Appendix A (For Online Publication). The corruption dataset in detail.

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Appendix A (For Online Publication). The corruption dataset in detail.

In this Appendix we engage in a precise description of the corruption database. In order to carry out our analysis, we constructed a novel database containing information on corrupt practices occurring at the municipal level between 1999 and 2011 in Spain. To do so, we analyzed several sources pub- lishing corruption scandals, on a case-by-case basis, in order to identify when the corruption scandal appearing in the media actually occurred. The underlying motivation was to collect information regarding the electoral term in which corruption was taking place, irrespective of when citizens had become aware about these practices, a key element in the empirical analysis. We define a local corruption incident as the “public accusation of corruption in the local sector that was brought to light through a corruption scandal appearing in a newspaper”.

For each case, we compile information in which municipality corruption was taking place; the main source of information and the main newspapers behind the information; the year(s) of the incident; the year(s) when the corruption scandal appeared in the media; the difference (in terms) between the incident and the scandal; the number of incidents in the same municipality; who was involved; the corruption type; a brief explanation of the incident; the stage of judicial intervention;

the party(ies) involved and, finally, the amount embezzled (if available).

Table A.1: Illustrative examples

Case 1 2

Source CORRUPTODROMO CORRUPTODROMO

Newspaper (1) EL PA´IS ABC

Newspaper (2) CASOS AISLADOS

Year (incident) 2007-2009 2003-2007

Year (scandal) 2010 2011

Difference incident-scandal 0 1

N. incident 2 1

Who MAYOR-MUN. COUNCILLORS-OTHER MAYOR-MUN. COUNCILLORS

Type URBAN PLOT 2 CRIMES

Description Caso Brugal: Caso Pretoria:

Irregular public contracts Embezzlement of public funds adjudication related to waste and prevarication in irregular

collection service buying and selling rustic non-developable land

J. Status CONVICTED ACCUSED

Party PP PSOE

Amount 190,000,000 11,000,000

Two illustrative examples are provided above. Focusing on the first case (Orihuela), we obtained information about an urban plot between 2007-2009, brought to light by the newspaper ‘El Pa´ıs’ in 2010, and summarized in two online blogs that we will describe later –Corrupt´odromoand Casos Aislados. This was the second incident in this municipality, involving the mayor, several municipal councillors (all of them belonging to the PP party) and other agents from outside the Town Hall.

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The case, known as ‘Caso Brugal’, closed with multiple charges for the accused and the amount embezzled estimated to be approximately 190 millione.

In the second case (Santa Coloma de Gramanet), we obtained information about the ‘Caso Pre- toria’, brought to light by the newspaper ‘ABC’ at the beginning of 2011, and summarized in the online blog Corrupt´odromo. It was the first incident in this municipality, involving the mayor and several municipal councillors (all of them belonging to the PSOE party). The case is still in court and the implicated parties are accused of embezzlement of public funds and prevarication, with a total estimated misappropriation of funds amounting to approximately 11 millione.

Our sources of information are as follows: Our starting point was the analysis of the corruption scandals database compiled byAlternativas(2007), a Spanish think-tank closely linked with the So- cialist party (PSOE) and the main left-wing newspaper in the country (’El Pais’). They investigated all corruption scandals involving municipalities (among others) appearing in national, regional or local newspapers between the beginning of 2000 and February 2007. In order to avoid a possible partisan bias, we followedCostas-P´erez et al.(2012) in analyzing a similar database conducted by

’El Mundo’, the main right-wing newspaper in Spain, covering the same period. The number of reported corruption scandals by parties is not significantly different in either database. Overall, a total of 205 analyzed corruption incidents come from this source, corresponding to the period 1999-2007.

Table A.2: Number of corruptions incidents by source

July-1999 to June-2003 to June-2007 to July-1999 to May-2003 May-2007 May-2011 May-2011

Fundaci´on Alternativas-El Mundo 79 126 0 205

Urbanismo Patas Arriba 14 38 32 84

National newspaper 17 67 48 132

Regional newspaper 6 15 15 36

Corrupt´odromo 18 74 70 162

Casos Aislados 6 16 38 60

Other 0 2 2 4

Total 140 337 206 683

As our sample covers an additional electoral cycle (2007-2011), we completed the database by conducting complementary internet-guided searches for additional news on corruption scandals, fol- lowingSaiz and Simonsohn(2013). Specifically, we analyzed three blogs –Urbanismo Patas Arriba, Corrupt´odromo, andCasos Aislados– compiling scandals involving corruption (local, regional, na- tional, or even related to other sectors such as banks), focusing again on corruption incidents at the local level between 1999 and 2011.²⁵ A total of 206 corruption incidents were selected from these

25The relevance of the online blogCasos Aisladosis well-acknowledged. Not only due to the number of cases, but

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sources, corresponding to the period 2007-2011, where we also found additional corruption cases relating to the period 1999-2007 that were published after the investigations carried out by ‘Fun- daci´on Alternativas’ and by ‘El Mundo’. In the end, the total number of corruption incidents in our database amounts to 683.

Figure A.1: Distribution of corruption cases by parties

PARTY PP (259) PSOE (193) CIU (15) PNV (8) Other (57)

Note: The figure shows the distribution of corruption cases by parties. Each dot represents a municipality with one (or more) corruption case, while its size shows the number of cases in the same municipality: Small dots = one case.

Medium dots = two cases. Big dots = three cases.

Similar to the map presented in the main text, Figure A.1 shows the distribution of corruption cases, this time by the main party involved. In the map, each dot represents a municipality with one or more corruptions case, while the size of the dot shows the number of cases in the same municipality for the three different electoral terms considered in the empirical analysis.²⁶ Visual inspection of the map suggests no partisan bias in the cases obtained, as it is pretty similar to other corruption maps compiled by different sources from the political ideology spectrum.²⁷

because it gave us useful information about the judicial status and the estimated embezzled amount for many other cases.

26Our database includes 561 municipalities affected by corruption in the period from July 1999 to May 2011. We classified them into seven sub-categories, depending on the persistence of corruption. First, 452 municipalities experienced just one corruption case –82 in the first period, 234 in the second period and 136 in the third one. Second, 96 municipalities experienced a corruption case in two different electoral cycles –39 in the first and the second, 51 in the second and the third and 6 in the first and the third. Finally, 13 municipalities experience a corruption case in the three electoral cycles considered in the empirical analysis. Notice that the map includes only 532 municipalities, as the Canary Islands are not shown to increase the visibility of the whole map.

27This is confirmed, when we compare the percentage of corruption cases by each political party, reported by each source described in Table A.2. For instance, the percentage of cases reported byFundaci´on Alternativasfor the two

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In TableA.3 we show the total number of corruption incidents, differentiating by the principal protagonist under accusation. These are classified into 7 different categories. In the first category only the mayor was accused of corruption (244 cases), while the second, third, and fourth categories involve the mayor plus other members of the City Hall (another 247 cases). Therefore, the table shows that the mayor, the highest representative authority of the municipality and the figure responsible for its government and administration, is involved in almost 75% of all detected cases.

Table A.3: Number of corruptions incidents by the person(s) involved

Mayor 51 113 80 244

Mayor-Mun. Councillors 19 45 35 99

Mayor-Mun. Councillors-Others 11 40 32 83

Mayor-Others 11 36 18 65

Mun. Councillors 15 24 4 43

City Hall 32 76 36 144

Other 1 3 1 5

Total 140 337 206 683

In addition, we identify 43 extra cases where municipal representatives (councillors) were accused and 144 cases where it was the city hall as an institution that was accused of illegal procedures.

Finally, we classify as “Other” 5 extra cases that did not fall into any of the previous six categories.

Table A.4: Number of corruptions incidents by type of charges

Bribery 5 12 4 21

Embezzlement of public funds 14 24 29 67

Prevarication 78 181 81 340

Fraud-falsification 1 7 7 15

Money laundering 2 2 1 5

2 crimes 17 44 36 97

3 crimes 3 23 15 41

Urban plot 9 31 30 70

Other 11 13 3 27

Total 140 337 206 683

The total number of corruption cases are further divided, distinguishing between the type of crime committed in Table A.4. In particular, we identify 21 cases related to bribes, 67 cases to embezzlement of public funds, 340 cases to prevarication, 15 to fraud or falsification, and 5 to

main parties in the country is, respectively, 23.33% for PSOE and 29.54% for PP. These figures change to 26.67% (PP) and 22.66% (PSOE) when usingCorrupt´odromoas source.

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money laundering. Furthermore, we identify 97(41) extra cases containing two (three) out of the five cases just described, while we denote by “urban plot” a case in which we have at least three of the five aforementioned cases. Finally, we classify as “Other” 27 cases for which we do not have a precise definition of any particular type of crime.

Next, we list our corruption data according to the type of judicial intervention in TableA.5.²⁸ Here, we distinguish, with the total number in parenthesis, between: i) Reported (102) = cases published by any of our sources but not reported to court (yet);ii)Investigated (63) = cases reported to court without further action;iii)Accused (360) = cases reported to court with an ongoing trial;iv) Convicted (140) = cases that ended with a formal corruption conviction;v)Acquitted (16) = cases that ended with acquittal; and vi) Other (2) = cases with insufficient information to be classified them in any of the other categories.

Table A.5: Number of corruptions incidents by type of judiciary intervention July-1999 to June-2003 to June-2007 to July-1999 to

May-2003 May-2007 May-2011 May-2011

Reported 20 59 23 102

Investigated 12 36 15 63

Accused 56 160 144 360

Convicted 48 69 23 140

Acquitted 3 12 1 16

Other 1 1 0 2

Total 140 337 206 683

In the robustness check analysis shown in AppendixB.8, we replicate our results for subsamples of cases considered to be more serious –i.e., those involving formal judicial accusation or conviction.

Results are almost unchanged to our baseline estimates presented in the main paper.²⁹

28In most of the cases, we did not have this information when we first collected all the data, either because it was too early to know about the outcome in court or directly due to lack of data. This is why we repeated the process after some time to check, on a case-by-case basis, for any update relating to the judicial stage of each case.

29In addition, results are also robust to the exclusion of a particular type of corruption charges, or to the exclusion of a particular protagonist that is being accused of corruption practices. Results available upon request.

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Appendix B (For Online Publication). Robustness checks

Although we consider that our empirical strategy is strong enough to support the causal interpre- tation of our main estimates, we recognize there were different procedures to identify the effect of being aligned, as well as several alternative definitions of alignment, and also alternative samples, that could have been employed. Hence, in this appendix we engage in an extensive procedure of robustness checks, in order to verify that the results are not sensitive to the choices used throughout the paper.

B.1 Alternative estimation procedure: RDD

B.1.1 Research design

We first recognize that the alignment effect may still be biased if unobserved time-varying factors that are correlated with alignment between local and regional governments remain omitted —as acknowledged in the main text. This is why, as an additional exercise, we use the “close-race” Re- gression Discontinuity Design (RDD) setup, in the spirit ofBrollo and Nannicini(2012), to explore the robustness of our results by comparing municipalities in which aligned candidates barely won the elections, with those where they barely lost.

However, the application of this methodology is not straightforward in the Spanish case for several reasons. First, as pointed out by Sol´e-Oll´e and Viladecans-Marsal(2013), the proportional representation system used at local elections generates multiple thresholds at which an additional vote brings one more seat to a party, and these are not necessarily located at the 50% vote threshold.

Second, the d’Hondt rule used to allocate votes to seats for those parties obtaining a vote share higher than 5% implies that each party and each seat has a specific vote threshold. Finally, being the most voted party does not imply gaining control of the local government, since coalitions can be formed with a majority of seats in the local council. Hence, the standard close-race RDD setup cannot be applied in our case.

To deal with these problems, we follow the methodology described inCurto-Grauet al.(2018).

Thus, we define the treatment of being aligned as a situation where the ideological bloc (left or right) of the regional government leader’s party also has a majority of seats in the local council, thereby taking into account that coalitions are formed along ideological lines.

However, close elections in terms of seats (-1 or +1 from a seat majority) does not imply close elections in terms of vote share.³⁰ Then, the forcing variable, hereafter termedDistit= distance in

30An illustrative example is the case of Albacete. In the 2007 local elections,PPandPSOEobtained 13 seats each withIUobtaining the remaining seat. PSOEobtained again the mayoralty withIUsupport in a coalition formed by 14

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the vote margin, is computed as the share of votes —as a fraction of the total votes cast at the local elections— that parties in the ideological bloc of the regional incumbent needs to lose (if holding the majority) or win (if in the opposition at the local level) to lose (win) a majority of seats in the municipality, thus switching the alignment status.³¹

We can then use the discontinuity at the 0% vote margin threshold, considering as close elections those in which the distance in the vote margin —to win the majority if negative, and to lose it if positive— of the aligned bloc is close to zero. At the threshold Distit = 0, partisan alignment Ait theoretically sharply changes from zero to one. However, this is not always the case. Hence, given that the probability of treatment jumps by less than one at the threshold, we estimate the alignment effect by instrumenting actual alignment with the theoretical alignment dummy (T A_it= 1 ifDistit > 0), using a “fuzzy” RDD (Hahnet al.,2001; Imbens and Lemieux,2008). Bearing all this in mind, we estimate the following specification:

Corrit=β0+β1Ait+f(Distit) +X_it^‘δ+ǫit1 (B.1) Ait =φ0+φ1T Ait+g(Distit) +X_it^‘ζ+ǫit₂ (B.2) where the parameter of interest in equation (B.1) is β1, which we interpret as the local effect of partisan alignment onCorrit. We estimate the effect of being aligned by 2SLS, usingT Ait equal to one if Distit > 0 as an instrument forAit. f(Distit)and g(Distit) are the RDD polynomials in the distance in the vote margin Distit, capturing the potential non-linear relationship betweeni) alignment andcorritandii)theoretical and actual alignment.³²

We first estimate equations (B.1) and (B.2) by using the whole sample and a flexible polynomial in the distance in the vote margin. We use a second order polynomial, since it is the optimal order of the polynomial using the Akaike information criteria. This specification, using all observations, has two advantages. First, we improve the efficiency of the estimates and, in addition, we do not lose statistical power when estimating potential heterogeneous effects.

out of 27 councillors in the municipality (+1 seat above majority). By contrast, the difference in the vote share between PSOE+IUandPPis almost 10 percentage points (0.5244-0.4317=0.0927). However, results tend not to vary when using either the seats margin or the vote margin, as shown byArt´es and Jurado(2018).

31Computing this variable is not straightforward. It is based on the d’Hondt method, which is used to translate votes into seats in Spanish local elections, initially assuming that abstention increases when the votes for the party holding the marginal seats decrease. For the sake of simplicity, we use this approach in this paper —seeCurto-Grauet al.(2018) (and the online appendix) for additional details on how to compute this variable under different scenarios.

32Substituting (B.2) into (B.1), i.e., the equation that describes the discontinuity in alignment that we use to identify the effect of interest on the equation used to estimate the effect of alignment on our outcomes of interest, we obtain the reduced form equation:

Corr_it=ϕ0+ϕ1T A_it+h(Dist_it) +X_it^‘µ+ǫ_it3 (B.3) whereϕ¹ = φ¹β¹ is the intent-to-treat effect (ITT). We can recover the effect of alignment by estimating equations (B.2) and (B.3) and by computingβˆ¹= ^ϕ^ˆˆ¹

φ¹. Both procedures should give the same estimate as long as we use the same polynomial order forf(Distit),g(Distit)andh(Distit).

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However, using observations far from the cutoff may have an influence on the estimated effect.

Hence, we also use a local linear regression by restricting the number of observations to those within an optimal bandwidth on either side of the threshold. FollowingCalonicoet al.(2014), the optimal bandwidth is 0.28.³³ Thus, followingLee and Lemieux(2010), we present results for the optimal one and we assess its sensitivity to using bandwidth values other than the optimal one.³⁴

In all cases, we estimate the discontinuity in the alignment probability, i.e., the strength of the first stage, which is important to assess since we use a fuzzy RDD. Then, we interpret our estimates as the local average effect of the treatment (LATE) on the population of compliers at the cutoff.

Finally, we include all the control variables described in Section 3.1 and we also cluster the error termǫitat the municipal level.

B.1.2 Research design validity

The RDD identifying assumptionis absence or imperfect sorting by municipalities on either side of the 0% vote margin cutoff. In other words, whether a municipality is barely ruled by an aligned or non-aligned mayor is practically random. Although the identifying assumption is not testable, our design is valid as long as no party is able to sort just above the threshold defined by the minimum number of votes needed for the ideological bloc of the regional incumbent to gain/lose the majority of seats in the local council. For example, aligned candidates should have the same winning probability as non-aligned ones. In particular, we provide evidence in Figure B.1 that the distribution of the distance in the vote margin, i.e. the forcing variable, is smooth at the 0% cutoff.³⁵

Panel A in FigureB.1 shows histograms of the distance in the vote margin with three different bin widths: 0.1, 0.05 and 0.025. This ensures that a jump can be detected visually as no bin contains the cutoff value 0. Visual inspection suggests no suspicious jump in the height of the histogram bins at the cutoff, hence supporting the validity of the research design. This result is confirmed by a test of the null hypothesis of no manipulation at the vote margin cutoff, using the procedure proposed in McCrary(2008), as shown by the overlapping confidence interval dashed lines at the cutoff in Panel B of FigureB.1.

33The optimal bandwidth is very similar (0.30) if we use instead the data-driven choice rule inImbens and Kalya- naraman(2011).

34In particular, we use the following bandwidths: 2h^∗ = 0.56; h^∗/2 = 0.14; h^∗/4 = 0.07. In all cases, we use a rectangular kernel, i.e. we estimate a linear regression over a window of the distance in the vote margin of width equal to the optimal bandwidth value on both sides of the cutoff. This choice is given by its simplicity with respect to more sophisticated ones, since kernel selection tends to have little impact in practice (Imbens and Lemieux,2008).

35SeeImbens and Lemieux(2008) andLee and Lemieux(2010) for additional details about the identifying assumption in a regression discontinuity design.

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Figure B.1: Distance in vote margin distribution Panel A: Histogram

0150300450600

Frequency

−1 −.5 0 .5 1

Distance in vote margin

Panel B: McCrary test

0.511.5

Density

−1 −.5 0 .5 1

Note:The figure shows histograms of the forcing variableDistitfor Spanish municipalities. The histogram in the left panel is obtained by using bin widths equal to 0.1 (black), 0.05 (grey) and 0.025 (light grey), while the right one shows smoothed histograms of the forcing variable by using the McCrary test routine described inMcCrary(2008). Visual inspection of the size of the bins of the histograms at the threshold suggests that this variable is smooth at the cutoff.

Finally, in the absence of manipulation, we expect no difference in baseline characteristics when we compare municipalities in which aligned candidates barely won the elections, with those where they barely lost, as such characteristics are predetermined. Panel B in FigureB.4shows estimates for each of our control variables using equations (B.1) and (B.2). For each variable, we present estimates using local linear regressions with the optimal bandwidthh^∗, which is also reported. Overall, the table shows that differences in control variables at the cutoff are small and not significant. Panels A1- A3 in FigureB.4report similar evidence using polynomial plots of these variables, fitted separately on either side of the zero threshold, thus ruling out the possibility that any other observable factor, besides alignment and capital transfers, is affecting corruption at the threshold.³⁶

B.1.3 First stage results

The plot in Figure B.2 shows the jump in the probability of alignment when the vote margin is barely greater than 0, as this what is required to obtain the majority of seats in the local council.

Mean values by the alignment dummy are shown as circles, fitted values of the RDD polynomials in the distance in the vote margin are shown using thick continuous lines and confidence intervals using dashed lines.³⁷

Panel A shows a significant jump, from about 15% to 80%, when the distance in the vote margin goes from negative to positive around the threshold. This means that there are cases in which ideology is not the main predictor of alignment. As explained above, this may be due to the fact that sometimes coalitions are different at the municipal and at the regional level.

36Given its length, FigureB.4is located at the end of AppendixB.1for the sake of visibility.

37The figure is obtained by regressing the dummy of being aligned on another dummy that equals 1 if the forcing variable is positive (T Ait = 1 if Distit > 0) and on the RDD polynomial. In particular, we use a second order polynomial, fitted separately on either side of the zero threshold.

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Figure B.2: First stage

Panel A: Discontinuity in the probability of alignment

0.2.4.6.81

Alignment (1st stage)

−.5 −.25 0 .25 .5

Panel B: RDD estimates of the first stage

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Panel B1: Without control variables

T Ait 0.646^∗∗∗ 0.685^∗∗∗ 0.641^∗∗∗ 0.671^∗∗∗ 0.698^∗∗∗

(0.025) (0.020) (0.027) (0.036) (0.050) Mean dep. var. non-treated 0.077 0.082 0.101 0.122 0.126

Polynomial order 2 1 1 1 1

Bandwidth 1 2h^∗= 0.56 h^∗= 0.28 ^h₂^∗ = 0.14 ^h₄^∗ = 0.07

N. Observations 5,088 4,355 2,823 1,516 809

Panel B2: With control variables

T Ait 0.611^∗∗∗ 0.664^∗∗∗ 0.626^∗∗∗ 0.653^∗∗∗ 0.680^∗∗∗

(0.025) (0.020) (0.027) (0.036) (0.051) Mean dep. var. non-treated 0.077 0.082 0.101 0.122 0.126

Bandwidth 1 2h^∗= 0.56 h^∗= 0.28 ^h₂^∗ = 0.14 ^h₄^∗ = 0.07

N. Observations 4,976 4,251 2,754 1,486 794

Note:The forcing variable is the distance in the vote margin. The top panel shows plots of estimates of the difference in alignment at the vote margin threshold. The figure is constructed using local linear regressions, fitted separately on either side of the zero threshold, using observations around the optimal bandwidth. The solid line shows predicted values while dashed ones 95% confidence intervals. Mean values are shown as circles. We also show rdd estimates of this effect in the bottom panel. Standard errors are clustered at the municipal level. The significance levels are as follows: *p <0.10,

**p <0.05, ***p <0.01.

In addition to the plot, we also estimate the first stage using equation (B.2) to quantify its precision. The results are shown in the bottom panel. The first row in panel B1 shows, in column (1) estimates using the whole sample and a second order polynomial in the vote margin. In the rest of the columns, we present local linear regressions using the optimal bandwidth (h^∗) and also different values around it. Similarly, the first row in panel B2 shows the same set of specifications but this time we include control variables. This is because precision should increase and, in addition, it also serves as a validation check, given that coefficients should not change dramatically when including them. The estimated value of the discontinuity is between 61%-70%, being robust to changes in the polynomial order, the bandwidth selection or the inclusion of control variables.

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B.1.4 Second stage results

Figure B.3 shows both plots and estimates of the alignment effect on corruption. The figure in the top panel is constructed using a second order polynomial, fitted separately on either side of the zero threshold, using all observations. Mean values by the corruption dummy are shown as circles, fitted values of the RDD polynomials in the vote margin are shown using thick continuous lines and confidence intervals using dashed lines. Overall, the plot shows a positive and weakly significant jump in corruption at the cutoff, as shown by the almost non-overlapping dashed lines of the confidence intervals.

Figure B.3: Second stage

Panel A: Discontinuity in the probability of being corrupt

0.02.04.06.08.1.12

Corruption (2nd stage)

−.5 −.25 0 .25 .5

Panel B: RDD estimates of the second stage

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Panel B1: Without control variables

Alignment (Ait) 0.050^∗ 0.041^∗ 0.054^∗ 0.032 0.031 (0.030) (0.023) (0.033) (0.046) (0.064) Mean dep. var. non-treated 0.077 0.076 0.078 0.083 0.090

Bandwidth 1 2h^∗= 0.56 h^∗= 0.28 ^h₂^∗= 0.14 ^h₄^∗= 0.07

N. Observations 5,088 4,355 2,823 1,516 809

Panel B2: With control variables

Alignment (Ait) 0.050^∗ 0.040^∗ 0.059^∗ 0.066 0.068 (0.029) (0.022) (0.031) (0.043) (0.057) Mean dep. var. non-treated 0.077 0.076 0.078 0.083 0.090

Bandwidth 1 2h^∗= 0.56 h^∗= 0.28 ^h₂^∗= 0.14 ^h₄^∗= 0.07

N. Observations 4,976 4,251 2,754 1,486 794

Note: The forcing variable is the distance in the vote margin. The top panel shows plots of estimates of the difference in corruption at the vote margin threshold. The figure is constructed using local linear regressions, fitted separately on either side of the zero threshold, using observations around the optimal bandwidth. Solid line shows predicted values while dashed ones 95% confidence intervals. Mean values are shown as circles. We also show rdd estimates of this effect in the bottom panel. Standard errors are clustered at the municipal level. The significance levels are as follows: * p <0.10, **p <0.05, ***p <0.01.

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Estimates of this effect are shown in the bottom panel. The structure of the table is the same as the one presented in FigureB.2. Then, we show estimates using the whole sample and a second order polynomial in the vote margin in the first column, while we present local linear regressions using the optimal bandwidth (h^∗) and also different values around it in the rest of the columns. In addition, we report estimates without(with) control variables in Panel B1(B2), respectively.

The estimates show a positive, albeit weakly significant effect on corruption. Focusing on column (1), where we use a second order polynomial in the vote margin with the whole sample, alignment significantly increases corruption by about 5 percentage points, regardless of the exclusion/inclusion of control variables. This result is very similar when using a local linear regression with the optimal bandwidth —column (3). By contrast, the alignment effect on corruption is still positive, although somewhat less precise because of the lower number of observations, when restricting the bandwidth to 50% and 25% of the optimal one. Altogether, this evidence suggests that our main results, shown in Table 2, are not likely to be biased by time-varying confounding factors, as the DiD estimate falls within the confidence interval of the close-race regression discontinuity design RDD—where elections won by a narrow margin in aligned municipalities are very similar to those elections won by a narrow margin in non-aligned ones.

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Figure B.4: Balance of baseline characteristics Panel A1: Discontinuity test for each baseline characteristic

.68.7.72.74.76.78

Participation rate

−.5 −.25 0 .25 .5

.4.45.5.55.6

Vote share incumbent

−.5 −.25 0 .25 .5

−.050.05.1

Change of mayor (different party)

−.5 −.25 0 .25 .5

0.05.1.15

Change of mayor (same party)

−.5 −.25 0 .25 .5

.2.3.4.5

PP in power (local)

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.25.3.35.4.45.5

PSOE in power (local)

−.5 −.25 0 .25 .5

0.05.1.15.2

Other party in power (local)

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.05.1.15.2.25.3

Other party in power (regional)

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.4.5.6.7.8.9

Absolute majority (local)

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.2.3.4.5.6

Absolute majority (regional)

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Continued on the next page...

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FigureB.4continued from the previous page...

Panel A2: Discontinuity test for each baseline characteristic

.2.3.4.5

PP in power (regional)

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.05.1.15.2.25.3

Other party in power (regional)

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0.05.1.15

Female

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46474849

Mayor’s age

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4.855.25.45.6

Mayor’s education

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1.522.533.5

Mayor’s superior education

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−.07−.06−.05−.04−.03

Population growth rate

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−.15−.1−.050

Unemployment growth rate

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051015

Number of saving banks

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100200300400500600

Number of small business

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Panel A3: Discontinuity test for each baseline characteristic

150200250300350

Direct taxes

−.5 −.25 0 .25 .5

405060708090

Indirect taxes

−.5 −.25 0 .25 .5

200250300350

Current transfers

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0204060

Property income

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100150200

Capital transfers

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Panel B: RDD estimate for each baseline characteristic

(1) (2) (3) (4)

Coef S.E. h^∗ N. Observations Electoral variables (local)

Participation rate 0.020^∗ (0.012) 0.204 2,121

Vote share current incumbent -0.003 (0.013) 0.158 1,702

PP in power (local) 0.015 (0.057) 0.297 2,955

PSOE in power (local) 0.039 (0.062) 0.259 2,666

Other party in power (local) -0.021 (0.023) 0.189 1,982

=1 if change in local party power 0.020 (0.032) 0.234 2,410

=1 if mayor’s change 0.032 (0.038) 0.243 2,490

=1 if incumbent re-elected -0.098 (0.067) 0.180 1,919

=1 if absolute majority at local level 0.045 (0.068) 0.188 1,973 Electoral variables (regional)

PP in power (regional) 0.038 (0.056) 0.265 2,706

Other party in power (regional) -0.021 (0.036) 0.217 2,265

=1 if absolute majority at regional level 0.016 (0.060) 0.218 2,271 Mayors’ characteristics

=1 if female 0.058 (0.036) 0.168 1,806

Mayor’s age -0.823 (1.136) 0.277 2,768

Mayor’s education 0.002 (0.218) 0.263 2,695

Mayor’s number of terms in office -0.084 (0.230) 0.189 1,983 Baseline characteristics

Population gr. rate 0.003 (0.009) 0.259 2,643

Unemployment gr. rate -0.012 (0.040) 0.190 1,978

Number of saving banks 2.931 (6.762) 0.185 1,938

Number of small business 45.252 (287.753) 0.209 2,169 Income variables (Euro per capita)

Direct taxes -11.437 (64.288) 0.260 2,670

Indirect taxes 3.220 (20.745) 0.240 2,462

Current transfers -18.732 (50.497) 0.207 2,160

Property income 8.420 (6.613) 0.116 1,272

Capital transfers 46.723^∗∗ (20.563) 0.140 1,518

Note: The forcing variable is the distance in the vote margin. Panels A1-A3 show plots of estimates of the difference in each control variable at the vote margin threshold. The figure is constructed using local linear regressions, fitted separately on either side of the zero threshold, using observations around the optimal bandwidth for each variable. The solid line shows predicted values while dashed ones 95% confidence intervals. Mean values are shown as circles. We also show rdd estimates for this test in panel B. Standard errors are clustered at the municipal level. The significance levels are as follows: *p <0.10, **p <0.05, ***p <0.01.

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B.2 Alternative estimation procedure: PSM

The basic problem in identifying a causal effect is that the variable of interest cannot be observed under the treatment and control regime at the same time. Taking the mean outcome of the control group as an approximation is not advisable, since municipalities in the treatment and control group usually differ even in the absence of treatment (Caliendo and Kopeinig,2008).

This well-known problem of selection bias can be solved, or at least mitigated, by using matching techniques. The basic idea behind is to choose, among the large groups of municipalities in the control group, those which are similar to municipalities in the treatment group in all relevant pre- treatment municipality characteristics X. Matching procedures based on the propensity score, i.e.

the probability of being treated given observed characteristics X, are known as propensity score matching (PSM).

Researchers are faced with a good number of questions regarding the implementation of matching techniques. According toDehejia and Wahba(2002), three issues arise in implementing matching. First, which matching model to choose and which variables to include within the model. Sec- ond, whether or not to match with replacement. Third, how many comparison units to match to each treated unit. Following the guidance provided in Caliendo and Kopeinig (2008), logit and probit models usually yield similar results when estimating the probability of being treated versus not being treated with a binary treatment. In addition, the literature is not clear enough when deciding how many variables to include in the propensity score model. Finally, among all the possible matching algorithms that can be used to estimate the difference in the outcome of treated and control units, it should be noted that PSM estimators differ in two aspects. First, in the way the neighbourhood for each treated unit is defined. Second, in how the weights are assigned to these neighbours.

Taking all this into account, we estimate the effect of being aligned (i.e., the treatment) on corruption by matching every treated municipality “i’ with the closest control in terms of the estimated propensity score. In particular, we combine all the municipality characteristics information into estimated treatment probabilities, known as propensity scores, and we use this single continuous variable as the matching variable. The propensity score (the probability of being aligned) is estimated using probit/logit models with all the available municipality characteristics. Alternatively, we also use a type of matching known as nearest-neighbour matching that performs bias correction to handle the case of more than one continuous control variable.³⁸

38As a robustness check, we also present estimates using different alternatives: RA (estimator based on a model for the outcome variable); IPW (estimator based on a model for treatment assignment, using a probit model); AIPW-IPWRA (estimators based on models for both the outcome variable and the treatment assignment).

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Table B.1 shows standardized differences and variance ratios between treatment and control groups, for the raw data and for the matched one. A perfectly balanced covariate has a standardized difference of zero and variance ratio of one. This is indeed the case for the selected municipality characteristics, as the weighted standardized differences are all close to zero –column (2)– and the variance ratios are all close to one –column (4).³⁹

Table B.1: Quality of matching

Variables Standardized differences Variance ratio

Raw Matched Raw Matched

Participation rate 0.068 0.002 0.972 0.944

=1 if change in local party power -0.176 -0.015 0.518 0.949

=1 if mayor’s change -0.082 -0.013 0.821 0.970

=1 if incumbent re-elected 0.183 0.004 0.975 0.999

=1 if absolute majority at local level 0.374 -0.003 0.792 1.002

PP in power (regional) 0.053 -0.008 1.036 0.994

Other party in power (regional) -0.051 0.001 0.886 1.002

=1 if absolute majority at regional level 0.309 -0.007 0.901 1.002

Population gr. rate 0.126 0.002 0.967 1.001

Unemployment gr. rate 0.013 -0.019 0.998 0.987

=1 if female mayor 0.017 -0.006 1.041 0.987

=1 if educ. superior -0.011 -0.002 0.997 0.999

=1 if above median terms 0.053 -0.007 0.982 1.002

=1 if 2003 electoral cycle -0.067 -0.029 0.953 0.980

=1 if 2007 electoral cycle -0.005 0.023 0.997 1.016

Note:Raw = unmatched sample; Matched = matched sample. The balancing property is satisfied.

We next graphically check whether the overlap assumption holds. Intuitively, the overlap assumption is satisfied when there is a chance of seeing observations in both the control and the treatment groups at each combination of covariate values. Several ways are suggested in the literature, where the most straightforward one is a visual density analysis of the predicted probabilities that a non-aligned municipality is not aligned and the estimated density of the predicted probabilities that an aligned municipality is aligned. The idea behind is that the overlap assumption is only violated when an estimated density has too much mass around 0 or 1 (Bussoet al.,2014). An implication of this point is that when the overlap assumption is violated, the estimated densities will have relatively little mass in the regions in which they overlap.

39The number of covariates is smaller with respect to the original set in order to properly compute the propensity score. In particular, including all controls implied having perfect predictors with the balancing property not satisfied.

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Figure B.5: Estimated density of the predicted probabilities of (not) being aligned

01234Density

.2 .4 .6 .8 1

Propensity Score

Control Treated

Note:The figure shows the estimated densities of the probability of getting the treatment. Visual inspection of the figure suggests that overlap assumption is not violated.

Visual inspection of FigureB.5 suggests that there is no evidence that the overlap assumption is violated. Neither plot indicates too much probability mass near 0 or 1, and the two estimated densities have most of their respective masses in regions in which they overlap each other.

Table B.2 shows PSM estimates of the alignment effect on corruption. Across rows, we show PSM estimates using probit (logit) models when necessary in Panel A (Panel B). In addition, we show the mean value of corruption for non-treated units, the number of observations for treated municipalities and the total number of observations used at the bottom of the table.

Table B.2: PSM estimates of the effect of alignment on corruption.

Methodology PSM NNM RA IPW AIPW IPWRA

(1) (2) (3) (4) (5) (6)

Panel A: Probit regressions

Alignment (Ait) 0.026^∗∗∗ 0.028^∗∗∗ 0.023^∗∗∗ 0.023^∗∗∗ 0.020^∗∗∗ 0.024^∗∗∗

(0.007) (0.007) (0.006) (0.006) (0.006) (0.006) Panel B: Logit regressions

Alignment (Ait) 0.032^∗∗∗ 0.028^∗∗∗ 0.023^∗∗∗ 0.024^∗∗∗ 0.021^∗∗∗ 0.024^∗∗∗

(0.007) (0.007) (0.006) (0.006) (0.006) (0.006) Mean dep. var. non-treated 0.062

N. Treated 4,934 4,934 4,934 4,934 4,934 4,934

N. Observations 9,296 9,296 9,296 9,296 9,296 9,296

Note:The table shows PSM estimates of the alignment effect on local corruption in Spain. We obtained the estimates in Panel A (Panel B) by using probit (logit) models when needed. In Panel B, we use our main specification including two dummies for local-national alignment and regional-national alignment. Across columns, we show six different specifications: PSM in column (1);

NNM in column (2); RA in column (3); IPW in column (4) and the combinations AIPW and IPWRA in columns (5) and (6). We regressed the outcome on a dummy equal to 1 if a municipality is aligned and 0 otherwise for the matched sample. Standard errors are clustered at the municipality level. The significance levels are as follows: *p <0.10, **p <0.05, ***p <0.01.

The first column in TableB.2shows the estimated effect using the propensity score technique by matching each municipality to a single municipality with the opposite treatment whose propensity

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score is closest. This procedure has the advantage of not needing bias correction, because PSM matches on a single continuous variable –the propensity score. By contrast, the nearest-neighbour matching estimator shown in column (2) uses a bias correction term when matching on more than one continuous control variable. Intuitively, it imputes the missing potential outcome for each municipality by using an average of the outcomes of similar municipalities receiving the other treatment level.

In the rest of the columns, we perform a sensitivity analysis by varying the estimation procedure chosen to estimate the model. The RA estimator presented in column (3) uses means of predicted outcomes for each treatment level to estimate each Potential Outcome Model (POM), while ATEs and ATETs are differences in estimated POMs. The IPW estimator in column (4) models the probability of treatment without any assumptions about the functional form for the outcome model. It uses weighted averages of the observed outcome variable to estimate means of the potential outcomes, where the weights (the inverse of the estimated probability that a municipality receives the treatment) account for the missing data inherent in the potential-outcome framework. In this setting, outcomes of municipalities receiving a probable treatment get a weight close to one.⁴⁰

Finally, AIPW and IPWRA estimators in columns (5) and (6) model both the outcome and the treatment probability. Intuitively, the AIPW estimator is an IPW that includes an augmentation term that corrects the estimator when the treatment model is misspecified, while the IPWRA uses the inverse of the estimated treatment-probability weights to estimate missing-data-corrected regression coefficients that are subsequently used to compute the POMs. In all cases, we focus on the ATET, the average treatment effect on the treated, because the assumptions required to estimate it are less restrictive than the assumptions required to estimate the ATE.⁴¹ Regardless of the method chosen, results prove robust to using a matched sample to estimate the alignment effect.

40IPW estimators become extremely unstable as the overlap assumption gets close to being violated. When this happens, some of the inverse-probability weights become very large, IPW estimators produce erratic estimates and the large-sample distribution provides a poor approximation to the finite-sample distribution of them.

41In particular, estimating the ATET requires a weaker form of the CI assumption and a weaker version of the overlap assumption. Result, available upon request, do not vary significantly when estimating the ATE.

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B.3 Alternative definitions of the treatment

We next study the robustness of the definition of our treatment variable. So far, we have considered a municipality as “treated” if it was ruled by the same party which was ruling the regional government. Alternatively, we could have used a broader definition for being considered as a “treated”

municipality. In particular, we define “coalition alignment” as a dummy equal to 1 if the party ruling the municipality is also in the coalition running the regional government, regardless of whether it is the main party or a secondary one.

TableB.3 provides an exemplary illustration for the main left-wing party in the country. In the top panel A (partisan alignment), alignment only occurs whenPSOEis the lead party at both levels, local and regional. In the bottom panel B (coalition alignment), there is an extra case of alignment:

when the local ruling party is further a junior member of the regional government coalition.

Table B.3: Capturing political alignment: an illustration

Panel A: Partisan alignment

PSOE (main party in regional gov.) PSOE (secondary party in regional gov.)

PSOE (ruling local gov.) Alignment= 1 Alignment= 0

PSOE (not ruling local gov.) Alignment= 0 Alignment= 0

Panel B: Partisan alignment within a coalition

PSOE (main party in regional gov.) PSOE (secondary party in regional gov.)

PSOE (ruling local gov.) Alignment= 1 Alignment= 1

PSOE (not ruling local gov.) Alignment= 0 Alignment= 0

In addition, we also explore whether alignment between other government tiers has an impact on the effect of alignment between the local and the regional government. To do so, we re-estimate equation (1) by including two dummies equal to 1 if the local government and the regional government are, respectively, aligned with the national one.

The results of these tests are shown in Table B.4. It shows that broadening the definition of alignment does not affect the main results, nor the inclusion of the two extra alignment dummies.⁴²

42If anything, alignment between other government layers decreases corruption, although the effect is small and weakly significant. Results available upon request.

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Table B.4: The effect of (coalition) alignment on corruption

(1) (2) (3) (4) (5) (6)

Panel A: Coalition alignment

Alignment (C) (Acoait) 0.018^∗∗∗ 0.021^∗∗∗ 0.018^∗∗∗ 0.020^∗∗∗ 0.019^∗∗∗ 0.018^∗∗

N. treated municipalities 5,477

N. Observations 9,733 9,733 9,733 8,988 8,988 8,988

Panel B: Including loc-national and reg-national alignment Alignment (Airt) 0.021^∗∗∗ 0.025^∗∗∗ 0.022^∗∗∗ 0.022^∗∗∗ 0.022^∗∗∗ 0.020^∗∗∗

N. treated municipalities 5,114

N. Observations 9,733 9,733 9,733 8,988 8,988 8,988

αi X X X X X X

γ_t X X X X

γ_t∗θ_r X X

Xit X X X

Note:The table shows estimates of the alignment effect on local corruption in Spain. We obtained the estimates in Panel A by broadening the definition of alignment to also include cases in which the ruling party at the local level is in the coalition government at the regional level, regardless of its position within the coalition. In Panel B, we use our main specification including two dummies for local-national alignment and regional-national alignment. Across columns, we show six different specifications: basic FE in column (1); FE with electoral cycle fixed effects in column (2); FE with electoral cycle fixed effects andelectoral_t×region_rfixed effects in column (3); and the same specifications but adding control variables in columns (4), (5) and (6). We regressed the outcome on a dummy equal to 1 if a municipality is aligned and 0 otherwise. Standard errors are clustered at the municipality level. The significance levels are as follows: *p <0.10, **

p <0.05, ***p <0.01.

B.4 Alternative definitions of control municipalities

We also check whether the broad control group we have been using is affecting our results. To do so, we use two alternative definitions of the control group. First, taking into account that in the main analysis the treatment is defined as partisan alignment and the control group includes municipalities that are not aligned and municipalities that have partisan alignment within a coalition, we restrict the control group to those municipalities without alignment and also without coalition alignment.

As a second alternative, we compare municipalities which are always not aligned with municipalities that are always aligned. The reason behind this is to provide a closer approach to a standard DiD, given that with this specification the identifying variation comes mostly from the comparison between municipalities —in contrast with our main results in which the inclusion of municipal FE implies a within municipalities identifying variation.

The results of these empirical exercises are included in TableB.5. Regarding panel A, our main results remain practically unchanged when including or excluding these municipalities —we observe 363 municipalities with partisan alignment within a coalition included in the control group. The same happens in panel B, where we compare municipalities with and without alignment instead of municipalities with themselves, thus suggesting that both our main control group and our main identifying strategy are not contaminated.