The Role of Local Media in Selecting and Disciplining Politicians

Carlos Varjao∗

Job Market Paper

12th December, 2019

Link to latest version

ABSTRACT

This paper examines how local media affects electoral accountability and the quality of public

education in Brazil. Exploiting the entry timing of the first local radio station in a municipality and

the geographical area it covers, I find that local media decrease (increase) the probability of mayors

being re-elected when they miss (exceed) the educational targets set by the federal government. I

then examine the two main channels through which this enhancement in accountability can impact

the quality of public schools. I estimate that local radio, by alleviating the moral hazard problem

and inducing politicians to exert more effort, increased test scores by 0.16 standard deviations. I

also show that local radio had negligible impact on educational outcomes through the selection

of higher quality candidates. I interpret these reduced form findings through a political agency

model, which I structurally estimate. The model fits my key findings and shows that the potentially

puzzling combination of large moral hazard and limited selection effects can be rationalized by low

heterogeneity in the quality of the candidate pool. Finally, the model allows me to aggregate

the reduced form results and recover the full effect of the expansion of local radios in Brazil on

educational outcomes.

∗Stanford GSB. Email: cvarjao@stanford.edu. I am especially indebted to my advisors Katherine Casey, MatthewGentzkow and Saumitra Jha for their comments and support, as well as to Pascaline Dupas, Zhao Li, Greg Martin,Marcos Salgado, Ashutosh Thakur, Ali Yurukoglu and participants at the IO workshop and Development EconomicsSeminar at Stanford for insightful feedback.

1 Introduction

A growing body of research indicates that the media play an important role in informing voters

and disciplining incumbent politicians in developing countries (Ferraz and Finan (2008), Larreguy

et al. (2015)). However, it remains unclear whether punishing low-performing incumbents actually

improves public service delivery. Moreover, there is currently little evidence regarding how the

media might improve public services. A canonical electoral accountability model posits that two

channels may play a role: the media may reduce the moral hazard problem, thereby inducing

politicians to exert more effort (Ferejohn (1986)), or it may increase the probability of high-quality

candidates being elected (Fearon (1999)).

This paper makes four contributions: (i) I show that the entry of the first local radio stations in

Brazilian municipalities facilitates the electoral punishment of incumbent mayors who miss federal

educational targets. I then exploit the entry timing of these stations in order to parse the two main

channels through which this enhancement in electoral accountability might impact the quality

of public schools. (ii) I demonstrate that local radios have improved educational outcomes by

alleviating the moral hazard problem and inducing politicians to exert more effort; (iii) I also show

that local radios had a negligible impact on educational outcomes through the selection of higher

quality candidates. (iv) I introduce and structurally estimate a simple political agency model to

link the three previous results and sharpen their interpretation. The model is able to fit my key

findings and shows that the potentially puzzling combination of large moral hazard and limited

selection effects is rationalized by low heterogeneity in the quality of the candidate pool. It also

allows me to aggregate up the reduced form variation and recover the full effect of the expansion

of local radios on educational outcomes in Brazil.

My empirical design first leverages the fact that, in 1998, the Brazilian federal government cre-

ated a new category of non-profit radio broadcasting called community radio. Since then, almost

3000 municipalities have received their first local radio stations as a result of this policy. As news-

paper penetration is low and television stations mostly cover stories related to population centers,

these radio stations are usually the only source of local news within these municipalities. Second,

every two years since 2005, almost all Brazilian students finishing primary school have participated

in the Prova Brasil examination, which is designed to assess the quality of public schools. The

results of this examination are released approximately two months before the municipal elections1

in the form of a simple score called the Basic Education Development Index (IDEB). Importantly,

the federal government has also created a trajectory of IDEB targets for each municipality up to

2021, based on the 2005 IDEB results. These targets provide a natural yardstick that enables voters

to evaluate an incumbent’s performance.

I show that community radio stations regularly report on IDEB scores by performing a content

analysis of their coverage of the public release of the 2017 IDEB results. I downloaded the audio

1Primary schooling is mainly provided by municipal government.

2

streams of 385 online community radio stations in the states of Sao Paulo and Parana 2 the day

after the IDEB results were released and used Google Speech AI technology to convert them into

text. The results of this exercise demonstrate that at least 25% of the recorded radio stations

commented on the IDEB3, and over 70% of the news segments used the federal targets as a yardstick

for performance. Importantly, this coverage was especially frequent in the municipalities where

the scores were far-removed from the targets, which is arguably the context within which the

information is going to be the most useful to voters.

First, my analysis compares the electoral outcomes of mayors between the municipalities that

received a local radio station immediately before or immediately after municipal elections. This

variation is largely driven by the idiosyncrasies of the long (on average four years) and uncertain

(25th percentile: 2.5 years, 75th percentile: 5 years) community radio licensing process, which are

plausibly orthogonal to electoral or educational outcomes. Using this design, I show that local media

facilitates the sanctioning of mayors and that its impact depends upon the divergence of the IDEB

scores from the federal targets for the municipality. Among the municipalities in the top quintile

of score deviations from the target, local media increased the incumbent’s likelihood of re-election

by 24 percentage points (p.p.), compared to the re-election rates in the municipalities without

local media. In contrast, among the municipalities in the bottom quintile, local radio decreased

the incumbent’s likelihood of re-election by 21 p.p. I also use within-municipality variation in the

range of the community radio signals and show that polling stations reached by community radio

stations are better able to hold politicians accountable than the ones that are not. I further show

that community radio stations’ ability to inform voters is severely reduced when they face high

competition from out-of-town radio stations, which do not cover local IDEB scores.

Second, while these results strongly suggest that local media facilitate sanctioning, they do not

answer the question of whether this actually improves educational outcomes. Information regarding

the incumbents’ performances can reduce moral hazard problems and induce mayors to exert more

effort in order to be re-elected. Whereas my previous analysis compares municipalities that received

a radio station before or after the election, I focus here on those that received a station before or

after the Prova Brasil examination. Stations that existed prior to the examination could pressure

mayors to exert effort toward increasing scores, whereas those that entered afterward could not.

This analysis suggests that local radio stations increased IDEB scores by 0.1 points (0.16 standard

deviations), with the effect fully concentrated on non-term-limited mayors. I then investigate

how mayors are improving scores. Using survey data from school principals, I show that radio

coverage increases the probability that schools have: initiatives that support grade promotion,

fewer financial difficulties, and less political interference problems. Finally, I find that receiving a

local radio station increases the probability of a mayor securing a discretionary federal grant from

2Out of a total of 928 community radio stations in these two states.3This is a lower bound on the proportion of recorded radio stations that actually covered the topic (see Section

6.1).

3

the Ministry of Education by 5 p.p.4

Third, I investigate whether local media improve public schools’ scores through the selection

of higher quality mayors. To test this hypothesis, I compare IDEB scores three years after the

municipal elections in the municipalities that received a local radio station immediately before and

immediately after the elections. This plausibly isolates the selection from moral hazard effects:

voters from the two groups had different information available at the time of the election and

selected different candidates, but three years after the elections, both groups had local media and

mayors who then faced the same level of scrutiny. The results of this exercise suggest no effects on

scores, with standard errors small enough to rule out effects as large as the moral hazard ones.

Fourth, I introduce and structurally estimate a simple political agency model to tie the reduced

form results together and sharpen their interpretation. This model allows community radio stations

to have an impact upon accountability and educational outcomes, depending on their entry timing.

The model is able to fit the key findings both qualitatively and quantitatively, and it also sheds ad-

ditional light on the mechanisms. In particular, it shows that the potentially puzzling combination

of large moral hazard and limited selection effects can be rationalized by the composition of the

pool of candidates running for office, which I estimate to be largely homogeneous and composed

mostly of opportunistic candidates. Under these circumstances, there is little space for selection

effects to play out as even when voters get rid of low-quality incumbents, the challenger is also

likely to be of low quality.

The model also allows me to aggregate up the reduced form variation to recover the full effect

community radios. Since mayors internalize the probability that a radio station will enter their

municipality in the future, they exert more effort today than if this threat did not exist. In other

words, previous results have underestimated the difference in scores between a scenario in which

community radios do not exist and one in which they do. Counterfactual estimations show that

the full impact on scores is 18% higher once we take this into account. Using this estimate and the

proportion of municipalities that have benefited from the expansion of community radios, back-

of-the-envelope calculations suggest that community radios have been responsible for 1.5% of the

large improvement in municipal school scores in Brazil since 2005.

Through counterfactual simulations, I also show that reducing the number of terms a mayor

can serve from two to one would reduce scores by 0.09 points (0.14 standard deviations) in the

municipalities with a local radio station, but it would barely affect the scores in those without one.

Finally, I estimate that low levels of incumbency advantage in my setting play an important role

in my results. For example, raising incumbents’ chances of reelection from 43% to levels similar to

US senatorial races (82%) would reduce the impact of local radio on scores by more than half.

I present three additional pieces of evidence in support of my identification strategy. First,

4Municipalities have limited taxing abilities, and the only major mechanism mayors can use to attract additionalresources is a discretionary federal matching grant known as ”convenio”. Unlike other transfers, the receipt of thesefunds is not formula-based, but depends on the mayor’s effort to procure them (Ferraz and Finan (2011)).

4

I demonstrate that municipalities that received radio stations around the event of interest were

largely similar in terms of their observable characteristics. Second, adding a rich vector of municipal

characteristics and, where possible, controlling for the lagged values of the dependent variables

barely alter my point estimates. Third, for all the main results of this paper, I perform placebo tests

in which I shift the time window around the event of interest. For example, instead of comparing

the municipalities that received a radio station before and after the municipal elections (the actual

comparison of interest), I shift the window forward and compare two groups of municipalities that

did not actually have radio stations at the time of the election. Conversely, I can move the window

backward, such that the test now compares two groups of municipalities that both had radio stations

at the time of the election. The results consistently suggest no effects from these placebo tests.

This paper relates to prior research showing that the media can increase electoral accountability

(Ferraz and Finan (2008), Larreguy et al. (2015),Marshall (2016)).5 It contributes to this literature

by not only showing that the media help voters sanction politicians, but also by providing evidence

of how this impacts the delivery of public goods, particularly primary education.6 My analysis

further relates to studies connecting politician performance with increase in voter knowledge due

to the media (Besley and Burgess (2002), Stromberg (2004), Snyder and Stromberg (2010), Reinikka

and Svensson (2011), Casey (2015)).7 I complement these studies in two ways. First, I untangle

the two main possible mechanisms linking media coverage to politician performance and find that

the moral hazard channel plays a stronger role in improving service delivery than the selection of

politicians. Second, I structurally estimate a political agency model to tie my results together,

demonstrate that the magnitude of the two mechanisms depends critically upon the quality of the

candidate pool, and recover the full effect of community radios.

The results in this paper bear some resemblance to the findings of Avis et al. (2018) in Brazil

and Bobonis et al. (2016) in Puerto Rico, who investigate the effects of audits on corruption and

find a negative impact in the short term due to a disciplining effect, but no evidence of an impact

due to the selection of better politicians.8 However, as Avis et al. (2018) argue, this reduction in

5More broadly, this paper also relates to literature investigating the role of information in political accountability.See Banerjee et al. (2011), Chong et al. (2010), Fujiwara and Wantchekon (2013), Kendall et al. (2015), Grossmanand Michelitch (2018), Cruz et al. (2018), Romero et al. (2018), Casey et al. (2019) for evidence on the positive effectsof information. Conversely, see Dunning et al. (2019) for evidence that typical information campaigns do not changevoter behavior.

6Dias and Ferraz (2017) and Boas et al. (2019) explore the connection between educational and electoral outcomesin Brazilian municipalities with mixed findings, but both studies do not take into account local media presence (orlack thereof) in their analyses.

7There is a large body of related literature on the effects of media on political outcomes. For example, the effectsof television on turnout (Gentzkow (2006)) or the effect on electoral performance of partisan media (DellaVignaand Kaplan (2007), Gerber et al. (2009), Enikolopov et al. (2011), Martin and Yurukoglu (2017)) and advertising (Da Silveira and De Mello (2011), Gerber et al. (2011), Krasno and Green (2008), Larreguy et al. (2018), Spenkuchand Toniatti (2018)). This paper is also particularly relevant to research that highlights the importance of localmedia (George and Waldfogel (2006), Gentzkow et al. (2011), Hopkins (2018), Martin and McCrain (2019)).

8On the other hand, Alt et al. (2011) and Aruoba et al. (2019) find that moral hazard and selection effects are ofcomparable magnitudes in U.S. gubernatorial elections.

5

corruption was derived mostly from non-electoral costs, with the audits increasing the perceived

legal costs of engaging in corruption. This is not the case in the current paper, as poor educational

performance can certainly be associated with electoral costs but not legal ones.

2 Institutional Background

2.1 Community Radio Stations

In 1998, the federal government created a new category of non-profit and low-powered radio

broadcasting to cover local, cultural and social affairs called community radio.9 The commercial

quality signal range of community radios is two miles,10 reaching on average close to 70% of the

voters in the municipalities in my setting.11

Radio is still very popular in Brazil. A 2016 federal government representative survey12 showed

that 67% of Brazilians listen to radio on a weekly basis. Most interviewees in small- and medium-

sized municipalities listen to radio using traditional radio equipment (85%) with only 10% using

cellphones as a listening device.13 The survey also showed that community radios are popular,

with at least 29% of respondents naming a community radio as their favorite radio station and on

average listening to over two hours of radio a day.

Figure 1a shows the expansion of the number of authorized community radio stations in Brazil.

The Ministry of Communication began the licensing process for creating community radio stations

by first choosing in which municipalities they would start a call for applications from local civic

associations to run a station. Priority to start these calls for applications was given according

to certain technical conditions14 and municipality size (see Figure 1b). Nonetheless, the federal

government plans to eventually license at least one community radio per Brazilian municipality.15

The roll out of the broadcasting licenses across the municipalities has been gradual. Due to the

extensive number of bureaucratic steps and shortage of federal staff to deal with the large number

of applications (Lima and Lopes (2007)), the Ministry of Communication license approval process

is long (on average four years) and uncertain (25th Percentile: 2.5 years and 75th Percentile: 5

9Although, in practice, the program content of these radios can end up being broadly similar to the commercialones with most of the airtime dedicated to music and entertainment (Borelli et al. (2012))

10Unless there are multiple community radios in the same municipality. In this case, since by law all communityradios within a municipality must operate in the same frequency, the reach of community radios might be just a littlemore than half a mile due to interference. But the municipalities of interest in my setting never have more than onecommunity radio.

11Number calculated by geographically merging geocoded information on community radios and poling stationslocations in municipalities that got their first local radio station between 1999 and 2017.

12Pesquisa Brasileira de Mıdia (2016)13During the elections of interest (2008 and 2012) prevalence of cellphone Internet usage to radio listening was

surely even lower than in 2016 given how much 3G cellphone coverage expanded in the intermediate period.14Mainly having a technical study showing an available radio frequency to be used by the community radio without

interfering with licensed commercial radios.15Plano Nacional de Outorgas 2011

6

years). Following ministry approval, the application is sent to the president’s cabinet for review.

The president’s cabinet then submits the application for congress approval. The radio station is

allowed to begin operating under a provisional license 90 days after the president’s cabinet submits

it to congress. Finally, a formal license is awarded with congress approval.

Between 1999 and 2017, as a result of this policy, almost 3,000 municipalities in Brazil acquired

their first local radio station. Among these municipalities only 6% had a local TV station and 26%

a local newspaper (Perfil do Municıpios Brasileiros 2014). Moreover, newspaper penetration is low

with only 13% of Brazilians in small- and medium-sized municipalities reading newspapers at least

once a week (Pesquisa Brasileira de Mıdia 2016).16 Hence, local radio stations are usually the only

relevant source of local news within these municipalities.

These radio stations are required by law to promote a diverse set of views and to not support

one specific party or politician. But, in practice, this might not always be the case. For example,

Boas and Hidalgo (2011) find evidence that winners in city council elections are more likely to be

connected to a community radio in the future and that candidates connected with a community

radio perform better in elections, suggesting that local politicians might use community radios to

advance their careers. Hence, whether community radios will actually increase accountability or

just be used as a political tool is an empirical question.

Figure 1: Community Radio Expansion

010

0020

0030

0040

0050

00N

umbe

r of A

utho

rized

Com

mun

ity R

adio

s

2000 2005 2010 2015 2020

(a) Number of Community Radios

5000

1000

015

000

2000

0M

edia

n Po

pula

tion

2000 2005 2010 2015

(b) Size of Municipalities Getting Community Radio

Notes: Figure 1a shows the total number of community radio licenses awarded across time. Figure 1b shows themedian size of municipalities getting their first local radio station within evenly sized bins.

16I do not have data to separate readership between local and non-local newspapers but actual readership of localnewspapers is likely substantially lower than 13%.

7

2.2 Public Education and Prova Brasil

Brazil is one of the most decentralized countries in the world (Ferraz and Finan (2008)). Mayors

receive large sums of money from the federal and state governments to provide public services, which

consist mainly of primary education and health care.

Close to 90% of Brazilian students are enrolled in a public school. Municipal governments are

mainly responsible for providing primary schooling while state governments mainly provide middle

and high schooling. Municipalities do not raise much revenue with taxes and rely on rule-based

and discretionary transfers from federal and state governments to finance schools.

In 2005, the federal government created a nationwide exam to evaluate the quality of public

schools called Prova Brasil. Every 2 years between the months of October and November, most

Brazilian students finishing primary school participate in the Prova Brasil.17 The exam is designed

to evaluate whether students have learned the primary school material that is required in Portuguese

and Math. Approximately one year after the exams, the results are released to the public in the

form of a 0 to 10 index named the IDEB.

The IDEB multiplies average test scores (measured by the Prova Brasil) by grade passing rates

(the percentage of primary school students in the municipality that advanced to the next grade in

school that year). This formula is designed to avoid municipalities artificially boosting their passing

rates (which could lower the exam scores) or holding back students (which could elevate the exam

scores). Realistically, it is hard to improve IDEB scores by increasing passing rates since they are

already very high. Only 6.5% of municipal primary school students were held back a grade in my

analysis period.

The IDEB is released at the school, municipality, state, and national levels. In 2006, based on the

2005 IDEB results, the federal government created a trajectory of IDEB targets for all municipalities

until 2021. These targets were set following a logistical function that projects municipal scores

with the goal of progressing all municipalities to a 9.9 score by 2096. Hence, municipalities that

performed poorly in 2005 have lower targets in the subsequent years than municipalities that

performed well. Figure 2 provides an example of the IDEB scores and targets of a municipality

over time.

Mayors have the ability to rapidly impact IDEB scores. First, they have full discretion regarding

the daily operations of schools: from hiring, paying and training staff, teachers and principals to

providing school lunches and transportation to students (Akhtari et al. (2017)). Second, there

are several cases in Brazil of local reforms that generated large and fast impacts on educational

scores. The dramatic improvement in educational outcomes in the relatively poor city of Sobral in

the northeast region of Brazil is a famous example.18 Sobral implemented a series of reforms to

17Students’ participation rate is above 90%. Very small schools with less than 20 students enrolled in the assessedgrades did not participate in the exams. Before the 2009 exam, rural schools also did not participate in the exam(≈ 12% of enrolled students are in a school classified as rural by the Ministry of Education).

18”Sobral in Brazil shows how to lift woeful school standards”, Financial Times, September 19, 2017.

8

improve school management including constantly monitoring students learning, establishing clear

learning targets, improving the selection of school principals and creating performance based pay

for teachers. These reforms increased the IDEB scores of the municipality from 4.0 in 2005 to 9.1

in 2017.19 Hence, scores improved more the 0.4 points a year on average for the last 12 years and

now scores in Sobral are substantially higher than the average municipal scores in the state of Sao

Paulo, the richest state in Brazil.20

The average difference between the IDEB score and the target for a municipality is 0.36 points

(standard deviation of 0.6 points) and 23% of municipalities score below the target.21 Although

IDEB scores are highly correlated with the municipality’s level of development, the difference

between the IDEB score and the target is not. A regression of IDEB scores on a rich vector of

municipal characteristics yields an r-squared of 0.63 while the same regression using the distance

to target as the dependent variable yields an r-squared of only 0.03.22

Figure 2: IDEB Scores and Targets for the municipality of Nova Friburgo

45

67

Scor

e

2005 2007 2009 2011 2013 2015 2017 2019 2021Year

IDEB Score IDEB Target

2.3 Municipal Elections in Brazil

Brazil has over 5,500 municipalities spread across 26 states. Every four years, each municipality

elects a mayor and a local council of legislators. Mayors are elected by direct ballot in a one-round

majority system in most cities. Once elected, mayors face a two-term limit. The median number of

19See Figure 23 in Appendix B20See Becskehazy (2018) for a detailed case study of Sobral.21See full distribution of distances to target in Figure 21 in Appendix B.22Figure 22 in Appendix B illustrates this point. The y-axis shows the normalized dependent variable (Mean = 0

and StandardDeviation = 1) and the x-axis is the median income of the municipality (which is the best predictor ofeducational performance). The plot clearly shows that income is highly correlated with IDEB scores but has basicallyzero correlation with the distance to target.

9

mayoral candidates in my setting is two and only 12% of elections have more than three candidates.

Mayoral elections are competitive; the median winning margin is 10.5% and 25% of races have a

winning margin of less than 4.6%. Moreover, only 43% of incumbent mayors are reelected. Unlike

the US, given that voting is mandatory and the average turnout for mayoral races is extremely

high (close to 90%), voter turnout is not decisive in Brazilian elections.

The timing of the release of the IDEB scores potentially makes it a very salient occurrence

during municipal elections. The 2007 IDEB results were released at the end of June 2008, just

before the 2008 municipal elections at the beginning of October. The 2011 IDEB results were

released in August 2012, just before the 2012 municipal elections at the beginning of October.

Hence, the scores could potentially provide a natural yardstick by which voters could evaluate

incumbents’ performances.

3 Data

3.1 Community Radios Date of Entry

I use the list of all community radio stations that were initially approved by the Ministry of

Communication as of 2017. This dataset includes the dates of the approval by the Ministry of

Communication, the geographic coordinates of the radio stations and the dates of final approval by

congress (if it already happened).

The main data component required for my empirical strategy is the entry timing of the com-

munity radio stations into the respective municipalities. As discussed in Section 2.1, a community

radio station is authorized to begin operating 90 days after the president’s cabinet submits the

application to congress for formal approval. This date of submission for each community radio

station in the Ministry of Communication dataset is scraped from the Brazilian federal congress

website.23

3.2 Radios’ Audience

I use microdata from the 2016 Pesquisa Brasileira de Mıdia24 (PBM 2016) from the Brazilian

Secretary of Social Communication to compile data on the community radio audience. The PBM

surveyed the population of a representative sample of 743 municipalities with regard to their media

consumption habits. The main pieces of information I use from this survey are whether or not an

individual listened to radio and, conditional upon that listening, how frequently he or she listened

and which radio station was his or her favorite.

I supplement this survey with two other datasets. First, I request the municipality of residency

of surveyed individuals from the Brazilian Secretary of Social Communication using the Lei de

23www.camara.leg.br/buscaProposicoesWeb24Brazilian Media Survey

10

Acesso a Informacao (Access to Information Law) as this is not part of the original public dataset.

Second, to assess the popularity of community radios, I merge the respondents’ named favorite radio

stations with the Ministry of Communications dataset of community radios described in section

3.1.25 This merging process underestimates the popularity of community radios, since sometimes

the official name of the community radio on the Ministry of Communication dataset does not match

the popular name used by the radio station within the city.

3.3 Community Radios’ Content Analysis

I start by scrapping all the radio stations that were online in the states of Sao Paulo and Parana

in an online radio aggregator website.26 The website organizes online radios by the municipality

and name of the radio station. I use this information to merge the online radio dataset with the

Ministry of Communications dataset of community radios described in section 3.1. This results in

a list of 385 community radios that are also online out of the 928 community radios within the two

states.

I download the audio stream of these community radios between 6 am and 9 pm on September

4, 2018. Following this, to avoid the cost of unnecessarily transcribing audio that only contained

music, I divide the audio files into 1-minute segments and use the pyAudioAnalysis python module

to classify the distribution of speech and audio in each minute. I transcribe only the segments that

contains at least 15 seconds of speech. I also do not transcribe recordings between 7pm and 8 pm

because during this hour all radios have to transmit the mandatory A Voz do Brasil (The Voice

of Brazil), which is a governmental radio program produced by the country’s public broadcaster.

Finally, all the segments that contain at least 15 seconds of speech (44%) are transcribed using

Google Cloud Speech-to-Text.

3.4 Radio Stations Coverage Area

The Anatel (National Telecommunications Agency) and the Brazilian Ministry of Communica-

tion provides data on all the commercial radio stations operating as of 2017.27 The dataset includes

antennae location (geographical coordinates), coverage area, and the date of the radio station’s cre-

ation. The coverage areas are defined as the protected contour around each radio station antenna.28

25I use both the official name of the radio station and frequency of the radio station to merge the datasets. Forexample, a lot of radio stations simply use as popular names their frequency (e.g. ”98.1 FM”), which are not theirofficial name.

26www.radios.com.br27http://sistemas.anatel.gov.br/se/public/view/b/srd.php28This is only directly available for FM stations. For AM stations, range depends on a more complex combination

of characteristics like vegetation, time of the day, humidity and others. As a first order approximation for AM stationsrange, I use the AM to FM migration plan. Currently, the Ministry of Communications is allowing AM radio stationsto migrate to the FM frequency to modernize the sector and, depending on the power and frequency of an AM station,it can migrate to a comparable coverage area in a FM frequency. I use this conversion table to assign an estimatedcoverage area for AM stations.

11

Outside the limits of the protected contour, signal strength cannot be above 66 dBuV/m, hence,

this is the area within which the radio station signal can be considered to be of commercial quality

(Ribeiro (2017)). This is the upper limit of the high-quality signal coverage area of these radio sta-

tions, as geographical obstacles may prevent the signal from reaching the borders of the protected

contour.

I use these data for three main purposes. Firstly, to identify the municipalities that did not

have a local commercial radio station before the entry of a community radio station. Secondly,

to measure the number of non-local radio stations against which a community radio in a given

municipality is competing. I spatially merge radio coverage areas with municipal boundaries and

consider a municipality to be reached by a non-local radio station if any region of the municipality

was inside the coverage area of the non-local radio stations. Thirdly, to identify which areas within a

municipality were actually included in the coverage area of a community radio, I spatially merge the

community radio coverage areas with polling station and school locations inside the municipality.

3.5 Polling Station Location

Brazil’s Superior Electoral Court provides the addresses of all the polling stations for the 2012

election. In total, there were 406,000 polling stations distributed across 94,000 voting locations in

the 2012 municipal election. To obtain the geographical coordinates corresponding to each voting

location, I search each address in Google Maps and collect the coordinates of the locations. I am

able to obtain the coordinates for 71,000 voting locations using this procedure. Google Maps is

not able to locate the remaining addresses, which is mostly the case in rural areas. Accordingly, I

supplement this dataset with the CNEFE (National Registry of Addresses for Statistical Purposes)

to locate some of the remaining addresses. This dataset is based on the 2010 population Census

and contains the addresses of the physical structures visited during the census. Moreover, for rural

addresses, it also contains the geographical coordinates that correspond to the addresses. I merge

voting location addresses at the neighborhood level with this database and obtain the approximate

coordinates for 16,000 of the remaining voting locations. For this reason, the final data set contains

87,000 georeferenced voting locations out of the original 94,000. Figure 3 demonstrates how the

final dataset looks.

12

Figure 3: Polling station locations and radio station geographical coverage

Notes: This figure illustrates how my datasets with geocoded voting locations and radio stations geographicalcoverage appear on a map. Gray circles correspond to the signal reach (protected contours) of commercial radiostations. Yellow circles correspond to the 2 miles radius signal reach of community radio stations. Blue dots correspondto voting locations with dot sizes proportional to the number of voters registered in a voting location. Black linescorrespond to municipality boundaries.

3.6 School Location

To obtain the geographical coordinates that correspond to the municipal schools’ locations, I

leverage the fact that many schools are used as voting locations (Dias and Ferraz (2017)). As

described in section 3.5, I was able to georeference most of the voting locations in Brazil. Hence,

I merge school names from the official data from the Ministry of Education with the school names

in the voting location data from Brazil’s Superior Electoral Court. I am able to obtain coordinates

for 28,000 of the 45,000 municipal schools that participated in the Prova Brasil in my period of

interest using this procedure. Figure 4 illustrates how the final dataset looks.

13

Figure 4: School locations and radio stations geographical coverage

Notes: This figure illustrates what my datasets with geocoded school locations and radio stations geographicalcoverage appear on a map. Gray circles correspond to the signal reach (protected contours) of commercial radiostations. Yellow circles correspond to the the 2 mile radius signal reach of community radio stations. Blue trianglescorrespond to municipal school locations. Black lines represent municipalities boundaries.

3.7 IDEB Scores, School Quality Measures, Election Data and Municipal Char-

acteristics

For the IDEB Scores and IDEB targets in primary education, I use the official data from the

Ministry of Education concerning the 2007, 2009, 2011, 2013, and 2015 results for every available

municipality.

When students take the Prova Brasil exam, the school principal also completes a survey that

includes questions about the school’s academic initiatives and problems during the year.29 I use

the answers to these survey questions to assess the quality of the schools and construct an index

of school quality.

Electoral outcomes for the 2008 and 2012 municipal elections are provided by the TSE (Brazil’s

Superior Electoral Court). The municipal characteristics used as controls are derived from the 2000

census.

29Approximately, 83% of school principals answer all the questions of interest.

14

4 Theoretical Framework

In this section, I present an adapted simple political agency model from Aruoba et al. (2019).

Voters cannot perfectly observe politicians preferences or actions in the model, which leads to

selection and moral hazard problems, respectively. In my framework, local media plays a role by

improving the quality of the signal voters receive regarding incumbents’ actions and, to closely fit

my empirical setting, I allow local media to enter at different points in time. I use this model

to show how, in my reduced form results, I can separate the impact of local media in a moral

hazard and a selection channel by exploiting the timing of entry of a local radio station. Moreover,

I subsequently structurally estimate this model to sharpen the interpretation of my results and

conduct counterfactual analyses.

4.1 Setup

Mayors can serve a maximum of two terms. In each period p, the incumbent mayor makes a

single political decision ep ∈ {L,H} that will impact the quality of local schools measured by the

IDEB scores sp. If the mayor exerts low effort (ep = L), the IDEB scores are drawn from a normal

distribution with mean µL and standard deviation σs and if he exerts high effort (ep = H), they

are drawn from a normal distribution with mean µH and standard deviation σs, with µH > µL.

The payoff for the politicians depends upon their type (good or opportunistic). With probability

π, a politician is a good type and always prefer to exert high effort. On the other hand, opportunistic

types pay a cost E × cp for exerting high effort, where E is the ego rent associated with being in

office. The opportunistic type payoff is E(1− cp) if ep = H and E otherwise. The cost cp is drawn

independently for each period from a uniform distribution with support [0, 1] and it is only observed

by the incumbent politician. The opportunistic type payoff is zero if he is not in office.

The representative voter lives forever and maximizes the discounted sum of her per period

utility. Her utility in period p is up = sp + reelectp−1 × εp−1, where reelect is equal to one if the

voter reelected the incumbent mayor and zero otherwise. Thus, the voter utility depends linearly

on the quality of the public school system and on whether or not she reelected the incumbent in the

previous period. The popularity shock εp is drawn independently each period before the election

from a normal distribution with mean µε and standard deviation σε. If the voter does not re-elect

the incumbent, a challenger is selected at random from the pool of potential politicians.

The role of local media in the model is to improve the precision of the signal observed by voters

regarding the quality of the school system before the election. In the absence of media voters

observe a noisy quality signal qp = sp +mnrp but if there is local media the signal is sp +mr

p. The

noises mnrp and mr

p are drawn independently each period from a normal distribution with mean

zero and standard deviation σnr and σr, with σnr > σr .

Initially, the municipality has no local media but before the IDEB exam in period 2 it will have

15

one with probability 1. During this transition period, there are three separate moments when a

radio station might enter a municipality: with probability θ1 a local station enters the municipality

before the mayor chooses his actions in the first period (”moment 1”); with probability θ2 the station

enters after the mayor chooses his action but before the election (”moment 2”); with probability θ3

the station enters after the election but before the mayor chooses his actions in the second period

(”moment 3”).30 Politicians and voters know the probability of local media entering in each of

these three moments but do not know exactly when it occurs. Hence, if the local media enters in

moments (1) and (2), voters observe a more precise school quality signal q but only if it enters in

moment (1) are mayors able to fully anticipate the occurrence. After this initial transition period,

the local media stays in the municipality forever. Figure 5 presents the timeline of the game. Note

that after this initial transition period, the environment is stationary, and the game is identical to

the one in which a municipality received the local media in ”moment 1”.

Figure 5: Game Timeline

(i) Radio enters with probability θ1

Mayor chooses effort e1

School exam

(ii) Radio enters with probability θ2

Voter observes quality signal with noise mnr or mr and popularity shock

Election

(iii) Radio enters with probability θ3

Mayor chooses effort e2

School exam

Voter observes quality signal with noise mr and popularity shock

Election

4.2 Mayor’s Problem

The only decision a mayor has to make is whether to exert high or low effort. Since exerting

high effort is costless for good mayors, I assume they always exert high effort in both terms. On

the other hand, opportunistic mayors in the second term will not exert high effort since they are

30θ1 + θ2 + θ3 = 1

16

term limited. Moreover, in the first term, the decision to exert effort will depend upon the effort

cost and the presence of local media. If the municipality acquired local media in moment 1 (as

illustrated in Figure 5), an opportunistic mayor will exert effort if and only if:

c < ρ1h − ρ1l (1)

Where ρ1h is the probability of reelection conditional upon exerting high effort and having

local media and ρ1l is the probability of reelection conditional upon exerting low effort and having

local media. I can define δ as the probability that an opportunistic mayors will exert high effort:

δ = ρ1h − ρ1l . Hence, an opportunistic mayor will exert high effort if the returns of exerting effort

in terms of reelection chances (ρ1h − ρ1l ) are higher than the cost of exerting effort c. Intuitively,

the returns of exerting effort should be higher when the quality signal the voter receives is more

precise - i.e., when the municipality has local media. If a municipality did not receive local media

in moment 1, the mayor will exert high effort if and only if:

c <θ2

θ2 + θ3(ρ2h − ρ2l ) +

θ3θ2 + θ3

(ρ3h − ρ3l ) (2)

Where ρ2h and ρ3h are the probabilities of reelection conditional on exerting high effort and

receiving local media before and after the election respectively. I can define δ′ as the probability

that an opportunistic mayors will exert high effort: δ′ = θ2θ2+θ3

(ρ2h − ρ2l ) + θ3θ2+θ3

(ρ3h − ρ3l ).

4.3 Voter’s Problem

The representative voter has to make a single decision each period: to re-elect or not re-elect

the incumbent mayor. I can define the voter’s problem at each period as:

maxreelect∈{0,1}

reelect(πk(q)µh + (1− πk(q))µl + ε+ βV) + (1− reelect)V (3)

Where V is the expected lifetime utility of the voter in the beginning of a two-term period. I

proceed as if V is a known constant, and it will be solved as a part of the equilibrium.31 πk(q) is

the voter’s posterior probability that the mayor is a good type given the quality signal observed by

her in a municipality that got local media in moments k = 1, 2, 3.32 Intuitively, voters will update

their priors more strongly according to quality signals in π1(q), π2(q) than in π3(q) since the quality

signal is more precise in the first two than in the last one.

Therefore, voter’s decision to reelect the incumbent mayor will depend on when local media

entered the municipality. Notice from equation 3 that, in a municipality that got local media in

moments k = 1, 2, 3, the voter will reelect the mayor if and only if:

31See Appendix D for details.32See Appendix D.2 for the explicit expressions of πk(.).

17

ε > (1− β)V− (πk(q)µh + (1− πk(q))µl) (4)

4.4 Equilibrium

The equilibrium concept I use is a Perfect Bayesian Equilibrium. A strategy for the mayor is

to choose whether or not to exert high effort in each term that he is in office given the cost of his

effort. A strategy for the voter is to choose whether or not to reelect the mayor given the observed

school quality signal and popularity shock. The voter updates his beliefs about the mayors type

according to Bayes’ rule (πk).33

PROPOSITION 1. There exists an unique Perfect Bayesian Equilibrium for the game described

above. Opportunistic incumbents always exert low effort in their second term. In period 1, if

the municipality received local media in moment 1, opportunistic incumbents exert high effort if

and only if inequality 1 holds; voters reelect incumbents if and only if inequality 4 holds and form

beliefs about the incumbent’s type based on π1. If the municipality received local media in moments

k = 2, 3, opportunistic incumbents exert high effort if and only if inequality 2 holds; voters reelect

incumbents if and only if inequality 4 holds and form beliefs about the incumbent’s type based on

πk for k = 2, 3. After period 1, opportunistic incumbents in their first term exert high effort if and

only if inequality 1 holds; voters reelect incumbents if and only if inequality 4 holds and form beliefs

about the incumbent’s type based on π1.

Proof. See Appendix D.3.

4.5 Estimated Moments in Reduced Form Results

Definition 1. The Accountability Effect is the difference in the correlation between the first period

test scores and the re-election results in municipalities that received their radio stations before the

election (”moment 2”) and immediately after the election (”moment 3”).

AccountabilityEffect = Correl(reelect, s1|R2 = 1)− Correl(reelect, s1|R3 = 1) (5)

The Accountability Effect captures to what degree local radio stations are causing voters to be

more likely to reward (punish) incumbents for high (low) performance in the IDEB. Using equation

4, I can write the Accountability Effect as:

Correl(1(ε+ π2(s1 +mr)(µh − µl) > (1− β)V− µl)), s1|R2 = 1)

− Correl(1(ε+ π3(s1 +mnr)(µh − µl) > (1− β)V− µl)), s1|R3 = 1) (6)

33See Appendix D for computational details of how to numerically solve for the equilibrium.

18

Where 1(.) is an indicator function equal to one if the inequality holds and zero otherwise; π2(.)

and π3(.) are the Bayes’ rule voters use to form their beliefs regarding the incumbent’s type.

Notice that, since π2(.) and π3(.) are always increasing in s1, Correl(reelect, s1|R2 = 1) > 0

and Correl(reelect, s1|R3 = 1) > 0 because higher values of s1 are always associated with a higher

probability of reelection.

Moreover, since σnr > σr, Correl(reelect, s1|R2 = 1) > Correl(reelect, s1|R3 = 1). Intuitively,

this happens because the noisier is the signal that voters receive regarding the incumbent’s per-

formance, the less voters update their prior on the incumbent’s type. Loosely speaking, imagine

σnr →∞, at this point π3(.) is close to a constant equal π because the signal is too noisy for voters

to actually update their priors. Hence, Correl(1(ε+ π(µh − µl) > (1− β)V− µl)), s1|R3 = 1) = 0,

since the decision to re-elect the incumbent or not is no longer a function of s1.

Definition 2. The Moral Hazard Effect is the difference in the expected first period test scores

between the municipalities that received their radio station before the exam (”moment 1”) and

immediately after the exam (”moment 2”).

MoralHazardEffect = E[s1|R1 = 1]− E[s1|R2 = 1] = (δ − δ′)(1− π)(µH − µL) (7)

Hence, the moral hazard effect depends upon the difference in the proportion of opportunistic

mayors exerting high effort between the municipalities that received radio in ”moment 1” versus

”moment 2” (δ − δ′). Intuitively, this difference is large when the incumbents anticipate radio

stations will render voters better informed about their performances and, consequently, more likely

to electorally punish mayors who obtain low scores. Notice that the moral hazard effect is driven

by only differences in behavior since the pool of elected politicians is the same regardless of the

entry timing of local media in this case.

Definition 3. The Selection Effect is the difference in the expected second period test scores between

the municipalities that received their radio stations immediately before the election (”moment 2”)

and immediately after the election (”moment 3”).

SelectionEffect = E[s2|R2 = 1]− E[s2|R3 = 1] = (8)

= π(µH − sNew)(ρ2h − ρ3h) + (1− π)(1− δ′)(sNew − µL)(ρ3l − ρ2l )− (1− π)δ′(sNew − µL)(ρ2h − ρ3h)

Increase Prob. Reel.

Good Mayor

Deacrease Prob. Reel.

Bad Mayor Exerting Low Effort

Increase Prob. Reel.

Bad Mayor Exerting High Effort

Where sNew = (π + (1 − π)δ)µH + (1 − π)(1 − δ)µL is the expected score in period two for

a newly elected mayor. Intuitively, the probability of reelecting a mayor who exerts high effort

19

increases if a the municipality has a local radio (ρ2h > ρ3h) since voters can now observe a more

precise signal regarding the quality of municipal schools. For the same reason, the probability of

reelecting a mayor who exerts low effort decreases if the municipality has a local radio (ρ2l < ρ3l ).

Hence, the selection effect has two components. First, municipalities benefit from an increase in

the probability of reelecting a good mayor. Second, municipalities benefit from an increase in the

probability of removing an opportunistic mayor.34 Notice that the selection effect is driven by only

differences in the pool of elected politicians since the behavior of each type of mayor is the same

regardless of the entry timing of local media in this case.

5 Empirical Strategy

5.1 Accountability Effect

To estimate the impact of local media upon electoral accountability and on education outcomes,

my research design exploits the entry timing of the first local radio station in a municipality. The

initial analysis compares the electoral outcomes of incumbent mayors between the municipalities

that received their first local radio station immediately before the release of the IDEB results and

immediately after the municipal elections.

Figure 6 illustrates the timing of the events for the 2012 election cycle. In November 2011,

the Prova Brasil exam took place and the results were released in mid-August 2012, less than 1.5

months before the municipal election at the beginning of October 2012. Hence, my treatment

group includes municipalities within which the first community radio station began working in the

12 months before the release of the IDEB results, and my control group includes the municipalities

within which the first community radio station began working in the 12 months after the election.35

To measure the impact of the first local radio station on electoral accountability, I estimate the

following equation by OLS:

Vit = α+ βTit + θIit + γIit × Tit + Yt + eit (9)

As discussed above, Tit = 1 if the municipality i received their first radio station before the

release of the IDEB results in year t = 2008, 2012 and Tit = 0 if the municipality i received their

first radio station after the election in year t = 2008, 2012; Vit is a dummy equal to one if the

incumbent mayor was reelected in year t = 2008, 2012 ; Iit is the different between the IDEB score

released in year t = 2008, 2012 and its target for that year; Yt is a year fixed effect. Importantly,

as discussed previously, I only include the municipalities that received their first local radio station

34This benefit is the difference of two effects: the decrease in probability of reelecting an opportunistic mayorexerting low effort minus the cost of increasing the probability of reelecting an opportunistic one who is exerting higheffort.

35Symmetrically, I proceed in the same manner for the 2008 election cycle.

20

in the 12 months before the release of the IDEB results and in the 12 months after the elections in

the regression.36

The coefficient that captures electoral sanctioning is γ. If local media is providing information

about the mayors performance and voters are taking that into account, the effect of local media on

re-election rates is likely to be positive if the municipality does well in the exam and negative if it

does not. The parameter γ estimates exactly this heterogeneous effect. Notice that this coefficient

captures the Accountability Effect predicted by the model (Definition 1).

Figure 6: Identification Strategy: Accountability Effect

2011

Prova Brasil 2011

Nov/2011

2012

IDEB Results

Released

Aug/2012

Elections

Oct/2012

2013 2014

Treated Municipalities

(Got Radio Before Results Release)

Control Municipalities

(Got Radio After Election)

By merging the geocoded information on the polling stations’ locations with the geographical

signal range of the community radio stations within a municipality, I also examine whether the

impact of community radio stations upon the incumbents’ electoral performances is stronger at

polling stations reached by the radio signals. Hence, I estimate the following triple difference

model by OLS:

Vjit = α+Mi + βRji + θRji × Iit + γRji × Tit + δRji × Iit × Tit + ejit (10)

Where j = R,NR is the group of polling stations reached or not by the community radio

signal37; Vjit is the incumbent mayor total vote share in polling stations j in municipality i in year

36Besides the main specification, I always show the results for alternative time windows (18 and 24 months) andincluding a vector of municipal characteristics as controls.

37Hence, each municipality in this analysis have 2 observations.

21

t = 2008, 2012 ; Rji is a dummy equal to one if polling stations j in municipality i are reached by a

community radio; Mi is a municipality fixed effect; Tit = 1 if the municipality i got their first radio

station before the release of the IDEB results in year t = 2008, 2012 and Tit = 0 if the municipality

i got their first radio station after the election; Iit is the different between the municipal IDEB

score released in year t = 2008, 2012 and its target for that year. I only include in the regression

municipalities that received their first local radio station in the 12 months before the release of the

IDEB results and in the 12 months after the elections.

The coefficient δ captures the difference within a municipality in the incumbent’s vote share

between the polling stations reached by community radio stations and those that are not reached

depending upon how well the municipality performed in the IDEB. The difference in the vote share

between the polling stations reached by community radio and those not reached should increase

more rapidly as the IDEB increases if a radio station entered before the release of the exam score.

Hence, δ should be positive.

5.2 Selection Effect

To evaluate whether local media help select better candidates, I compare the IDEB scores

three years after the municipal elections in the municipalities that received a local radio station

immediately before and immediately after elections.38 This basically consists of the same design

as before (Section 5.1); the only difference now is that the dependent variable is the exam scores

three years after the election. Figure 7 illustrates the design for the 2012 election cycle. This

design plausibly isolates the selection from moral hazard effects: voters from the two groups of

municipalities had different information available at the time of the election and selected different

candidates, but three years after the elections both groups had local media and the mayors then

faced the same level of scrutiny.

To measure the impact of the first local radio station on selection, I estimate the following

equation by OLS:

Iit+3 = α+ βTit + Yt + eit (11)

As discussed above, Tit = 1 if the municipality i got their first radio station before the release

of the IDEB results in year t = 2008, 2012 and Tit = 0 if the municipality i got their first radio

station after the election in year t = 2008, 2012; Iit+3 is the difference between the IDEB score

three years after the election and its target for that year; Yt is a year fixed effect. Importantly, as

discussed previously, I only include in the main specification municipalities that got their first local

radio station in the 12 months before the release of the IDEB results and in the 12 months after

the elections.

38This is the last IDEB score under the newly elected mayor’s term.

22

The coefficient that captures the impact of an improved selection of politicians upon educational

outcomes is β. If local media provided information that helped voters retain good incumbents and

remove bad ones, the effect of local media upon educational outcomes is likely to be positive. Notice

that this coefficient captures the Selection Effect predicted by the model (Definition 3).

Figure 7: Identification Strategy: Selection Effect

20162011

Prova Brasil 2011

2012

IDEB Results

Released

Elections

2013 2014

Prova Brasil 2015

Treated

Municipalities

Control

Municipalities

The identification hypothesis underpinning this empirical design is that the entry timing of

community radio stations can be seen as exogenous when I focus on small time-windows around

the event of interest (12 months in my preferred specification). This strategy allows me to compare a

largely homogeneous group of municipalities, and it is reasonable to argue that those cities received

their first radio stations either before or after an election largely due to the idiosyncrasies of the

long and uncertain community radio licensing process, which are likely to be orthogonal to electoral

or educational outcomes.

Table 1 supports this intuition. It shows that the municipalities that received their first radio

station the year before the election are largely similar in observable characteristics to the munici-

palities that received a station following the election.39 The only statistically significant difference

between the treatment and control groups is the size of the municipalities. This is explained by

the fact that, as discussed in section 2.1, the Ministry of Communication explicitly prioritized the

licensing of community radios in larger municipalities. In the Results section, I show that all my

main results remain largely unchanged regardless of whether or not I flexibly controlled for pop-

ulation in my regressions. Moreover, I run a series of placebo tests in section 8 that show that

this tendency to award larger municipalities community radios first cannot be the driver of my

results. Finally, Figure 25 in the Appendix plots the geographical distribution of the municipalities

39Figure 24a in Appendix B also shows no signs of bunching in the entry timing of community radios around theelection time.

23

that received community radio stations before and after the election. The map shows that the two

groups also largely overlap geographically.

Table 1: Balance of Municipal Characteristics: Accountability and Selection Effects

Dependent Variable Control Mean Observations Difference in Means

(1) (2) (3)

Ideb Score minus Target 0.333 425 -0.049

(0.059)

Median Income 295.74 425 -13.692

(15.043)

Illiteracy Rate 18.130 425 0.648

(1.129)

Share Urban 62.254 425 1.549

(2.267)

Gini Coefficient 0.489 425 0.0037

(0.0069)

Population (log) 9.320 425 0.164*

(0.100)

Population 20332.6 425 4576.0

(5539.9)

Radio Penetration Rate 80.811 425 -0.551

( 1.432)

TV Penetration Rate 74.922 425 -2.483

( 2.076)

Distance to State Capital 247.29 425 5.891

(16.826)

Non Local Radio Stations 7.504 425 0.337

(1.042)

Second Term Mayor 0.285 425 -0.031

(0.0482)

Incumbent V. Share Last Election 0.56 425 -0.006

(0.014)

Notes: This table shows the balance between the observable characteristics of ”treatment” and ”control” munic-ipalities. Municipalities that got their first radio station in the 12 months prior to the 2008 or 2012 release of IDEBresults are in the treatment group and municipalities that got their first radio station in the 12 months after to the2008 or 2012 municipal elections are in the control group. The difference in means column presents the OLS coefficientof a regression of the municipal characteristic on a treatment dummy and year fixed effects. Robust standard errorsin parentheses.

5.3 Moral Hazard Effect

I also test whether local media affect educational outcomes by alleviating the mayors’ moral

hazard problems. To do so, I compare the IDEB scores in the municipalities that received radio in

the 12 months before the exam with the ones that received it in the 12 months after. This strategy

24

is illustrated in Figure 8. Information regarding the incumbents’ performances may reduce moral

hazard problems and allows voters to better sanction politicians. Hence, local media might improve

educational outcomes by inducing mayors to exert more effort in order to be re-elected

Figure 8: Identification Strategy: Moral Hazard Effect

2010

Prova Brasil 2011

2011 2012 2013

Treated Municipalities

(Got Radio Before Exam)

Control Municipalities

(Got Radio After Exam)

Specifically, to measure the impact of the first local radio station on test scores, I estimate the

following equation by OLS:

Iit = α+ βTit + Yt + eit (12)

As discussed above, Tit = 1 if the municipality i got their first radio station before the Prova

Brasil exam in year t = 2007, 2009, 2011, 2013, 2015 and Tit = 0 if the municipality i got their

first radio station after the Prova Brasil exam in year t = 2007, 2009, 2011, 2013, 2015; Iit is the

difference between the IDEB score in year t and its target for that year ; Yt is an year fixed effect.

Importantly, as discussed previously, I only included in the regression the municipalities that got

their first local radio station in the 12 months before the exam and in the 12 months after the

exam.

The coefficient that captures the impact of improved incentives for politicians on educational

outcomes is β. If mayors anticipate that they are more likely to be held accountable for bad scores

in the presence of local media, they might exert more effort in order to be re-elected. Notice that

this coefficient captures the Moral Hazard effect predicted by the model (Definition 2).

Table 2 shows the balance in the observable municipal characteristics between the treatment and

control groups. Similarly to Table 1, it demonstrates that the municipalities that received their first

radio station the year before the Prova Brasil exam are largely similar in observable characteristics

to the municipalities that received a station after the exam. Again, the only statistically significant

25

difference between the treatment and control groups is the size of the municipalities. As discussed

in section 5.2, I perform a series of robustness tests that show that this tendency to award larger

municipalities community radios first cannot be the driver of my results.40

Table 2: Balance of Municipal Characteristics: Moral Hazard Effect

Dependent Variable Control Mean Observations Difference in Means

(1) (2) (3)

Lagged Ideb Score minus Target 0.336 778 0.051

(0.045)

Median Income 283.68 1152 4.48

(4.47)

Illiteracy Rate 18.34 1152 -0.1

(0.323)

Share Urban 60.91 1152 1.06

(1.18)

Gini Coefficient 0.491 1152 0.001

(0.005)

Population (log) 9.20 1152 0.155**

(0.065)

Population 16894.5 1152 5909.2**

(2401.5)

Radio Penetration Rate 80.27 1152 -0.411

( 0.476)

TV Penetration Rate 72.39 1152 1.215

(0.807)

Distance to State Capital 249.52 1152 -1.296

(9.50)

Non Local Radio Stations 7.02 1152 0.487

(0.551)

Second Term Mayor 0.315 1152 0.005

(0.0296)

Incumbent V. Share Past Election 0.560 1152 -0.007

(0.008)

Notes: This table shows the balance in observable characteristics between ”treatment” and ”control” municipal-ities. Municipalities that got their first radio station in the 12 months prior to the Prova Brasil exams in 2007, 2009,2011, 2013 and 2015 are in the treatment group and municipalities that got their first radio station in the 12 monthsafter are in the control group. The difference in means column presents the OLS coefficient of a regression of themunicipal characteristic on a treatment dummy, year and state fixed effects. Robust standard errors in parentheses.

40Figure 24b in Appendix B also shows no signs of bunching in the entry timing of community radios around theProva Brasil time.

26

6 Main Results

6.1 Community Radios Coverage

First, I provide direct evidence that community radios do report upon the results of the IDEB.

It is not obvious ex-ante that community radios will cover the results appropriately. For example,

they may not have the ability to find and interpret the IDEB results or they may face political

pressures from mayors to only comment upon the results when they are good. They may even

believe that this news is not of interest to their audience and decide not to cover it.

As described in section 3.3, I perform a content analysis of the community radio coverage of

the 2017 IDEB results. I downloaded the streams of 385 community radios that are online in the

states of Sao Paulo and Parana the day after the IDEB results were released (9/4/2018). I then

used Google Speech AI technology to convert this audio into text. To identify passages where the

radio stations covered the IDEB scores, I simply searched for the acronym IDEB in the text data.

Since IDEB is not a word in Portuguese, any mention of IDEB refers to the exam.41

The results of this exercise indicate that 25% of the recorded radio stations commented upon

the IDEB results. This is a lower bound of the proportion of recorded radios that actually cover

the topic since audio transcriptions are likely to miss some mentions of the IDEB results.42 In

addition, I did not record on the day that the IDEB scores were publicly released, only on the

following day.43 Moreover, 2018 did not coincide with a municipal election year which might have

made the topic less salient and not as likely to be covered by the local radios.

The following transcriptions exemplify the kind of coverage given to the IDEB in a municipality

that did well and in a municipality that did poorly in the exam:

”We have here the website with the IDEB results open [...]. And we are going to

bring to you the results of Oswaldo Cruz. [...] You can also go to the website and check

the results for yourself in [...]. The target for municipal schools of Oswaldo Cruz was 6.5

and the result was 6.3 [...]. So we were below the target [...] the number is worrisome.

This is it for today but we are obviously going to discuss this further in the coming

shows[...].”

”We have to talk about what is good. Yesterday the IDEB results were released and

Tanabi is doing very well with a 7.7 score [...]. We have here with us the Education

Secretary of Tanabi [...] , before anything else we would like to congratulate you for

the excellent score since our target was around 6 [...]. We also want to congratulate the

school principals for their efforts [...], the mayor who invested in our schools [...].”

41I also manually checked part of the audio segments to check if segments flagged for the word ”IDEB” were actuallytalking about the score of interest. I did not find any segment with a false positive.

42Due to poor audio quality or too much background noise, for example.43Due to technical problems with the server used to store the radio recordings.

27

Similarly to the transcriptions above, 72% of the news coverage segments used the federal targets

as a yardstick of performance. Importantly, as Figure 9 illustrates, this coverage was particularly

frequent in municipalities where the scores were far from the targets, which is arguably exactly

the context in which the information will be the most useful to voters. Moreover, this behavior is

more likely to be compatible with an independent media and not media captured by the incumbent

mayors.

Figure 9: Community Radios IDEB Coverage

0.2

.4.6

.81

Talke

d ab

out I

deb

-.5 0 .5 1Ideb Score minus Target

95% CI Second Order Polynomial

Notes: This figure illustrates the relationship between the probability that a community radio covered the IDEBresults and how well the municipality did in the IDEB compared to its target. The line corresponds to the predictionfor the coverage probability from a linear regression of a coverage dummy on score difference from the target andscore difference from the target squared, along with a 95% confidence interval. The dots represent averages withinevenly spaced bins.

6.2 Accountability Effect

Do local radio stations increase the probability that voters will re-elect mayors who do well in

the exam and help remove the ones that do not? Figure 10 strongly suggests that the answers is

yes. The plot shows that for the municipalities that received their first community radio station

in the 12 months before the release of the IDEB results, there was a strong positive correlation

between the score in the exam and the re-election probability of the incumbent. On the other hand,

for municipalities that received a local radio station after the election, there was no statistically

significant correlation between the scores and the re-election rates.

28

Figure 10: Impact of community radios on accountability

Notes: This figure illustrates the relationship between the incumbent’s re-election probability and the differencebetween a municipality IDEB score and its target. The blue line and blue dots present the relationship for mu-nicipalities that got their first community radio station in the 12 months before the release of the IDEB results in2008 and 2012. The orange line and orange diamonds present the relationship for municipalities that got their firstcommunity radio station in the 12 months after the 2008 and 2012 municipal elections. Each line corresponds to theprediction for the re-election probability from a linear regression of a re-election dummy on score difference from thetarget, along with a 95% confidence interval. Each set of dots represent re-election averages within evenly sized bins(quintiles). The sample only includes municipalities where the mayor was not term-limited.

In Table 3, I quantify the impact illustrated by Figure 10 by estimating Equation 9 as described

in Section 5. The table provides results for different estimation windows and for specifications with

and without a vector of municipal characteristics as control. The variable DistanceToIdebTarget

was demeaned to facilitate the interpretation of the coefficient of Radio. The results suggest that

local radios facilitate the sanctioning of politicians. The main coefficient of interest is the inter-

action between Radio and DistanceToIdebTarget. Community radios increased the incumbents

re-election probability by between 3.7 percentage points (p.p.) and 1.7 p.p. for an increase of 0.1 in

the difference between the IDEB scores and the target.44 Note also that the effect of receiving a com-

munity radio before the election is not statistically different from zero when DistanceToIdebTarget

is equal to its mean.45

44Unlike the US, since voting is mandatory in Brazil and average turnout in mayoral races is extremely high (closeto 90%), voter turnout is not decisive in Brazilian elections. Indeed, replicating this analysis using voter turnout asdependent variable, does not suggest any impact of community radios.

45Table 14 in the Appendix further explores the results from Table 3 and shows that the impact on the incumbents’re-election was a combination of incumbents deciding not to run and not winning the election conditional uponrunning.

29

Table 3: Impact of community radios on incumbents re-election

Dependent Variable: Incumbent Mayor Reelected (Mean = 0.46)

Estimation Window: 12 Months 18 Months 24 Months

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

Radio 0.0190 0.0165 -0.0338 -0.0375 -0.0144 -0.0178

(0.0713) (0.0745) (0.0496) (0.0503) (0.0425) (0.0429)

Dist. IDEB Target -0.0872 -0.0907 -0.0889 -0.0927 -0.0640 -0.0659

(0.0593) (0.0616) (0.0567) (0.0579) (0.0474) (0.0477)

Radio x Dist. IDEB Target 0.376∗∗∗ 0.373∗∗∗ 0.284∗∗∗ 0.277∗∗∗ 0.187∗∗ 0.175∗∗

(0.132) (0.136) (0.0926) (0.0928) (0.0760) (0.0756)

Controls N Y N Y N Y

Observations 305 305 426 426 578 578Notes: This table presents the OLS estimation of Equation 9. Incumbent mayor reelected is a dummy that equals

one if the incumbent mayor was reelected; Radio is a dummy that equals one if the municipality got their first radiostation before the release of the IDEB results in year t = 2008, 2012 and zero if the municipality got their first radiostation after the election in year t = 2008, 2012; Dist. IDEB Target is the demeaned difference between the IDEBscore released in year t = 2008, 2012 and its target for that year. In columns 1 and 2, I only included in the regressionmunicipalities that got their first local radio station in the 12 months before the release of IDEB results and in the 12months after the elections; in columns 3 and 4, the window is 18 months; and in columns 5 and 6, the window is 24months. In odd columns the specification has no controls and in even columns a vector of municipal characteristics isincluded as controls (median income, % population urban, % population finished primary school, log of population,population, distance to state capital, % population has TV and % population has radio equipment). The sample onlyincludes municipalities where the incumbent mayor was not term-limited. Robust standard errors in parenthesis. *p < 0.10, ** p < 0.05, *** p < 0.01.

I also add another layer of variation in order to identify the effects of community radios on

accountability. Merging geocoded information on polling station locations with the geographical

range of the community radio stations within a municipality, I examine whether the impact of

community radio stations upon incumbents’ electoral performances was stronger at the polling

stations reached by the radio signals. Hence, this is a triple difference model: the first difference is

whether the municipality received radio before or after the election; the second whether the voting

location within a municipality was reached or not by the community radio; and the third how well

the municipality performed in the exam. Figure 11 suggests that the polling stations reached by

community radio were more capable of holding politicians accountable. The plot shows that for

the municipalities that received their first community radio station in the 12 months before the

election, there was a strong positive correlation between the score in the exam and the difference

in the incumbent’s vote share between the polling stations that were reached and those that were

not reached by the radio stations. On the other hand, for the municipalities that received a local

radio station after the election, there was no correlation between the scores and the difference in

the incumbent’s vote share in reached versus unreached areas.

30

Figure 11: Impact of community radios on accountability: within municipality variation

Notes: This figure illustrates the relationship between the difference in the incumbent’s vote share in pollingstation reached versus unreached by the community radio signal and the distance of the IDEB score to its target.The blue line and blue dots present the relationship for municipalities that got their first community radio station inthe 12 months before the release of the IDEB results in 2008 and 2012. The orange line and orange diamonds presentthe relationship for municipalities that got their first community radio station in the 12 months after the 2008 and2012 municipal elections. Each line corresponds to the prediction for the difference in the incumbent’s vote sharefrom a linear regression of the difference in the incumbent’s vote share on score distance from the target, along witha 95% confidence interval. Each set of dots represent the difference in the incumbent’s vote share averages withinevenly sized bins (quintiles). The sample only includes municipalities where the mayor ran for re-election and wherethere is at least one polling station reached and one unreached by the community radio signal.

In Table 4, I quantify the impact illustrated by Figure 11. I proceeded as described in Section

5 and estimated Equation 10 for different time windows. In the odd-numbered columns, I show the

results with traditional robust standard errors and, in the even-numbered columns, with standard

errors clustered at the municipality level. The results suggest that community radios increased the

incumbents’ vote share by between 0.5 p.p. and 0.3 p.p. for an increase of 0.1 in the IDEB score

distance to the target.

In Table 17 in Appendix A, I investigate if community radios are also helping voters hold

politicians accountable for performance in other dimensions. I focus on health care outcomes since,

next to education, this is the biggest responsibility of municipal governments. It is important to

note that there is no clear equivalent to the IDEB scores for the health sector, i.e. an index that

is easily interpretable and heavily covered by the media. Hence, I replicate the analysis in Table 3

using broad measures that capture the underlying quality of the municipal health care system in

the election year (infant mortality, prenatal exams’ frequency and newborns’ weight). The results

31

do not suggest that local radios help voters hold politicians accountable for performance in these

dimensions.46 Obviously, this is just a first order attempt to capture the ability of local radios to

hold politicians accountable in other dimensions and I cannot rule out this possibility.

Table 4: Impact of community radios on accountability: within municipality variation

Dependent Variable: Incumbent Vote Share (Mean = 0.50)

Estimation Window: 12 Months 18 Months 24 Months

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

Radio Covered -0.0129∗ -0.0129 -0.0132∗∗ -0.0132 -0.00800 -0.00800

(0.00674) (0.00953) (0.00635) (0.00898) (0.00542) (0.00767)

Radio Covered x Pre Election -0.0130 -0.0130 -0.00677 -0.00677 -0.00252 -0.00252

(0.0108) (0.0153) (0.0104) (0.0147) (0.00868) (0.0123)

Radio Covered x Dist. Target -0.00879 -0.00879 -0.00591 -0.00591 -0.00389 -0.00389

(0.0106) (0.0150) (0.0104) (0.0147) (0.00909) (0.0129)

Radio Covered x Dist. Target x Pre Election 0.0577∗∗∗ 0.0577∗∗∗ 0.0361∗∗ 0.0361 0.0377∗∗∗ 0.0377∗∗

(0.0150) (0.0212) (0.0164) (0.0232) (0.0130) (0.0183)

Municipality Fixed Effect Y Y Y Y Y Y

Clustered S.E. at Municipal Level N Y N Y N Y

Observations 298 298 398 398 512 512Notes: This table presents the OLS estimation of Equation 10. Incumbent Vote Share is the vote share of the

incumbent in poling stations reached or unreached by the communitty radio signal; Radio Covered is a dummy thatequals one if the polling station are reached by the communitty radio signal and zero if they are unreached; Pre electionis a dummy that equals one if the municipality got their first radio station before the release of the IDEB results inyear t = 2008, 2012 and zero if the municipality got their first radio station after the election in year t = 2008, 2012;Dist. Target is the difference between the municipality IDEB score released in year t = 2008, 2012 and its target forthat year. In columns 1 and 2, I only included in the regression municipalities that got their first local radio stationin the 12 months before the release of IDEB results and in the 12 months after the elections; in columns 3 and 4,the window is 18 months; and in columns 5 and 6, the window is 24 months. In odd columns, robust standard errorsin parenthesis and, in even columns, standard errors clustered at the municipality level. The sample only includesmunicipalities where the incumbent mayor ran for reelection and where there is at least one polling station reachedand one unreached by the community radio signal. This regression is on the Municipality × RadioCoverage level.Hence, all municipalities included have two observation.* p < 0.10, ** p < 0.05, *** p < 0.01.

6.3 Moral Hazard Effect

While the results of section 6.2 strongly suggest that local media facilitate sanctioning, they

do not answer the question of whether this actually improves educational outcomes. It is possible

that local media affect educational outcomes by alleviating the moral hazard problem and inducing

46Using variations of these performance indicators yield equivalent results. For example, using improvement inthese health outcomes instead of their level or using mayoral term’s average of the outcomes instead of the outcomesjust in the election year.

32

mayors to exert more effort in order to be re-elected. I test this hypothesis, using the municipal-

ities where the incumbent was not term limited, by comparing the educational outcomes in the

municipalities that received a radio station before the Prova Brasil exam and the municipalities

that received a station after the exam.

In Table 5, I estimate Equation 12 as described in Section 5. I estimate the model with no

controls in columns 1, 4, 7; I add a vector of municipal characteristics as controls in columns 2, 5, 8;

and I finally add the lagged dependent variable as control in columns 3, 6, 9.47 Results indicate

that local radios improved IDEB scores. The results suggest an effect between 0.1 and 0.04 points

(or 0.16 and 0.07 standard deviation of the dependent variable).48 It is important to highlight how

my point estimates barely change within estimation windows regardless of adding the vector of

controls or even the lag of the dependent variable.

Table 5: Impact of community radios on scores: moral hazard channel

Dependent Variable: Distance to IDEB Target (Mean = 0.36)

Estimation Window: 12 Months 18 Months 24 Months

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio 0.0885∗∗ 0.0972∗∗ 0.0940∗∗ 0.0655 0.0724∗ 0.0763∗ 0.0422 0.0516 0.0583

(0.0446) (0.0435) (0.0460) (0.0401) (0.0396) (0.0432) (0.0328) (0.0326) (0.0370)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 787 787 518 953 953 594 1445 1445 866Notes: This table presents the OLS estimation of Equation 12. The dependent variable is the difference between

the IDEB score and the municipality target in years t = 2007, 2009, 2011, 2013, 2015 ; Radio is a dummy that equalsone if the municipality got their first radio station before the Prova Brasil exam in year t = 2007, 2009, 2011, 2013, 2015and zero if the municipality got their first radio station after the exam. In columns 1, 2 and 3, I only included inthe regression municipalities that got their first local radio station in the 12 months before the exam and in the 12months after the exam; in columns 4, 5 and 6, the window is 18 months; and in columns 7, 8 and 9, the window is24 months. In columns 1, 4 and 7, the specification has no controls. In columns 2, 5 and 8, a vector of municipalcharacteristics is included as controls (median income, % population urban, % population finished primary school,log of population, population, distance to state capital, % population has TV, % population has radio equipment andstate fixed effects). In columns, 3, 6 and 9, the lagged dependent variable is also added as a control. The sample onlyincludes municipalities where the incumbent mayor was not term-limited. Robust standard errors in parenthesis. *p < 0.10, ** p < 0.05, *** p < 0.01.

How are mayors improving scores? In the schools that participate in the Prova Brasil exam, the

school principals complete a survey answering questions regarding the school’s academic initiatives

and problems for that year. For example, 29% of school principals stated that school operations were

severely affected by teacher absenteeism and 40% by lack of pedagogical resources and personnel.

47Notice that columns 3, 6, 9 have less observations because for the 2007 exam there is no lagged dependent variable.48Notice that, for estimation windows larger than 12 months and ”Prova Brasil” exams that happened in the first

year of mayoral terms (2009 and 2013), some municipalities in the treatment group already had radio before theprevious election. This risks conflating moral hazard effects with selection effects when I use larger time windows.Hence, in Appendix A Table 20, I replicate these results including only ”Prova Brasil” exams that happened in thethird year of mayoral terms (2007, 2011 and 2015). Results show largely similar or stronger effects when we focus onthis subgroup of exams.

33

The survey also shows that 57% of school principals claimed that a lack of financial resources

interfered with the school’s operations. Moreover, 40% stated that political interference was also a

serious problem. Finally, the survey also revealed that only 24% of schools have special programs

supporting learning and grade advancement.

For this reason, I am able to evaluate whether schools covered by local radios faced fewer

problems or had more initiatives for improving student scores. I proceeded as described in section

5.3 and estimated Equation 12 for different measures of school quality49 and an aggregate index

of school quality created by taking the average across all the individual school quality measures.

Figure 12 presents the results. Each line presents the coefficient β of Equation 12 for a different

dependent variable.

Although most of the estimated coefficients are not statistically significant, the results in Figure

12 provide some suggestive evidence that radio coverage increases the probability that schools will

have initiatives to support grade promotion and will have fewer financial difficulties and problems

related to a lack of pedagogical resources and political interference. Importantly, when I aggregated

the different measures of quality in an index, the coefficient is more precisely estimated and indicates

an improvement in the quality of the public schools.

Finally, I evaluate whether mayors are exerting more effort to obtain federal funds for their

municipalities. The only key mechanism mayors have to attract additional resources are discre-

tionary federal grants known as ”convenios”. Unlike other transfers, the receipt of these funds is

not formula-based, but depends upon the ability and effort of the mayor to procure them (Ferraz

and Finan (2011)). Hence, I am able to test whether mayors under the scrutiny of local radios tried

to secure these funds for education more often. Table 6 performs the same analysis of Table 5 but

using as the dependent variable whether the municipality got a discretionary federal transfer from

the Ministry of Education in the 12 months before the Prova Brasil exam. I estimate the model

with no controls in columns 1, 4, 7; I add a vector of municipal characteristics as controls in columns

2, 5, 8; and I finally add the lagged dependent variable as control in columns 3, 6, 9. The results

suggest that community radios increased the probability that mayors secure education ”convenios”

by around 5 percentage points. I also do not find evidence that local radios impact federal grants

for non-education sectors like health care and agriculture, for example (see Table 18 in Appendix

A).

49Each measure is normalized to have mean zero and standard deviation one.

34

Figure 12: Impact of community radios on measures of school quality

Has Anti Dropout InitiativeHas Grade Promotion InitiativeHas Learning Support Initiative

No Financial ProblemNo Lack of Teachers Problem

No Lack of Administrative PersonnelNo Lack of Pedagogical ResourcesNo Lack of Pedagogical Personnel

No School Closure ProblemNo Absent Teacher Problem

No Teacher Turnover ProblemNo Political Interference ProblemHas Upper Government Support

Aggregate Index

-.2 0 .2 .4

Notes: This figure presents the impact of community radios on different measures of school quality. Eachline presents the coefficient β of Equation 12 for a different dependent variable. I include a vector of municipalcharacteristics as controls in all specifications (lagged of the dependent variable, median income, % populationurban, % population finished primary school, log of population, population, distance to state capital, % populationhas TV, % population has radio equipment and state fixed effects). I only included in the regression municipalitiesthat got their first local radio station in the 12 months before the Prova Brasil exam and in the 12 months after theexam. The sample only includes municipalities where the incumbent mayor was not term-limited. Each coefficientestimate is presented with its corresponding 95% confidence interval using robust standard errors.

Table 6: Impact of community radios on educational federal discretionary transfers

Dependent Variable: Mayor got Discretionary Transfer for Education Sector (Mean = 0.26)

Estimation Window: 12 Months 18 Months 24 Months

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio 0.0548∗ 0.0439 0.0411 0.0715∗∗ 0.0552∗∗ 0.0532∗ 0.0524∗∗ 0.0397∗ 0.0408∗

(0.0299) (0.0294) (0.0289) (0.0279) (0.0275) (0.0272) (0.0240) (0.0237) (0.0232)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 787 787 787 953 953 953 1445 1445 1445Notes: This table presents the OLS estimation of Equation 12 but using a different dependent variable. The

dependent variable is a dummy equal to one if the municipality received an education discretionary transfer from thefederal government in the 12 months prior to the Prova Brasil exam in years t = 2007, 2009, 2011, 2013, 2015 ; Radiois a dummy that equals one if the municipality got their first radio station before the Prova Brasil exam in yeart = 2007, 2009, 2011, 2013, 2015 and zero if the municipality got their first radio station after the exam. In columns 1,2 and 3, I only included in the regression municipalities that got their first local radio station in the 12 months beforethe exam and in the 12 months after the exam; in columns 4, 5 and 6, the window is 18 months; and in columns 7,8 and 9, the window is 24 months. In columns 1, 4 and 7, the specification has no controls. In columns 2, 5 and8, a vector of municipal characteristics is included as controls (median income, % population urban, % populationfinished primary school, log of population, population, distance to state capital, % population has TV, % populationhas radio equipment and state fixed effects). In columns, 3, 6 and 9, the lagged dependent variable is also added as acontrol. The sample only includes municipalities where the incumbent mayor was not term-limited. Robust standarderrors in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01.

35

In Table 19 in Appendix A, I investigate if community radios might be creating a multitasking

problem (Holmstrom and Milgrom (1991)). As discussed previously, I do not find evidence that

local radios help voters hold politicians accountable for performance in the health sector. Hence,

since local radios might be making outcomes in the education sector more visible compared to the

health sector, mayors might be diverting resources from the health to the educational sector to

satisfy voters’ demand for high scores. Notice that it is not obvious that this is the case since

mayors can also, for example, be improving educational outcomes by embezzling less funds, which

could help both sectors. I replicate the analysis in Table 5 using broad measures that capture

the underlying quality of the municipal health care system in the election year (infant mortality,

prenatal exams’ frequency and newborns’ weight). Results suggest that community radios have

no impact on health outcomes, alleviating concerns that the improvement in educational outcomes

comes at the cost of health outcomes.

6.4 Selection Effect

The results from section 6.2 suggest that in the municipalities that performed very well in the

exam, local media increased the re-election rate of the incumbent mayors, while in municipalities

that performed very poorly, local media decreased the re-election rates of the incumbents. How-

ever, does this actually lead to an improvement in candidate selection and educational outcomes?

It is possible that the community radio stations helped to both remove bad incumbents and retain

competent ones. To test this hypothesis, I compare the IDEB scores three years after the munic-

ipal elections in the municipalities that had received a local radio station immediately before and

immediately after the elections. This plausibly isolates the selection from moral hazard effects: the

voters from the two groups of municipalities had different information available at the time of the

election and selected different candidates, but three years after the elections, both groups had local

media

In Table 7, I estimate Equation 11 as described in Section 5. The results indicate that local

radios have not improved the exam scores in subsequent mayoral terms. The null results are

precise enough to rule out large effects above 0.08 standard deviation in the scores. This suggests

that providing voters with information regarding the incumbent’s performance does not help select

better politicians.

In Table 15 in Appendix A, I further investigate the impact of community radio on the selection

of politicians. I replicate the analysis in Table 7 using different characteristics of the elected

candidates that might be correlated with candidate quality as dependent variables. The results do

not indicate that the local media had any impact upon the observable characteristics of the elected

candidates.

Due to the very nature of my identification strategy, it is possible that high-quality challengers

did not have time to enter the race in response to the creation of the community radio station in

36

the short term. In fact, Table 16 in Appendix A shows no effect of the entry of community radio on

the characteristics of the pool of candidates. Hence, it is better to consider my previous results to

be the impact of local media, maintaining the quality of the candidate pool as fixed. It is possible

that, in the long term, local media improve the pool of candidates, and, therefore, selection but my

setting is not well suited to estimating these long-term impacts.

Table 7: Impact of community radios on scores: selection channel

Dependent Variable: Distance to IDEB Target 3 Years after Election (Mean = 0.37)

Estimation Window: 12 Months 18 Months 24 Months

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

Radio -0.0425 -0.0315 -0.0684 -0.0649 -0.0530 -0.0434

(0.0713) (0.0748) (0.0551) (0.0550) (0.0486) (0.0485)

Controls N Y N Y N Y

Observations 305 305 426 426 578 578Notes: This table presents the OLS estimation of Equation 11. The dependent variable is the difference between

the IDEB score and the municipality target 3 years after the municipal elections in years t = 2008, 2012; Radio isa dummy that equals one if the municipality got their first radio station before the release of the IDEB results inyear t = 2008, 2012 and zero if the municipality got their first radio station after the election in year t = 2008, 2012.In columns 1 and 2, I only included in the regression municipalities that got their first local radio station in the 12months before the release of IDEB results and in the 12 months after the elections; in columns 3 and 4, the window is18 months; and in columns 5 and 6, the window is 24 months. In odd columns the specification has no controls andin even columns a vector of municipal characteristics is included as controls (median income, % population urban,% population finished primary school, log of population, population, distance to state capital, % population has TVand % population has radio equipment). The sample only includes municipalities where the incumbent mayor wasnot term-limited. Robust standard errors in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01.

7 Heterogeneous Effects

7.1 Out-of-town Radios Competition

The capacity of community radios to inform voters is likely to depend upon the degree of

competition they have with out-of-town radio stations (which will not report on local IDEB scores)

because non-local radio stations may reduce the number of community radio listeners. To test this

hypothesis, I used geocoded geographical coverage of all the Brazilian commercial radio stations

and data on the community radio audience from the 2016 Brazilian Media Survey. Figure 13a shows

the relationship between the community radio audience in the municipalities where the community

radio was the only local radio station and the number of out-of-town radio stations that reached

that municipality. The plot shows that the community radio audience is highly negatively correlated

with competition with non-local radio stations.50

50The plot shows that the audience share of community radios is not 100% even when it faces no competitionagainst non-local radios. This happens because of measurement errors both in the audience of community radios

37

In 13b, I estimate how the impact of local radios on sanctioning varied depending upon how

much competition the local radios faced against out-of-town radio stations. I divide the sample

according to terciles of non-local competition and estimate the γ coefficient from Equation 9 for

each subsample. The results show that the power of community radios to enhance accountability

greatly depends on outside competition. For example, the impact of a 0.1 increase in the exam

score on the mayor’s re-election probability is 6 p.p. in municipalities with two or less outside

radios and zero in municipalities with nine or more.

Figure 13: Heterogeneous Accountability Effect by Out-of-town Radios Competition

0.1

.2.3

.4.5

Audi

ence

Sha

re

0 10 20 30 40Number of Non Local Radios

(a)

-.50

.51

0-2 Non Local Radios 2-8 Non Local Radios 9+ Non Local Radios

(b)

Notes: This Figure presents the heterogeneous effect of community radios depending on how much competitionthey face against out-of-town radios. Figure 13a shows the relationship between the audience share of communityradios (number of listeners who claim the community radio is their favorite radio station over number of radio listenersin the municipality) in municipalities where the community radio is the only local radio and the number of out-of-townradio stations reaching that municipality. Each dot represents the average Audience Share within evenly sized bins.Figure 13b illustrates how the impact of local radios on sanctioning varies depending on how much competition thelocal radios face against out-of-town radio stations. It plots the γ coefficient from equation 9 and their respective95% confidence interval estimated separately by terciles of non-local competition.

7.2 Term-limited Mayors

The results in Section 6.3 suggest that the presence of community radios induce mayors to exert

more effort and improve educational outcomes in the presence of re-election incentives. Tables 8, 9

and Figure 14 present the results of the same analysis but focusing on term-limited mayors. Gener-

ally, I find no effect of local radio stations on scores or federal discretionary transfers when mayors

are term-limited. Moreover, point estimates of the analysis using term-limited mayors are almost

always smaller than their counterparts in the analysis using non-term-limited ones. This heteroge-

neous impact of local radio stations provides support for the moral hazard channel, as mayors who

can seek re-election are precisely those with an incentive to improve schools’ performances.

and the presence of non-local radios. As described in Section 3.2, the merging process between the audience surveyand the Ministry of Communication data on community radios presence is underestimating the popularity of thesestations. Moreover, the coverage areas of non-local radio stations are an approximation (especially for AM stations)as described in Section 3.4.

38

Table 8: Impact of community radios on scores: term-limited mayors

Dependent Variable: Distance to IDEB Target (Mean = 0.38)

Estimation Window: 12 Months 18 Months 24 Months

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio 0.0146 0.00173 0.0131 -0.00552 -0.00817 0.0219 -0.0222 -0.0335 0.00474

(0.0688) (0.0708) (0.0679) (0.0612) (0.0620) (0.0615) (0.0515) (0.0520) (0.0515)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 363 363 259 453 453 319 670 670 463Notes: This table presents the OLS estimation of Equation 12. The dependent variable is the difference between

the IDEB score and the municipality target in years t = 2007, 2009, 2011, 2013, 2015 ; Radio is a dummy that equalsone if the municipality got their first radio station before the Prova Brasil exam in year t = 2007, 2009, 2011, 2013, 2015and zero if the municipality got their first radio station after the exam. In columns 1, 2 and 3, I only included inthe regression municipalities that got their first local radio station in the 12 months before the exam and in the 12months after the exam; in columns 4, 5 and 6, the window is 18 months; and in columns 7, 8 and 9, the window is24 months. In columns 1, 4 and 7, the specification has no controls. In columns 2, 5 and 8, a vector of municipalcharacteristics is included as controls (median income, % population urban, % population finished primary school,log of population, population, distance to state capital, % population has TV, % population has radio equipmentand state fixed effects). In columns, 3, 6 and 9, the lagged dependent variable is also added as a control. The sampleonly includes municipalities where the incumbent mayor was term-limited. Robust standard errors in parenthesis. *p < 0.10, ** p < 0.05, *** p < 0.01.

Figure 14: Impact of community radios on measures of school quality: term-limitedmayors

Has Anti Dropout InitiativeHas Grade Promotion InitiativeHas Learning Support Initiative

No Financial ProblemNo Lack of Teachers Problem

No Lack of Administrative PersonnelNo Lack of Pedagogical ResourcesNo Lack of Pedagogical Personnel

No School Closure ProblemNo Absent Teacher Problem

No Teacher Turnover ProblemNo Political Interference ProblemHas Upper Government Support

Aggregate Index

-.4 -.2 0 .2 .4

Notes: This figure presents the impact of community radios on different measures of school quality. Eachline presents the coefficient β of Equation 12 for a different dependent variable. I only included in the regressionmunicipalities that got their first local radio station in the 12 months before the Prova Brasil exam and in the 12months after the exam. The sample only includes municipalities where the incumbent mayor was term-limited. Eachcoefficient estimate is presented with its corresponding 95% confidence interval using robust standard errors.

39

Table 9: Impact of community radios on educational federal discretionary transfers:term-limited mayors

Dependent Variable: Mayor got Discretionary Transfer for the Education Sector (Mean = 0.28)

Estimation Window: 12 Months 18 Months 24 Months

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio 0.00259 -0.00115 0.00856 -0.00895 -0.00344 0.00664 0.0513 0.0459 0.0501

(0.0480) (0.0498) (0.0505) (0.0433) (0.0434) (0.0436) (0.0373) (0.0371) (0.0371)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 363 363 363 453 453 453 670 670 670Notes: This table presents the OLS estimation of Equation 12 but using a different dependent variable. The

dependent variable is a dummy equal to one if the municipality received an education discretionary transfer from thefederal government in the 12 months prior to the Prova Brasil exam in years t = 2007, 2009, 2011, 2013, 2015 ; Radiois a dummy that equals one if the municipality got their first radio station before the Prova Brasil exam in yeart = 2007, 2009, 2011, 2013, 2015 and zero if the municipality got their first radio station after the exam. In columns 1,2 and 3, I only included in the regression municipalities that got their first local radio station in the 12 months beforethe exam and in the 12 months after the exam; in columns 4, 5 and 6, the window is 18 months; and in columns 7,8 and 9, the window is 24 months. In columns 1, 4 and 7, the specification has no controls. In columns 2, 5 and8, a vector of municipal characteristics is included as controls (median income, % population urban, % populationfinished primary school, log of population, population, distance to state capital, % population has TV, % populationhas radio equipment and state fixed effects). In columns, 3, 6 and 9, the lagged dependent variable is also added asa control. The sample only includes municipalities where the incumbent mayor was term-limited. Robust standarderrors in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01.

7.3 Heterogeneous Selection Effect by Pre Election IDEB Scores

The results in Section 6.4 suggest that community radio stations do not improve the selection

of politicians and subsequent educational outcomes. However, this result may be hiding some

interesting heterogeneous effects. For example, perhaps there is an improvement in selection but

only in the tails of the initial IDEB score distribution: local radios help voters retain really good

incumbents in the municipalities that performed very well in the exam and remove really bad

incumbents in the municipalities that performed very poorly. Figure 15a illustrates this point.

In Figure 15b, I visually present the heterogeneous effect of community radios depending on the

initial performance of the municipality. Although I am underpowered to draw strong conclusions

from this exercise, the figure suggests no improvement in the scores even in the tails of the initial

score distribution, further indicating that community radios cannot improve educational outcomes

through the selection of better politicians.

40

Figure 15: Heterogeneous Selection Effect by Pre Election IDEB

(a) Reelection x Pre Election IDEB (b) Post Election IDEB x Pre Election IDEB

Notes: Figure 15a is the same as Figure 10 but with the difference in reelection rates in the tails of the distributionhighlighted. Figure 15b illustrates the relationship between the distance to IDEB target 3 years after the election andthe distance to IDEB target before the election. The blue line and blue dots present the relationship for municipalitiesthat got their first community radio station in the 12 months before the release of the IDEB results in 2008 and2012. The yellow line and yellow dots present the relationship for municipalities that got their first community radiostation in the 12 months after the 2008 and 2012 municipal elections. Each line corresponds to the prediction forthe distance to IDEB target 3 years after the election from a linear regression of the distance to IDEB target 3 yearsafter the election on distance to IDEB target before the election, along with a 95% confidence interval. Each set ofdots represent the distance to IDEB target 3 years after the election averages within evenly sized bins (quintiles).The sample only includes municipalities where the mayor was not term-limited.

7.4 Moral Hazard Effect and Community Radios Signal Reach

The impact of local radios on exam scores is not necessarily uniform within the municipality.

Mayors might be incentivized to exert more effort to improve scores specifically in schools in areas

where voters will be better informed about their performance. Hence, I used geocoded information

on the schools’ locations to separately identify the impact upon the schools that were reached by

the community radio stations versus the ones that were not. Hence, I estimate the following model

by OLS:

Ijit = α+Mi + βRjit + γRjit × Tit + ejit (13)

Where j = R,NR is the the group of schools reached or not by the community radio signal51;

Ijit is the average IDEB score for schools j in municipality i in year t = 2007, 2009, 2011, 2013, 2015

; Rjit is a dummy equal to one if schools j in municipality i are reached by a community radio;

Mi is a municipality fixed effect; Tit = 1 if the municipality i got their first radio station before

the Prova Brasil exam in year t and Tit = 0 if the municipality i got their first radio station after

the Prova Brasil exam in year t. I only included in the regression municipalities that got their first

local radio station in the 12 months before the exam and in the 12 months after the exam.

51Hence, each municipality in this analysis have 2 observations.

41

The coefficient γ captures the difference within a municipality in the IDEB scores between

schools reached by community radios and those that are not. Intuitively, the difference in scores

between schools reached by community radio and those that are not might increase if a radio station

entered before the exam. Hence, γ should be positive.

Table 10 presents the results. In the odd-numbered columns I present the results with traditional

robust standard errors and in the even-numbered columns with standard errors clustered at the

municipality level. The results suggest that the improvement in scores is concentrated in school

near community radios, with the difference in scores between the reached and unreached schools

increasing by 0.25 points.

Table 10: Impact of community radios on scores: moral hazard within municipality

Dependent Variable: IDEB Score (Mean = 4.15)

Estimation Window: 12 Months 18 Months 24 Months

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

Radio Covered -0.0220 -0.0220 0.0115 0.0115 0.0569∗ 0.0569

(0.0489) (0.0688) (0.0415) (0.0585) (0.0312) (0.0440)

Radio Covered x Pre Exam 0.249∗∗∗ 0.249∗∗∗ 0.214∗∗∗ 0.214∗∗∗ 0.134∗∗∗ 0.134∗∗

(0.0623) (0.0877) (0.0539) (0.0759) (0.0400) (0.0564)

Municipality Fixed Effect Y Y Y Y Y Y

Clustered S.E. at Municipal Level N Y N Y N Y

Observations 410 410 496 496 778 778Notes: This table presents the OLS estimation of Equation 13. The dependent variable is the average IDEB scores

of schools reached or unreached by the community radio signal; Radio Covered is a dummy that equals one if theschools are reached by the communitty radio signal and zero if they are unreached; Pre election is a dummy that equalsone if the municipality got their first radio station before the Prova Brasil exam in year t = 2007, 2009, 2011, 2013, 2015and zero if the municipality got their first radio station after the exam. In columns 1 and 2, I only included in theregression municipalities that got their first local radio station in the 12 months before the Prova Brasil exam andin the 12 months after the exam; in columns 3 and 4, the window is 18 months; and in columns 5 and 6, the windowis 24 months. In odd columns, robust standard errors in parenthesis and, in even columns, standard errors clusteredat the municipality level. The sample only includes municipalities where the incumbent mayor was not term-limitedand where there is at least one school reached and one unreached by the community radio signal. This regression ison the Municipality ×RadioCoverage level. Hence, all municipalities included have two observation.* p < 0.10, **p < 0.05, *** p < 0.01.

8 Placebo Tests

The identification hypothesis underpinning my main results is that the entry timing of com-

munity radios is exogenous when I focus on small time windows around the events of interest.

Still, a reasonable concern with respect to my identification strategy may be that even inside the

small time-windows used to estimate my results, the municipalities that receive community radio

stations earlier are different to those that get one later. For example, such municipalities may be

those in which the demand for information is stronger, which may be correlated with electoral and

42

educational outcomes. Moreover, as discussed in Section 5, municipalities that get radios earlier

are usually larger than the ones that get later, which could also potentially be a confounder.

To alleviate these concerns, I perform placebo tests by shifting the time window around the

event of interest for all the main results in the paper. For example, instead of comparing the

municipalities that received a radio station before and after the municipal elections (the actual

comparison of interest), I can shift the window forward and compare the municipalities that received

radio stations 1 to 12 months after the election with the municipalities that received a station 13

months to 24 months after the election. This is a placebo test because none of these groups actually

had radio stations at the time of the election. Conversely, I also move the window backward so

that the placebo test is now comparing municipalities that both had radio stations at the time of

the election. Figure 16 illustrates this exercise.

Figure 16: Illustration of Placebo Tests

(a) Both Get Radio After Election

2012 2013

Elections

2014 2015

Placebo Treated Placebo Control

(b) Both Get Radio Before Election

2011 2012

Elections

2013 2014

Placebo Treated Placebo Control

Figure 17 presents the outcome of this exercise for each of the six main results in the paper. In

each plot, the black triangle represent the coefficient of interest presented in the Results section with

its corresponding 95% confidence interval. The blue dots correspond to the separately estimated

placebo coefficients. Each number on the x-axis corresponds to the number of years I am moving

the time window (e.g. -5 corresponds to moving the window back 5 years and +2 corresponds

to moving it forward 2 years). For example, in Figures 17a, 17b and 17c, instead of comparing

municipalities that got radio before and after the municipal elections,52 I can shift the window

forward one year and compare municipalities that got radio 1 to 12 months after the election with

municipalities that got it 13 months to 24 months after the election (corresponding to 1 in the

x-axis of the plot). This is a placebo test because none of these groups actually had radio at the

time of the election, but I could still see an ”effect” when I perform this exercise if, for example,

the information demand confounder described earlier was driving my results. Symmetrically, I also

move the window back one year so that the placebo test is now comparing municipalities that both

had radio at the time of the election (corresponding to −1 in the x-axis of the plot).

52Or the Prova Brasil Exam in Figures 17d, 17e and 17f.

43

Figure 17: Placebo Tests

-.4-.2

0.2

.4.6

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4

Placebo Treatment

(a) Accountability Effect

-.1-.0

50

.05

.1

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3

Placebo Treatment

(b) Accountability Effect Within Municipality

-.20

.2.4

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4

Placebo Treatment

(c) Selection Effect on Scores

-.2-.1

0.1

.2

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4

Placebo Treatment

(d) Moral Hazard Effect on Scores

-.2-.1

0.1

.2.3

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4

Placebo Treatment

(e) Moral Hazard Effect on School Quality

-.2-.1

0.1

.2

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3

Placebo Treatment

(f) Moral Hazard Effect on Federal Grants

Notes: In Figure 17, I perform placebo tests for all the main results in the paper by shifting the estimation timewindow around the events of interest. In each subplot, the black dot presents the coefficient of interest presented inthe results section with its corresponding 95% confidence interval. The blue dots correspond to estimated placebocoefficients from separate regressions. As illustrated by Figure 16, in Figures 17a, 17b and 17c, I shift the windowback and forth around the municipal elections. In Figures 17d, 17e and 17f, I proceed the same way but movingthe window around the Prova Brasil exam. Each number on the x-axis corresponds to the number of years I ammoving the time window (e.g. -5 corresponds to moving the window back 5 years and +2 corresponds to moving itforward 2 years). Figure 17a presents the estimated coefficient γ from equation 9; Figure 17b presents the estimatedcoefficient ∆ from equation 10; Figure 17c presents the estimated coefficient β from equation 11; Figures 17d, 17eand 17f present the estimated coefficient β from equation 12.

44

The results of this exercise provide strong evidence that my results are not driven by an omitted

variable like the ones described above. Plots 17a, 17b, 17d, 17e and 17f clearly only show a positive

and significant effect around the window of interest. Finally, Plot 17c confirms the null effects of

selection on educational outcomes.53

9 Structural Estimation

9.1 Estimation

In this section, I structurally estimate the parameters of the game described in Section 4 by

Maximum Likelihood. Reflecting the spirit of the previous reduced form analysis, my data set

consists of the municipalities that had a first-term mayor in the 2008 and 2012 election cycles and

that received their first local radio station between two years before and one year after the elections.

This yields a total of 405 municipalities. Each observation consists of a mayor stint, in which I

observe the vector (s1, reelect, R1, R2, R3, s2).

Hence, I observe the IDEB Scores minus the target (released prior to the election (s1) and 3

years after the election (s2)), whether the mayor was reelected (reelect) and in which moment the

municipality got the radio station. If the municipality got a radio station before the Prova Brasil

exam R1 = 1 (otherwise R1 = 0) , if it got the station after the exam but before the election

R2 = 1 and if it got the station after the election R3 = 1. I present the detailed expressions for the

likelihood function in Appendix E. Intuitively, the likelihood function captures the probability of

jointly observing a vector of values for the outcomes of the model (s1, reelect, R1, R2, R3, s2) for a

given set of structural parameters of the game (µH , µL, π, µε, σε, σs, σr, σnr, θ1, θ2).

9.2 Structural Estimation Results

I maximize the log likelihood described in Appendix E with standard numerical optimization

routines to estimate 10 structural parameters (µH , µL, π, µε, σε, σs, σr, σnr, θ1, θ2). I set the 4 years

discount factor β = 0.65 following Avis et al. (2018). Table 11 shows the estimated structural

parameters and Table 12 shows the relevant equilibrium outcomes.

The results in Table 11 suggest that there is a large difference in the average scores when a

mayors exerts high effort versus low effort. The average scores for a high effort mayor is 1.33 points

and for a low effort mayor is 0.19 points. Moreover, the estimates show that only 10% of the mayors

are good types. This implies that the vast majority of candidates need re-election incentives in

order too act according to voters preferences and are susceptible to changing their effort choice if

53Alternatively, the exercise in Figure 17 can be illustrated by Figure 26 in the Appendix which shows the distri-bution of t-statistics from the placebo tests above. The results also clearly only suggest a positive and significanteffect around the window of interest.

45

voters are better informed. Finally, I estimate that σr < σnr indicating the local radios increase

the quality of the signal that voters receive regarding mayoral behavior.54

Table 11: Parameter Estimates

Estimate Standard Errors

µh - Expected Score given High Effort 1.33 (0.07)

µl - Expected Score given Low Effort 0.19 (0.03)

σs - S.D. of Score Shock 0.48 (0.02)

π - Proportion of Good Candidates 0.10 (0.03)

µε - Mean of Popularity Shock 0.12 (0.07)

σε - S.D. of Popularity Shock 0.18 (0.12)

σr - S.D. of Noise Shock with Radio 1.32 (0.71)

σnr - S.D. of Noise Shock without Radio 40

θ1 - Probability of Getting Radio in Moment 1 0.30 (0.02)

θ2 - Probability of Getting Radio in Moment 2 0.27 (0.02)

β - Discount Factor 0.65Notes: This table reports maximum likelihood estimates of the parameters of the model.

Table 12 illustrates how local radios can improve test scores. Notice that for the municipalities

that received radio stations before the election the probability of a high effort mayor being reelected

is 52% (ρ1h), while a low effort mayor only has a 40% (ρ1l ) probability of being reelected. On the

other hand, in the municipalities that did not receive a radio station, the probability of re-election

for high and low effort mayors is the same (ρ3h = ρ3l = 0.42). This generates differential incentives

for mayors to exert effort. In the municipalities with radio, 12% of opportunistic mayors exert effort

(δ) while in the municipalities without radio only 5% exert effort (δ′). I can use these estimates to

calculate the Moral Hazard Effect as I defined in section 4.5.55 The estimated Moral Hazard Effect

is 0.07 points which is close to my reduced form results (between 0.04 and 0.1 points).

Moreover, the introduction of radio increases the probability that mayors will be punished

(rewarded) for low (high) scores. The first three rows of Table 12 illustrate this effect. The

estimated probability for the re-election of a mayor in a municipality with radio who got a score of

2 points above the target is 20 percentage points higher than in a municipality without radio, while

54The parameter σnr is estimated at the upper bound imposed for the numerical optimization. This reflects thefact that the correlation between reelection rates and IDEB scores is close to zero in municipalities without radio.Hence, I will just treat σnr as calibrated parameter and not an estimated one from this point forward. Differentchoices of upper bound for this parameter do not substantively change the results.

55See Definition 2: MoralHazardEffect = E[s1|R1 = 1]− E[s1|R2 = 1] = (δ − δ′)(1− π)(µH − µL)

46

for a mayor who scored -1 local radio reduces the reelection probability by 11 percentage points.

Furthermore, notice that the effect of radio close to the average score (s1 = 0.4) is zero just as in

the reduced form results.

Table 12: Estimated Equilibrium Outcomes

Estimate Standard Errors

Accountability(x) = P (reelect|s1 = x,R2 = 1)− P (reelect|s1 = x,R3 = 1):

Accountability(−1) -0.11 (0.06)

Accountability(0.4) 0.00 (0.02)

Accountability(2) 0.20 (0.07)

Moral Hazard Effect 0.07 (0.03)

Selection Effect 0.01 (0.005)

Proportion of Opportunistic Mayors Exerting High Effort:

δ - If radio entered in moment 1 0.12 (0.04)

δ′ - If radio entered in moment 2 or 3 0.05 (0.02)

Probability of Reelection depending on Effort and Radio Entry:

ρ1h - High Effort and Radio in Moment 1 0.52 (0.05)

ρ1l - Low Effort and Radio in Moment 1 0.40 (0.05)

ρ2h - High Effort and Radio in Moment 2 0.54 (0.06)

ρ2l - Low Effort and Radio in Moment 2 0.41 (0.05)

ρ3h - High Effort and Radio in Moment 3 0.42 (0.05)

ρ3l - Low Effort and Radio in Moment 3 0.42 (0.05)

Probability of Good Mayor in Period 2:

If radio entered in moment 2 0.11 (0.03)

If radio entered in moment 3 0.10 (0.03)

Notes: This table reports estimated equilibrium outcomes. MoralHazard = E[s1|R1 = 1] − E[s1|R2 = 1] andSelection = E[s2|R2 = 1] − E[s2|R3 = 1]. Accountability(x) = P (reel|s1 = x,R2 = 1) − P (reel|s1 = x,R3 = 1).Standard errors for the equilibrium outcomes are computed using the delta method.

Finally, Table 12 presents estimates for the Selection Effect as I defined in section 4.5.56 The

estimated Selection Effect is 0.01 points, which is seven times smaller than the Moral Hazard Effect.

The direct reason for this small effect is that getting a radio before the election only increases the

proportion of good mayors in office in the second period from 10% to 11%. It is important to notice

here how the estimated low proportion of good types (π) already puts a low upper bound on the

56See Definition 3: SelectionEffect = E[s2|R2 = 1]− E[s2|R3 = 1]

47

potential for selection effects. Even if radio could perfectly inform the voter about the type of the

incumbent mayor, the maximum proportion of good mayors in the second period would be 19%.57

Table 13 shows how the main moments predicted by the model compare with the actual moments

in the data. Figure 18 further explores the fit of the model to the data. The plot shows the

relationship between the reelection rates and the IDEB scores depending on when the municipality

received its first radio station. First, notice that the noise generated by σnr = 40 implies no

relationship between the electoral performance and the scores. This visually fits with the data,

which in fact presents a non significant negative correlation between reelection and the scores.

Second, σr = 1.3 implies a positive correlation between reelection and the scores similar to the

observed correlation in the data for municipalities with radio.

Table 13: Model Fit

Data Model Data Model

E[reelect|R1 = 1] 0.46 0.42 E[R1] 0.30 0.30

E[reelect|R2 = 1] 0.39 0.42 E[R2] 0.27 0.27

E[reelect|R3 = 1] 0.44 0.42

E[s1|R1 = 1] 0.42 0.43 E[s2|R1 = 1] 0.39 0.38

E[s1|R2 = 1] 0.35 0.36 E[s2|R2 = 1] 0.37 0.39

E[s1|R3 = 1] 0.35 0.36 E[s2|R3 = 1] 0.41 0.38

std(s1) 0.59 0.62 std(s2) 0.63 0.63

Moral Hazard Effect 0.08 0.07

Selection Effect -0.04 0.01

Notes: This table reports estimated equilibrium outcomes from the model and the equivalent moments in thedata.

57Scenario in which the voter reelects all good mayors and kick out all opportunistic mayors =⇒ 1×0.1+0.1×0.9 =0.19

48

Figure 18: Model Fit: Reelection vs IDEB Scores

Notes: The equally sized bins represent average reelection rates in municipalities that got radio before and afterthe election. The blue line is the structurally estimated P (reel|s1, R2 = 1) for different values of s1. The red line isthe structurally estimated P (reel|s1, R3 = 1) for different values of s1. Standard errors are computed using the deltamethod.

In Figure 19, I further explore why the selection effects are so small considering the estimated

large increase in accountability generated by the introduction of radio. The upper plot in Figure

19 shows what the selection effect of radio is depending on the IDEB score in the municipality.

The first thing to notice is that the impact of radio is not uniform and is asymmetric. As would be

expected, the selection effect is smallest when the scores are between µH and µL since this signal

provides little information to help voters reelect (not reelect) good (opportunistic) incumbents.

Interestingly, low scores also lead to small selection effects. The intuition behind this is simple:

when an opportunistic incumbent is removed from office there is only a 10% chance that in the

next period a good mayor will be elected. On the other hand, when radio helps reelect a good

incumbent it guarantees a high effort mayor in the next period. In other words, as good mayors

are rarer the value of keeping them is higher. Finally, the lower plot illustrates why on average (

across s1) the selection effects are small. The histogram displays the distribution of s1 obtained by

100,000 simulation of the model and shows that large selection effects only occur a low percentage

of the time.58

58It is not possible to test if the asymmetric selection effect described above holds when looking at the raw databecause I am underpowered to estimate the selection effect conditioning on very high scores.

49

Figure 19: Selection Effect by First Term IDEB Scores

Notes: The blue line in the upper plot is the structurally estimated E[s2|R2 = 1, s1] − E[s2|R3 = 1, s1] fordifferent values of s1. Standard errors are computed using the delta method. The lower plot corresponds to thehistogram of s1 obtained by 100,000 simulation of the model. Vertical dotted line corresponds to the 84th percentileof the s1 distribution.

In Figure 20, I estimate the counterfactuals for the impact of local radios depending upon the

quality of the candidates pool. The plot shows that relatively small selection effects and large

moral hazard effects only occur when the pool of candidates is largelly homogenous and most of

the politicians are opportunistic types. When the pool of candidates is homogeneous but mostly

comprised of good types both effects are small. The selection effects are largest when the pool of

candidates is most heterogenous around π = 0.5.59

These results demonstrate that the impact that information can have on the selection of better

politicians is highly dependent upon the quality of the pool from which voters can pick from. In

the particular context of this paper, the introduction of local radio lead to an improvement in the

test scores by inducing opportunistic mayors to exert effort. However, because of the estimated low

heterogeneity of the candidate pool, this did not lead to a meaningful improvement in exam scores

through the selection of better politicians.

59In Appendix C, I explore other counterfactual scenarios like allowing voters to perfectly observe scores when themunicipality gets a radio station or setting δ′ = 0 to evaluate how much the fact that opportunistic mayors sometimesexert high effort diminishes the ability of voters to hold politicians accountable and select better candidates. Resultsshow that, although in both these cases scores rise, the moral hazard effect is always much larger than the selectioneffect.

50

Figure 20: Counterfactuals: Increasing the Proportion of Good Mayors

Notes: The blue line corresponds to the estimation of counterfactual Moral Hazard Effects (E[s1|R1 = 1] −E[s1|R2 = 1]) for different values of the parameter π. The blue line corresponds to the estimation of counterfactualSelection Effects ( E[s2|R2 = 1] − E[s2|R3 = 1]) for different values of the parameter π. The vertical dotted linemarks the actual estimated value of π = 0.1.

I can also use my structural estimates to recover the full effect of community radios. Notice

that in the model the mayors internalize the probability that a community radio will enter their

municipality in the near future. Hence, they exert more effort even if they do not currently have

a radio station in their municipality. To fully capture the impact of the expansion of community

radios on scores, I estimate two counterfactual scenarios.In the first one, the municipality already

has a local radios station at the beginning of the game and, in the second one, the municipality

does not have a local radio and does not expect to receive one in the future. Next, I calculate

the expected score for a first term mayor and the expected score for a second term mayor for each

one of the scenarios. Finally, I calculate the stationary proportion of first and second term mayors

implied by the probability of reelection for each one of the counterfactuals.60 The expected score

for a given municipality will be equal to the proportion of first term mayors times the expected

score of first term mayors plus the analogous expression for second term mayors.

The results of this exercise indicate that local radio increases the scores difference to the target

from 0.305 points to 0.399 points.61 This effect is 18% larger than would be obtained by naively

adding the moral hazard and selection effects previously estimated. I can use the results of this

exercise to make simple back-of-the-envelope calculations of the total impact that the expansion

of community radio stations had upon the scores of Brazilian primary municipal schools. Ap-

60In other words, what is the proportion of first and second term mayors such that, given a probability of reelection,the proportion of first and second term mayors in the next periods is always the same. The proportion of first termmayors can be calculated by the formula 1

1+P (reelected).

61With 89% of the improvement attributed to first term mayors and 11% to second term mayors.

51

proximately one-third of Brazilian students are in municipalities within which community radios

were the first local radio stations. If community radio raised scores by 0.094 points on average

in municipalities that benefited as previously estimated, the total impact of community radios is

approximately 0.03 points. Consequently, the creation of community radios was responsible for

approximately 1.5% of the large improvement in the municipal schools’ scores between 2005 and

2017 (2.2 points).

I also estimate what would be the effect of reducing the number of terms a mayor can serve

from two to one. In municipalities with a local radio station, limiting mayors to one term would

reduce scores from 0.399 to 0.304. On the other hand, the effect for the municipalities without

radio would be almost zero (from 0.305 to 0.304). These results highlight the importance of the

interaction between term limits and how informed voters are. In contexts within which voters are

very uniformed, there is little gains from allowing mayors to remain more than one term because

voters are poorly equipped to generate any real accountability.

Finally, I assess what role the competitiveness of mayoral elections play in my results. As

discussed earlier, mayoral elections are very competitive in Brazil with only 43% of incumbents

getting reelected. I conduct two counterfactual analysis to understand what would be the impact

of local radios if (1) I doubled incumbents’ average popularity (µε), bringing the reelection chance

of incumbents’ to 63% (similarly to US gubernatorial races) , and if (2) I tripled it, bringing the

reelection chance of incumbents’ to 82% (similarly to US senatorial races). The results show that

in (1) the positive impact of community radios would be reduced to 0.072 points and in (2) to 0.044

points, which is less than half of the original impact of 0.094 points. Hence, the impact of local

radios is amplified by the competitiveness of Brazilian municipal elections and we should be careful

to extrapolate my results to settings in which politicians enjoy a large incumbency advantage.

10 Conclusion

This paper examined how local media affects electoral accountability and the quality of public

education in Brazil. Exploiting the entry timing of the first local radio station in a municipality and

its geographical coverage area, I found that local media decreases (increases) the probability that

mayors will be re-elected when they miss (exceed) educational targets set by the federal government.

Back-of-the-envelope calculations suggest a persuasion rate (DellaVigna and Kaplan (2007)) of

approximately 15% when mayors score one standard deviation above or below the neutral distance

to target.62 This is close to the middle of the range of voter persuasion rates (6% to 20%) calculated

by DellaVigna and Gentzkow (2010) using several different studies. However, this persuasion rate

is considerably smaller than, for example, Fox News persuasion rates (27% to 58%) estimated more

62Neutral distance to target is the distance to target such that the impact of local media on incumbent mayors’vote share is zero according to the estimated coefficients in column 3 of Table 14. See details of the persuasion ratecalculation in Appendix F.

52

recently by Martin and Yurukoglu (2017).

I also examined two channels through which this increase in accountability might improve the

quality of public schools. The results do not suggest an improvement in school exam scores through

the selection of better candidates, but they do indicate that local radio stations discipline mayors,

inducing politicians to exert more effort to improve scores. Community radio increased test scores

by 0.16 standard deviations, with the effect fully concentrated in non-term-limited mayors.

Untangling these two mechanisms leads to different practical recommendations for policy-makers

interested in improving outcomes. If moral hazard is the main channel through which information

impacts policy, then interventions should be designed not only to inform the electorate about the in-

cumbents’ performances, but, just as importantly, to also inform politicians about this. Conversely,

if selection is the main channel, then informing the electorate about politicians’ performances is

the most important concern.

My findings suggest that local media plays an important role in disciplining politicians and

improving the quality of public services, but also indicate that it has a limited impact on the

ability of voters to select higher quality candidates. This lack of selection effects seems puzzling in

light of the large estimated accountability effects. Hence, I adapt a simple political agency model

from Aruoba et al. (2019) to closely fit my reduced form empirical setting and interpret my previous

results through the lenses of this model, which I subsequently structurally estimate. The structural

estimates confirm the key findings of the reduced form analysis, showing that the direction and

magnitudes of my reduced form results can be explained by a standard political agency model.

Moreover, the structural exercise explains the apparent puzzling lack of selection effects. I estimate

that the pool of candidates running for office is largely homogeneous and composed mostly of

opportunistic candidates. Under these circumstances, there is little space for selection effects to

play out as even when voters get rid of low quality incumbents, the challenger is also very likely to

be of low quality.

This highlights the importance of improving the pool of candidates who run for office.63 Due

to the very nature of my identification strategy, it is possible that high-quality challengers did not,

in the short term, have time to enter the race in response to the creation of the community radio

station. Hence, it is preferable to consider my results to be the impact of local media, holding the

quality of the candidate pool as fixed. It is possible that, in the long term, local media improves

the pool of candidates, and, therefore, selection, but my setting is not well suited to estimating

these long-term impacts.

This paper also draws attention to the impact that simple performance targets might have on

electoral accountability. Speculatively, it seems unlikely that mayors would be held accountable in

such a strong fashion if municipalities had no targets. Voters may be unable to infer what the score

by itself means, and local radio stations would not have a simple way to frame the results. Hence,

63See Dal Bo and Finan (2018) for an overview of the literature on candidate entry.

53

radio stations would be less likely to report upon the results and, even if they covered them, voters

would be less likely to use that information when making their voting decisions.

Finally, it is useful to place the magnitude of the impact mayors’ increase in effort had on

scores (between 0.07 and 0.16 standard deviations) into context. Recent high-quality empirical

evidence in Brazil suggests that party turnover in municipal governments reduces scores by up to

0.08 standard deviations (Akhtari et al. (2017)), and the embezzlement of public funds reduces

scores by up to 0.35 standard deviations (Ferraz et al. (2012)). Hence, although not having a

community radio station to keep politicians in check has a sizable impact, it is still less than half

of the estimated impact that corruption can have on students’ outcomes. Moreover, the impact

is smaller than but comparable to successful policy interventions such as reducing classroom sizes

in the US (0.22 standard deviations), introducing teacher performance pay in India (0.27 and 0.17

standard deviations in math and language, respectively), and outsourcing public schools in Liberia

(0.18 standard deviations).64

64See Krueger (1999), Muralidharan and Sundararaman (2011), and Romero et al. (2017), respectively. Morebroadly, Kremer et al. (2013) showed that the average impact on scores across 30 primary school programs subjectto randomized evaluation was 0.2 standard deviations.

54

References

Akhtari, M., Moreira, D., and Trucco, L. (2017). Political turnover, bureaucratic turnover, and the

quality of public services. Bureaucratic Turnover, and the Quality of Public Services (April 25,

2017).

Alt, J., Bueno de Mesquita, E., and Rose, S. (2011). Disentangling accountability and competence

in elections: Evidence from us term limits. The Journal of Politics, 73(1):171–186.

Aruoba, S. B., Drazen, A., and Vlaicu, R. (2019). A structural model of electoral accountability.

International Economic Review, 60(2):517–545.

Avis, E., Ferraz, C., and Finan, F. (2018). Do government audits reduce corruption? estimating

the impacts of exposing corrupt politicians. Journal of Political Economy, 126(5):1912–1964.

Banerjee, A., Kumar, S., Pande, R., and Su, F. (2011). Do informed voters make better choices?

experimental evidence from urban india. Unpublished manuscript.

Becskehazy, I. (2018). Institucionalizacao do direito a educacao de qualidade: o caso de sobral, ce.

Besley, T. and Burgess, R. (2002). The political economy of government responsiveness: Theory

and evidence from india. The Quarterly Journal of Economics, 117(4):1415–1451.

Boas, T., Hidalgo, D., and Toral, G. (2019). Accountability backlash: Negative electoral responses

to public service delivery in brazil.

Boas, T. C. and Hidalgo, F. D. (2011). Controlling the airwaves: Incumbency advantage and

community radio in brazil. American Journal of Political Science, 55(4):869–885.

Bobonis, G. J., Camara Fuertes, L. R., and Schwabe, R. (2016). Monitoring corruptible politicians.

American Economic Review, 106(8):2371–2405.

Borelli, V., Silva, G. L. P. d., Garcia, A. D., and Carbonel, F. L. (2012). Radios comunitarias e

legislacao: um estudo da programacao da caraı fm e da comnorte fm, santa maria-rs. Revista

Alterjor, 1(1).

Casey, K. (2015). Crossing party lines: The effects of information on redistributive politics. Amer-

ican Economic Review, 105(8):2410–48.

Casey, K., Glennerster, R., Bidwell, K., et al. (2019). Debates: Voting and expenditure responses

to political communication. Technical report.

Chong, A., De La O, A., Karlan, D., and Wantchekon, L. (2010). Information dissemination and

local governments? electoral returns, evidence from a field experiment in mexico. In Annual

Meeting of the American Political Science Association, Seattle.

55

Cruz, C., Keefer, P., Labonne, J., and Trebbi, F. (2018). Making policies matter: Voter responses

to campaign promises. Technical report, National Bureau of Economic Research.

Da Silveira, B. S. and De Mello, J. M. (2011). Campaign advertising and election outcomes: Quasi-

natural experiment evidence from gubernatorial elections in brazil. The Review of Economic

Studies, 78(2):590–612.

Dal Bo, E. and Finan, F. (2018). Progress and perspectives in the study of political selection.

Annual Review of Economics, 10:541–575.

DellaVigna, S. and Gentzkow, M. (2010). Persuasion: Empirical evidence. Annual Review of

Economics, 2(1):643–669.

DellaVigna, S. and Kaplan, E. (2007). The fox news effect: Media bias and voting. The Quarterly

Journal of Economics, 122(3):1187–1234.

Dias, M. and Ferraz, C. (2017). Voting for quality? the impact of school quality information

on electoral outcomes. Departamento de Economia, Pontifıcia Universidade Catolica do Rio de

Janeiro, Rio de Janeiro.

Dunning, T., Grossman, G., Humphreys, M., Hyde, S. D., McIntosh, C., Nellis, G., Adida, C. L.,

Arias, E., Bicalho, C., Boas, T. C., et al. (2019). Voter information campaigns and political

accountability: Cumulative findings from a preregistered meta-analysis of coordinated trials.

Science advances, 5(7):eaaw2612.

Enikolopov, R., Petrova, M., and Zhuravskaya, E. (2011). Media and political persuasion: Evidence

from russia. American Economic Review, 101(7):3253–85.

Fearon, J. D. (1999). Electoral accountability and the control of politicians: selecting good types

versus sanctioning poor performance. Democracy, accountability, and representation, 55:61.

Ferejohn, J. (1986). Incumbent performance and electoral control. Public choice, 50(1):5–25.

Ferraz, C. and Finan, F. (2008). Exposing corrupt politicians: the effects of brazil’s publicly

released audits on electoral outcomes. The Quarterly journal of economics, 123(2):703–745.

Ferraz, C. and Finan, F. (2011). Electoral accountability and corruption: Evidence from the audits

of local governments. American Economic Review, 101(4):1274–1311.

Ferraz, C., Finan, F., and Moreira, D. B. (2012). Corrupting learning: Evidence from missing

federal education funds in brazil. Journal of Public Economics, 96(9-10):712–726.

Fujiwara, T. and Wantchekon, L. (2013). Can informed public deliberation overcome clientelism?

experimental evidence from benin. American Economic Journal: Applied Economics, 5(4):241–

55.

56

Gentzkow, M. (2006). Television and voter turnout. The Quarterly Journal of Economics,

121(3):931–972.

Gentzkow, M., Shapiro, J. M., and Sinkinson, M. (2011). The effect of newspaper entry and exit

on electoral politics. American Economic Review, 101(7):2980–3018.

George, L. M. and Waldfogel, J. (2006). The new york times and the market for local newspapers.

American Economic Review, 96(1):435–447.

Gerber, A. S., Gimpel, J. G., Green, D. P., and Shaw, D. R. (2011). How large and long-lasting

are the persuasive effects of televised campaign ads? results from a randomized field experiment.

American Political Science Review, 105(1):135–150.

Gerber, A. S., Karlan, D., and Bergan, D. (2009). Does the media matter? a field experiment

measuring the effect of newspapers on voting behavior and political opinions. American Economic

Journal: Applied Economics, 1(2):35–52.

Grossman, G. and Michelitch, K. (2018). Information dissemination, competitive pressure, and

politician performance between elections: A field experiment in uganda. American Political

Science Review, 112(2):280?301.

Holmstrom, B. and Milgrom, P. (1991). Multitask principal-agent analyses: Incentive contracts,

asset ownership, and job design. JL Econ. & Org., 7:24.

Hopkins, D. J. (2018). The increasingly United States: How and why American political behavior

nationalized. University of Chicago Press.

Kendall, C., Nannicini, T., and Trebbi, F. (2015). How do voters respond to information? evidence

from a randomized campaign. American Economic Review, 105(1):322–53.

Krasno, J. S. and Green, D. P. (2008). Do televised presidential ads increase voter turnout? evidence

from a natural experiment. The Journal of Politics, 70(1):245–261.

Kremer, M., Brannen, C., and Glennerster, R. (2013). The challenge of education and learning in

the developing world. Science, 340(6130):297–300.

Krueger, A. B. (1999). Experimental estimates of education production functions. The quarterly

journal of economics, 114(2):497–532.

Larreguy, H. A., Marshall, J., and Snyder, J. M. (2015). Publicizing malfeasance: When media

facilitates electoral accountability in mexico. Cambridge, Harvard University, manuscrito.

Larreguy, H. A., Marshall, J., and Snyder Jr, J. M. (2018). Leveling the playing field: How campaign

advertising can help non-dominant parties. Journal of the European Economic Association,

16(6):1812–1849.

57

Lima, V. d. and Lopes, C. A. (2007). Radios comunitarias: coronelismo eletronico de novo tipo

(1999-2004). em Observatorio da Imprensa (26/06/07-http://observatorio. ultimosegundo. ig.

com. br/artigos. asp.

Marshall, J. (2016). Political information cycles: When do voters sanction incumbent parties for

high homicide rates? Cambridge, Harvard University, manuscrito.

Martin, G. J. and McCrain, J. (2019). Local news and national politics. American Political Science

Review, 113(2):372–384.

Martin, G. J. and Yurukoglu, A. (2017). Bias in cable news: Persuasion and polarization. American

Economic Review, 107(9):2565–99.

Muralidharan, K. and Sundararaman, V. (2011). Teacher performance pay: Experimental evidence

from india. Journal of political Economy, 119(1):39–77.

Reinikka, R. and Svensson, J. (2011). The power of information in public services: Evidence from

education in uganda. Journal of Public Economics, 95(7-8):956–966.

Ribeiro, B. (2017). The politics of government advertisement: evidence from brazil. Departamento

de Economia, Pontifıcia Universidade Catolica do Rio de Janeiro, Rio de Janeiro.

Romero, M., Sandefur, J., and Sandholtz, W. (2017). Can outsourcing improve liberia’s schools?

preliminary results from year one of a three-year randomized evaluation of partnership schools

for liberia. Center for Global Development Working Paper, (462).

Romero, M., Sandefur, J., and Sandholtz, W. (2018). Electoral accountability and public good

provision: Evidence from a randomized school reform in liberia. Unpublished manuscript.

Snyder, J. M. and Stromberg, D. (2010). Press coverage and political accountability. Journal of

political Economy, 118(2):355–408.

Spenkuch, J. L. and Toniatti, D. (2018). Political advertising and election results. The Quarterly

Journal of Economics, 133(4):1981–2036.

Stromberg, D. (2004). Radio’s Impact on Public Spending*. The Quarterly Journal of Economics,

119(1):189–221.

58

A Additional Tables

Table 14: Impact of community radios on accountability

All Eligible Incumbents Conditional on Incumbent Running All Municipalities

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

Dependent Variable: Incumbent Ran Reelected Inc. Vote Share Party Reelected

Mean of Dep. Var. 0.72 0.64 0.53 0.34

Radio -0.00703 -0.0498 -0.00411 -0.0224

(0.0453) (0.0586) (0.0195) (0.0386)

Dist. IDEB Target -0.112∗∗ -0.0156 -0.00514 -0.0559

(0.0527) (0.0671) (0.0223) (0.0417)

Radio x Dist. IDEB Target 0.204∗∗ 0.176∗ 0.0538∗ 0.171∗∗

(0.0795) (0.101) (0.0320) (0.0696)

Controls Y Y Y Y

Observations 426 311 311 643Notes: This table presents the OLS estimation of Equation 9 with different dependent variables. Radio is a

dummy that equals one if the municipality got their first radio station before the release of the IDEB results in yeart = 2008, 2012 and zero if the municipality got their first radio station after the election in year t = 2008, 2012;Dist.IDEBTarget is the demeaned difference between the IDEB score released in year t = 2008, 2012 and its targetfor that year. I only included in the regression municipalities that got their first local radio station in the 18 monthsbefore the release of IDEB results and in the 18 months after the elections. In all specifications a vector of municipalcharacteristics is included as controls (median income, % population urban, % population finished primary school, logof population, population, distance to state capital, % population has TV and % population has radio equipment).In column 1, the sample only includes municipalities where the incumbent mayor was not term-limited; In column 2and 3, the sample only includes municipalities where the incumbent ran for re-election; and in column 4 the sampleincludes all municipalities regardless of term limits. Robust standard errors in parenthesis. * p < 0.10, ** p < 0.05,*** p < 0.01.

59

Table 15: Impact of community radios on Election Winner Characteristics

Estimation Window: 12 Months 18 Months 24 Months

Mean Dep. Var. (1) (2) (3) (4) (5) (6)

Incumbent 0.46 -0.00718 -0.00642 -0.0339 -0.0375 -0.0138 -0.0172

(0.0727) (0.0751) (0.0503) (0.0508) (0.0427) (0.0430)

Ln(Wealth+1) 11.9 0.188 0.274 0.0100 0.0800 0.214 0.270

(0.296) (0.314) (0.270) (0.275) (0.224) (0.226)

College Degree 0.42 -0.0447 -0.0356 -0.0228 -0.0217 0.00708 0.0105

(0.0585) (0.0568) (0.0501) (0.0485) (0.0427) (0.0421)

Age 48.1 1.112 1.437 1.409 1.605∗ 1.253 1.312

(1.069) (1.078) (0.935) (0.951) (0.810) (0.820)

Female 0.11 0.0139 0.00214 0.0206 0.0187 0.0210 0.0193

(0.0376) (0.0369) (0.0323) (0.0325) (0.0269) (0.0270)

Controls N Y N Y N Y

Observations 305 305 426 426 578 578Notes: This table presents the OLS estimation of Equation 11 for different dependent variables in each line.

The dependent variables are observable characteristics of the municipal elections winners in years t = 2008, 2012.In columns 1 and 2, I only included in the regression municipalities that got their first local radio station in the 12months before the release of IDEB results and in the 12 months after the elections; in columns 3 and 4, the window is18 months; and in columns 5 and 6, the window is 24 months. In odd columns the specification has no controls andin even columns a vector of municipal characteristics is included as controls (median income, % population urban,% population finished primary school, log of population, population, distance to state capital, % population has TVand % population has radio equipment). The sample only includes municipalities where the incumbent mayor wasnot term-limited. Robust standard errors in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01.

60

Table 16: Impact of community radios on Pool of Candidates Characteristics

Estimation Window: 12 Months 18 Months 24 Months

Mean Dep. Var. (1) (2) (3) (4) (5) (6)

Ln(Wealth+1) 12.4 -0.0672 -0.0393 -0.0504 0.00745 0.0254 0.0642

(0.181) (0.180) (0.144) (0.139) (0.117) (0.114)

College Degree 0.41 0.0259 0.0286 0.0116 0.0112 0.0107 0.00914

(0.0380) (0.0363) (0.0318) (0.0302) (0.0276) (0.0266)

Age 48.7 -0.110 0.0726 0.326 0.402 0.529 0.566

(0.685) (0.667) (0.622) (0.623) (0.544) (0.542)

Female 0.10 0.0336 0.0275 0.0301 0.0264 0.0292∗ 0.0262

(0.0247) (0.0245) (0.0196) (0.0196) (0.0174) (0.0175)

Controls N Y N Y N Y

Observations 305 305 426 426 578 578Notes: This table presents the OLS estimation of Equation 11 for different dependent variables in each line.

The dependent variables are average observable characteristics of the municipal elections candidates in years t =2008, 2012. In columns 1 and 2, I only included in the regression municipalities that got their first local radio stationin the 12 months before the release of IDEB results and in the 12 months after the elections; in columns 3 and4, the window is 18 months; and in columns 5 and 6, the window is 24 months. In odd columns the specificationhas no controls and in even columns a vector of municipal characteristics is included as controls (median income,% population urban, % population finished primary school, log of population, population, distance to state capital,% population has TV and % population has radio equipment). The sample only includes municipalities where theincumbent mayor was not term-limited. Robust standard errors in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01.

61

Table 17: Impact of community radios on incumbents re-election

Dependent Variable: Incumbent Mayor Reelected (Mean = 0.46)

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

Radio -0.00437 -0.00483 0.0413 0.0670 -0.0352 -0.0194

(0.189) (0.189) (0.191) (0.195) (0.290) (0.293)

Infant Mortality -0.00375 -0.00546

(0.00387) (0.00442)

Radio x Infant Mortality -0.000243 -0.000358

(0.0107) (0.0105)

Prenatal Exams ≥ 7 0.0748 0.212

(0.136) (0.212) )

Radio x Prenatal Exams ≥ 7 -0.0868 -0.130

(0.336) (0.347)

Low Weight Births -0.640 -0.916

(1.671) (1.807)

Radio x Low Weight Births 0.369 0.153

(3.763) (3.850)

Controls N Y N Y N Y

Observations 305 305 305 305 305 305Notes: This table presents the OLS estimation of Equation 9. Incumbent mayor reelected is a dummy that equals

one if the incumbent mayor was reelected; Radio is a dummy that equals one if the municipality got their first radiostation before the release of the IDEB results in year t = 2008, 2012 and zero if the municipality got their first radiostation after the election in year t = 2008, 2012; Infant Mortality is the infant mortality rate per 1000 live births inyear t = 2008, 2012; Prenatal Visits ≥ 7 is the proportion of mothers that had more than 6 prenatal exams duringtheir term in year t = 2008, 2012; Low Weight Birth is the proportion of births where the newborn weighted less than2.5 kg in year t = 2008, 2012. In all specifications, I only included in the regression municipalities that got their firstlocal radio station in the 12 months before the release of IDEB results and in the 12 months after the elections. Inodd columns the specification has no controls and in even columns a vector of municipal characteristics is included ascontrols (median income, % population urban, % population finished primary school, log of population, population,distance to state capital, % population has TV and % population has radio equipment). The sample only includesmunicipalities where the incumbent mayor was not term-limited. Robust standard errors in parenthesis. * p < 0.10,** p < 0.05, *** p < 0.01.

62

Table 18: Impact of community radios on non-education federal discretionary transfers

Dependent Variable: Mayor got Discretionary Transfer for non-education Sector (Mean = 0.76)

Estimation Window: 12 Months 18 Months 24 Months

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio 0.0219 0.0138 0.0187 0.0269 0.0211 0.0256 0.0143 0.0124 0.0179

(0.0320) (0.0319) (0.0315) (0.0280) (0.0277) (0.0272) (0.0224) (0.0221) (0.0216)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 787 787 787 953 953 953 1445 1445 1445Notes: This table presents the OLS estimation of Equation 12 but using a different dependent variable. The

dependent variable is a dummy equal to one if the municipality received an non-education discretionary transfer fromthe federal government in the 12 months prior to the Prova Brasil exam in years t = 2007, 2009, 2011, 2013, 2015 ;Radio is a dummy that equals one if the municipality got their first radio station before the Prova Brasil exam in yeart = 2007, 2009, 2011, 2013, 2015 and zero if the municipality got their first radio station after the exam. In columns 1,2 and 3, I only included in the regression municipalities that got their first local radio station in the 12 months beforethe exam and in the 12 months after the exam; in columns 4, 5 and 6, the window is 18 months; and in columns 7,8 and 9, the window is 24 months. In columns 1, 4 and 7, the specification has no controls. In columns 2, 5 and8, a vector of municipal characteristics is included as controls (median income, % population urban, % populationfinished primary school, log of population, population, distance to state capital, % population has TV, % populationhas radio equipment and state fixed effects). In columns, 3, 6 and 9, the lagged dependent variable is also added as acontrol. The sample only includes municipalities where the incumbent mayor was not term-limited. Robust standarderrors in parenthesis. * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 19: Impact of community radios on Health Outcomes

Dependent Variable Mean: 17.1 0.49 0.08

Dependent Variable: Infant Mort. Prenatal Exams ≥ 7 Low Weight Births

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio -1.01 -0.68 -0.62 -0.01 0.007 0.002 -0.001 -0.001 -0.001

(0.91) (0.78) (0.79) (0.02) (0.01) (0.01) (0.002) (0.001) (0.001)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 451 451 451 451 451 451 451 451 451Notes: This table presents the OLS estimation of Equation 12. The dependent variable in columns 1, 2 and 3

is he infant mortality rate per 1000 live births in year t = 2008, 2012 ; ; in columns 4, 5 and 6 is the proportionof mothers that had more than 6 prenatal exams during their term in year t = 2008, 2012; and in columns 7, 8and 9 is the proportion of births where the newborn weighted less than 2.5 kg in year t = 2008, 2012. Radio is adummy that equals one if the municipality got their first radio station in year t− 1 and zero if the municipality gottheir first radio station in year t + 1. In columns 1, 4 and 7, the specification has no controls. In columns 2, 5 and8, a vector of municipal characteristics is included as controls (median income, % population urban, % populationfinished primary school, log of population, population, distance to state capital, % population has TV, % populationhas radio equipment). In columns, 3, 6 and 9, the lagged dependent variable is also added as a control. The sampleonly includes municipalities where the incumbent mayor was not term-limited. Robust standard errors in parenthesis.* p < 0.10, ** p < 0.05, *** p < 0.01.

63

Table 20: Impact of community radios on scores: moral hazard channel

Dependent Variable: Distance to IDEB Target

Estimation Window: 12 Months 18 Months 24 Months

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Radio 0.126∗∗ 0.112∗ 0.154∗ 0.0961∗ 0.0833 0.0747 0.0568 0.0574 0.0186

(0.0603) (0.0600) (0.0874) (0.0528) (0.0526) (0.0766) (0.0401) (0.0405) (0.0555)

Controls N Y Y N Y Y N Y Y

Lagged Dep. Var. N N Y N N Y N N Y

Observations 399 399 161 535 535 212 960 960 423Notes: This table presents the OLS estimation of Equation 12. The dependent variable is the difference between

the IDEB score and the municipality target in years t = 2007, 2011, 2015 ; Radio is a dummy that equals one if themunicipality got their first radio station before the Prova Brasil exam in year t = 2007, 2011, 2015 and zero if themunicipality got their first radio station after the exam. In columns 1, 2 and 3, I only included in the regressionmunicipalities that got their first local radio station in the 12 months before the exam and in the 12 months afterthe exam; in columns 4, 5 and 6, the window is 18 months; and in columns 7, 8 and 9, the window is 24 months. Incolumns 1, 4 and 7, the specification has no controls. In columns 2, 5 and 8, a vector of municipal characteristics isincluded as controls (median income, % population urban, % population finished primary school, log of population,population, distance to state capital, % population has TV, % population has radio equipment and state fixedeffects). In columns, 3, 6 and 9, the lagged dependent variable is also added as a control. The sample only includesmunicipalities where the incumbent mayor was not term-limited. Robust standard errors in parenthesis. * p < 0.10,** p < 0.05, *** p < 0.01.

B Additional Figures

Figure 21: Distribution of IDEB Score Distance to Target

Notes: This figure plot the histogram of the municipality level IDEB Score Distance to Target (IDEB Scoreminus IDEB target) for municipal primary schools. The sample includes all municipalities and exams between 2007and 2015.

64

Figure 22: Correlation between IDEB Scores and Median Income

-2-1

01

2N

orm

aliz

ed P

erfo

rman

ce

0 200 400 600Median Income

IDEB IDEB Minus Target

Notes: This figure illustrates the municipality level correlation between the 2007 IDEB Score and Median Incomeaccording to the 2000 Census; and the correlation between 2007 IDEB Score minus 2007 IDEB target and MedianIncome. Both IDEB Score and IDEB Score minus IDEB target were normalized to have the mean equal to zero andthe Standard Deviation equal to one. Each line corresponds to the prediction for the dependent variable from a linearregression of the dependent variable on Median Income, along with a 95% confidence interval.

Figure 23: IDEB Scores of the Municipality of Sobral

45

67

89

IDEB

Sco

re

2005 2007 2009 2011 2013 2015 2017 2019 2021Year

IDEB Score - Sobral IDEB Target - SobralMean IDEB in the State of São Paulo

65

Figure 24: Entry Timing of Community Radios

020

4060

80Fr

eque

ncy

-40 -20 0 20 40Months to Election

(a)

020

4060

8010

0Fr

eque

ncy

-40 -20 0 20 40Months to Prova Brasil

(b)

Figure 25: Geographical Distribution Balance

Notes: This map shows the geographical distribution of ”treatment” and ”control” municipalities. Municipalitiesthat got their first radio station in the 12 months prior to the 2008 or 2012 release of IDEB results are in the treatmentgroup (red circles) and municipalities that got their first radio station in the 12 months after to the 2008 or 2012municipal elections are in the control group (blue triangles).

66

Figure 26: t-statistics Distribution of Placebo Tests

Accountability Effect

Account. Within Munic.

Selection Effect

Moral Hazard Effect

Quality Index

Federal Grants-4 -2 0 2 4

t-statistic

Placebo Treatment

Notes: In Figure 26, I perform placebo tests for all the main results in the paper by shifting the estimation timewindow around the events of interest. In each line, the black dot presents the t-statistic of the coefficient of interestpresented in the results section. The blue dots correspond to estimated t-statistic of placebo coefficients from separateregressions. As illustrated by Figure 16, in lines ”Accountability Effect”, ”Accountability Effect Within Municipality” and ”Selection Effect”, I shift the window back and forth around the municipal elections. In lines ”Moral HazardEffect”, ”Quality Index” and ”Federal Grants”, I proceed the same way but moving the window around the ProvaBrasil exam. Line ”Accountability Effect”, presents the estimated coefficient γ from equation 9; line ”AccountabilityEffect Within Municipality ” presents the estimated coefficient ∆ from equation 10; line ”Selection Effect” presentsthe estimated coefficient β from equation 11; lines ”Moral Hazard Effect”, ”Quality Index” and ”Federal Grants”present the estimated coefficient β from equation 12.

C Other Counterfactuals

Table 21 shows the impact of radio in counterfactual scenarios. In the second row, I set δ′ = 0

to evaluate how much the fact that opportunistic mayors sometimes exert high effort diminishes

the ability of voters to hold politicians accountable and select better candidates. Results show

largely similar results to the original ones, because δ′ was already very low originally. In the third

row, I examine the effect of allowing voters to perfectly observe scores when the municipality gets

a radio station. The proportion of opportunistic mayors exerting high effort raise substantially

both for municipalities that got radio before the exam (δ) and for municipalities that got it after

(δ′). The probability of reelecting a mayor with a high scores also substantially increases. The

selection effect remains relatively small probably because the proportion of opportunistic mayors

exerting high effort increased to 18% . Hence, in the fourth row, I show that selection effect increase

substantially only when δ′ and σnr are equal to zero. At the same time, this generates even larger

increases in both the accountability and moral hazard effects.

67

Table 21: Counterfactuals

Selection Moral Hazard δ δ′ Account.(−1) Account.(2)

Original Estimation 0.01 0.07 0.12 0.05 -0.11 0.20

(0.005) (0.03) (0.04) (0.02) (0.06) (0.07)

δ′ = 0 0.014 0.12 0.12 0 -0.12 0.22

(0.006) (0.05) (0.05) (0.07) (0.12)

σr = 0 0.018 0.12 0.30 0.18 -0.10 0.63

(0.01) (0.06) (0.07) (0.12) (0.27) (0.38)

σr = 0 and δ′ = 0 0.06 0.30 0.30 0 -0.10 0.86

(0.05) (0.07) (0.07) (0.20) (0.37)Notes: Each row in the table corresponds to a different counterfactual estimation of the model and each column

to a different equilibrium outcome and its respective standard error. I make δ′ = 0 in the conterfactual scenariosby setting the parameter θ2 = 0. MoralHazard = E[s1|R1 = 1] − E[s1|R2 = 1] and Selection = E[s2|R2 =1]− E[s2|R3 = 1]. Accountability(x) = P (reelect|s1 = x,R2 = 1)− P (reelect|s1 = x,R3 = 1)

D Theoretical Model Details

D.1 Voter’s Problem

The representative voter has to make a single decision each period: to re-elect or not re-elect

the incumbent mayor. I can recursively define W (s1, ε,mr) as the voter’s life-time expected utility

in a municipality that acquired local media in moment 1 after observing the first term performance

of a mayor, the quality signal and the popularity shock:

W (s1, ε,mr) = s1+β max

reelect∈{0,1}E{reelect(s2+ε+βW (s1′, ε′,mr′))+(1−reelect)W (s1′, ε′,mr′)|s1, ε, r}

(14)

W (s1, ε,mr) = s1+β max

reelect∈{0,1}{reelect(π1(s1+mr)µh+(1−π1(s1+mr))µl+ε+βV)+(1−reelect)V}

(15)

Where V = E[W (s1, ε,mr)] is the expected lifetime utility of the voter in the beginning of a

two-term period and π1(s1 +mr) = P (GoodMayor|s1 +mr) is the probability that the mayor is a

good type given the quality signal observed by the voter. Finally, we can write V as:

68

V = (π + (1− π)δ)

∫ ∫ ∫W (s1, ε,m

r)φuh(s1)φε(ε)φmr(mr)ds1dmrdε

+ (1− π)(1− δ)∫ ∫ ∫

W (s1, ε,mr)φul(s1)φε(ε)φmr(mr)ds1dm

rdε (16)

• φuh() is the pdf of a Normal(uh, σs);

• φul() is the pdf of a Normal(ul, σs);

• φε() is the pdf of a Normal(µε, σε);

• φmr() is the pdf of a Normal(0, σr).

D.2 Election

Voter’s decision to reelect the incumbent mayor will depend on when local media entered the

municipality. Notice from equation 15 that, in a municipality that got local media in moments

k = 1, 2, 3, the voter will reelect the mayor if and only if:

ε > (1− β)V− (πk(q)µh + (1− πk(q))µl) (17)

With:

π1(q) =πφuhr (q)

(π + (1− π)δ)φuhr (q) + (1− π)(1− δ)φulr (q)(18)

π2(q) =πφuhr (q)

(π + (1− π)δ′)φuhr (q) + (1− π)(1− δ′)φulr (q)(19)

π3(q) =πφuhnr (q)

(π + (1− π)δ′)φuhnr (q) + (1− π)(1− δ′)φulnr(q)(20)

• φuhr () is the pdf of a Normal(uh,√

(σs)2 + (σr)2);

• φulr () is the pdf of a Normal(ul,√

(σs)2 + (σr)2);

• φuhnr () is the pdf of a Normal(uh,√

(σs)2 + (σnr)2);

• φulnr() is the pdf of a Normal(ul,√

(σs)2 + (σnr)2)

We can now obtain the probability of reelection conditional on the quality signal q for k = 1, 2, 3:

ψk(q) = 1− Φe((1− β)V− (πk(q)µh + (1− πk(q))µl)) (21)

69

• Φe() is the cdf of a φe()

We can now obtain the probability of reelection conditional on s1 scores for k = 1, 2:

ωk(s1) =

∫1− Φe((1− β)V− (πk(s1 +mr)µh + (1− πk(s1 +mr))µl))φmr(mr)dmr (22)

ω3(s1) =

∫1− Φe((1− β)V− (π3(s1 +mnr)µh + (1− π3(s1 +mnr))µl))φmnr(mnr)dmnr (23)

• φmnr() is the pdf of a Normal(0, σnr);

Finally, we can recover the probabilities of reelection conditional upon effort for k=1,2,3 by:

ρkh =

∫ωk(s1)φuh(s1)ds1 (24)

ρkl =

∫ωk(s1)φul(s1)ds1 (25)

D.3 Proof of Proposition 1

This proof is adapted from Aruoba et al. (2019). First, note that πk(q) is strictly increasing in

the quality signal q. Since ρkh, ρkl ∈ (0, 1) in any equilibrium, there is always a positive probability

that an opportunistic mayors will exert low effort (by equations 1 and 2). Moreover the distribution

of q when a mayor exerts high effort first-order stochastically dominate the distribution of q when

a mayor exerts low effort. Hence, a higher value of q must raise the posterior probability that a

mayor is good. This can also be seen by taking the derivative of πk(q) for k = 1, 2, 3 with respect

to q.

The solution to the voter’s problem given by inequality 4 yields a critical value for the popularity

shock ε for any quality signal q. Given the monotonicity of πk(q), there exists an unique cut off

value of ε for each q below which the voter does not reelect the incumbent and above which he does.

This voting rule generates the reelection probability ψk(q) (defined in equation 21) which is unique

and strictly increasing in q. Hence, the reelection probabilities ρkh, ρkl (obtained by integrating over

the probability distribution of q given an effort level) are unique and belong to the (0, 1) interval.

Moreover, ρkh > ρkl by first order stochastic dominance and the difference ρkh − ρkl is unique and

belong to the (0, 1) interval.

Now, let’s consider the mayors’ problem. The decision problem of an opportunistic mayor in

equations 1 and 2 implies that his effort decision in the first term can be described by a single

70

cutoff point c?1 = ρ1h − ρ1l ∈ (0, 1) if local media entered in moment 1 and c?2 = θ2θ2+θ3

(ρ2h − ρ2l ) +θ3

θ2+θ3(ρ3h − ρ3l ) ∈ (0, 1) if local media entered in moments 2 or 3, where c?1 and c?2 are unique given

that ρkh − ρkl is unique for k = 1, 2, 3.

D.4 Computational Details

Following Aruoba et al. (2019), I numerically solve for the Perfect Bayesian Equilibrium of the

game. Notice that if V, δ, δ′ are known, the voting rule in equation 17 defines whether an incumbent

mayor is reelected or not for any pair q, ε randomly drawn. Hence, I can recover the probability of

reelections conditional on effort ρkh, ρkl for k = 1, 2, 3 by integrating over q, ε. Finally, once I have

ρkh, ρkl for k = 1, 2, 3, I can recover c?1 = ρ1h − ρ1l and c?2 = θ2

θ2+θ3(ρ2h − ρ2l ) + θ3

θ2+θ3(ρ3h − ρ3l ). Hence,

solving for the equilibrium amounts to solving the following nonlinear system of three equations

and three unknowns (V, δ, δ′):

δ = ρ1h − ρ1l (26)

δ′ = θ2θ2 + θ3

(ρ2h − ρ2l ) +θ3

θ2 + θ3(ρ3h − ρ3l ) (27)

V = (π + (1− π)δ)

∫ ∫ ∫W (s1, ε,m

r)φuh(s1)φε(ε)φmr(mr)ds1dmrdε

+ (1− π)(1− δ)∫ ∫ ∫

W (s1, ε,mr)φul(s1)φε(ε)φmr(mr)ds1dm

rdε (28)

Further details on how to numerically evaluate the integrals on the right side of the equations

above can be found in the online appendix of Aruoba et al. (2019).

E Likelihood Function Expression

L =n∏i=1

(θ1p winreelecti1i p lose

(1−reelecti)1i )R1i(θ2p win

reelecti2i p lose

(1−reelecti)2i )R2i(θ3p win

reelecti3i p lose

(1−reelecti)3i )R3i

(29)

Where p win1i and p lose1i are defined as:

p win1i = πφuh(s1i)ω1(s1i)φuh(s2i) + (1− π)(1− δ)φul(s1i)ω1(s1i)φul(s2i)+

(1− π)δφuh(s1i)ω1(s1i)φul(s2i) (30)

71

p lose1i = πφuh(s1i)(1− ω1(s1i)) + (1− π)(1− δ)φul(s1i)(1− ω1(s1i))+

(1− π)δφuh(s1i)(1− ω1(s1i)) (31)

And p winki and p loseki for k = 2, 3 are defined as:

p winki = πφuh(s1i)ωk(s1i)φuh(s2i) + (1− π)(1− δ′)φul(s1i)ωk(s1i)φul(s2i)+

(1− π)δ′φuh(s1i)ωk(s1i)φul(s2i) (32)

p loseki = πφuh(s1i)(1− ωk(s1i)) + (1− π)(1− δ′)φul(s1i)(1− ωk(s1i))+

(1− π)δ′φuh(s1i)(1− ωk(s1i)) (33)

F Persuasion Rate

Following DellaVigna and Gentzkow (2010), I calculate the persuation rate using the following

formula:

100× γt − γcet − ec

1

1− γ0= 100× 0.053× 0.6

0.4

1

1− 0.53u 15 (34)

γt − γc = 0.053× 0.6 is the estimated interaction coefficient in column 3 of Table 14 times one

standard deviation of the Distance to Target variable (0.6).

et − ec = 0.4 is the approximate audience of community radio stations in municipalities with

one radio station. Notice that I am implicitly assuming that in municipalities with no community

radio zero voters hear about the IDEB results.

γ0 = 0.53 is the incumbents vote share in municipalities that did not have community radio

before the election.

72