Do Higher Salaries Lead to Higher Performance? Evidence fromState Politicians∗
Mitchell Hoffman†

Elizabeth Lyons‡

July 2, 2014

Abstract
We study the impact of politician salary on electoral competitiveness and political performanceusing new data on US state legislators and governors over the last sixty years. Higher salary is
associated with statistically significant, but economically small, increases in electoral competitiveness and legislative productivity, the latter measured with bill-passing and missed roll call votes.
Salary has no effect on politician quality, corruption, or fiscal policy. To address the possible
concern of salary changes being correlated with politicians’ outside options, we implement a spatial discontinuity design using legislative district pairs straddling state borders and find modest
impacts of salary, similar as in our other research designs (fixed-effects, selection-on-observables,
and a narrative analysis). The impact of politician salary is weakest (i.e., totally absent) in stateswith strong political parties, suggesting that parties may reduce entry. Despite small impacts on
performance, higher salary is significantly correlated with behavior on another margin, namely
time-use; time-use data suggests that politicians in higher wage states spend greater time on
fund-raising and on constituent services, but no more time on legislative activities. Our results
lend caution to common claims that increasing politician salary would significantly increase thequality of US state government.

JEL Classifications: H70, D72, D78, M52, J33
Keywords: Politician salary; Productivity; Moral hazard; Political parties∗

We thank Alan Auerbach, Varanya Chaubey, Dan Elfenbein, Rui de Figueiredo, Fred Finan, Yosh Halberstam,
Nikolaj Harmon, Deepak Hegde, John Morgan, Gerard Roland, Michel Serafinelli, Nathan Yang, and various seminar
participants for helpful comments. We are grateful to the staff at the library at UC Berkeley and the University ofToronto for help with archival material. We are grateful to Kathleen Bratton, Ernesto Dal Bo, Ray Fisman, Enrico
Moretti, and James Snyder for sharing data with us. We thank a number of people from state offices, including
Jessica Eisenbrey, Matthew Fedorchuk, John D. Moran, Simon Powell, Clifford Strachan, and Sara VanderFeltz. Ania
Jaroszewicz, Nicholas Roth, Kristjan Sigurdson, and Trent Turnbull provided outstanding research assistance. Financial
support from the Kauffman Foundation, the National Science Foundation, and the Social Science and Humanities
Research Council of Canada is gratefully acknowledged.
†
University of Toronto Rotman School of Management; mitchell.hoffman@rotman.utoronto.ca‡
University of California, San Diego, IR/PS; lizlyons@ucsd.edu

11Introduction

How do political institutions affect government performance? Designing institutions that promotetalented people to run for office and to govern well is a central issue in political economy (Barro,1973). A large literature focuses on understanding how incentives regarding re-election (e.g., term
limits) affect politician effort (e.g., Besley and Case, 1995; List and Sturm, 2006). Another potentially
important, but much less studied institutional feature is politician salary. Although politicians, likemost workers, generally do not receive explicit pay-for-performance contracts, higher salaries may
reduce moral hazard, both by incentivizing effort due to fear of losing one’s job (Becker and Stigler,
1974; Shapiro and Stiglitz, 1984) and by drawing in higher ability job-seekers (Weiss, 1980). In thispaper, we investigate how politician salary affects the productivity and quality of politicians, as wellas the competitiveness of political markets.
In addition to importance for theory, how salary affects political performance is important forpolicymakers. In the US, this is particularly true in state politics, where there is wide variationin salaries, and there is significant concern about low performance by politicians. In discussion
about the quality of US state government, a common concern is that politician salaries are toolow. For example, the Council of State Governments, a national organization representing state
governments, often argues that low legislative compensation has deleterious consequences, statingthat “If legislators are not paid adequately, then candidates are drawn from a smaller pool. ...You
can’t expect to attract good candidates with pay that is lower when compared to other jobs andprofessions.”1
We study the impact of politician salary on election competitiveness and politician performanceby looking at two different types of politicians—governors and legislators—both of which performcritical roles but may respond differently to incentives. We study how salaries affect the number andtypes of individuals who run for election, their quality, and their effort and performance if elected.
We do so exploiting 60 years of new US data. We take advantage of the large variation in politiciansalary across states and over time, and employing fixed effect, selection on observables, and spatial
discontinuity research designs we find that salary has statistically significant, but economically verysmall positive impacts on election and performance outcomes. Interestingly, we find that strong
political parties may be acting as a barrier to the impact of salary on politician behavior.
We draw on the citizen candidate model of Besley and Coate (1997) and Osborne and Slivinski(1996) in a conceptual framework of how politician salary may affect performance. In this framework,
citizens choose whether to run for office by weighing the gains from office against their outside option.
Higher ability workers have higher outside options, so increasing salary makes higher ability workers
1
Quotation from Keon Chi, editor-in-chief of the Council of State Government’s Book of the States. Quoted in“Legislators’ pay falling behind” by Eric Kelderman, February 13, 2007, article on Stateline.org. A more recent article
from July 23, 2013 in USA Today, “Most governors paid less than state execs,” argues that state governors may besignificantly under-paid relative to the private sector and other state officials, and thus that governorships may not
be drawing in the best people. General popular discussion on whether politicians should be paid more is also very
common. For example, business magnate Richard Branson, founder of Virgin Airlines, in a July 13, 2013 blog post
entitled “Should politicians be paid more?” argues that increasing pay is critical for attracting high-quality politiciansin the context of recent discussion about whether to raise pay for British members of parliament.

2

want to work in politics instead of as lawyers and businesspeople. Thus, salary will increase thenumber of candidates running for office. In addition, since a politician’s chance of getting re-elected
depends on them exerting effort to provide public goods, higher salaries mean that politicians become
more concerned with getting re-elected, and thus exert more effort. Theory by itself, however,cannot determine the likely magnitude of these effects, nor assess how these magnitudes comparewith alternative countervailing forces.
We study the predictions of the conceptual framework using the large variation across statesand over time in salary for state legislator and governors. Our analysis focuses more on legislators, forwhom we have richer data, but have similar findings for both legislators and governors. Specifically,
we analyze the impact of politician salary on election outcomes, including the number of candidates
and re-election rates; politician selection, measured by quality proxies such as politician schoolingand diversity; and political effort and performance, measured by bill-passing, fiscal policy, and rollcall voting. For legislators, we find that increasing salary by 50% is associated with a one percentage
point increase in the probability an election is contested and increases the number of candidates inthe election by 0.025. It increases by share of bills approved by the legislature by 0.7 percentage
points and decreases the probability of a missed roll call vote by 1 percentage point. These effects
are statistically different from zero, precisely estimated, and are very small compared with variation
over time or across states. We find no connection between legislator salary and having more diverseor educated legislators. For governors, we find that salary appears to have almost no impact onrunning for office, on selection, or on performance.
We lay out three main concerns related to whether salary could be correlated with unobservables and address them in turn, including by using a spatial discontinuity analysis. Absent randomvariation or a good instrument for politician salary in the US, that salary could be correlated withunobservables is the central concern for our study. First, states may be likely to increase salaries
when times are (unobservedly) economically “good” so as to match politicians’ outside options, which
could potentially bias, for example, the relationship between salary and electoral competitiveness.
Although we control for state GDP, what is relevant for potential political candidates may not be theoverall labor market, but rather, say, the labor market for lawyers. While California and Nevada may
have very different labor markets for lawyers in a given year, it may be that the market in a districtin eastern California and the market in a bordering district in western Nevada are more similar. To
address the possibility that increases in salary could correlate with unobserved local labor market
conditions such as the market for lawyers, we use a spatial discontinuity design, comparing legislative
districts on either side of state borders. Through our spatial discontinuity design, we can analyze
the impact of salary on electoral competitiveness, comparing only between bordering districts in agiven year, and we find very similar results as in the baseline.
Second, as we describe in Section 2, pay is set by the upper legislative house for governors
and by one of several means (compensation committee, statute, or the legislators themselves) for
legislators. One may worry that pay could be set to co-vary with unobservable information about
performance or with unobserved dimensions of politician quality. Salaries may be increased whenpoliticians expect to do well, though this would tend to upward bias the results away from our finding3

of small impacts of salary on performance. Alternatively, unobservedly higher quality politicians may
be averse to raising salaries. However, based on observable measures of quality, we find no evidence
that higher quality politicians are less likely to have their salaries raised. We also find that results are
similar when we restrict the sample to states where politicians have little direct control over salary.
Third, it is possible politician salaries could be correlated with other institutions in a state.
Although we collected a large amount of data on changes in state legislative institutions over time,
it is certainly possible that changes in salary may correlate with unobserved changes in institutions.
To address this, we perform a selection-on-observables research strategy, where we gradually control
for observable aspects of legislature professionalism. Our results are highly robust to this strategy,
suggesting that bias is limited.
In addition, in order to assuage any lingering concerns related to these issues, we implementa narrative analysis. We use the historical record to identify a set of 17 salary changes or nonchanges which we believe are plausibly exogenous for our analysis (e.g., whether the salary change
went through or not was determined by a close vote of a salary commission or the state legislature).
Although a limitation of the narrative analysis is that the sample is much smaller, we find similar
results as in our other analyses.
We find support for two interesting mechanisms which could explain the modest impact ofpolitician salary: (1) That politicians are responding on other effort margins and (2) That strong
political parties act as a barrier to electoral competition. We also fail to find support for othermechanisms. For one, politician salaries in many states are low relative to other corporate andprofessional jobs, and many politicians are independently wealthy. It could simply be that salaries are
too small to affect politician incomes or behavior. We show, however, that legislator salaries comprisea significant portion of household earnings and have a significant impact on whether legislators workoutside of politics. We also fail to find a correlation between legislator salary and legislators’ desire
to start a carry in lobbying following their time in office. In support of the hypothesis that politicians
respond on other effort margins, unique data on legislator time use show that when politicians receive
higher salaries, they spend more time on fund-raising and on constituent services, as opposed to on
developing legislation. This evidence seems inconsistent with an explanation based on politicians
have strong intrinsic motivations, where they exert effort because of a “calling” for politics (Weber,2004) and not because of a desire to get re-elected.
We provide one of the first analyses (to our knowledge) of the impact of politician salary in
the United States.2 We suspect this absence may be due to data limitations. There is no central
database on politician salary, and a significant portion of our data is hand-collected. In addition,
we assemble data on electoral competitiveness, partisan electoral advantage, politician quality (asmeasured through politician schooling and other characteristics), politician productivity (as measured
2

For the US, we know of very little prior empirical work on the impact of politician salary. In political science,
we have found some papers that have included salary in some analyses related to state legislatures (Berkman, 1994;
Fiorina, 1994; Squire, 1997), but the focus of these papers is not on the impact of politician salary. In economics, Besley(2004) shows that higher salary for governors is associated with greater ideological congruence between governors andtheir electorate. Although not directly related to our paper, two papers in political science, Groseclose and Krehbiel(1994) and Hall and Van Houweling (1995), show that US congressmen respond to financial incentives to retire.

4

by bill-passing), shirking (as measured by missed roll call votes), outside labor supply, and politician
time use. In economics, the only work on US politician salary we are aware of is by Diermeier
et al. (2005) and Keane and Merlo (2010). These two papers estimate structural models of careerdecisions for US congressmen. Their counterfactual simulations include an investigation of howchanges in congressional wages affect congressional career decision-making. By analyzing the impact
of politician salary using wage variation across states, our analysis complements their counterfactual
results. Given that there is very little work on US politician salary, some of the most related workto our paper is (arguably) that on other institutions for US politicians, such as term limits (Besley
and Case, 1995; List and Sturm, 2006), term length (e.g., Kalt and Zupan, 1990; Titiunik, 2014),
and district size (e.g., Baqir, 2002; Gilligan and Matsusaka, 1995).
Our study suggests that the impact of political salary may be context dependent. In economic theory, there is substantial work analyzing the impact of politician salary (e.g., Besley, 2004;
Caselli and Morelli, 2004; Mattozzi and Merlo, 2008; Messner and Polborn, 2004; Poutvaara and
Takalo, 2007); as we discuss in Section 3, this work points out that the impact of increasing salaryis not always straightforward. Compared to several recent empirical papers finding predominantly
large impacts of politician salary in other countries, the impacts we estimate are an order of magnitude smaller. Ferraz and Finan (2010) and Gagliarducci and Nannicini (2010) study the impact ofpolitician salary for Brazilian municipal legislators and Italian mayors, respectively. For Brazilian
municipal legislators and Italian mayors, salaries change discontinuously with city population, allowing for regression discontinuity estimates of the impact of politician salary. Fisman et al. (2013) andMocan and Altindag (2013) study politician performance in the European Union, exploiting a recent
pay equalization policy that significantly increased salaries for politicians from certain countries. Kotakorpi and Poutvaara (2011) analyze a recent pay increase for members of parliament in Finland.
These papers tend to find large impacts on competitiveness, selection, and performance.3 While it
is of course possible that politicians in the US may simply be “different” than politicians in othercountries, or that differences in methods between the papers are important, we believe that it may
be useful to consider differences in political institutions. For instance, in the Brazilian municipalities
studied by Ferraz and Finan (2010), politics is highly decentralized and political parties are very
weak. In contrast, the two main political parties in the United States are very strong.
Section 2 provides background on compensation and other institutions for US state legislatorsand governors. Section 3 provides a conceptual framework for how salary affects political behavior
and describes the data. Section 4 shows the estimation results. Section 5 interprets our findings andconsiders possible explanations. Section 6 concludes.
3
For electoral competitiveness, Ferraz and Finan (2010) and Fisman et al. (2013) find large positive impacts onwhether politicians seek re-election. For selection, Ferraz and Finan (2010), Gagliarducci and Nannicini (2010), andKotakorpi and Poutvaara (2011) find large positive impacts on politician quality, whereas Fisman et al. (2013) findlarge negative impacts on politician quality. (For Kotakorpi and Poutvaara (2011), this is observed only for female
parliamentarians.) For political performance, Ferraz and Finan (2010) and Gagliarducci and Nannicini (2010) findlarge positive impacts on legislative productivity, whereas Mocan and Altindag (2013) and Fisman et al. (2013) findlarge negative effects or zero effect, depending on how the standard errors are clustered. We find modest impacts
across electoral competitiveness, selection, and performance. It is worth pointing out that some of these papers alsofind smaller effects along some dimensions of behavior.

52Institutional Context

In this section, we give a brief overview of the key factors that characterize the pay structure andresponsibilities of US governors and legislators in order to give context to our data, empirical strategies, and the implications of our analysis. There is substantial variation in salaries across statesand over time. Understanding the sources and implications of these differences is critical for careful
investigation of our research question. While much more can be said about the institutional context
beyond what is included in this section, our goal is to limit the focus to the points that are most
relevant for our setting.
We study the impact of salary both for state legislators and for governors because both roles
are important for the successful functioning of state governments. The two roles also may be affectedby salary differently and they may have different implications for the effect of salary on outcomes
more generally. In particular, the number of legislators per state ranges from about 50 to 400 whereas
each state has only one governor. Legislators are responsible for state legislation and, importantly,
for distributing state funds (Rehavi, 2007). Governors are chief administrators of the state and also
play a key role in advancing legislation (e.g., Bernick, 1979).
While nominal salaries for legislators averaged across all states have been increasing at a steadyclip, as seen in Figure 1, there is substantial variation both within and across states. As seen in
Figure 2, in some states, salaries have increased a great deal over time, whereas in others, salary hasremained somewhat flat. In states where salary has increased substantially over the sample frame,salary has been increased in large irregular discontinuous jumps, though in other states the increases
have been more gradual.
How pay is determined for governors and legislators varies between the positions and acrossstates, and has important implications for our research design. For governors, pay is determined by astate’s upper assembly, and the amount is frequently adjusted.4 For legislators, how pay is determinedvaries across states. In 31% of the contiguous 48 states, pay for legislatures is determined by a statecompensation commission. Commissions set salaries based on a number of criteria, including thepay of other government officials and bureaucrats. As of 1993, in a relatively small number of thecontiguous states, 14.5%, pay is set by the constitution. New Mexico is an extreme example of thiswhere the state constitution specifies that legislators will receive no pay (except for some expenses.)
In states where pay is constitutionally determined, it is changed only if the constitution is amended.
Finally, in the remainder of the states, legislator pay is set by statute, either a law outside theconstitution, or the legislators essentially set their own pay by passing compensation bills. In manycases, legislators vote on their own pay, but the increases only take effect for the next term.5 We
later exploit this variation in how pay is set for legislators to assuage concerns that endogenous wage
setting is driving our results.
What determines salary changes over time for legislators and governors? First, for legislators,
salaries have increased as legislatures have become more “professionalized.” In the past, many legis4

For a rich analysis of how compensation is set for governors, see Di Tella and Fisman (2004).
Per the 27th amendment to the US constitution passed in 1992, this is the method use by the US congress insetting its compensation.
5

6

latures would meet every other year or be in session for only several months. In the mid 20th century,
however, a push began where legislatures began a shift toward more responsibilities. As of 2010,
only five legislatures meet every other year: Montana, Nevada, North Dakota, Oregon, and Texas.
The National Conference of State Legislatures classifies states into categories based on whether theirlegislatures are full-time or part-time. Ten states are categorized as full-time or almost full-time, andthe rest are considered as part-time or almost part-time (National Conference of State Legislatures,2009). In general, full-time legislators tend to receive higher pay. In all our analysis of state legislators, we control for length of legislative sessions in days, probably the most important measure ofsession length, and we also explore other measures of professionalization such as a legislator’s numberof staff.6
Second, as documented by Di Tella and Fisman (2004), salaries have often been increased in
response to economic growth. As the economy improves and there are greater tax revenues at boththe national and state level, salaries for politicians tend to increase. To account for this in our
regressions, we always control for state GDP.
Third, and most important for our analysis, even conditional on professionalization and economic growth, as well other differences across states, there are a substantial number of large, abrupt,
and seemingly irregular salary changes in the historical record. As an example for legislator compensation, consider the nearby states of Michigan and Minnesota. In the 1980s and 1990s, legislator
salaries were higher in Michigan than in Minnesota, but both were growing at a slow, steady rate.
In 2000, Michigan legislators made $55,054 and Minnesota legislators made $31,440. However, due
to a vote of the Michigan Compensation Commission, salaries were increased roughly 40% from
$55,054 to $77,400, where it remained at a roughly similar level throughout the 2000s. In contrast,
the Minnesota salary stayed flat during this time period. Or in another example, legislator salary in
Oklahoma increased by 60% in 1990, from $20,000 to $32,000, changes which were not observed in
nearby states. There are also numerous example of seemingly irregular changes in governor salary.7
Looking deeper into the historical record, these seemingly irregular changes appear to stemfrom a number of different sources. The membership of compensation committees changes overtime, and their votes on proposed changes are frequently divided. When salary changes are passedby the legislature itself, votes are sometimes close and sometimes divided between the upper andlower house. In a few instances that we have located, salary increases that have been passed bycompensation commissions or legislatures have later been undone by courts or governor vetoes. We
believe that such changes (and non-changes) are likely to be driven by forces external to those affectelectoral competitiveness and productivity. In Section 4.7, we use these events to create a sample ofsalary change events which are likely to be exogenous for our regressions.
Other factors that may contribute to both politician salary and election outcomes are whether
6

Governorships have historically been full-time jobs with professional staff, so the issue of professionalization issomewhat less relevant than for legislators.
7
For example, in Tennessee, governor salary was increased to $160,000 in 2008, up from $85,000 where it had been
for over 15 years. Bordering states made no changes or relatively small changes to governor salaries. In a much older
example, governor salary in Alabama shot up in the early 1950s, while surrounding states again kept governor salaries
relatively constant.

7

or not a seat is term-limited and the length of a term. Some states have term-limited seats forlegislators and governors and some do not. In addition, in some states, legislator terms are four
years and in some they are two years. The form of legislative compensation also differs across states.
In some states, legislators are paid a yearly annual salary. For example, in California legislators made$125,000 per year in 2008. This amount is paid irrespective of the number of days the legislature
meets. In other states, legislators are paid a daily wage.8
Conditioning on important institutional features, there remains a great deal of variation inlegislator and governors. In a regression of real salary on session length, frequency of meeting, term
length, and term limits, the R2 is only 0.213. To demonstrate this variation, Table 1 shows the realsalary in two different years for legislators and governors. For legislators, pay in some states has
been persistently very low. Legislator pay over time is shown graphically in Figure 1 and nominal
legislator salaries over time by state are shown in Appendix D. Appendix D also shows real legislator
salaries over time by state.
The differences across states in session lengths, meeting frequencies, modes of pay determination and pay frequency are useful for testing mechanisms for our results but they also imply that
we need to be especially careful in our analysis of the effect of salary on electoral outcomes in the
US. Whenever possible, use state and year fixed effects and controls for session length, frequencyof meetings, term-limited seats and length of terms to control for this variation.9 Our estimation
methodology is described in more detail in Sections 3 and 4.

3

Conceptual Framework and Data

This section provides theoretical motivation for our analysis and information on our data. We begin
by laying out a conceptual framework on how a politician’s salary would affect their performanceand electoral competitiveness. A formal model accompanying the discussion is given in Appendix C.
Following this framework, we describe the sources and structure of our data, and delineate how wemeasure our key variables.

3.1

Conceptual Framework

Paying politicians a higher salary serves to incentivize them to exert more effort once they are in
office. As in a standard efficiency wage model, the cost of shirking for politicians is the cost of getting
fired (or in their case, not re-elected). Raising the salary increases the return to getting re-elected,
even when a significant part of being in office may be benefits outside of the formal salary.10
In addition, paying politicians a higher salary may affect the quality of people who are willingto run for office. Suppose that perspective politicians differ on a single dimension of quality, which
could represent their ability to get legislation done or to act in the interest of their constituents. It is
8

In Vermont, legislators receive a weekly wage.
We do not have observations over time for some of our dependent variables, including our measure of politician
effort. For these cases, we do not include state and year fixed effects.
10Such benefits include utility from public service (Weber, 2004), the opportunity to be a lobbyist after politics(Blanes i Vidal et al., 2012), or the opportunity to accumulate political rents (Querubin and Snyder, 2011).
98

likely that higher ability politicians also have higher outside options (Gagliarducci et al., 2010). Thus,by raising the return for running for office, a higher salary could draw in higher quality politicians.
Although this logic may seem relatively straightforward, it is not theoretically obvious thatraising salary should increase politician quality. Mattozzi and Merlo (2008) present a dynamic
equilibrium model of politician careers. If higher quality politicians receive higher financial payoffs
after serving in office, increasing the wage may reduce politician quality, since doing so reduces thecomparative advantage of being a high quality politician. It is thus primarily an empirical question
whether politician salary will increase political effort and politician quality.
By increasing politician salary, more citizen-candidates may be willing to run for office, soelections have more candidates and are more likely to be contested. As emphasized in Ferraz and
Finan (2010), impacts on re-elections may be more theoretically ambiguous. On the one hand, byspurring more candidates and/or higher quality candidates to run, re-election rates for incumbents
should decrease. On the other hand, increasing salary increases the value of a seat for incumbents,
so they may exert more effect in providing public goods and/or other efforts to get re-elected.
In Appendix C, we also address the issue of political parties. As described in Mayhew’s (1986)
seminal political science book on parties in US politics, political parties have often play a central
role in state politics, but the role has differed by state. In “strong party” states such as New York,famous for the Tammany Hall political machine, or Indiana, with strong local party organizations
across the state, political parties traditionally have had a major say in which candidate is chosen to
run for office, as well as the interests of politicians once they enter office. However, in “weak party”
states like Massachusetts, Michigan, and California, politics has been much more individualistic.
To formalize how political parties interact with the impact of politician salary, we conceptualize
a strong political party as a tax that politicians pay. When the benefits of public office increase, it is
not the case that anyone can receive them. Rather, only those with tight connections to one of the
major political parties are likely to have a reasonable chance of running for office and winning.11

3.2

Data Sources

Our paper combines data from several sources, and includes a substantial portion of hand-collected
data. A complete list of data sources is given in Appendix B. We discuss the most important elements
here, including how they relate to our identification strategies.
Salaries. A sizeable portion of the data was hand-collected from the Book of the States,
a periodical providing extensive information about policies and programs in different states. The
most important data here are the politician salary data.12 As in Di Tella and Fisman (2004), we
ignore non-salary benefits that politicians receive (e.g., living per diems, transportation expenses,etc). Governors all receive an annual salary. For legislators, we create a single annual salary figure11
We note that this is not the only way by which parties could affect electoral competition and performance. For
example, Mayhew (2008) describes how parties may make it easier for incumbents to get re-elected via access to theresources of political machines, helping with canvassing and voter outreach.
12
We hand-collected legislator salary data for 1967 to 2008. Gubernatorial salary data were collected for 1950-1994
by Di Tella and Fisman (2004) and were graciously provided by Ray Fisman. We hand-collected gubernatorial salarydata for 1995 - 2008.

9for each state by annualizing the relevant time frame. Salaries that are given for biennial legislative
sessions are divided by two to obtain an annual salary figure. Daily salaries are multiplied by theaverage number of days a legislature is in session over time. For legislators, salary data are from1967 to 2008. For governors, salary data are from 1950 - 2008. We use real salaries deflating with
the national CPI; in Section 4.6, we discuss how we obtain similar results deflating with separate
state level price indices over time.
Other Characteristics of Legislatures and Legislative Productivity. From the Bookof the States, we also hand-collected data on methods used to set salaries, session length over time,bill-passing over time, and on legislative support staff over time. We use the data on methods ofcompensation to help assuage concerns about endogenous wage-setting, examining how the impact ofpolitician salary varies by method of compensation. Session length is an important control variable.
Bill-passing is a standard measure of legislative productivity. We use data on legislative support staff
as a control, one that is particularly useful in proxying a legislature’s degree of professionalisation
over time.
Border Districts. To implement our border discontinuity design, we obtained data on electoral border districts from the website of the US census. The website provides coordinates for allstate lower-house and upper-house districts, which were then read into ArcGIS. Using ArcGIS, a map
was created of all districts which lay on a state boundary, based on the 2006 cartographic boundary
files. These districts were then paired with the legislative districts bordering these districts in otherstates.
Politician Characteristics. To examine the impact of salary on political selection, we gathered data on politician characteristics. Data on legislator characteristics were kindly provided by
Kathleen Bratton and are an extension of the characteristics given in Bratton and Haynie (1999).
We hand-collected data on gubernatorial primary candidates using Google searches. Full details anddescription are provided in Appendix B.5.
Political Parties. To measure the strength of political parties, we use the measures constructed by the political scientist David Mayhew, first given in Mayhew (1986). His work performs a
detailed state-by-state analysis on whether political parties have historically been powerful, focusing
on the degree to which local politics are run by organized party machines. All states are given a‘Total Party Organization’ score from 1 to 5, and states are divided into Organization States andNon-Organization states. For our analysis, we focus on a simple binary comparison of strong andweak party states. Following the discussion in Mayhew (1986), strong party states are ones with a
Total Party Organization score of 4 or 5, whereas weak party states have a Total Party Organization
score of 1, 2, or 3. This is also the classification system used by Primo and Snyder (2010).

4

Results

We begin this section by describing our estimation equation and follow by presenting our estimation
results. The first set of results are on the impact of salary on electoral competitiveness, measured as

10the likelihood that an election is contested, the number of candidates running for office, the likelihoodthat the incumbent wins the election, and the margin of victory. We find an economically small but
statistically significant positive relationship between politician salary and election competitiveness.
We run several robustness checks on these results to try and reduce concerns associated with theendogeneity of our findings. We follow this by presenting our results on the link between salary andpolitician characteristics which suggest a small and statistically insignificant relationship between
the two. Subsequently, we discuss our estimates of the relationship between salary and bill-passing
and between salary and state fiscal policy (i.e. taxes and spending). We then turn to the relationshipbetween salary and roll-call voting, a measure of politician shirking, and find a small, statisticallysignificant, negative relationship. The rest of the section is spent addressing additional threats to
identification.
Our baseline specification for the empirical analysis is a fixed effects model:
yest = α0 + α1 wst + Xst β + fs + ft + est(1)

where yest is an outcome variable for election e in state s in year t; wst is the salary in state s inyear t; Xst is a vector of covariates; fs and ft are state and year fixed effects, respectively; andest is an error.13 We estimate equations of this form for both state legislators and governors. The
outcome variable y will include electoral competitiveness; candidate quality; legislative productivity;
shirking; public goods provision; measures of outside-of-politics labor supply; and time allocation
toward law-making, constituent services, and fundraising. We cluster standard errors at the statelevel (Bertrand et al., 2004).

4.1

Effects on Electoral Competitiveness and Partisan Advantage

Legislators. Table 3 shows OLS results of the impact of salary on different measures of electoralcompetitiveness, showing mostly modest impacts. The effect of Log Salary on an election being
contested and on the number of candidates is positive and statistically significant, though the effectis small. The coefficient on 0.025 in column 1 means that a 100% increase in the salary is roughly
associated with a 2.5 percentage point increase in the chance that the election is contested (73% ofelections are contested overall).14 A 100% increase in salary is also associated with an increase in
the number of candidates by 0.05 (the average number of candidates is 1.93). In addition, a higher
salary for politicians is associated with a smaller margin of victory and a lower chance the incumbent
is re-elected, but the effects are not statistically significant.15
13

Our initial estimates use fixed effects for states instead of for electoral districts. Estimating using electoral districts
fixed effects is somewhat challenging due to the fact that electoral districts change over time due to re-distributing.
It is not conceptually difficult, however, to use electoral district-year fixed effects as we do later on in our analysis ofelectoral districts straddling state boundaries.
14
Interpreting log changes in percentage changes is imperfect for larger changes. Given that log(2) is approximately
0.7, we are actually finding that a 70% increase in salary is associated with a 2.5 percentage point increase in the
chance that the election is contested. These numbers are still an order of magnitude below what others have found.
15
The interpretation of results related to margin of victory and whether the incumbent is re-elected are less clear thanthose related to number of candidates. On one hand, non-competitive elections are often associated with incumbents
who are repeatedly re-elected by wide margins. On the other hand, by working hard and providing high quality
governance, incumbents may make themselves more likely to be re-elected and to win by a larger margin.

11

Our estimates related to number of candidates are precise, and are small in comparison to
those in other countries and to the effect of other variables. For Brazil, Ferraz and Finan (2010)
find that an increase of 100% in politician salary is associated with 2.7 more candidates per election(where the baseline number of candidates per seat is 6.1). For the European Union, Fisman et al.
(2013) find that a 100% increase in Member of the European Parliament (MEP) salary increases thelikelihood of running for re-election by 23 percentage points and increases the number of parties thatfield a candidate by a economically large amount.
Panels B and C of Table 3 separately examine the effects of politician salary for upper and lower
house elections. Politician salary only has a statistically significant effect in lower house elections.
This could occur for several reasons. For example, political parties may exercise more control overupper house election, and better be able to regulate which candidates run. In addition, there may
be greater non-salary returns for running for the upper house of a state legislature compared to thelower house; thus, the calculus for citizens of whether to run or not hinges less on the official salary.
Three other results in Table 3 are also of interest. First, column 5 shows that increased salaryis associated with a greater probability that Democrats will win the election. This result may reflectthat Republican candidates are often businesspeople with significant independent wealth or income,whereas Democratic candidates are often lawyers or public servants who may be more responsive
to political salary in choosing whether to run for office. Indeed, Table D1 shows that Republican
legislators tend to have higher family income than Democratic legislators and are less likely to haveprior governmental experience, using a large survey of state legislators that we make more use oflater.16
Second, there are the expected (and somewhat mechanical) effects from term limits; term limits
increase the likelihood of a contested election and the number of candidates running for election, anddecrease margins of victory. Third, there does not appear to be any effect of term length on electorial
competitiveness.
Legislator Robustness. In all these regressions, we use log wage as the independent variableof interest. We have also run the regressions in un-logged form, using instead the salary in terms oftens of thousands of dollars. The effects of politician salary on electorial competitiveness decline insignificance in their un-logged form. Table D2 shows that the results of these regressions controllingfor heterogeneity in salary trends across states with state-specific year trends are virtually unchanged.
We also split the sample by full-time vs. part-time legislatures in Table D4 to test whether the results
vary with the demands of the job. We find that the effect of salary is more pronounced for part-timethan full-time legislatures, and, in particular, the coefficients using the full-time legislature sample
are not statistically different from zero for any outcome except margin of victory.17
Addressing Local Shocks. In these regressions, the identifying assumption is that politician
16

The argument that salaries could benefit Democratic legislators has been made previously in the political science
literature, and a basic correlation (though without any time or state controls) was pointed out by Fiorina (1994).
17
We do not have strong evidence as to why we see a modest positive effect of salary on competitiveness for part-timelegislators, but not for full-time legislators. That the coefficients are only significant for part-time legislators highlights
our general conclusion that impacts of salary seem to be modest (if they are present).

12

salary is uncorrelated with the error term conditional on the spatial fixed effects, the time fixed effects,
and other controls. In Section 4.6, we address a number of ways by which this assumption could
potentially be violated. In the context of salary and electoral competitiveness for state legislators,
one important confound we address here is the possibility of local economic shocks. As discussed
in the Introduction and the Conceptual Framework, a key component of political competition is a
prospective politician’s outside offer, for example, the economic opportunities for lawyers in his local
area. One might worry that state legislative salaries could be raised or lowered in step with shocks toprospective politicians’ outside option. To address this, we exploit that local economic shocks need
not respect state borders. While the market for lawyers in California may be a poor comparison tothat in Nevada, the markets for lawyers in two bordering legislative districts in eastern California
and western Nevada may be similar.
To address shocks of this type, we implement a spatial discontinuity design, analyzing pairs ofelectoral districts straddling state borders. We consider regressions of the form:
yp(d)dst = α0 + α1 wst + Xst β + fs + fp(d)t + p(d)dst(2)

where p(d) refers to a border district pairing and fp(d)t is a border district pair-year fixed effect we
perform the above regression in a sample restricted to electoral districts that lie on state borders,
including all pairs to which a given district belongs.18,19
To highlight the logic of the spatial discontinuity strategy, consider a year where politiciansalary is raised in California, but not raised in Nevada. We wish to examine whether the increase insalary leads to greater competition. In estimating (2), the only electoral districts that will factor inare those along the state border. By including border pair-year fixed effects, we make comparisonsof one district to another in a given year. For (2) to be biased by a local economic shock, there needs
to be a shock to the market for lawyers in 1976 in western Nevada, but not in eastern California.20,21
18
For some border district pairs, the states have elections in different years. Thus, including border district pair-year
fixed effects is the most conservative approach, but doing so may also lead to over-controlling. We have, thus, also
estimated equation (2) using border pair-two year fixed effects and border pair-four year fixed effects, and obtain similar
results.
19
Thus, districts that belong to multiple pairings will be included multiple times. To address any impacts of thison standard errors, one approach is to use multi-way clustering (Miller et al., 2009). We experimented with several
different methods of clustering, including two-way clustering by state and by electoral district pair fixed effects, andtwo-way clustering by state and by electoral district pair times year fixed effects (similar to Naidu (2009), who uses
two-way clustering both across states and county pair time year fixed effects). We find very similar standard errors
across the different methods. However, because the two-way clustering was sometimes highly computationally taxing,
sometimes taking days to run a single regression, we present results clustering by state.
20
Of course, there are broader differences between California and Nevada, such as there is more gambling in Nevada,but time-invariant differences will be picked up in the state fixed effects. Our identifying assumption is that changes in
unobserved conditions affecting one district in a border district pair are uncorrelated with changes in salary, conditionalon all of our controls.
21
In the US, because of restrictions placed on lawyers by state bar associations, it may be difficult for lawyers toeasily cross state borders to practice law. However, what matters for us is not that the lawyers can cross the borderbut merely that the relative demand for lawyers is similar in neighboring districts in a given year. We use lawyers as an
example because they are a common occupational group for state legislators. However, lawyers actually only compriseabout 20% of state legislators, and are outnumbered by state legislators with a business occupation, as tabulated in
the survey of Carey et al. (2002). Businesspeople are generally able to freely move across state borders. One concern
could be that businesspeople would not move across state borders if tax rates were much higher in one state than in
the other, however, our results are similar when we include controls for state corporate tax rates.

13

In addition to helping with shocks to legislator outside options, our border district methodology
is also useful for addressing other local shocks that could affect voting. For example, it could be that
voters’ preferences change depending on the economic climate. By focusing on two nearby districts,
we help generate conditions with a similar economic climate.22
Table 4 implements the border district methodology and finds similar results to those in thebasic fixed effect design. The coefficients are positive and slightly larger than in the basic fixed
effect design. The standard errors are also larger, so the estimates are not statistically significant
from zero. Based on the overlapping 95% confidence intervals, we cannot reject that the estimateswith the border pair design in Table 4 are statistically different from the ones in Table 3. The main
message is similar here, with increased legislator salary associated with slightly greater electoralcompetitiveness.
For our strategy to be effective in addressing concerns about salaries being increased in responseto changes in the outside option, we need that the outside option of legislators be comparable on bothsides of the border. However, some border legislative districts may be not good comparisons with
one another. For example, in the Western US, legislative districts are big and bordering districts may
encompass wide geographies. However, our results are similar if we eliminate Western states fromthe analysis. This does not completely address the issue, as even when districts are small, they may
still not be good comparisons. Consider, for example, the cities of Philadelphia, PA and Camden,
NJ, lying on opposite sides of a state border, but differing substantially in their level of economic
activity (Camden is far more disadvantaged). To address this, we would like to run our analysis
restrict to districts that are “similar” to one another. Unfortunately, we were unable to collect dataover time on the demographics of state legislative districts. Instead, we do our analysis restricting
to districts where the two states are similar to one another; when we do this, our results are similar(see Table D5).23
Governors. In Table 5, we analyze the effect of politician salary in gubernatorial primaries.
Since the general elections for governors usually have exactly two major candidates (one Democrat
and one Republican), we focus on the results for gubernatorial primaries. The data shows no systematic relationship between gubernatorial salary and electoral competitiveness. The estimates areclose to zero, though the standard errors are larger than in the legislator estimates. We are able,
though, to rule out large effects. For the impact on number of candidates, a 50% increase in salarywould have a 95% confidence interval of [-0.18,0.24]. Given a mean of 2.56 candidates and a standard
deviation of 1.43, we can rule out that the 50% salary increase would increase the number of candi22

For example, voter labor market shocks could affect the degree of accountability pressures for politicians or affectvoter preferences.
23
To measure whether two states are similar to one another, we calculate the squared distance between two statesacross several normalized covariates (share Black, share between age 5-17, share over age 65, unemployment, GDP per
capita, and population). We normalize the covariates by year using our full sample of all legislative districts. We thenrank border districts by comparability and restrict to border districts spanning comparable states. For the results inTable D5, we perform our analysis restricting to border districts in the top 75 percent of state comparability (that is,
excluding the less comparable districts). The coefficients are similar in size and direction when we restrict instead tostates in the top 50th percentile, though we have less power.

14

dates by more than 0.24 candidates or 0.17 standard deviations in candidates.24 There are no clear
differences in effects for Republican vs. Democratic primaries, though our estimates are somewhat
imprecise.

4.2

Effects on Political Selection

Table 6 turns to analyzing the impact of politician salary on various politician characteristics including candidate diversity, candidate education and occupation, and past political experience. As
discussed above, the measures are used simply because they are what can be observed and clearly do
not represent all relevant measures of quality. The data are only available for some states, so there
are fewer observations in Table 6 than in Table 3.
Panel A shows the effect of log salary on the characteristics of state legislators. There are no
statistically significant correlations. Politician salary is actually negatively (though insignificantly)
associated with the probability of a politician having a college degree. The coefficient of -0.014, andthe standard error of 0.025 means that we can rule out an effect of greater than 3.6% at the 95%
confidence level (in the baseline, the share of college graduates is 77%). In contrast, the estimatesof Gagliarducci and Nannicini (2010) indicate that a 100% increase in salary to be associated withan Italian mayor having an additional 2.7 years of schooling. Kotakorpi and Poutvaara (2011) findthat a 35% increase in the salary of Finnish Members of Parliament increased the number of female
candidates with higher education by 5 percentage points.25

4.3

Effects on Passing Legislation and on Fiscal Outcomes

Table 7 shows modest impacts of politician salary on legislative productivity, which we measure using
the introduction and passing of bills, and income, expenditure, and taxes per capita. The results
are presented both with and without session length, the issue being whether one wishes to interpretsession length itself as possibly reflecting productivity.26 In columns 1-4 of Panel A, we see positive,
but statistically insignificant impacts of salary on bill introduction and approval. For example, theelasticity of 0.03 estimated in column 4 indicates that a 100% increase in salary is associated with a3 percentage point increase in the number of bills approved. Given the standard error of 0.04, we canrule out that a 100% increase in salary would increase the share of bills approved by more than 11%.
In columns 5 and 6, the impact on the share of bills approved is positively statistically significant(and is not affected much by including session length), but is economically small. The coefficient of
0.013 means that a 100% increase in salary increases the share of bills passed by roughly 1%, which
is about 3% of the mean. These results are robust to including state-specific year trends (see Table
D3).
24

It is important to keep in mind, however, that there is far less variation in salaries for governors than there is forstate legislators. An increase in 100% for gubernatorial salary is thus a very large amount.
25
The reform did not affect the educational background of male candidates.
26
Since session length is chosen by the legislators, one might imagine that very unmotivated legislators might choose
to have short session lengths. Thus, controlling for session length, there may be no effect of log salary on session length,even though the salary is affecting their productivity.

15

Panel B shows no significant impacts of salary on expenditure or taxes per capita, which areoften used as measures of fiscal discipline (e.g., Besley and Case, 1995; Peltzman, 1992), or on income
per capita. The impacts on income per capita are negative and insignificant, whereas the impacts on
taxes and expenditure are positive and insignificant. Thus, there is no evidence that paying higher
salary helps improve fiscal discipline.
In Panel C, we observe that salary for governors has no robust significant impact on fiscal
outcomes. Increasing salary is associated with slightly lower income per capita and slightly higher
spending and taxes per capita. There is no evidence that increasing salary would lead to greater
fiscal discipline in terms of lower taxes and spending.27
Our findings differ substantially from others in the literature. Gagliarducci and Nannicini(2010) find that a 33% increase in Italian mayoral salary decreases expenditure and revenue percapita by about 18% and increases the speed of revenue collection by 7%. Ferraz and Finan (2010)
show that a 20% increase in salary for Brazilian municipal legislators increases the share of bills
submitted by 25%, and also substantially increases the share of bills approved and whether there
is a local commission. Ferraz and Finan (2010) also show that raising salary raises spending on
education and health. Our estimates are sufficiently precise that we can rule out magnitudes of thislevel in US data. In terms of bills introduced, our preferred estimate in column 2 corresponds to aconfidence interval from -2.2% to 2.6% for a 33% increase in salary, meaning we can rule out effectsizes 6 times smaller than those in Gagliarducci and Nannicini (2010). In terms of bills approved,
our preferred estimate in column 4 corresponds to a 95% confidence interval from -0.9% to 2.2% fora 20% increase in salary, meaning we can rule out effects over 10 times smaller than those in Ferraz
and Finan (2010).

4.4

Effects on Shirking: Roll-Call Voting

Table 8 examines the effect of salary on a performance outcome that legislators have individual
control over, namely, whether or not they miss a roll-call vote during 1999-2000. Missing roll-callvotes is considered a form of shirking in the political science literature (e.g., Baughman and Nokken,
2011; Rothenberg and Sanders, 2000; Wright, 2007). Other studies have examined whether politicians
miss roll call votes or have examined similar measures of basic effort (e.g., whether or not they attend
sessions of the legislature), and include Gagliarducci et al. (2010) and Lott (1990), with a review
article given in Bender and Lott (1996).
Column 1 of Table 8 indicates a statistically significant negative association of salary withwhether legislators miss a vote. The coefficient of -0.02 means that a 100% increase in salary is
associated with a two percentage decrease in the probability that a legislator will miss their rollcall vote (compared to a baseline missed vote percentage of 9 percent). This economically small
magnitude is consistent with Fisman et al. (2013) who find no impact of MEP salary on attendance
27

For example, using a 95% confidence interval for expenditure per capita, we find that increasing salary by 50%
would not decrease spending per capita by any more than 1.6%. Although we find it more plausible that governors
influence fiscal policy (Besley and Case, 1995) as opposed to whether bills are introduced and passed, we also lookedat the relationship between governor salary and bills introduced, bills passed, and share of bills passed. We found no
significant impacts.

16

or shirking.28 Because our data on roll-call voting is essentially from a cross-section, we do notinclude time effects, and because salary varies at the state level, there are no state fixed effects.
Lacking state and time fixed effects, our conclusions here should be regarded as more tentative.
Table D6 analyzes the impact of salary on missed roll-call votes using the border-pair research
design similar to as in Equation (2). In our conceptual framework, politicians exert more effort themore they value keeping their job (vs. instead getting their outside options); if changes in salary arecorrelated with unobserved increases in politician outside options, our estimates may be downward
biased. In the border-pair design, we compare roll-call voting records of legislators in districts on
two sides of state boundaries, the idea being that such legislators will face more similar outside
options. The difference from Equation (2) is that we are using border pair fixed effects instead ofborder pair-year fixed effects because the roll-call data is a cross-section. Table D6 shows that the
impact of salary is small and statistically insignificant. Although the coefficient on salary is actuallyslightly positive (meaning higher salary is associated with slightly more missed roll call votes), wecannot reject that the coefficients in the baseline and border-pair design are different (i.e. the 95%
confidence intervals on the salary coefficient overlap). Thus, the border pair design confirms ourbaseline finding that salary has only a modest impact on roll call voting.

4.5

Effects on Corruption

Table 9 shows that politician salary has no significant impact on corruption, our final measureof politician performance. Corruption is frequently studied as a critical measure of governmental
performance both in developing countries (e.g., Ferraz and Finan, 2011; Olken, 2007) and in the
United States (e.g., Glaeser and Saks, 2006). We measure corruption using two standard measuresfrom political science: the number of convictions per 1,000 state governmental officials (Maxwell
and Winters, 2005) and the level of perceived corruption in state government based on a survey ofState House news reporters (Boylan and Long, 2003). It should be noted that data on perceptionsof corruption is cross-sectional.
Looking at convictions, column 1 shows that politician salary fives years prior has no statistically significant impact on the number of convictions.29 Convictions has been used to measurecorruption in a number of papers, both in political science (e.g., Meier and Holbrook, 1992; Goel andNelson, 1998) and in economics (e.g., Campante and Do, 2013), and has the advantage of representing corruption validated in court. However, it is possible that the number of convictions could also
reflect factors unrelated to corruption, such as the diligence of prosecutors, so we also investigate
corruption perceptions. Using the cross-sectional survey of corruption perceptions in column 2, we
also see no statistically significant impact.30
28
Fisman et al. (2013) define shirking as the fraction of times an MEP attended a session without casting any roll-callvotes.
29
Recognizing that there may be significant variation across cases in time from crime commission to conviction, we
experimented with a number of different lengths of leads in sensitivity checks. The estimates vary by specification.
However, we found no significant evidence that higher salaries lead to less corruption.
30
Measuring corruption through survey perceptions has been used in many economics papers, (e.g., Mauro, 1995;
Treisman, 2000).

17

4.6

Additional Threats to Identification

We discuss three additional important threats to identification and why we believe them to be unlikely
to affect our main results.
Reverse causality: politician quality and wage-setting. One broad concern for the
analysis is whether politician salary is set anticipating future performance or set differently dependingon the unobserved quality of the politicians. As discussed in Section 2, governor salaries are set by
the upper legislative house, whereas legislator salaries are set by different methods in different states.
For example, salaries may be set to conform to voters’ beliefs about appropriate compensation given
politicians’ quality and behavior. For example, salary-setters may be reluctant to raise politiciansalary if they have unobservable (to the econometrician) information that politicians will shirk andhave an unproductive year due to fear of popular backlash against higher salaries for low performance.
If salaries are set to equal politicians’ expected marginal products, this would lead us to overestimate
the positive impact of salary, with higher salary being driven by higher performance instead of theother way around. Such a bias would work against us finding very modest impacts of politiciansalary on outcomes (compared to other papers in the literature).
However, it is possible that the bias could go in the other direction. As noted by Gagliarducci
and Nannicini (2010), if politicians are setting salaries for themselves, it may be that higher-quality
politicians do not wish to set themselves higher salaries, either because they are honest or they do
not need the money. Such a bias could confound our main results.
We attempt to address this concern with two robustness checks. First, we would imagine thatthe bias from unobservedly higher quality politicians not wishing to raise their salaries would differ
by the method of compensation. The bias might presumably be stronger when politicians are voting
on setting salaries for themselves by voting on compensation bills. Tables D7 and D8 repeat our
main analysis of politician salary on electoral outcomes and performance, respectively, restricting
the sample to states where politicians do not essentially set their own salary. The results are quite
similar.31
Second, we examine whether observedly better performing politicians are less likely to receive
increased salaries. We measure observable quality in several different ways. First, we measurequality using performance in year t − 1, regressing salary in period t on performance in t − 1 inTable D9.32 Second, we measure quality using average performance to date; Table D10 shows nosignificant relationship between average performance to date for legislators and salary. Third, we
measure quality in terms of observables, asking whether more educated politicians are less likely toreceive a salary increase. To the extent that observable measures of politician quality are correlatedwith unobservable measures, we find little evidence that higher quality politicians are less likely toreceive increased salaries.
Unobserved changes in legislature characteristics or political institutions. Another31

Note that we can only do this robustness check legislators and not for governors because gubernatorial salary are
set in all states by the upper house (Di Tella and Fisman, 2004).
32
Di Tella and Fisman (2004) show that when governors perform well, their salaries are often increased in the future,so there is no certainly no evidence there that higher performance is associated with lower salaries.

18

broad concern is that changes in politician salary may be accompanied by unobserved changes inlegislature characteristics or political institutions that affect performance. The late 1960s and 1970s
saw a large increase in the “professionalization” of state legislatures, where wages were increased,along with other changes such as lengthening legislative sessions and providing increased legislative
staff Fiorina (1994). These changes have continued to the present, with gradually longer sessions
and annual instead of bi-annual meetings. Although we collected data on staff and session lengthand control for these variables, it is possible that wage changes may be accompanied by unobservedchanges in legislature characteristics, e.g., the capital buildings or the facilities. While we cannot
eliminate this concern altogether, we attempt a selection on observables design (Altonji et al., 2005,2008) as a robustness test.33 Because our concern is about changes in state-year legislative unobservables, we use measures of legislative staff and aides, the number of meetings per year, and thelength of meetings to estimate the model. As seen in Table D11, our estimates are very stables as
observable proxies of professionalization are gradually added. As long as these proxies of professionalization also correlate with unobservable measures of professionalization, there would need to be
substantial selection on unobservables to explain the result if it were spurious.
Differences in cost of living over time. For the analysis so far, we have deflated politiciansalaries using the CPI, and have accounted for time-invariant differences across states by usingstate fixed effects. However, as detailed in a growing body of work (e.g., Moretti, 2013), the costof living has changed very differently in different states over the last several decades. Althoughlegislators in California receive high salaries compared to those in other states, the cost of living has
increased substantially in California over time compared to many other states. Thus, to account for
this, we created a state-level CPI using housing and grocery store price data, closely following themethodology in Moretti (2013). As seen in Appendix Table D12, our results are similar when we
deflate salaries using state-level CPIs.34

4.7

Narrative Analysis of Salary Changes and Non-Changes

The identifying assumption for our analysis is that politician salary is uncorrelated with the error,
conditional on controls. To address the issue of local economic shocks biasing the relationship between
salary and competitiveness in legislative elections, we used a spatial discontinuity methodology. We
tried to rule out additional threats to identification in Section 4.6. To try to address any lingering
concerns about endogeneity, we perform a narrative analysis.
Using old newspapers, we searched the historical record for salary changes or non-changes thatare plausibly exogenous with respect to various concerns. We looked for proposed salary increases
whose passages were contested. For example, we located cases where the salary change passed or notaccording to a vote of a divided state salary commission, based off of a close vote of the electorate, or33This approach is similar to an approach used in Gentzkow et al. (2012) in that we assume that the correlation
between state-year unobservable shocks and election competition and outcomes is the same as the correlation between
state-year observable shocks and our outcome variables.
34
Pricing data used to calculate the state level CPIs is available starting in 1980. For years prior to 1980, we use thenational level CPI.

19

because of some other event (e.g., the bill was vetoed, but then the veto declared unconstitutional;
an unknown citizen led a drive to defeat the bill, etc.). A full explanation of our methodology andall the salary changes are given in Appendix B.12.35
The purpose of the narrative analysis is to identify changes and non-changes which are plausibly
exogenous for our analysis. That is, the events are plausibly exogenous with respect to factors suchas shocks to politicians’ outside options, expectations about future performance, and unobservedchanges in political institutions. A disadvantage of the narrative approach is that it is based on amuch smaller sample size, based off of 17 salary change or non-change events. As such, we believethat our full analysis and our narrative approach are complementary.36
Table D14 shows very limited impact of salary electoral competitiveness using our narrative
sample. We analyze elections in states in the 8 years before and after the salary event.37 The
estimates are relatively precise, e.g., in column 1, looking at whether an election was contested as
the outcome, the 95% confidence interval on “Log Salary” is [-0.05,0.08]. Repeating the regressions
in Table 7 with our narrative sample, we also see limited impact of salary on bill introduction andbill passing (results omitted for brevity).

5

Interpretation

We discuss several explanations and interpretations of our results:• That salaries are too small to be meaningful to politicians.
• That politicians are driven by intrinsic motivations.
• That salary impacts are small because salary is not a good measure of the pecuniary value ofserving in office. Specifically, state politicians could have many future opportunities to become
lobbyists and consultants, particularly in low salary states.
• That the small impacts of salary on entry and performance are driven by political parties
serving as a barrier to entry.
Although not dispositive by any means, additional evidence fails to support the first three explanations, and seems more supportive of the fourth explanation.
Politician Salaries are too Small to be Meaningful. One explanation for our results isthat politician salaries are too small to have much of an influence on outcomes. Potential politicians are reasonably well-educated, high-ability people, so differences in several hundred or thousand
35

Our methodology is similar in spirit to the work of Romer and Romer (2010), who identify exogenous tax changes
using a narrative approach.
36
The full analysis has the strength of a larger sample, whereas the narrative analysis has the virtue of using events
which we are more confident are plausibly exogenous. Both approaches deliver similar results. Beyond the limitationof only having 17 event observations, we also only have 11 states. Because we are clustering by state, this means wehave relatively few clusters and asymptotic inference may not be valid (Bertrand et al., 2004). Cameron et al. (2008)
show that one approach to address this is to perform inference using G − 2 degrees of freedom, where G is the numberof clusters. Using G − 2 degrees of freedom decreases the statistical significance of the log salary coefficient, meaning
the coefficients on log salary remain statistically insignificant.
37
Thus, elections will be counted multiple times if they are within 8 years of multiple events. Our results are similar
if we look at 4 year windows instead of 8 year windows.

20

dollars may be too small to significantly affect household finances or politician behavior. However,Table 10 shows that politician salary is significantly and positively associated politician family’sself-reported income and decreases outside labor supply (i.e. “moonlighting”).38 In Panel A, thedependent variable is family income, which has a mean of $101,394. Thus, formal politician salaries
comprise about 24% of household income, on average. Each $1 increase in politician salary is associated with a 50 cent increase in family income.
Of course, that politician salaries are a substantial portion of family income does not mean thatthey would actually affect behavior. For example, it may be that post-office returns, e.g. lobbying,
are much larger than compensation received in office (we address this further below). However, PanelB suggests that an increased salary does affect behavior, namely by decreasing the probability thepolitician has an outside job. An increase in politician salary by $10,000 is associated with a fourto six percentage point decrease in the likelihood that a politician has an outside job (corresponding
to a change of 6-9 percent).39 The effect holds conditional on session length, suggesting the effect is
not driven simply by legislatures with short sessions having politicians who are both more likely toearn lower salaries and have outside jobs. Given that politicians’ decisions about whether to workoutside of politics change significantly with changes in salary, it does not seem likely that salaries
are too small to potentially meaningfully shift performance.
Intrinsic Motivations. An important body of work shows that workers are often driven byintrinsic motivations, thereby blunting or reversing the impact of external rewards via crowd-out(Gneezy and Rustichini, 2000; Ariely et al., 2009). Though we are dealing with salary instead of
performance pay, the relatively small impact of salary on behaviors is consistent with this view.
We show, however, that salary is strongly correlated with the way that politicians spend their time,suggesting that politicians are to an important degree motivated by external incentives. In particular,
higher-paid politicians spend much more time on fund-raising, and no more or less time on legislative
activities.
We use self-reported time use data from Carey et al. (1995, 2002). State legislators in 1995 and2002 were asked to report how much time they actually spent on one of several activities going from
1=‘Hardly Any’ to 5=‘A Great Deal.’ We normalize these variables to use as outcomes in Table 11.
Panel A of Table 11 shows that when politicians receive higher pay, they do not seem to report
spending time on legislative activities. Indeed, they report spending significantly less time on building
coalitions across parties. Panel B shows that higher paid politicians do report spending significantly
more time on constituent services, such as keeping in touch with constituents and helping constituentswith problems. Finally, Panel C shows a significant positive relationship between politician salaryand fundraising. In states where the wage is higher, politicians are much more likely to report38

For an analysis of moonlighting by Italian politicians, see Gagliarducci et al. (2010).
It is important to caveat that the analysis in Tables 10 is cross-sectional. While we control for various statecharacteristics that seem relevant for the analysis, such as state population, GDP per capita, session length, session
frequency, and legislative staff controls, it is possible that salary could correlate with unobserved state characteristics.
Although we speculate that unobserved state characteristics are not a significant source of bias here, the results shouldbe treated as more tentative than those with state fixed effects. Our table on time-use, Table 11, is also based oncross-sectional comparisons.
39

21

spending more time fundraising. Specifically, an increase in salary of $30,000 is associated with
0.16 more standard deviations of time spent fund-raising. It is noteworthy that a higher salary is
associated with spending more time fundraising for oneself (a $30,000 salary increase is associatedwith 0.20 more standard deviations of time fundraising), but that it is not associated with time spent
fundraising for others.
Rather than either significantly increasing performance (as we saw was not the case in Section4) or having no impact on behavior, higher salaries seem to increase time spent on fund-raisingand constituent services. This suggests that politicians are not non-responsive to external incentives
due to strong intrinsic motivations, but rather that they may respond by changing other behaviors.
Although we are dealing with salary instead of performance, these results have parallels to the multitasking literature, where incentives on one dimension affect behavior on other dimensions (Holmstrom
and Milgrom, 1991).40
Political Office Leads to Lobbying Opportunities. One reason why we see limited
impact of salary on performance is that salary may not be a good measure of the pecuniary valuethat legislators get from serving in office. In particular, it could be that many legislators will later
have the opportunity to be lobbyists or consultants on issues related to public policy. However,in the state legislator surveys of Carey et al. (1995, 2002), only 15% of legislators said that it was
likely that they would later pursue lobbying or consulting after leaving office. Furthermore, there
is no correlation between legislative salary and whether legislators say it is likely they will later
pursue lobbying or consulting (see Table D15). While it is possible that legislators could deliberately
mis-report this information, the survey was anonymous and reported via mail; thus, there was little
incentive to mis-report.41
Strong Political Parties. Political scientists have long emphasized the importance of political parties for understanding differences across states. In particular, they have argued that parties
play a role in determining who runs for office, in maintaining patronage networks, and in regulating
whether incumbents face challengers (e.g., Mayhew, 1986, 2008; Primo and Snyder, 2010). Thus, it
was natural for us to look to differences in party strength in helping understand why we find modest
impacts of salary on electoral competition and performance relative to several other papers on theimpact of politician salary.42
In states with strong parties, potential candidates may not be able to respond to salary increases
because, for instance, the party might decide who runs for office, in addition to driving political effort
and other outcomes. As a result, while individual politicians may be responsive to salary changes,
the decisions of a strong party may override these incentives. Table 12 performs the regressions on
40
Although it seems (to us) that having politicians spend more time on fundraising, but no more time on legislative
activities may not be beneficial, we do not need to take a stand here on whether the data patterns found here are“good” or “bad.” Spending lots of time on constituent services may be thought clientelistic or it may be thought sociallyefficient (compared to, say, drafting a bill that will never pass). Our point is merely that salary is correlated with timeuse, suggesting that intrinsic motivations do not make politicians impervious to extrinsic incentives.
41
See Carey et al. (1995, 2002) for further discussion and justification of the survey methodology.
42
In theoretical work in economics, there is also growing interest in understanding how party strength affects thepolitical process and the impact of political institutions (e.g., Buisseret and Prato, 2014).

22

electoral competitiveness and adds an interaction between log salary and an indicator for whether
or not a state has strong political parties. We focus for now on two measures: The number ofcandidates running and the probability that the incumbent is re-elected. The results suggest thatin states with weak political parties, there is a positive relationship between log salary and thenumber of candidates while this relationship is almost zero for strong political parties. Similarly, in
weak party states, there is a negative (though insignificant) relationship between the likelihood anincumbent is re-elected and this relationship is positive (though insignificant) in strong party states.
These results are consistent with strong parties being a barrier to entry.43

6

Conclusion

In this paper, we examine the impact of salaries for state legislators and governors on political competition, politician quality, and political performance in the US. Our main finding is that effects aremodest. Some effects are positive and statistically significant, some are statistical zeroes, but almost
all are economically insignificant. Most of the estimates are relatively precise, with enough precision to reject moderate-sized effects. Although mostly inconsistent with reduced-form international
evidence on the impact of politician salary, our reduced-form results are consistent with simulations
from structural models (Diermeier et al., 2005; Keane and Merlo, 2010), which also find that theeffect of increasing politician salary on selection and performance is likely be small for the US. Our
results are of interest both for policy debates about politician pay and for agency theory.
The central concern for our paper is whether the results may be biased due to correlation ofwages with unobservables. We examine several possible sources of bias. To address the concernthat estimates of salary on political competition may be biased by correlation between salary andunobserved local economic conditions, we consider a spatial discontinuity design of legislative districts
straddling state borders. As an example, this strategy helps deal with the concern that politiciansalaries could be increased in response to local outside options, under the assumption that outside
options are likely to be similar in a given year for two districts straddling state borders. To address
the concern that wages may be correlated with changes in legislature characteristics or politicalinstitutions, we examine how our coefficients change as we control for observable proxies, in the spiritof Altonji et al. (2005). To assuage the concern that higher quality politicians may strategically try to
avoid receiving higher salaries, we show that our results are similar when we restrict to states where
politicians have less control over their salary, and that past observable measures of quality do notappear correlated with reluctance to raise salary. While our results are quite robust to these tests,
we recognize that these tests are imperfect, and without quasi-experimental variation in politiciansalary in the US, possible concerns about endogeneity may still remain. Based on our accumulated
43

In unreported results, we also studied the interaction effect of salary and party strength on bill-passing, fiscal
policy, missed roll call votes, and time use. In these regressions, we do not generally significantly different impacts ofsalary for weak vs. strong party states. One interpretation of this is that party strength is only a mediating factor
for electoral competition outcomes, but not for performance outcomes. This is consistent with some of the discussionin Mayhew (2008), which emphasizes the role of parties in their impact on electoral competition rather than otheroutcomes.

23evidence, however, we feel comfortable concluding that moderate increases in politician salary, in therange of salaries that we study, are unlikely to have large impacts on the performance of US state
politicians.
While our paper shows that within-state variation in politician salary appears to have onlya modest impact on selection and performance, what can our paper tell us about a policy, say, of
permanently increasing salary for US state politicians? Two important issues are, (1) to what extent
do politicians regard historical increases in their salary as permanent, and (2) can we extrapolate
our linear estimates to salary levels observed outside our sample. (Both these issues arise in many
reduced-form panel estimation papers which attempt to use variation over time to inform the impact
of policies.) On (1), nominal salaries usually go up, so it is likely that salary increases will not later
be reversed. On (2), it is difficult to say what would happen if Alabama increased legislator salaryto, say, $150,000 per year, because Alabama is never observed paying near that level. Structural
studies simulating larger salary changes (Diermeier et al., 2005; Keane and Merlo, 2010) have tended
to confirm our reduced-form result that increasing salary would have a modest impact.44 Thus, while
it is important to note the limitations of our analysis to extrapolate on changes outside observedsalary variation, we believe our estimates are still generally informative for policy discussions.
The obvious question given our findings is why are impacts modest. We take a first stab at
this issue by showing that the impact of politician salary is especially muted in states classified in
the political science literature as having historically strong political parties. Based on time use data,
we show that US politicians may respond to salary increases on other effort margins, such as timespent fund-raising. Further research is clearly warranted.

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26

Figure 1: Average Salary of Legislators Over Time27

Figure 2: Legislator Pay Over Time By State, In Nominal Terms

28

Figure 2: Legislator Pay Over Time By State, In Nominal Terms (Cont.)

29

Figure 3: Lower House State Legislative Districts Lying Along State Boundaries

Source: 2006 US Census cartographic boundary file.

30

Table 1: Real Salary for Governors and State Legislators Over Time (1982 Dollars)State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
FloridaGeorgia
Idaho
Illinois
Indiana
Iowa
KansasKentuckyLouisiana
Maine
Maryland
Massachusetts
MichiganMinnesota
Mississippi
MissouriMontana
Nebraska
NevadaNew Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
OregonPennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Di Tella and Fisman (2004)
Governor Salary
Governor Salaryin 1950
in 1990
24928
41547
41547
103867
41547
49856
31160
49856
49856
31160
49856
33237
49856
33237
41547
49856
41547
16619
83094
93480
49856
41547
41547
31160
41547
31576
24928
83094
41547
103867
62320
24928
54011
27005
41547
103867
62320
31160
35315
49856
49856
31160
35315
62320
62320
41547
51934
33237

53744
57400
26787
65054
53574
59696
61227
77209
68017
42093
71380
55974
55487
55974
53368
50586
53574
65054
57400
81654
79488
57859
67764
39578
44390
54229
57977
65054
68880
99494
94136
49897
49747
53574
59314
65054
52808
64975
46547
65054
71507
53567
58012
65054
74008
55104
65933
53574

31

Book of the States
Legislator Salary
Legislator Salaryin 1967
in 2008
1142
5389
5566
17964
9581
4865
13473
3593
1146
1771
26946
2038
7270
2038
2398
7279
2994
7186
22455
29940
14371
4491
14371
4444
7186
5709
277
22455
0
29940
3673
485
23952
9220
8982
21557
1082
5389
4491
1423
14371
1497
2838
1617
3593
4491
16168
976

177
11147
6995
53923
13934
13005
19856
14831
8055
7485
30354
2719
11612
2719
2738
7803
5905
20204
27049
36994
14463
4645
14463
1615
5574
3033
93
22759
0
36925
6480
1955
28139
17835
9235
35375
6079
4830
557
8417
3344
1835
3379
8193
19173
6967
22022
1893

Table 2: Summary Statistics
Variable

Obs

Mean

Std. Dev.

Min

Max

84,59286,401
86,401
86,37884,04386,222
86,212
63,51015420.23
0.73
1.93
1.92
47.04
0.68
0.74
0.93

15609.71
0.43
0.74
0.70
35.51
0.46
0.44
0.26

0
0
1
1
0
0
0
0

99,000
1
20
6
100
1
1
1

41,312
85,88986,404
86,404
86,404
71,18286,404

0.90
0.59
0.06
0.18
0.82
0.16
0.20

0.30
0.49
0.24
0.38
0.38
0.36
0.40

0
0
0
0
0
0
0

1
1
1
1
1
1
1

Female legislator
Black legislator
Latino legislator
Legislator age
College Degree
Graduate Degree
LawyerHas political experience
Legislature Productivity

12,052
12,052
11,978
11,313
11,012
11,012
11,041
11,204

0.21
0.10
0.06
49.61
0.77
0.44
0.23
0.39

0.41
0.30
0.23
11.44
0.42
0.50
0.42
0.49

0
0
0
19
0
0
0
0

1
1
1
94
1
1
1
1

Number of bills introduced
Number of bills introduced per legislator
Number of bills approved
Number of bills approved per legislator
Share of bills approved
Miss a roll-call vote
Other

1,771
1,305
1,771
1,305
1,771
3,282,096

2054.1213.87
467.91
3.51
0.31
0.09

2339.1512.50
337.56
2.53
0.17
0.28

6.00
0.06
0.00
0.05
0.00
0

21435.00
101.592361.00
17.51
0.97
1

48

0.27

0.45

0

1

Panel A - State Legislators
Legislature & Election Characteristics
Nominal salaryContested election
Number of candidates
Number of candidates, 6 or fewer candidates
Margin of victory (percentage points)
Incumbent is re-elected
Incumbent runs for re-election
Incumbent re-elected conditional on running
Incumbent re-elected conditional on running an facing
an opponent
Election won by a Democrat
Election for term-limited seat
Election for seat with four-year term length
Election for seat with two-year term length
Election for seat where pay is set by constitution
Election in a border district
Legislator Characteristics

Strong State Party

32

Table 2: Summary Statistics, Continued
Variable

Obs

Mean

Std. Dev.

Min

Max

Panel B - State Governors
Real Salary (in 1982 dollars)

1,326

61,035

19,714

16,598172,414

1,323
1,325
1,232
1,325
1,062
1,326

0.80
3.12
2.70
0.46
0.33
0.50

0.40
2.17
1.43
0.36
0.27
0.50

0
1
1
0
0
0

1
18
6
1
1
1

Female governor
Governor age
College degree
LawyerHas military experience
Born out of state
Worked in private sector after officeWorked in public sector after office
Retired after office
Gubernatorial Candidate Characteristics

344
344
344
343
344
344
231
231
231

0.03
48.30
0.95
0.55
0.58
0.30
0.44
0.60
0.05

0.16
8.03
0.22
0.50
0.49
0.46
0.50
0.49
0.21

0
33
0
0
0
0
0
0
0

1
71
1
1
1
1
1
1
1

Female candidate
College degree
Graduate degree
LawyerHas military experience
Born out of state
Worked in private sector after officeWorked in public sector after office

600
429
379
423
417
427
310
382

0.05
0.95
0.66
0.52
0.56
0.34
0.69
0.52

0.21
0.23
0.47
0.50
0.50
0.47
0.46
0.50

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

Gubernatorial Primary Election Characteristics
Contested election
Number of candidates
Number of candidates, 6 or fewer candidates
Margin of victory (percentage points)
Margin of victory (percentage points), contested election
Election for term-limited seat
Governor Characteristics

33

Table 3: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes
Panel A - All Elections

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.026**(0.013)0.070***(0.021)0.011(0.014)0.068**(0.032)-0.023(0.023)0.050**(0.020)0.114***(0.031)
0.015(0.028)0.109*(0.062)-0.026(0.033)-0.747(1.199)-3.546**(1.526)-0.191(0.948)-3.494(2.708)-0.576(1.665)-0.015(0.013)-0.017(0.011)0.008(0.010)-0.142***(0.030)-0.110***(0.016)

0.066**(0.025)-0.013(0.022)0.020(0.012)0.019(0.035)0.018(0.026)

82,5330.161
0.734

82,4470.175
1.933

80,2480.162
47.04

82,3530.141
0.685

82,0270.143
0.590

Panel B - Lower House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.039**(0.017)0.014(0.017)0.088**(0.034)-0.102*(0.053)0.070***(0.026)0.015(0.032)0.131*(0.067)0.251**(0.114)-1.041(1.615)-0.076(1.088)-4.831(2.965)-9.551(12.284)-0.020(0.016)0.013(0.010)-0.143***(0.033)-0.173***(0.061)0.071**(0.033)0.024*(0.013)0.020(0.035)0.407***(0.149)62,7330.168
0.719

62,6730.181
1.908

60,8700.167
48.30

62,5790.141
0.706

62,3250.137
0.597

Panel C - Upper House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.006
(0.011)-0.001(0.014)-0.006(0.037)-0.026(0.026)0.019(0.019)0.014(0.029)0.020(0.060)-0.022(0.038)-0.388(1.424)-1.039(1.224)1.000(3.480)0.837(1.007)-0.003(0.012)0.004(0.017)-0.133***(0.033)0.005(0.030)0.053***(0.019)0.008(0.015)0.022(0.039)0.023*(0.013)19,8000.145
0.782

19,7740.167
2.011

19,3780.150
43.05

19,7740.149
0.617

19,7020.175
0.565

Notes: This table anlayzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003. An observation is an election. Robust standard errors clustered by state in parentheses. All regressions include stateand year fixed effects. The contested election variable is a dummy for whether the election had more than one candidate.
Column 2 is restricted to elections with 6 or fewer candidates. Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

34

Table 4: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes,
Analysis for Border Districts
Panel A - All Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.018
(0.021)0.001(0.033)0.143**(0.062)0.000(0.036)-0.000(0.034)-0.057(0.051)0.213***(0.073)-0.027(0.051)-1.416(1.967)2.325(2.382)-13.433**(5.746)-3.964(2.567)0.037(0.024)0.000(0.022)-0.242***(0.045)-0.092**(0.041)

0.027(0.033)-0.046*(0.025)-0.100*(0.059)0.028(0.069)

46803
0.546
0.735

46755
0.574
1.926

46181
0.567
47.61

46746
0.545
0.685

46605
0.583
0.577

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.060
(0.053)-0.006(0.042)
0.234***(0.078)-0.656(0.580)0.044(0.079)-0.063(0.066)
0.313***(0.103)-1.239(0.808)-3.226(4.355)4.359(3.165)-16.981**(8.035)42.460(40.701)-0.021(0.043)0.018(0.029)-0.237***(0.083)0.341(0.284)0.054(0.057)-0.051(0.046)-0.137(0.087)0.848(0.652)

30881
0.685
0.712

30853
0.707
1.881

30434
0.716
49.79

30838
0.694
0.717

30748
0.717
0.598

Panel - Lower House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var
Panel C - Upper House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins-0.000(0.032)0.062(0.105)0.032(0.143)-0.018(0.070)-0.068(0.067)0.111(0.191)
0.053(0.181)-0.055(0.113)0.238(3.299)-2.441(6.488)-3.349(16.190)
4.865(4.437)0.143***(0.045)-0.020(0.079)-0.200**(0.091)
0.057(0.090)0.007(0.048)-0.022(0.071)-0.101(0.120)0.031(0.074)

15922
0.822
0.781

15902
0.843
2.013

15747
0.830
43.37

15908
0.823
0.623

15857
0.853
0.535

Notes: This table anlayzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003, restricting to elections in districts on state borders. An observation is an election. Robust standard errors clusteredby state in parentheses. All regressions include border district pair-year fixed effects (e.g. fixed effect for elections in District A
in eastern California and District B in western Nevada in 1970) and state fixed effects. The contested election variable is adummy for whether the election had more than one candidate. Column 2 is restricted to elections with 6 or fewer candidates.
Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

35

Table 5: The Effect of Governor Salary on Electoral Competitiveness and Electoral Outcomes,
Gubernatorial Elections
Panel A - All Primaries(1)

Log salary
Democratic primaryElection for term-limited seat

Observations
R-squared
Mean dep var

(2)

(3)

(4)

Contested
Election

Number of
Candidates

Margin of Victory (inpercentage points)

Margin of Victory forContested elections (inpercentage points)

0.034(0.049)-0.012(0.017)0.084(0.054)0.016(0.276)0.303*(0.161)-0.151(0.352)-0.291(6.032)-1.421(2.785)-3.111(5.037)-0.921(5.832)-1.879(2.690)-1.408(4.998)

1137
0.407
0.878

1150
0.508
2.659

962
0.483
34.29

947
0.481
33.30

(1)

(2)

(3)

(4)

Contested
Election

Number of
Candidates

Margin of Victory (inpercentage points)

Margin of Victory forContested elections (inpercentage points)

0.022(0.043)0.053(0.055)0.041(0.346)-0.077(0.401)-4.052(7.027)-17.837**(7.429)-2.951(7.162)-15.948**(7.112)

537
0.305
0.887

545
0.373
2.713

452
0.302
33.06

440
0.274
31.15

(1)

(2)

(3)

(4)

Contested
Election

Number of
Candidates

Margin of Victory (inpercentage points)

Margin of Victory forContested elections (inpercentage points)

0.044(0.064)0.044(0.054)0.103(0.436)-0.107(0.410)20.456**(8.421)
5.819(9.260)17.136**(7.623)5.998(9.435)

676
0.391
0.850

683
0.509
2.574

588
0.476
37.17

552
0.495
35.49

Panel B - Democratic Primaries

Log salaryElection for term-limited seat

Observations
R-squared
Mean dep var

Panel C - Republican Primaries

Log salaryElection for term-limited seat

Observations
R-squared
Mean dep var

Notes: This table anlayzes the effect of legislative salary on candidate selection using gubernatorial primary elections from
1950-1994. An observation is an election. Robust standard errors clustered by state in parentheses. All regressions include stateand year fixed effects. The contested election variable is a dummy for whether the election had more than one candidate. Salaryis given in terms of 1982 prices.
* significant at 10%; ** significant at 5%; *** significant at 1%

36

Table 6: State Politician Salary and Politician Characteristics
Panel A - State Legislative Salary and Legislator Characteristics
(1)

(2)

(3)

(4)

(5)

(6)

(7)

LawyerHas Political
Experience

Dep var:FemaleBlack

LatinoAgeCollege
DegreeSession length in election year
(in hundreds of days)

0.024(0.015)-0.029(0.022)-0.005(0.010)-0.017(0.010)-0.004(0.012)0.001(0.005)0.027(0.023)0.026(0.024)0.004(0.011)-0.615(0.775)-3.110***(0.755)0.133(0.424)-0.019(0.024)-0.009(0.016)0.001(0.020)0.015(0.021)0.000(0.013)-0.021*(0.010)0.016(0.027)0.040(0.029)-0.018(0.021)

Observations
R-squared
Mean dep var

9,904
0.034
0.208

9,903
0.017
0.100

9,839
0.076
0.0571

9,286
0.077
49.61

8,981
0.025
0.771

9,000
0.032
0.235

9,094
0.084
0.391

Log salaryElection for term-limited seat

Panel B - Governor Salary and Governor Characteristics
(1)

(2)

(3)

(4)

(5)

FemaleAge

College
DegreeLawyerHas Political
Experience-0.042(0.088)-0.110**(0.046)6.540**(2.524)-0.936(2.802)0.004(0.048)0.005(0.044)-0.132(0.165)-0.186(0.190)0.117(0.175)0.055(0.180)1710
0.276
0.0532

1710
0.269
49.10

1710
0.284
0.950

1707
0.277
0.538

1710
0.286
0.492

Dep var:Log salaryElection for term-limited seat

Observations
R-squared
Mean dep var

Notes: Panel A analyzes the effect of legislative salary on candidate selection using US state legislative elections from 1967-2003.
Panel B analyzes the effect of gubernatorial salary on candidate selection using gubernatorial primaries from 1950-1990. An
observation is one candidate. The dependent variable is a candidate characteristic. For example, we examine whether higher
pay is associated with a greater chance that a legislator is female or is a lawyer. Panel A is restricted to data from 11 states,
whereas Panel B contains data from all states. Robust standard errors clustered by state in parentheses. All regressions include
state and year fixed effects.
* significant at 10%; ** significant at 5%; *** significant at 1%

37

Table 7: The Effect of Politician Salary on Productivity
Panel A - Legislator Salary and Bill Introduction, and Approval
(1)

(2)

(3)

Log(Number of Bills
Introduce)
Log salary

(5)

(6)

Share of Bills Approved

0.017(0.036)0.004(0.037)0.215*(0.119)0.047(0.040)0.034(0.040)0.242**(0.114)0.013*(0.007)0.013*(0.007)0.012(0.012)

No
1,467
0.777
7.269

Yes
1,463
0.783
7.269

No
1,466
0.639
5.897

Yes
1,462
0.648
5.897

No
1,467
0.679
0.309

Yes
1,463
0.680
0.309

(4)

(5)

(6)

Session length in election year
(in hundreds of days)
Additional Controls
Observations
R-squared
Mean dep var(4)

Log(Number of Bills
Approved)

Panel B - Legislator Salary and Taxes, Expenditures, and Income
(1)

(2)

(3)

Log(Income per Capita)
Log salary

Log(Taxes per Capita)-0.015(0.009)-0.005(0.003)-0.001(0.002)0.009(0.010)0.013(0.008)0.002(0.010)0.003(0.017)0.016(0.010)-0.012(0.011)

No
981
0.994
2.700

Yes
981
0.998
2.700

No
981
0.988
7.535

Yes
981
0.989
7.535

No
981
0.979
6.839

Yes
981
0.985
6.839

Session length in election year
(in hundreds of days)
Additional Controls
Observations
R-squared
Mean dep var

Log(Expenditure per Capita)

Panel C - Governor Salary and Taxes, Expenditures, and Income

Log SalaryElection for term-limited seat

Observations
R-squared
Mean dep var

(1)

(2)

(3)

Log(Income
per Capita)

Log(Expenditure
per Capita)

Log(Taxes
per Capita)

0.016(0.020)-0.032***(0.011)-0.001(0.031)-0.031(0.019)0.023(0.032)0.002(0.023)

1943
0.995
2.674

1943
0.991
7.500

1943
0.987
6.809

Notes: This table analyzes the effect of salary for state politicians on politician performance. Panel A studies the impact oflegislator salary on bill-passing using data from 1968-2007. Panel B studies the impact of legislator salary on state finances
using data from 1972 and 1977-2000, as described in the Data Appendix. Panel C studies the impact of governor salary on state
finances using data from 1972 and 1977-1994, as described in the Data Appendix. All regressions include state fixed effects, year
fixed effects, and a dummy for the state having term limits for legislature seats in that year. An observation is alegislature-year. Observations are missing for some states in some years due to missing data on bills introduced and enacted.
Robust standard errors clustered by state in parentheses.
* significant at 10%; ** significant at 5%; *** significant at 1%

38

Table 8: Legislative Salary and Missed Roll Call Votes
(1)

(2)

Dep var:

Missed Vote (0 or 1)

Log salary-0.024***(0.007)0.009(0.016)0.071(0.046)-0.079***(0.015)-0.023***(0.006)0.008(0.016)
0.091*(0.046)-0.087***(0.016)
0.031*(0.017)0.048**(0.019)0.018(0.021)

3,282,096
0.021
0.0897

3,282,096
0.025
0.0897

Session length in election year
(in hundreds of days)
Log(Population)
Biennial session frequency
Any personal staff
Any shared staff
Any district staff
Observations
R-squared
Mean dep var

Notes: This table analyzes the effect of legislative salary on missed roll call votes using OLS. Standard errors clustered by statein parentheses. An observation is a roll call vote for one legislator in 1999-2000. The roll call vote data does not indicate
whether the vote took place in 1999 or 2000; the salary date is from 2000, and is merged to all observations from acorresponding state. All columns include census region (South, Northeast, Midwest, and West) fixed effects.
* significant at 10%; ** significant at 5%; *** significant at 1%

Table 9: Legislator Salary and Corruption

Dep var:Log salary
Observations
R-squared
Mean dep var(1)
Log Number of Convictions(2)
Perceptions of Corruption-0.024(0.218)9255
0.95-2.7530.040(0.151)
14597
0.301
0.058

Notes: OLS regressions with standard errors clustered by state in parentheses. The dependent variable in column 1 is the log
number of convictions relative to the number of elected officials (Maxwell and Winters, 2005). The dependent variable incolumn 2 is an overall corruption scale, computed as the average of several normalized variables on perceived corruption levels
from a survey of state house news reporters (Boylan and Long, 2003). Controls in both regressions are session length, a dummy
for term limited seats, a dummy for seats with 4 year terms, a dummy for states with biennial session frequency, and measuresof legislator staff. Corruption committed today may take time before it shows up in convictions or in perceptions of corruption.
Thus, in column 1, five-year lagged salary is used. Likewise, in column 2, the sample is restricted to observations between 1994
and 1999 (five years before the corruption survey).
* significant at 10%; ** significant at 5%; *** significant at 1%

39

Table 10: Legislative Salary, Family Income, and Outside Labor Supply
Panel A

(1)
(2)
(3)
Dep var: Family Income (in $)Total salary (in dollars)

0.759***(0.102)0.436***(0.143)-3,823.909(2,685.972)

0.706***(0.106)

0.367**(0.139)-2,740.866(2,505.795)

No
5,703
0.080
101394

No
5,703
0.111
101394

Yes
5,703
0.096
101394

Yes
5,703
0.115
101394

(1)

(2)

(3)

(4)

Session length in election year (in hundreds of days)
Year fixed effects
Observations
R-squared
Mean dep var
Panel B(4)

Dep var: Has Outside Job (0 or 1)
Total salary (in thousands of dollars)-0.006***(0.001)-0.004***(0.001)-0.047**(0.022)-0.006***(0.001)-0.003***(0.001)-0.056**(0.023)

No
5,539
0.088
0.651

No
5,539
0.094
0.651

Yes
5,539
0.091
0.651

Yes
5,539
0.099
0.651

Session length in election year (in hundreds of days)
Year fixed effects
Observations
R-squared
Mean dep var

Notes: OLS regressions with standard errors clustered by state in parentheses. The data is from the surveys of state legislators
done by Carey et al. (1995) and Carey et al. (2002). The log of state population, log of GDP per capita, a dummy for stateswith biennial session frequency, and measures of legislator staff are also included in regressions 2 & 4.
* significant at 10%; ** significant at 5%; *** significant at 1%

40

Table 11: Politician Salary and (Normalized) Time Use
Panel A: Time on Legislative Activities
Dep var:(1)
Studying proposed
legislation(2)
Developing newlegislation(3)
Building coalitions
within party(4)
Building coalitions
across parties

Total salary (in tens of thousands of dollars)
Session length in election year
(in hundreds of days)-0.025(0.018)-0.008(0.049)0.034(0.021)-0.126**(0.047)-0.010(0.016)0.028(0.050)-0.038***(0.014)-0.107***(0.037)

Observations
R-squared

2,869
0.019

2,859
0.053

2,832
0.003

2,844
0.018

Panel B: Time on Constituent Services
Dep var:(5)
Keeping in touchwith constituents(6)
Helping
constituents withproblems(7)
Making sure the
district gets a fairshare

Total salary (in tens of thousands of dollars)
Session length in election year
(in hundreds of days)

0.043*(0.024)0.121*(0.064)0.046*(0.025)0.079(0.066)

0.048**(0.022)0.085(0.065)Observations
R-squared

2,854
0.093

2,859
0.103

2,867
0.104

(8)
Campaigning andfundraising(9)
Fundraising for self

(10)
Fundraising for
caucus

0.054***(0.019)-0.013(0.042)0.065***(0.021)
0.023(0.051)0.028(0.026)-0.082(0.073)

2,857
0.087

2,799
0.103

2,735
0.034

Panel C: Time on Fundraising
Dep var:
Total salary (in tens of thousands of dollars)
Session length in election year
(in hundreds of days)
Observations
R-squared

Note: OLS regressions with standard errors clustered by state in parentheses. The dependent variable in each regression is astandardized measure from 1 to 5 of the amount of time a politician spends with each activity. The question was ”How muchtime do you actually spend on each of the following activities?” with 1=Hardly Any to 5=Great Deal. Data is from the surveyof Carey et al. (2002).
* significant at 10%; ** significant at 5%; *** significant at 1%

41

Table 12: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes,
Effects By Party Strength (Mayhew Ratings)
Panel A - All Elections

Log salary
Strong party * Log salary

Observations
R-squared
Mean dep var(1)
Contested
Election(2)
Number of
Candidates(3)
Margin ofVictory(4)
Incumbent
Re-elected(5)
Democrat
Wins

0.040**(0.018)-0.034(0.023)0.066**(0.025)-0.038(0.046)-3.062*(1.795)5.396**(2.013)-0.025(0.017)0.024(0.016)0.086**(0.032)-0.046(0.038)82,5330.162
0.734

82,4470.176
1.933

80,2480.164
47.04

82,3530.141
0.685

82,027
0.144
0.590

(1)
Contested
Election(2)
Number of
Candidates(3)
Margin ofVictory(4)
Incumbent
Re-elected(5)
Democrat
Wins

0.056**(0.024)-0.038(0.027)

0.097***(0.032)-0.057(0.049)-3.401(2.288)4.916**(2.346)-0.034*(0.020)0.031*(0.017)

0.096**(0.039)-0.054(0.041)62,7330.168
0.719

62,6730.181
1.908

60,8700.168
48.30

62,5790.141
0.706

62,325
0.138
0.597

(1)
Contested
Election(2)
Number of
Candidates(3)
Margin ofVictory(4)
Incumbent
Re-elected(5)
Democrat
Wins

0.018
(0.016)-0.032(0.022)0.021(0.024)-0.004(0.050)-2.864(1.802)
6.785***(1.766)-0.007(0.016)0.012(0.028)0.064**(0.026)-0.031(0.033)19,8000.146
0.782

19,7740.167
2.011

19,3780.153
43.05

19,7740.149
0.617

19,7020.175
0.565

Panel B - Lower House Elections

Log salary
Strong party * Log salary

Observations
R-squared
Mean dep var

Panel C - Upper House Elections

Log salary
Strong party * Log salary

Observations
R-squared
Mean dep var

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003. An observation is an election. Robust standard errors clustered by state in parentheses. All regressions include stateand year fixed effects. Controls for term limited seats, session length 4 year term limited seats, the log of state population, andlog of GDP per capita are included in all regressions. The contested election variable is a dummy for whether the election had
more than one candidate. Column 2 is restricted to elections with 6 or fewer candidates.
* significant at 10%; ** significant at 5%; *** significant at 1%

42

Web Appendix: For Online Publication Only
This Appendix consists of four parts. Appendix A provides discussion on additional potential identification concern. Appendix B describes the data. Appendix C presents a formal model accompanying
our conceptual framework. Appendix D presents additional results.

Appendix A

Additional Discussion

In the main text, we discuss a number of potential endogeneity concerns, focusing on the ones that
we believe to be of greatest concern. For brevity, we do not attempt to address all possible concerns,
even though there are others which may be important.
One additional concern is that politicians could be more or less willing to have their salaries
raised depending on public perception of government (which could also correlate with whether people
are interested in running for office, or how easy it is to pass bills). For example, if public perception isvery negative, it could be that politicians may be reluctant to have their salaries increased. Further,
only certain types of people may be willing to run in toxic political climates. By including year fixed
effects, we address national shocks to public perception of government (e.g., the Watergate scandal
in the 1970s), but there may be state-level shocks (i.e., a state-level scandal). One way we test forthis possibility is by looking at whether legislator salary is less likely to increase after periods of highcorruption levels within states, where corruption is measured by the number of convictions per 1,000
state government officials. It is likely that public perceptions about state governments are lower than
average after an unusually high number of state official convictions. To examine this, for each state,
we identify the year with the greatest number of corruption convictions. Then, we create a dummy
for the four years following that year, identifying a period of time in each state where corruption
convictions may be most salient in the public mind. We regress salary on this dummy, controllingfor state and year fixed effects, state population and GDP, and legislature characteristics. We findno evidence that salary is more or less likely to change during periods following a high number of
corruption convictions.1
Assuming that the public takes a more negative view of government after state officials areconvicted of corruption, this provides some evidence that negative changes in public perception do
not seem to inhibit salary increases. We acknowledge, however, that such a concern could still be
relevant for our results.

Appendix B

Data Appendix

In this section, we explain the sources for different variables, as well as provide variable definitions
for the variables in our regressions.

B.1

Politician Salaries

Data on politician salaries was collected from the Book of the States. For legislators, we create asingle annual salary figure for each state by annualizing the relevant time frame. Salaries that are
given for biennial legislative sessions are divided by two to obtain an annual salary figure. Daily
salaries are multiplied by the average number of days a legislature is in session over time. Governors
are paid annual salaries. Following Di Tella and Fisman (2004), we focus on base salary excluding
non-salary benefits.
1
In case memory of corruption convictions is more fleeting, we also looked at two years following convictions, andgot similar results.

1

Log Salary. Logarithm of state politician salary in 1982 dollars.
Total Salary. State politician salary in 1982 dollars.
State level CPIs. State level CPIs are calculated using a weighted sum of food, apparel,transportation, medical, educational, recreational, and other prices in states. Weights capture average consumer income allocation, and are consistent with Moretti (2013), who constructs price indicesat the city (MSA) level. We have data on the prices necessary to calculate the state CPIs for 13
years between 1980 and 2001. For years missing within that period, state CPIs are filled in using
linear interpolation. For years before 1980 and after 2001, we use the national CPI.

B.2

Election Results.

Data on state legislature elections comes from ICPSR 21480, containing almost all state legislature
general elections from 1967 to 2003. It includes information on the number of candidates, the marginof victory, and office term length for each contested office. (Term length is believed to be a potentially
important determinant of political performance; for a recent exploration of the effect of term length onpolitician productivity in Argentina, see Dal Bo and Rossi (2010).) Data on gubernatorial primaries
are from Ansolabehere et al. (2007) and were kindly provided by James Snyder.
Contested Election. A dummy for whether or not an election is contested.
Number of Candidates. A measure of the number of candidates running in an election. In
our analysis, we restrict the sample to those elections with six or fewer candidates.
Margin of Victory. This measures how much the elected politician won the vote by. It isequal to the election winner’s votes minus second the place candidate’s votes divided by the total
votes cast.
Incumbent Re-elected. A dummy for whether or not the incumbent is re-elected.
Democrat Wins. A dummy for whether or not the elected politician is a Democrat. Thisvariable is equal to zero if the politician is a Republican, and Independent, or a member of anotherparty.
Election for seat with four year term length. A dummy for whether or not an election
is for a seat with a four year term limit.
Democratic Primary A dummy for whether or not a Gubernatorial election is a Democratic
primary. This variable is equal to zero if the election is a Republican primary.

B.3

Term Limits.

Obtained from the website of the Council of State Governments for state legislators and from Listand Sturm (2006) for governors.
Election for Term-Limited Seat. A dummy for whether or not an election is for a termlimited seat.

B.4

Other Legislature Characteristics.

We classify legislatures as part-time or full-time based on the classification of the National Councilof State Legislatures. We collected data on legislator staff and legislative session frequency from theCouncil of State Governments.

2

Any Personal Staff A dummy for whether a legislator has any staff working directly for
him/her.
Any Shared Staff A dummy for whether a legislator has any shared staff.
Any District Staff A dummy for whether a legislator has access to any legislative district
staff.
Biennial Session Frequency A dummy for whether or not legislative sessions occur biennially. This variable is equal to zero if sessions occur annually.

B.5

Politician Characteristics.

For state legislators, information on characteristics were kindly provided by Kathleen Bratton. The
data are an extension of characteristics in Bratton and Haynie (1999). The data contain informationon legislators in 11 states: Arizona, Arkansas, California, Connecticut, Florida, Illinois, Nevada,Ohio, Texas, South Carolina, and North Carolina. The legislator characteristics data go from 1967
- 2003.
For governors and candidates in gubernatorial primaries, we collected our own using using
the biographies on the website of the National Governor’s Association and Google Searches. This
information was given in paragraph form and coded into attributes by hand. The attributes ofinterest including gender, age, having a college degree, having a law degree, and having military
experience were chosen (1) because they were straightforward to measure and (2) because they weresimilar to the variables provided by Bratton. Gender was coded by use of gender-specific pronouns
in the biography (e.g. “he”, “wife”, and “her”). Age was coded up using the information on thegovernor’s date of birth, and was defined as the governor’s age upon entering office. Information on
having a college degree or law degree was coded up based on the mention of this information in the
biographical paragraph. For governors, characteristics data are from 1950 - 1990.
Female. A dummy for whether or not a candidate is a female.
Black. A dummy for whether or not a candidate is black.
Latino. A dummy for whether or not a candidate is of Latin American origin.
Age. Candidate age.
College Degree. A dummy for whether or not a candidate has a college degree.
Lawyer. A dummy for whether or not a candidate has a law degree.
Has Political Experience. A dummy for whether or not a candidate has political experience
prior to the focal election.

B.6

Bill-passing.

Information on legislative productivity, that is, the number of bills proposed and passed was handcollected from the The Book of the States. Information here is given in terms of the number of bills
during regular sessions and during special sessions (there is no division by upper and lower house).
Most of our analysis focuses on using the data on bill introduction during regular sessions.
Number of Bills Introduced. A count of the number of bills introduced by the legislature.
Number of Bills Approved. A count of the number of bills approved by the legislature.
Share of Bills Approved. A count of the number of bills introduced by the legislature
divided by a count of the number of bills approved by the legislature.
3

B.7

Roll call voting.

Roll call voting data were obtained from Wright (2004). The data are a comprehensive record of all
roll-call voting in state legislatures during the years 1999 and 2000. Our main outcome measure iswhether a legislator was present for a vote.
Missed Roll Call Vote A dummy for whether or not a legislator missed a given roll call vote.

B.8

Politician Corruption.

We use two measures of politician corruption: the number of convictions per 1,000 state government
officials Maxwell and Winters (2005), and a survey measure of the level of perceived corruption instate government developed by Boylan and Long (2003).
Number of Convictions. The number of convictions of state government officials in corruption cases per 1,000 state government officials.
Perceptions of Corruption. A survey measure of State House new reporters’ perceptionsof state public officials’ corruption level from Boylan and Long (2003).

B.9

State Fiscal Policy.

We also use state fiscal policy to measure the quality of state politicians. We collected data on state
GDP, expenditures, and taxes from U.S. Census Bureau’s Annual Survey of Government Series.
Income per Capita. State income per capita.
Expenditure per Capita. State government expenditures per capita.
Taxes per capita Total state taxes per capita. This includes income, sales, and corporate
taxes.

B.10

Politician Surveys.

Information on politician outside labor supply, family income, and time use was collected from thesurvey of legislators done by Carey et al. (1995) and Carey et al. (2002). Surveys were conducted
anonymously through the mail. The 1995 survey was mailed to every upper house legislator and 77
percent of lower house legislators. In addition, it was mailed to all former legislators who last servedin 1993 or 1994. For the 1995 survey, there were 3,542 legislators who responded and the response
rate was 47 percent. For the 2002 survey, there were 2,982 state legislators who responded and the
response rate was 40.1 percent.
Family Income. Reported family income in US$ across 5 categories. We assign the categoryof $0-$25,000 to $25,000, the category of $25,000-$50,000 to $37,500, category of $50,000-$75,000 to$62,500, the category of $75,000-$250,000 to $175,000, and the category of $250,000+ to $250,000.
Has Outside Job. A dummy for whether or not a legislator has an outside job while serving
as a legislator.
Studying Proposed Legislation The amount of time on a scale from one to five a legislator
reports to spend studying proposed legislation.
Studying Proposed Legislation. The amount of time on a scale from one to five a legislator
reports to spend studying proposed legislation.

4

Developing New Legislation. The amount of time on a scale from one to five a legislator
reports to spend developing new legislation.
Building Coalitions Within Parties. The amount of time on a scale from one to five a
legislator reports to spend building coalitions within his/her own party to develop legislation.
Building Coalitions Across Parties. The amount of time on a scale from one to five a
legislator reports to spend building coalitions across parties to develop legislation.
Keeping in Touch with Constituents. The amount of time on a scale from one to five a
legislator reports to spend keeping in touch with constituents.
Helping Constituents with Problems. The amount of time on a scale from one to five a
legislator reports to spend helping constituents with problems related to government.
Making Sure the District Gets a Fair Share. The amount of time on a scale from one to
five a legislator reports to spend making sure his/her district gets a fair share of government money
and projects.
Campaigning and Fundraising. The total amount of time on a scale from one to five a
legislator reports to spend campaigning and/or fundraising.
Fundraising for Self. The amount of time on a scale from one to five a legislator reports tospend fundraising for him/her self.
Fundraising for Caucus. The amount of time on a scale from one to five a legislator reports
to spend fundraising for caucus.

B.11

Party Strength.

We measure political party strength using the Mayhew (1986) measure of Total Party Organization
which captures the degree to which local politics are run by organized party machines. Each stateis scored from 1 to 5 on the Total Party Organization scale.
Strong Party. A dummy for whether or not a state has strong political parties. A strong
party state has a Total Party Organization score of 4 or 5.

B.12

Narrative Analysis

In this section, we describe how we assembled the sample for our narrative analysis. We attempted
to create a list of potential salary increases whose passage or failure was determined by forces which
are plausibly exogenous for our analysis. The list was assembled by a research assistant.
We began with an extensive systematic search of historical newspapers. We used search terms
such as “legislative salary increase” and “legislative compensation commission.” We conducted oursearches on ProQuest’s historical newspaper database and the Google newspaper archive (http://news.google.com/newspapers). Most searches were done on Google newspaper archive because it
has a wide range of local newspapers, whereas ProQuest is more concentrated in national newspapers.
We began our searches aiming at large absolute salary changes that were observed in our data.2
For example, we would start the Google newspaper archive searches with “california legislative salary
increase 1998.” We looked for information about the nature of the decision, such as whether thecommission vote was contested, or if the executive or different branches of the legislature were atodds with one another about the decision.
2

We began with large changes because they seem to be more widely covered in the news, and news coverage ofsalary changes several decades back was often incomplete.

5After doing searches related to large salary changes, we broadened the search within particularstates. We looked for other salary changes within that state (e.g., “california legislative salary increase”), as well as proposed and rejected salary changes (e.g., “california legislators salary rejected”).
We looked for the same sort of information about the nature of the decision.
We emphasize that this list is non-exhaustive. It should thought more of as a list of changes(or potential changes) that were covered in newspapers or that we were able to find information forin some other manner (e.g., because someone from a state compensation commission was willing toscan and send us minutes from past meetings of the compensation board). Although the list is surely
not all non-assured salary changes, we believe that it represents some of the most important changes
or non-changes, including ones that would be salient for politicians or potential candidates.3• Florida, 1970. Increase from $1,000/year to $12,000/year. Passed by the legislature. The
governor tried to veto, but a judge eventually ordered him to approve it because his veto was
unconstitutional.
• Rhode Island, 1973. Failed proposed increase from $300/year to $2,000/year. “Narrowly
defeated” by the electorate, taken directly from the article: “In Rhode Island, a proposal tolift legislative salaries from $300 a year to $2,000 by 1975 was narrowly defeated”.
• Texas, 1973. Failed proposed increase from $4,800 to $15,000. Rejected by the electorate,with the proposed increase gaining 43.3% of the vote.
• Washington, 1973. Failed proposed large increase of 193%; enacted instead a small increaseof 5.5%. The proposed increase was passed by the legislature. However, a Seattle furnituresalesman initiated a ballot measure to lower the proposed raise to only 5.5%. The balletmeasured was passed by the electorate.
• Washington, 1976. Increase from $3,800 to $7,200. Passed by the House on the relatively
narrow margin of 53 to 42. (After passing the House, the bill was sent to the Senate, but wecould not locate the Senate vote total.)• Arizona, 1978. Failed proposed increase from $6,000/year to $9,200/year. Rejected by theelectorate, 54.6% to 46.4.4• Arizona, 1980. Increase from $6,000/year to $15,000/year. Passed by the electorate, 55.1%
to 44.9%.
• Pennsylvania, 1982. Increase of $10,000/year. Passed by the legislature, but controversial.
According to one account, the bill drew heavy criticism from legislators and public interest
groups, but passed nonetheless. It was not possible to locate a record of the vote tally.
• California, 1990. Increase of $13,282/year. The increase was instituted by a salary commission.5
3
As an example of what our list does not cover, many small proposed salary changes which passed or did not pass
may not be in our sample, as many of them are not covered in newspapers.
4
In Arizona, the state compensation commission does not have a final say. Instead, the compensation commission
proposes the salary change; the proposed salary changes goes to a public ballot and is passed by a simple majority.
5
No record of a vote by the salary commission could be located. In 1990, California voters approved proposition
112, which included a series of ethical reforms proposed by the legislature. Reforms included giving up honorariums
for making speeches to special interest groups, and placing a $250 limit on gifts from most sources. Prop 112 also
created the compensation commission, which set new legislator salaries in 1990. Normal legislators were granted a
28.5% increase from $40,816 to $52,500 yearly “partially to help make up for the loss of speaking fees” (Gillam, 1993,
May 10).

6

• Alabama, 1991. Failed proposed salary increase of 30%. Passed by the House, but rejectedby the Senate.6• Idaho, 1991. $5,400/year increase. While it was passed by the legislature, the GOP caucus
leader noted that his caucus was divided on the subject.
• Michigan, 1993. Increase from $45,450 to $47,722.50. Passed by the state compensationcommission, but nearly overturned by the State House.7• Arizona, 1994. Failed proposed increase from $15,000/year to $19,750/year. Rejected by theelectorate, 60.47% to 39.53%.
• California, 1994. Increase of $22,449/year. Passed by the compensation commission in a
divided vote of 5 to 2.8• Arizona, 1998. Increase from $15,000/year to $24,000/year. Passed by the electorate, 56.6%
to 43.4%.
• California, 1998. Increase of $23,837/year. Passed by the compensation commission in a
divided vote of 4 to 3.9• New Jersey, 1999. Increase from $35,000/year to $49,000/year. Passed by the House, 48 to
24 and by the Senate, 21 to 14.
• Michigan, 2000. Increase from $55,054 to $77,400. Passed by the Compensation Commission
despite disagreement in the state legislature.

6

The House voted for a 30% raise but the senate refused to even consider it and instead voted 19-16 for a separate
bill to create a compensation commission. The House then passed the senate proposed bill.
7
The House tried to overturn the state compensation commission, but missed the two-thirds threshold by 3 votes.
8
The large California legislator salary increase appearing in our data in year 1996 resulted from a proposal made in1994 by the California Citizens Compensation Committee.
9
The increase that appears in our data for the year 2000 was the result of a Citizens Compensation Commission
decision in 1998, in which the group decided in a 4-to-3 vote to increase legislator salaries by 25.9%.

7

Appendix C

Formal Model

We develop a very simple model to analyze how politician wages affect the number of candidates
running for an office; the quality of candidates and politicians; and politician productivity and public
good provision, accompanying our conceptual framework in Section 3. The purpose of this model
is to lay out a framework for interpreting our comparative statics rather than to provide a realistic
account for how salary impacts performance. The basic idea is that when politicians are paid more,they face a greater incentive to work hard once in office in order to keep their seat. In addition, thegreater reward from being in office leads to both higher quality people wishing to run, as well as
more candidates.
Politicians are risk-neutral citizen-candidates endowed with quality θ, where θ is distributed
across the population with distribution function F (·). The size of the population is set to unity.
We denote the measure of candidates who chose to run by N . Politicians receive a salary of w perperiod in office, whereas working in the private sector leads to a salary of r(θ) per period. We assume
that r0 (θ) > 0, that is, higher ability people have higher outside options. In addition to their salary,
politicians also receive a non-pecuniary benefit, z, from serving in office. While in office, politicians
choose a costly effort (productivity) level e, with a convex cost function c(·). Effort and quality leadto public goods Gt , with Gt = Gt (et ), G0 > 0. Politicians can serve for two periods so those whoare elected in the first period can be re-elected for the second period but cannot run again after thesecond period. Politicians are re-elected with probability π, which is increasing, but concave withrespect to public goods (π 0 > 0 and π 00 < 0) . That is, we assume that voters vote retrospectively.
Running for office is assumed to be costless.
For simplicity, we will assume that G (et ) = et . (The analysis is similar with a more generalpublic good production, where public goods are weakly concave in e, i.e. G00 (e) ≤ 0.)
Many models of politician salary analyze an infinite period economy (Besley, 2004; Ferraz and
Finan, 2010). For our purposes, however, we are merely using the model to illustrate the maineconomic forces. Thus, we assume a 2-period economy with no discounting. We consider the caseof an office that has been newly created with no incumbent going into the first period. Thus, atthe start of the first period, one candidate is randomly selected to serve among those who choose
to run. At the end of the first period, if the incumbent loses, a new politician is randomly selectedfrom among the challengers.
We deal with two cases. First, we analyze elections where there are no political parties. In thesecond case, we add political parties.

C.1

No Political Parties

In the second period, a politician will exert 0 effort because there is no incentive to get re-elected.
Conditional on having exert e1 in the first period, the politician’s expected utility from runningis π(G1 (e1 ))(w + z) + (1 − π(G1 (e1 ))r(θ), whereas the utility from not running is r (θ) ; thus, theincumbent politician runs if w + z > r (θ) .Challenger politicians will run for office if w + z > r (θ) ,which occurs when θ < θ∗ ≡ r−1 (w + z) . By inspection, the share of candidates running, N = F (θ∗ )is increasing in politician salary. In addition, since only citizens below a certain quality thresholdchoose to run, increasing the quantity of candidates also increases the average quality.
The incumbent politician optimally chooses first period effort trading off the benefit from being
re-elected against the cost of effort, leading to the first order condition: M = π 0 (e1 ) (w + z − r (θ)) −c0 (e1 ) = 0. To examine the incumbent politician’s effort changes with respect to wage, we apply the
implicit function theorem:

8

− ∂M
de1
−π 0
π0
∂w
= 00
= − ∂M=dw
π (e1 ) (w + z − r (θ)) − c00 (e1 )c00 (e1 ) − π 00 (e1 ) (w + z − r (θ))∂e1Thus, we have that sgn



de1
dw



= sgn (c00 (e1 ) − π 00 (e1 ) (w + z − r (θ))) . Since the politician will only

choose to run if w + z > r (θ), we have that c00 (e1 ) − π 00 (e1 ) (w + z − r (θ)) > 0, conditional on the1politician intending to run. Given c00 > 0 and π 00 < 0, we have thus shown that dedw > 0 for first
period politicians who intend to run in the second period.
In our highly stylized model, since challengers are selected randomly from among those whochoose to run, increasing the wage will unambiguously increase the chance the incumbent politician
is re-elected. However, if having more candidates decreases the probability that the incumbent is
re-elected, than the impact of the wage on the probability of re-election is ambiguous, as in thesimulation of the infinite period model in Ferraz and Finan.
A politician is willing to serve in office in the first period if maxe1 [w+z −c (e1 )+π (e1 ) (w + z)+(1 − π (e1 )) r (θ)] > 2r (θ) . If w + z < r (θ), it is not worthwhile for the citizen in the first period toenter politics. If w + z > r (θ) , he can always obtain at least w + z + r (θ) , the return from exerting
0 effort, and therefore he prefers to run. Thus, the same people will choose to run in the first period
election are the same people who choose to run in the second period election. Hence, we have thatde1
dw > 0 for all politicians who run for office at time 0. To summarize, we have proven the following
the result:
Result: An increase in politician salary (1) Increases the number of candidates running forboth open and non-open seats; (2) Increases the quality of politicians running for both open andnon-open seats; and (3) Increases the amount of effort politicians provide and increases the quantityof public goods provided.
We now consider the impact of political parties on the relationship between politician salaryand outcomes.

C.2

Political Party Strength as Barrier to Entry

In the empirical results, we show that there the relationship between wage and the number ofcandidates is very small. One possibility, that we now illustrate, is that in states with strong politicalparties, entry is decided by the party. As a result, potential candidates are less able to respond tosalary increases.
When political parties are strong, a citizen who would like to run for office may not be able
to simply enter a race. Rather, he must affiliate himself with a party and choose to fulfill certain
responsibilities for the party. These may come before the party backs him to run for office or they
may come after has assumed office. In either case, such party responsibilities may be viewed as a tax
on the return from political office. That is, a certain share, τ , of the gains from office are “taxed”
away by a political party. The stronger are political parties, the larger the presumed tax; thus, τ isa measure of the strength of a political party.
When there is a tax, a politician will run for office in the second period if (1 − τ ) (w + z) >r (θ) , or if θ < θ∗ ≡ r−1 ((1 − τ ) (w + z)) . Denoting g(·) = r−1 (·), we have, similar as before,∗
0that dθdw = (1 − τ ) g . To examine how the impact of salary on candidate quality varies with partyd2 θ∗
d2 θ ∗
strength, we would like to compute the cross partial derivative dwdτ. We get that dwdτ= −g 0 −00(1 − τ ) (w + z) g . We have not made assumptions so far on the second derivatives of r (θ) . However,
if r00 (θ) < 0, then g 00 (θ) > 0, since g 00 (θ) = −[r0 (g (θ))]2 · r00 (g (θ)) · g 0 (θ) . Thus, if r00 < 0, we haved2 θ∗that dwdτ
< 0.
9

Likewise, increasing τ may dampen the impact of w on e1 .
It is important to reiterate that our model is highly stylized and is simply intended to groundthe empirical analysis, and not provide new theoretical insights. The contribution of the paper isthe empirical analysis.

10Appendix D

Additional Results

Figure D1: Legislator Pay Over Time By State, In Real Terms

11

Figure D1: Legislator Pay Over Time By State, In Real Terms (Cont.)

12

Figure D2: Legislative Session Length Over Time By State

13

Figure D2: Legislative Session Length Over Time By State (Cont.)

14

Figure D3: Legislative Productivity Over Time, Number Of Bills Introduced and Passed

15

Figure D3: Legislative Productivity Over Time, Number Of Bills Introduced and Passed (Cont.)

16

Figure D4: Governor Pay Over Time By State, In Real Terms

17

Figure D4: Governor Pay Over Time By State, In Real Terms (Cont.)

18

Table D1: Political Parties, Family Income, and Prior Experience in Government
(1)

(2)

Dep Var:Family Income

Prior Office in Government

Republican

6,906.484***(2,446.366)-0.053***(0.011)

Observations
R-squared
Mean dep var

3118
0.110
101,394

3410
0.044
0.051

Notes: This table analyzes whether Republican candidates have different family incomes and prior experience inpublic office than Democratic candidates. The data is from the surveys of state legislators done by Carey et al. (1995)
and Carey et al. (2002). The sample is restricted to Republican or Democratic candidates. State and year fixed
effects are included. Standard errors clustered by state in parentheses. Controls for session length, the logarithm of
population, and the logarithm of state GDP are included.
* significant at 10%; ** significant at 5%; *** significant at 1%

19

Table D2: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomeswith State-Specific Year Trends
Panel A - All Elections

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.031**(0.012)0.070***(0.021)0.011(0.014)0.070**(0.032)-0.023(0.022)0.055***(0.020)0.114***(0.031)
0.015(0.028)0.111*(0.062)-0.026(0.033)-0.903(1.227)-3.557**(1.525)-0.238(0.950)-3.533(2.726)-0.561(1.664)-0.007(0.010)-0.017(0.011)0.009(0.010)-0.139***(0.030)-0.111***(0.015)0.062**(0.025)-0.013(0.022)0.019(0.012)0.018(0.035)0.018(0.026)

82,5330.162
0.734

82,4470.176
1.933

80,2480.162
47.04

82,3530.142
0.685

82,0270.144
0.590

Panel B - Lower House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.044***(0.016)0.015(0.017)0.090**(0.034)-16.077***(3.120)0.074***(0.026)0.015(0.032)0.132*(0.067)-10.029**(4.209)-0.957(1.666)-0.066(1.093)-4.803(2.967)-192.811(281.242)-0.012(0.012)0.014(0.010)-0.141***(0.032)-22.822***(2.446)

0.067**(0.033)0.023*(0.013)0.018(0.036)12.813**(4.770)62,7330.168
0.719

62,6730.181
1.908

60,8700.167
48.30

62,5790.142
0.706

62,3250.137
0.597

Panel C - Upper House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.012
(0.011)0.000(0.014)-0.003(0.037)-0.026(0.026)0.027(0.018)0.016(0.028)0.024(0.060)-0.022(0.038)-0.848(1.437)-1.234(1.220)0.832(3.532)0.868(0.992)0.004(0.010)0.006(0.018)-0.129***(0.033)0.005(0.030)0.051***(0.017)0.005(0.014)0.023(0.038)0.024*(0.013)19,8000.146
0.782

19,7740.167
2.011

19,3780.151
43.05

19,7740.150
0.617

19,7020.177
0.565

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003. An observation is an election. Robust standard errors clustered by state in parentheses. All regressions include stateand year fixed effects. The contested election variable is a dummy for whether the election had more than one candidate.
Column 2 is restricted to elections with 6 or fewer candidates. Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

20

Table D3: The Effect of Legislative Salary on Legislative Productivity with State-Specific Year
TrendsLog(Number of Bills
Introduce)

Log salary

Log(Number of Bills
Approved)

Share of Bills Approved

(1)

(2)

(3)

(4)

(5)

(6)

0.010
(0.042)0.049(0.040)0.003(0.007)0.340***(0.101)0.014(0.043)0.247*(0.126)0.121(0.084)-0.001(0.017)0.003(0.007)0.002(0.010)-0.001(0.017)

1,466
0.640
5.897

1,462
0.712
5.897

1,467
0.742
0.309

1,463
0.742
0.309

Election for term-limited seat

0.042
(0.059)-0.002(0.040)0.250*(0.127)0.099(0.077)

Observations
R-squared
Mean dep var

1,467
0.805
7.269

1,463
0.812
7.269

Session length in election year (in hundreds of days)

Notes: This table analyzes the effect of legislative salary on legislative productivity using data from 1968-2007. Robust standard
errors in parentheses. All regressions include state and year fixed effects. An observation is a legislature-year. Observations are
missing for some states in some years due to missing data on bills introduced and enacted. Robust standard errors clustered bystate in parentheses.
* significant at 10%; ** significant at 5%; *** significant at 1%

21

Table D4: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomesby Full-Time and Part-Time Legislatures
Panel A - Full-Time Legislatures

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins-0.020(0.060)0.075(0.067)0.022*(0.012)0.122(0.075)-0.042(0.065)-0.096(0.156)0.142(0.098)0.030(0.030)0.219*(0.106)-0.035(0.097)8.189*(4.020)-0.747(0.728)-1.849*(0.962)-8.027(4.746)-3.859***(1.131)-0.052(0.042)0.000(0.026)-0.006(0.007)-0.193***(0.041)-0.147***(0.036)-0.001(0.061)-0.082(0.055)0.002(0.008)-0.020(0.029)0.053(0.054)

23,4630.159
0.734

23,4620.216
1.933

22,8500.158
47.04

23,3990.181
0.685

23,3540.048
0.590

Panel B - Part-Time Legislatures

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.031**(0.012)0.067***(0.018)0.019(0.045)0.048(0.035)-0.018(0.021)0.060***(0.016)
0.095***(0.030)0.068(0.087)0.050(0.089)-0.010(0.035)-1.342(1.109)-4.513**(1.891)
2.131(3.022)-2.125(3.299)0.707(2.106)-0.014(0.014)-0.021**(0.009)0.044(0.036)-0.117***(0.035)-0.099***(0.016)

0.068**(0.026)0.013(0.013)0.063(0.061)
0.033(0.046)0.006(0.023)59,5290.148
0.734

59,5060.129
1.933

57,8570.165
47.04

59,4130.127
0.685

59,1320.183
0.590

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003. An observation is an election. Robust standard errors clustered by state in parentheses. All regressions include stateand year fixed effects. The contested election variable is a dummy for whether the election had more than one candidate.
Column 2 is restricted to elections with 6 or fewer candidates. Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

22

Table D5: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes,
Analysis for Border Districts in Similar States
All Elections

Log salary(mean) upperhou
Session length in election year (in hundreds of days)
Election for seat with four year term length

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.015
(0.031)
0.056(0.057)0.033(0.061)-0.007(0.060)0.004(0.041)0.149*(0.075)0.049(0.068)-0.037(0.086)0.849(1.460)2.684(4.010)1.243(2.943)-7.863*(4.407)0.040(0.033)-0.001(0.034)-0.026(0.050)-0.136***(0.048)0.101***(0.026)-0.013(0.036)-0.028(0.039)0.035(0.048)15,6390.612
0.770

15,6360.612
1.960

15,461
0.622
46.13

15,6380.534
0.716

15,6390.662
0.565

Observations
R-squared
Mean dep var

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003, restricting to elections in districts on state borders. States in the top 25th percentile of distance in terms of
population, GDP, unemployment, and demographics are excluded from the sample. An observation is an election. Robust
standard errors clustered by state in parentheses. All regressions include border district pair-year fixed effects (e.g. fixed effectfor elections in District A in eastern California and District B in western Nevada in 1970) and state fixed effects. The contested
election variable is a dummy for whether the election had more than one candidate. Column 2 is restricted to elections with 6
or fewer candidates. Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

Table D6: Legislative Salary and Missed Roll Call Votes, Analysis for Border Districts

Log salary
Session length in election year
(in hundreds of days)
Population
Biennial session frequency(1)
Missed Vote

(2)
Missed Vote

0.017
(0.016)-0.047*(0.027)0.082**(0.035)0.007(0.038)0.013(0.020)-0.031(0.022)0.054(0.041)-0.028(0.052)-0.130(0.087)-0.045(0.087)0.139(0.094)39,0780.714
0.894

39,0780.732
0.894

Any personal staff
Any shared staff
Any district staff
Observations
R-squared
Mean dep var

Notes: This table analyzes the effect of legislative salary on missed roll call votes for legislative districts straddlingstate borders, using a spatial discontinuity design. Fixed effects are included for each pair of border districts.
Standard errors clustered by state in parentheses. An observation is a roll call vote for one legislator in 1999-2000.
The roll call vote data does not indicate whether the vote took place in 1999 or 2000; the salary date is from 2000,
and is merged to all observations from a corresponding state. Both columns include census region (South, Northeast,
Midwest, and West) fixed effects.
* significant at 10%; ** significant at 5%; *** significant at 1%

23

Table D7: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes,
Restricted to States that Do Not Allow Legislators to Choose Own Salaries
Panel A - All Elections

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.030*(0.016)0.079***(0.025)0.024(0.027)0.064(0.046)-0.040(0.024)0.056**(0.021)0.126***(0.029)0.075*(0.039)0.074(0.069)-0.040(0.033)-0.172(1.434)-6.261**(2.459)-0.715(2.102)-5.066(4.839)2.630(2.400)-0.021(0.018)-0.039***(0.012)
0.053**(0.024)-0.123**(0.048)-0.083***(0.025)0.062*(0.031)
0.003(0.015)0.049(0.044)-0.017(0.064)0.040(0.031)

33,6250.162
0.734

33,6020.142
1.933

32,2670.144
47.04

33,6060.134
0.685

33,5270.182
0.590

Panel B - Lower House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.054**(0.023)0.027(0.032)0.073(0.053)-0.210(0.287)0.088***(0.030)0.068(0.045)0.082(0.076)0.202(0.368)-1.082(2.271)-0.966(2.528)-6.280(5.606)34.710(20.840)-0.028(0.021)
0.043*(0.023)-0.133**(0.057)-0.051(0.067)0.063(0.043)0.056(0.044)-0.008(0.069)0.727(.)

23,9940.178
0.719

23,9790.150
1.908

22,8850.157
48.30

23,9750.141
0.706

23,9190.179
0.597

Panel C - Upper House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.004
(0.015)0.015(0.026)0.022(0.052)-0.003(0.021)

0.022(0.021)0.077(0.049)0.008(0.066)-0.004(0.035)0.419(1.591)-0.397(1.853)-1.220(5.476)0.573(1.084)-0.015(0.014)0.090**(0.032)-0.089*(0.046)-0.024(0.033)0.050**(0.022)0.035(0.049)-0.032(0.057)0.041*(0.020)

9,631
0.124
0.782

9,623
0.130
2.011

9,382
0.124
43.05

9,631
0.142
0.617

9,608
0.200
0.565

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003. All regressions include state and year fixed effects. An observation is a legislature-year. Observations are missing forsome states in some years due to missing data on bills introduced and enacted. Robust standard errors clustered by state inparentheses. States that allow legislators to set their own salaries are excluded (i.e. we restrict to states with salaries set by acompensation board or by the constitution).
* significant at 10%; ** significant at 5%; *** significant at 1%

24

Table D8: The Effect of Legislative Salary on Legislative Productivity, Restricted to States thatDo Not Allow Legislators to Choose Own Salaries(1)
Log(Number ofBills Introduced)(2)
Log(Number ofBills Approved)(3)
Share ofBills Approved(4)
Income to Corporate
Tax Ratio

0.022(0.048)0.195(0.140)0.033(0.089)0.020(0.075)0.216(0.143)0.323***(0.107)-0.002(0.016)0.013(0.014)0.067***(0.023)0.281(0.793)-0.394(0.293)-0.214(0.703)

807
0.810
7.269

807
0.693
5.897

807
0.676
0.309

503
0.770
4.796

Log salary
Session length in election year
(in hundreds of days)
Election for term-limited seat
Observations
R-squared
Mean dep var

Notes: Notes: This table analyzes the effect of legislative salary on legislative productivity using data from 1968-2007 incolumns 1-3 and from 1972-2000 in column 4. An observation is an election. Robust standard errors clustered by state inparentheses. Log salary is the logarithm of the real salary in 1982 dollars. Additional controls included in all regressions are thelog of state population, and the log of GDP per capita. State and year fixed effects are included in all regressions. States thatallow legislators to set their own salaries are excluded (i.e. we restrict to states with salaries set by a compensation board or by
the constitution).
* significant at 10%; ** significant at 5%; *** significant at 1%

Table D9: Lagged Productivity and Salary: Ruling Out that Observably More Productive
Politicians Are Less Likely to Experience Salary Increases
Panel A - Legislator Salary(1) Log(Number of Bills Introduced)t−1(2) Log(Number of Bills Approved)t−1(3) Share of Bills Approvedt−1(4) Log(Income per Capita)t−1(5) Log(Expenditures per Capita)t−1(6) Log(Taxes per Capita)t−1

Dependent Variable: Log Salary

Observations

R-Squared

Mean dep var

0.039(0.032)0.041(0.030)0.146(0.100)0.0362(0.765)0.400*(0.211)0.527*(0.289)

1,215

0.945

7.269

1,214

0.945

5.897

1,215

0.945

0.309

986

0.951

2.764

986

0.951

7.580

986

0.951

6.890

Dependent Variable: Log Salary

Observations

R-Squared

Mean dep var-0.548(0.765)0.065(0.228)0.279(0.270)

665

0.759

2.716

665

0.757

7.545

665

0.760

6.847

Panel B - Governor Salary(1) Log(Income per Capita)t−1(2) Log(Expenditures per Capita)t−1(3) Log(Taxes per Capita)t−1

Notes: This table analyzes the effect of legislative productivity on legislative salary in the subsequent year using data from1968-2007 in rows 1-3 and from 1972-2000 in rows 4-6 of Panel A, and, in Panel B, the effect of governor productivity on
governor salary in the subsequent year using data from 1968-1994 in rows 1-3 and from 1972-1994 in rows 4-6. An observation isan election. Robust standard errors clustered by state in parentheses. Log salary is the logarithm of the real salary in 1982
dollars. Additional controls included in all regressions are the log of state population, and the log of GDP per capita. State andyear fixed effects are included in all regressions.
* significant at 10%; ** significant at 5%; *** significant at 1%

25

Table D10: Lagged Average Productivity to Date and Salary: Ruling Out that Observably More
Productive Politicians Are Less Likely to Experience Salary Increases

L. Avg Bills introduced per year

(1)

(2)

0.001
(0.001)0.001(0.001)

L. Avg Number of Bills Approved

(3)

(4)

0.001
(0.001)0.001(0.001)

L. Avg Share of bills approved
Session length in election year
(in hundreds of days)

0.082**(0.039)

(5)

(6)

0.011
(0.022)0.010(0.022)0.081**(0.039)0.081**(0.039)

Election for term-limited seat

0.169
(0.156)0.183(0.159)0.169(0.156)0.183(0.159)0.170(0.155)0.184(0.158)Observations
R-squared
Mean dep var

1,359
0.934
7.269

1,356
0.935
7.269

1,359
0.934
5.897

1,356
0.935
5.897

1,359
0.934
0.309

1,356
0.935
0.309

Notes: This table analyzes whether politicians with higher average productivity to date, measured in terms of lagged
values of bill-introduction and bill-passing, are more or less likely to receive salary increases. Additional controls
included in all regressions are the log of state population, and the log of GDP per capita. State and year fixed effects
are included in all regressions. Standard errors clustered by state in parentheses.

26

Table D11: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes, Robustness to Controlling forLegislature Characteristics
Panel A - All Elections
Contested

Log salary

(1)

(2)

(3)

(4)

Number of
Candidates(5)0.025**(0.012)0.026**(0.013)0.026**(0.012)0.009(0.013)0.046**(0.020)0.052**(0.020)

Session length in election year
(in hundreds of days)
Any personal staff

0.034(0.020)-0.021(0.020)-0.058*(0.030)-0.009(0.021)

Any shared staff
Any district staff
Biennial session frequency

27

Other Controls
Observations
R-squared
Mean dep var

No82,992
0.155
0.734

Yes
82,992
0.161
0.734

Yes
82,992
0.162
0.734

(6)

(7)

Margin ofVictory(8)0.052***(0.018)0.022(0.025)0.014(1.399)-0.730(1.187)0.046(0.035)0.008(0.033)-0.162***(0.054)0.036(0.054)
No82,992
0.158
1.933

Yes
82,992
0.164
1.933

Yes
82,992
0.166
1.933

(9)
-0.685(1.144)0.118(0.885)-0.933(1.554)1.852(1.246)1.769(2.315)2.212(1.794)

No
80,707
0.157
47.04

Yes
80,707
0.162
47.04

Yes
80,707
0.162
47.04

(6)

(7)

Margin ofVictory(8)(9)

0.073***(0.024)0.017(0.028)0.119(1.925)-1.014(1.603)

Panel B - Lower House Elections
Contested

Log salary

(1)

(2)

(3)

(4)

Number of
Candidates(5)0.035*(0.018)0.039**(0.017)0.039**(0.017)0.012(0.016)

0.062**(0.027)0.071***(0.026)

Session length in election year
(in hundreds of days)
Any personal staff

0.035(0.022)-0.028(0.023)-0.060*(0.035)-0.011(0.025)

Any shared staff
Any district staff
Biennial session frequency
Other Controls
Observations
R-squared
Mean dep var

No63,095
0.164
0.719

Yes
63,095
0.167
0.719

Yes
63,095
0.168
0.719

0.044
(0.035)-0.005(0.036)-0.168***(0.059)0.034(0.059)
No
63,095
0.169
1.908

Yes
63,095
0.171
1.908

Yes
63,0950.173
1.908-0.952(1.551)0.212(1.045)-1.040(1.715)2.037(1.322)1.014(2.614)2.057(2.181)

No
61,232
0.163
48.30

Yes
61,232
0.167
48.30

Yes
61,2320.167
48.30

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from 1967-2003. An observation is an election. Robust
standard errors clustered by state in parentheses. The contested election variable is a dummy for whether the election had more than one candidate. Log salary is the logarithmof the real salary in 1982 dollars. Additional controls are the log of state population, a dummy for upper-house elections in panel one only, a dummy for elections for term
limited seats and a dummy for elections for four-year term lengths term limits.
* significant at 10%; ** significant at 5%; *** significant at 1%

Table D12: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomes
Using State Level CPI
Panel A - All Elections

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.027
(0.018)0.063***(0.022)0.003(0.015)0.072**(0.034)-0.016(0.023)0.056*(0.029)0.106***(0.030)0.007(0.031)0.120*(0.068)-0.016(0.032)-0.349(1.572)-3.298**(1.526)-0.484(1.061)-2.832(2.708)-0.884(1.670)-0.023(0.017)-0.017(0.012)0.005(0.009)-0.149***(0.031)-0.110***(0.016)

0.069**(0.028)-0.012(0.027)0.019(0.012)0.019(0.038)0.017(0.030)74,8350.173
0.734

74,7620.190
1.933

72,6290.167
47.04

74,7340.152
0.685

74,4280.140
0.590

Panel B - Lower House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.043
(0.026)0.007(0.019)0.095**(0.036)0.227***(0.065)0.081**(0.039)0.010(0.036)0.142*(0.073)0.728***(0.218)-0.267(2.101)-0.380(1.215)-4.280(3.081)-14.063(15.329)-0.029(0.020)0.009(0.009)-0.150***(0.035)0.027(0.180)0.079**(0.036)0.023*(0.013)0.018(0.039)0.461**(0.192)57,0300.181
0.719

56,9800.195
1.908

55,2200.172
48.30

56,9390.152
0.706

56,700
0.134
0.597

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins

0.005
(0.015)-0.010(0.018)-0.008(0.038)-0.019(0.026)0.021(0.028)0.001(0.034)0.030(0.065)-0.012(0.039)-0.471(1.803)-1.380(1.368)1.627(3.442)0.639(1.062)-0.011(0.020)0.004(0.018)-0.138***(0.035)-0.001(0.032)0.048**(0.023)0.008(0.015)0.023(0.042)0.021(0.015)17,8050.153
0.782

17,7820.182
2.011

17,4090.150
43.05

17,7950.159
0.617

17,7280.173
0.565

Panel C - Upper House Elections

Log salary
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003. An observation is an election. Robust standard errors clustered by state in parentheses. All regressions include stateand year fixed effects. The contested election variable is a dummy for whether the election had more than one candidate.
Column 2 is restricted to elections with 6 or fewer candidates. Log salary is the logarithm of the real salary. Salaries after 1979
are in deflated by state level CPIs in 1980 dollars. Salaries before 1980 are deflated by the national CPI in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

28

Table D13: Legislator Salary and Bill Introduction, and Approval Using State Level CPI
Panel A - Legislator Salary and Bill Introduction, and Approval
(1)

(2)

(3)

Log(Number of Bills
Introduce)
Log salary
Session length in election year
(in hundreds of days)
Election for term-limited seat
Observations
R-squared
Mean dep var

00.032(0.033)0.033(0.077)0.017(0.035)0.219*(0.120)0.073(0.079)

1444
0.774
7.269

1440
0.780
7.269

(4)

Log(Number of Bills
Approved)
0.075*(0.042)(5)(6)

Share of Bills Approved

0.321***(0.099)0.060(0.043)0.245**(0.114)0.358***(0.106)0.014(0.009)0.077***(0.018)0.014(0.009)0.013(0.012)0.078***(0.019)

1443
0.640
5.888

1439
0.649
5.888

1444
0.677
0.306

1440
0.677
0.306

(5)

(6)

Panel B - Legislator Salary and Taxes, Expenditures, and Income(1)

Log salary
Session length in election year
(in hundreds of days)
Election for term-limited seat
Observations
R-squared
Mean dep var

(2)

(3)

(4)

Log(Income per Capita)

Log(Expenditure per Capita)

Log(Taxes per Capita)-0.016*(0.009)0.009(0.009)-0.004(0.020)-0.042(0.059)0.009(0.011)-0.009(0.009)-0.017(0.040)

958
0.980
6.858

958
0.985
6.858

-0.033**(0.015)-0.006(0.004)-0.001(0.002)-0.011(0.009)-0.047**(0.021)

0.013(0.009)0.001(0.009)-0.035*(0.018)

958
0.994
2.720

958
0.998
2.720

958
0.989
7.555

958
0.990
7.555

Notes: This table analyzes the effect of salary for state politicians on politician performance. Panel A studies the impact oflegislator salary on bill-passing using data from 1968-2007. Panel B studies the impact of legislator salary on state finances using
data from 1972 and 1977-2000, as described in the Data Appendix. An observation is an election. All regressions include state
fixed effects, year fixed effects, and a dummy for the state having term limits for legislature seats in that year. An observation isa legislature-year. Observations are missing for some states in some years due to missing data on bills introduced and enacted.
Robust standard errors clustered by state in parentheses. Log salary is the logarithm of the real salary. Salaries after 1979 are
in deflated by state level CPIs in 1980 dollars. Salaries before 1980 are deflated by the national CPI in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

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Table D14: Narrative Sample Analysis: The Effect of Legislative Salary on Electoral
Competitiveness and Electoral Outcomes
All Elections

Log salaryUpper house
Session length in election year (in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var

(1)

(2)

(3)

(4)

(5)

Contested
Election

Number of
Candidates

Margin ofVictory

Incumbent
Re-elected

Democrat
Wins-0.002(0.034)0.017*(0.009)0.010(0.008)0.086(0.098)-0.010(0.010)-0.053(0.090)0.015(0.031)
0.066***(0.016)0.230*(0.124)-0.000(0.028)

3.212(3.194)-0.669(1.225)0.171(0.803)-5.270(7.398)-1.458(1.731)

0.005(0.041)-0.037(0.033)0.007(0.007)-0.155***(0.040)0.002(0.037)-0.034(0.025)0.009(0.014)0.017***(0.004)0.002(0.050)-0.012(0.017)

24443
0.116
0.848

24331
0.223
2.244

24388
0.091
39.52

24439
0.046
0.340

24442
0.047
0.297

Notes: This table analyzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003, restricting to the 8 year periods preceding and following the sample of salary change events described in Section
Appendix B. Robust standard errors clustered by state in parentheses. All regressions include year, event, and state fixed
effects. The contested election variable is a dummy for whether the election had more than one candidate. Column 2 isrestricted to elections with 6 or fewer candidates. Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

Table D15: Is Salary Correlated with whether Politicians Plan on Becoming Lobbyists After
Office?
Dep var: Politician Plans on Doing Lobbying or Consulting
after Serving in Office (0-1)
Log(1+Salary)

(1)

(2)

(3)

0.002
(0.003)0.004(0.003)-0.010(0.011)-0.007(0.017)0.005(0.018)

No
6,367
0.000
0.148

No
6,367
0.002
0.148

Yes
6,367
0.013
0.148

Session length in election year (in hundreds of days)
State & Year fixed effects
Observations
R-squared
Mean dep var

Notes: OLS regressions with standard errors clustered by state in parentheses. An observation is a politician. The
dependent variable is whether a politician indicated that they were likely to do lobbying or consulting after service inthe legislature. The data is from the surveys of state legislators done by Carey et al. (1995) and Carey et al. (2002).
The log of state population, log of GDP per capita, a dummy for states with biennial session frequency, and measuresof legislator staff are also included in regressions 2 & 3.
This table illustrate two points. First, the share of politicians intending to become lobbyists or consultants is 15%.
Second, there is no relationship between salary and whether politicians later intending to become lobbyists or
consultants.

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Table D16: The Effect of Legislative Salary on Electoral Competitiveness and Electoral Outcomeswith Large States Excluded

Log salaryUpper house
Session length in election year
(in hundreds of days)
Election for term-limited seat
Election for seat with four year term length
Observations
R-squared
Mean dep var(1)
Contested
Election(2)
Number of
Candidates(3)
Margin ofVictory(4)
Incumbent
Re-elected(5)
Democrat
Wins

0.026**(0.013)0.081***(0.022)0.002(0.017)0.046(0.033)-0.032(0.024)0.044***(0.016)0.129***(0.033)0.003(0.031)
0.051(0.069)-0.035(0.037)-0.982(1.208)-4.047**(1.771)0.247(1.090)-3.079(2.759)0.063(1.976)-0.011(0.014)-0.016*(0.009)0.016(0.011)-0.116***(0.031)-0.113***(0.015)0.059**(0.025)0.013(0.011)0.019(0.015)0.022(0.040)0.001(0.021)

64950
0.150
0.720

64868
0.145
1.898

63278
0.158
47.41

64834
0.132
0.671

64552
0.167
0.595

Notes: This table anlayzes the effect of legislative salary on candidate selection using US state legislative elections from
1967-2003 with California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas excluded. An observation is an election.
Robust standard errors clustered by state in parentheses. All regressions include state and year fixed effects. The contested
election variable is a dummy for whether the election had more than one candidate. Column 2 is restricted to elections with 6
or fewer candidates. Log salary is the logarithm of the real salary in 1982 dollars.
* significant at 10%; ** significant at 5%; *** significant at 1%

31

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Ansolabehere, Stephen, John Mark Hansen, Shigeo Hirano, and James M. Snyder Jr., “The Incumbency Advantage
in U.S. Primary Elections,” Electoral Studies, 2007, 26 (3), 660 – 668.
Besley, Timothy, “Paying Politicians: Theory and Evidence,” Journal of the European Economic Association, 2004, 2 (2-3),
193–215.
Boylan, Richard T and Cheryl X Long, “Measuring public corruption in the American states: A survey of state housereporters,” State Politics & Policy Quarterly, 2003, 3 (4), 420–438.
Bratton, Kathleen A. and Kerry L. Haynie, “Agenda Setting and Legislative Success in State Legislatures: The Effects ofGender and Race,” Journal of Politics, 1999, 61 (3), pp. 658–679.
Carey, John M., Richard G. Niemi, and Lynda W. Powell, “State Legislative Survey and Contextual Data, 1995,”
Technical Report, ICPSR03021-v1, Rochester, NY: University of Rochester 1995.
,, and, “State Legislative Survey and Contextual Data, 2002,” Technical Report, ICPSR20960-v1, Rochester, NY:
University of Rochester 2002.
Dal Bo, Ernesto and Martin Rossi, “Term Length and Political Performance,” Working Paper, UC Berkeley 2010.
Ferraz, Claudio and Federico Finan, “Motivating Politicians: The Impacts of Monetary Incentives on Quality and Performance,” 2010. Working paper, UC Berkeley.
List, John A and Daniel M Sturm, “How Elections Matter: Theory and Evidence from Environmental Policy,” Quarterly
Journal of Economics, 2006, 121 (4), 1249–1281.
Maxwell, Amanda and Richard F. Winters, “Political Corruption in America,” Manuscript, Dartmouth College, May,
2005.
Mayhew, David, Placing Parties in American Politics, Princeton, NH: Princeton Unverity Press, 1986.
Moretti, Enrico, “Real Wage Inequality,” American Economic Journal: Applied Economics, January 2013, 5 (1), 65–103.
Tella, Rafael Di and Raymond Fisman, “Are Politicians Really Paid Like Bureaucrats?,” Journal of Law & Economics,
2004, 47 (2), 477–513.
Wright, Gerald C., “Representation in America’s Legislatures,” 2004. Indiana University: National Science Foundation Grant.

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