 1 2

Polit Demographics Income

32 Figure 9 Policy Targeting - Simulated Transfers 22 20 18 Avg. of Recipients Share 16 0 50 100 150 Simulated Transfers - shifting schedule by 100 Euros each

Note: 160 simulated transfers, shifting the original schedule by 100 euro each. The red line indicates the real transfer structure.

33 Figure 10 Event Study Estimates of the Eﬀect of Bonus Intensity, Pre/Post Policy .6 1 .4 .5 .2 eligible_2013 bonus80_2014 0 0 -.5 -.2 1994 2000 2004 2008 2013 2016 1994 2000 2004 2008 2013 2016 1996 2001 2006 2009 2014 2018 1996 2001 2006 2009 2014 2018 Years Years

Coefﬁcent 95% CI Coefﬁcent 95% CI

Coeﬃcients βk from the regression Pk=+T Ymt = + k=−t βk ∗ [t = T − k]Recipientsm,2014 + γt + δpt + αm + mt

34 Figure 11 Incumbent’s Electoral Performance and Policy Intensity

Figure 12 Electoral Rewards - Coeﬃcients from Table 3

Share PD Turnout 4 3 2 1 0

Turnout Female Votes Left (log) 4 3 2 1 0 Bonus(Z-sc)*POST Bonus(Z-sc)*POST

No Controls Controls

35 Figure 13 DID Coeﬃcients from Table 4

Share PD Turnout 6 4 2 0 -2

Turnout Female Votes Left (log) 6 4 2 0 -2

postyear=2014 # Bonus Recipients (Z-score)postyear=2016 # Bonus Recipients (Z-score)postyear=2018 # Bonus Recipients (Z-score)postyear=2014 # Bonus Recipients (Z-score)postyear=2016 # Bonus Recipients (Z-score)postyear=2018 # Bonus Recipients (Z-score)

Figure 14 DID Coeﬃcients from Table 5

Votes PD Votes Right .2 0 -.2

2014 2016 2018

Votes 5stars .2 0 -.2

2014 2016 2018

No Controls Controls

36 Figure 15 Local Elections per Year 5,000 4,000 3,000 Total Elections 2,000 1,000 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 16 DID Coeﬃcients from 6 .2 .1 0 -.1 POST*Bonus (Z-Scores)

Incumbent Won Left Mayor Won

37 Figure 17 DID Coeﬃcients from Table 7

Incumbent Won Left Mayor .4 .2 0 -.2

2014 2015 2016 2017 2014 2015 2016 2017

Turnout 2 1 0 -1 -2 -3

2014 2015 2016 2017

38 Table 1 Summary Statistics

Mean Median S.D. Min. Max. N Change in PD Share 2013-2014 11.41 11.94 6.22 -22.58 51.68 7,819 Change in PD Share 2013-2016 15.40 15.08 9.68 -27.18 81.85 7,816 Change in Turnout 2013-2014 -13.49 -13.31 13.11 -67.81 38.76 7,791 Change in Turnout 2013-2018 -6.47 -6.18 4.84 -42.41 40.85 7,788 Turnout in 2014 61.34 64.07 15.59 13.67 97.25 7,790 Turnout in 2013 74.84 76.50 7.71 20.68 93.33 7,790 Democrat share 2014 35.45 35.03 9.73 0.75 74.00 7,915 Democrat share 2013 23.95 23.10 8.00 0.94 59.31 7,819 mayorPD 0.12 0.00 0.33 0.00 1.00 7,952 Mean Share Left 1994-2013 33.19 31.97 11.09 5.93 86.52 7,791 SD Share Left 1994-2013 6.87 6.27 2.77 1.45 28.69 7,791 share80 Recipients 22.01 21.81 7.42 0.82 386.71 7,912 People below 10000 income % 33.18 30.23 10.63 14.10 71.77 7,951 Exlcluded Share 74.43 71.65 14.46 34.88 140.32 7,951 share just above 2014 0.97 0.89 0.55 0.00 9.45 7,724 Unemployment rate 10.58 6.59 8.63 0.00 51.32 7,224 Urban City 52.45 100.00 49.94 0.00 100.00 7,317 maintown 0.03 0.00 0.18 0.00 1.00 7,317 High-School graduates 45.26 45.83 11.44 9.49 100.00 7,224 University graduates 9.57 8.82 4.42 0.00 74.49 7,224 Active Labor Force % 84.73 88.24 20.93 23.51 150.70 7,224 Females % 50.93 51.09 1.73 35.62 61.80 7,039 % Immigrants 7.98 6.90 5.43 0.00 42.50 7,162 Mean Share Left 1994-2009 33.75 32.45 11.42 6.06 87.22 7,791 SD Share Left 1994-2009 6.75 6.06 2.94 0.22 30.00 7,791 avg income growth -0.02 -0.02 0.03 -0.40 0.35 7,317 Income Ineq. (Gini) 0.38 0.38 0.04 0.22 0.75 7,317

39 Table 2 Determinants of Policy Intensity (1) (2) (3) (4) Bonus Recipients (Share) Bonus Recipients (Share) Bonus Recipients (Share) Bonus Recipients (Share) Mean Share Left 1.201∗∗∗ 0.473∗∗ 1994-2009 [0.271] [0.232] SD Share Left 0.136 0.120 1994-2009 [0.109] [0.105] shareLEFT2013 -0.00461 0.0246 [0.198] [0.171] shareLEFT2009 -0.531∗∗∗ -0.345∗∗ [0.160] [0.135] shareLEFT2008 -0.742∗∗∗ -0.364 [0.279] [0.220] share5s 2013 0.844∗∗∗ 0.190∗ [0.124] [0.0991] turnout avg 1 1.434∗∗∗ 0.286∗∗ [0.150] [0.116] maintown 0.179∗∗∗ 0.143∗∗ [0.0673] [0.0594] University graduates -0.835∗∗∗ -0.397∗∗∗ [0.107] [0.0934] High-School 0.464∗∗∗ -0.406∗∗∗ graduates [0.0787] [0.129] Females 1.019∗∗∗ 1.016∗∗∗ [0.0688] [0.0756] public Sector -0.0527 -0.0122 Employees [0.0911] [0.0694] Pensioners -1.462∗∗∗ -1.224∗∗∗ [0.155] [0.145] lnavg income growth 0.00887 0.119∗∗∗ [0.0472] [0.0419] Income Ineq. (Gini) -0.841∗∗∗ -0.870∗∗∗ [0.162] [0.154] Avg. Income (2013) 0.161 0.139 [0.360] [0.379] Ratio 50-10 1.494∗∗∗ 0.963∗∗∗ [0.331] [0.260] Unemp. Rate -0.0602 -0.478∗∗∗ [0.122] [0.120] Active Labor Force 0.738∗∗∗ 0.788∗∗∗ [0.0641] [0.0998] Bonus Average 21.41 21.41 21.41 21.41 Observations 7693 7625 6736 6724 R2-Adj 0.640 0.669 0.672 0.728 Notes: Robust standard errors in parenthesis. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

40 Table 3 Panel DID, Static (1) (2) (3) (4) (5) (6) (7) (8) PD Share PD Share Turnout Turnout Turnout (F) Turnout (F) Votes PD (ln) Votes PD (ln) POST=1 × Bonus Recipients (Z-score) 3.255∗∗∗ 2.964∗∗∗ 1.126∗∗∗ 1.619∗∗∗ 1.337∗∗∗ 1.246∗∗∗ 0.175∗∗∗ 0.109∗∗∗ [0.164] [0.329] [0.109] [0.288] [0.123] [0.310] [0.00650] [0.00994] Pre-T Mean 28.41 28.41 74.92 74.92 72.22 72.22 5.969 5.969 Observations 73382 73382 73383 73383 73378 73378 73382 73382 R2 0.812 0.868 0.788 0.809 0.777 0.808 0.991 0.994 Controls No Yes No Yes No Yes No Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Local Linear Trends No Yes No Yes No Yes No Yes Notes: Robust standard errors in parenthesis. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 4 Panel DID, Dynamic (1) (2) (3) (4) (5) (6) (7) (8) PD Share PD Share Turnout Turnout Turnout (F) Turnout (F) Votes PD (log) Votes PD (log) postyear=2014 × Bonus Recipients (Z-score) 3.059∗∗∗ 3.076∗∗∗ 2.608∗∗∗ 3.025∗∗∗ 2.905∗∗∗ 2.745∗∗∗ 0.176∗∗∗ 0.123∗∗∗ [0.149] [0.340] [0.284] [0.416] [0.281] [0.424] [0.00704] [0.0110] postyear=2016 × Bonus Recipients (Z-score) 4.553∗∗∗ 4.164∗∗∗ 1.364∗∗∗ 1.803∗∗∗ 1.505∗∗∗ 1.374∗∗∗ 0.187∗∗∗ 0.118∗∗∗ [0.202] [0.361] [0.249] [0.270] [0.271] [0.289] [0.00745] [0.0106] postyear=2018 × Bonus Recipients (Z-score) 2.159∗∗∗ 1.475∗∗∗ -0.633∗∗∗ -0.323 -0.440∗∗∗ -0.748∗∗ 0.163∗∗∗ 0.0814∗∗∗ [0.209] [0.323] [0.106] [0.299] [0.133] [0.327] [0.00992] [0.0112] Pre-T Mean 28.41 28.41 74.92 74.92 72.22 72.22 5.969 5.969 Observations 73382 73382 73383 73383 73378 73378 73382 73382 R2 0.813 0.869 0.790 0.812 0.779 0.811 0.991 0.994 Controls No Yes No Yes No Yes No Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Local Linear Trends No Yes No Yes No Yes No Yes Notes: Robust standard errors in parenthesis. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 5 Panel DID, Vote Flows (1) (2) (3) (4) (5) (6) Votes PD (log) Votes PD (log) Votes Right (log) Votes Right (log) Votes 5Stars (log) Votes 5Stars (log) postyear=2014 0.176∗∗∗ 0.123∗∗∗ -0.0161 -0.00597 -0.0150 -0.0251 × Bonus Recipients (Z-score) [0.00647] [0.0110] [0.0120] [0.0156] [0.00926] [0.0157] postyear=2016 0.188∗∗∗ 0.118∗∗∗ × Bonus Recipients (Z-score) [0.00749] [0.0106] postyear=2018 0.164∗∗∗ 0.0814∗∗∗ -0.0857∗∗∗ -0.0556∗∗∗ -0.216∗∗∗ -0.0540∗∗∗ × Bonus Recipients (Z-score) [0.0110] [0.0112] [0.0154] [0.0168] [0.0208] [0.0155] Observations 73382 73382 67874 67874 17122 17122 R2 0.982 0.994 0.981 0.994 0.995 0.998 Controls No Yes No Yes No Yes Year FE Yes Yes Yes Yes Yes Yes Municipality FE Yes Yes Yes Yes Yes Yes Local Linear Trends No Yes No Yes No Yes Weighted Notes: Robust standard errors in parenthesis. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

41 Table 6 Panel DID, Down-Ballot Consequences, Static (1) (2) (3) (4) (5) (6) Incumbent Won Incumbent Won Left Mayor Won Left Mayor Won Turnout Turnout post=1 × -0.0132 -0.0128 0.0146 0.0843∗ -0.185 0.205 Standardized values of (bonus80 2014) [0.0276] [0.0466] [0.0644] [0.0510] [0.479] [0.538] Pre-T Mean 0.299 0.299 0.191 0.191 67.83 67.83 Observations 28530 23253 28530 23253 28530 23253 R2 0.266 0.326 0.484 0.555 0.891 0.900 Controls No Yes No Yes No Yes Year FE Yes Yes Yes Yes Yes Yes Municipality FE Yes Yes Yes Yes Yes Yes Local Linear Trends Yes Yes Yes Yes Yes Yes Notes: Robust standard errors in parenthesis. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 7 Panel DID, Down-Ballot Consequences, Dynamic (1) (2) (3) (4) (5) (6) Incumbent Won Incumbent Won Aligned Mayor Won Aligned Mayor Won Turnout Turnout postyear=0 × 0 0 0 0 0 0 Standardized values of (bonus80 2014) [.] [.] [.] [.] [.] [.] postyear=2014 0.00975 0.00266 0.0357 0.0655 0.176 1.272∗∗ × Standardized values of (bonus80 2014) [0.0304] [0.0508] [0.0532] [0.0531] [0.310] [0.508] postyear=2015 -0.0435 -0.0703 0.0446 0.0571 -2.602∗∗∗ -1.704∗∗ × Standardized values of (bonus80 2014) [0.0521] [0.0704] [0.0904] [0.0763] [0.689] [0.749] postyear=2016 -0.0784 -0.0212 0.00412 0.137∗∗ 0.993 -0.175 × Standardized values of (bonus80 2014) [0.0840] [0.0660] [0.136] [0.0681] [1.961] [0.828] postyear=2017 0.0431 -0.00323 -0.0606 0.0576 -2.138∗∗∗ -1.091∗ × Standardized values of (bonus80 2014) [0.0548] [0.0636] [0.0783] [0.0675] [0.463] [0.560] Pre-T Mean 0.299 0.299 0.191 0.191 67.83 67.83 Observations 28530 23253 28530 23253 28530 23253 R2 0.267 0.327 0.485 0.555 0.893 0.901 Controls Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Municipality FE Yes Yes Yes Yes Yes Yes Notes: Robust standard errors in parenthesis. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

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