Public Choice (2014) 161:141–156DOI 10.1007/s11127-013-0144-0

The effect of the election of prosecutors on criminal trials

Siddhartha Bandyopadhyay · Bryan C. McCannon

Received: 14 October 2012 / Accepted: 19 November 2013 / Published online: 29 November 2013© Springer Science+Business Media New York 2013

Abstract We examine whether elections of public prosecutors influence the mix of casestaken to trial versus plea bargained. A theoretical model is constructed wherein voters useoutcomes of the criminal justice system as a signal of prosecutors’ quality, leading to adistortion in the mix of cases taken to trial. Using data from North Carolina we test whetherreelection pressures lead to (a) an increase in the number and proportion of convictions fromjury trials and (b) a decrease in the average sanction obtained in both jury trials and pleas.Our empirical findings are consistent with our theoretical predictions.

Keywords Crime · Election · Plea bargaining · Prosecutor · Trials

1 Introduction

Local prosecutors in the United States exercise enormous discretion in how they handlecriminal cases. There are 2344 local US prosecutor offices, which collectively handle 2.3million felony cases each year (Perry 2006). This number represents approximately 95 % ofall criminal prosecutions (Simmons 2004). A common feature of almost all of these localoffices is that the ‘chief’ prosecutor is elected by popular vote.1 Given prosecutors’ impor-tance in the functioning of the criminal justice system, one would like to understand theimpact elections have on how they exercise their discretion. However, with a few exceptionsthis has not been formally analyzed. We fill the void by asking: do elections of prosecu-tors influence the way they handle cases? In particular, do such elections affect which casesthey take to trial and which cases are plea bargained? We show how jury trials are used by

1The three states that do not elect public prosecutors are Alaska, Connecticut, and New Jersey (Perry 2006).

S. BandyopadhyayDepartment of Economics, University of Birmingham, Birmingham B152TT, UK

B.C. McCannon (B)Saint Bonaventure University, Saint Bonaventure, NY 14778, USAe-mail: bmccannon@sbu.edu

142 Public Choice (2014) 161:141–156

prosecutors to signal their quality to voters. We analyze a model in which there is asymmet-ric information about prosecutor quality. Prosecutors signal by distorting the mix of casesthey take to trial versus those plea bargained. We use a detailed dataset from North Carolinato assess whether empirical evidence exists of the distortions predicted by our theoreticalmodel.

The model we use is an adaptation of Bandyopadhyay and McCannon (2014). That the-oretical model assumes that higher quality prosecutors are more likely than their colleaguesto be successful in the courtroom and can thus secure stiffer penalties. As quality is not di-rectly observable, voters can use information on sentences obtained. Incumbent prosecutorsrespond to the information asymmetry by taking more cases to trial. In pooling equilibria,lower quality prosecutors aggressively take cases to trial to mimic the high-quality type,while in separating equilibria higher quality prosecutors increase the number to differentiatethemselves. In both situations the model predicts that when reelection pressures are strong:(i) the number of cases obtained from jury trials rises, (ii) the number of convictions fromtrial increases and the number of guilty pleas decreases, so that the proportion of convic-tions that are obtained from jury trials increases, and (iii) the average sentence obtainedin both trials and plea bargaining goes down, as marginal cases with weaker evidence areprosecuted.

We test these hypotheses using a district-level, panel dataset from North Carolina. Thedata cover all 43 prosecutorial districts between 1997 and 2009. Data on electoral contestsover this period are also collected.

Our theoretical results receive empirical support. We find that the number of convictionsobtained in jury trials (relative to the total number of convictions) increases in the yearbefore an incumbent runs for reelection. In the year of reelection, if the incumbent facesa challenger either in the primary or the general election, we observe a further increase inthe relative number of convictions obtained from jury trials. We estimate that the reelectionincentives result in a 9.7 % increase in the proportion of cases taken to trial, while thepresence of a challenger results in an additional 14.7 % increase. If a district does not have acontested election in any year within the sample, fewer convictions arise from trials. Anotherfinding, as predicted by the theory, is that the yearly average maximum sanction obtainedfalls.

Early analysis of prosecutors focused on the allocation of the office’s budget and the roleof plea bargaining (Landes 1971). Boylan (2005) analyzes data on chief federal prosecutorsand their subsequent careers. He shows that prosecutors maximize sentence length, whichsupports our choice of evaluation metric used in the theory presented here. Boylan and Long(2005) show that prosecutors use trial experience as a career advancement strategy. Glaesaret al. (2000) find evidence that career concerns affect the decisions of US Attorneys andhave led to an expansion in the federalization of drug crimes.

The focus in this paper is on state-level prosecutors who face a different retention process.A work closely related to ours is that of Rasmusen et al. (2009), who investigate the decisionabout whether to prosecute a case or to dismiss it. They consider an environment whereina prosecutor is interested in allocating the office’s budget and effort over convictions, im-proving conviction rates, and fulfilling ‘personal goals’. The authors use a cross-sectionaldataset to assess the impact of an increase in the office’s budget on case outcomes, find-ing that larger budgets raise both conviction rates and the number of convictions. They donot consider the effect of the signaling problem arising from reelection pressure and do notanalyze the decision about whether to engage in plea bargaining or to proceed to trial, aswe do here. Our findings complement theirs since they show that elected prosecutors haverelatively high conviction rates, while we show that the greater success of elected prosecu-tors coincides with an expanded use of the courtroom. Another paper closely connected to

Public Choice (2014) 161:141–156 143

our theoretical model is that of Gordon and Huber (2002), who consider the moral hazardproblem arising when voters use observable signals of performance to monitor prosecutorialeffort. They do not consider adverse selection issues.

A qualitative analysis of the related matter of the retention of local prosecutors is Wright(2009), who presents stylized facts concerning media coverage of prosecutor elections.

The model of this paper has similarities to incumbent-challenger models (Besley 2006),which consider the agency issues involved in elections. Such models have been used toanalyze a host of situations, including political business cycles (Rogoff and Siebert 1988;Rogoff 1990), the continuation of inefficient policy (Majumdar and Mukand 2004), and thepersistence of conflict (Bandyopadhyay and Oak 2010). Our model predicts an excessiveuse of trials, in line with most results in the literature.

The growing literature on how career concerns affect the behavior of public officials bor-rows the central insight that politicians manipulate outcomes before elections. Leaver (2009)and Shotts and Wiseman (2010) both consider environments of asymmetric information onthe quality of regulators. Hanssen (1999, 2000), Shepherd (2009), Berdejo and Yuchtman(2010), and Lim (2013) all provide empirical verification of the hypothesis that the inde-pendence and retention concerns of judges affect their decisionmaking. While not aboutprosecutorial behavior, these papers demonstrate that retention motives affect outcomes ofthe justice system.

2 Model

We now develop a model of prosecutorial decisionmaking that will allow us to investigatethe incentives created by elections. Consider a two-period model. In the first period thereis a single prosecutor of unknown quality who is to decide how to handle cases broughtbefore her. She takes one of two quality types q ∈ {H,L}, referred to as ‘high’ and ‘low’respectively. She is high quality with probability γ ∈ (0,1). Let the parameter θ be a sum-mary measure of the strength of evidence she has against the defendant in any given case.The parameter may also capture information the prosecutor has concerning the skill of thedefendant’s attorney. Assume that θ ∈ [0, θM ] where θM < ∞, with this being known to thedefendant as well. Observing θ for a particular case, the prosecutor may either take the caseto trial or engage in plea bargaining.2 Assume that a large number of cases come up in thefirst period, which we can think of as a term in office.

Denote s as the sanction received if the prosecutor is successful in the courtroom. Thesanction is known and is exogenously set by, for example, sentencing guidelines.3 Alter-natively, if there are judicial discretion, uncertain parole outcomes, or appeals, s is bestthought of as the expected sanction. We also take account of the probability that the prose-cutor is successful at trial, a probability which depends on the quality of the prosecutor andthe evidence. A high-quality prosecutor wins at trial with probability pH (θ), while if sheis low quality she wins with probability pL(θ). Assume that 1 > pH (θ) > pL(θ) > 0 ∀θ ,pq(0) = 0, and dpq

dθ≥ 0 ∀q . Finally, if she takes the case to trial and obtains a conviction, a

cost c > 0 is borne by the prosecutor and the defendant.4 For simplicity we assume that c

does not depend on the strength of the evidence or the prosecutor’s ability.

2We assume that the choices of whether to file charges and which charges to file have already been made.3The analysis is unaffected if one assumes that s = s(θ), where ds

dθ≥ 0.

4Assuming different costs of trials for the two parties does not make any substantive change in the analysis.

144 Public Choice (2014) 161:141–156

With regard to plea bargaining, let g(θ) denote the agreed plea bargain sentence. Weignore the possibility that the plea outcome depends on the quality of the prosecutor. Asthe prosecutor’s type is assumed to be the private information to the defendant, we assumethe outcome under plea bargaining cannot be conditioned on type. One could alternativelyargue, however, that even though the skill of the incumbent is not known to the votingpublic, defense attorneys may, in view of their repeated interaction, gain this information.In that case, g would be a function of the prosecutor’s type as well as of θ . Extending theenvironment to allow for type-dependent plea bargains simply adjusts the bounds of theset of separating and pooling equilibrium. Being cumbersome, the analysis is not presentedhere, but is available from the authors upon request.

How might the function g be determined? One way is to assume that a prosecutor makesthe best offer that the defendant will accept. Since trials are lengthy and the final sanctionof a successful trial comes with a delay, compared with the outcome of plea bargain, whichis settled more quickly, the defendant will discount the future disutility of the cost of trial.Thus, he or she will agree to a sanction no greater than δps +c where p = γpH +(1−γ )pL.Alternatively, with optimism or self-serving bias (as is often assumed in the plea bargainingliterature; see Burke 2007 and Farmer and Pecorino 2002 for a discussion), a defendant maybelieve that he will receive a sanction kps where k < 1, and so even with a take it or leaveit offer will not accept a sanction greater than kps + c. Consequently dpq

dθs >

dg

dθ≥ 0 and

g(θ) > 0 ∀θ . Such results are consistent with the usual assumption that in a plea bargain thesentence received is less than after a trial conviction. Finally, assume that if the prosecutoris indifferent between the two options then she chooses to plea bargain.

Let w(g(θ)) be the welfare generated from a case that results in g(θ), and w(pq(θ)s)− c

be the welfare generated from a case that goes to trial. Assume that w is a strictly increasingfunction. Let W(q) be the welfare generated over the prosecutor’s entire first term if she is ofquality q . To link the two concepts, assume that θ for each case is a random variable. Acrosscases, values of θ are independent and identically distributed. The likelihood of a particularvalue is determined by the distribution function F : [0, θM ] → [0,1]. Assume that a largenumber of cases arise during the prosecutor’s term and that the number of cases disposed ofdoes not depend on the manner in which they are handled. The expected welfare from a casethen equals the average welfare generated from each case over the course of the term. Giventhat this is so, assume that first-period welfare equals the expected welfare from a randomlyselected case. Thus, if a prosecutor chooses to take every case to trial where θ ≥ θ and pleaout those with θ < θ̄ , then first-period welfare is

W(q) =∫ θ

θ=0w

(g(θ)

)dF(θ) +

∫ θM

θ=θ

[w

(pq(θ)s

) − c]dF(θ). (1)

The assumption of welfare being monotonic in sanctions is based on the presumption thatthe sanction has been optimally chosen by society. This does not necessarily mean thatsociety benefits from having every defendant punished as harshly as possible, but ratherthat given the option to impose the harsher sanction deemed appropriate by the judge (orchosen by the sentencing board or legislature) or to accept a plea offer, welfare is greaterif the prosecutor achieves the stiffer of the two penalties. One may also think of welfareas the expected welfare allowing for the possibility of wrongful convictions.5 The cost oftrial appears as a negative term because with greater expenditures on a trial, the resourcesavailable for disposing of other cases shrink. Finally, let V (q ′) be second-period welfare ifthe prosecutor is of quality q ′, where dV

dq ′ > 0. Consequently, total welfare is W(q) + V (q ′).

5See McCannon (2013) for an analysis of the effect of prosecutor elections on mistakes and appeal[s].

Public Choice (2014) 161:141–156 145

2.1 First best

We now describe the first-best outcome in this environment as it is a useful benchmark. Tak-ing a case to trial is best if w(pq(θ)s) − c > w(g(θ)). Trial costs are assumed not to be toogreat: c < w(pL(θM)s) − w(g(θM)). Otherwise, even with very good evidence the expectedwelfare of going to trial is insufficient to motivate that action. Given the way we have mod-eled plea bargaining, dpq

dθs >

dg

dθand pq(0)s = 0 < c, it follows that there exists a threshold

value of θ , denoted θ̃q , where welfare is equal between the two options.6 The threshold valuedepends on the prosecutor’s quality, since the probability of conviction depends on her abil-ities. Consequently, θ̃L > θ̃H > 0. If θ > θ̃q , then W(q) is improved if the case proceedsto trial. The evidence is so substantial that the expected sanction is severe enough to makethe trial preferable. If θ < θ̃q , then the best outcome is for the cases to be resolved by pleabargaining. The expected sanction is small and, given the high cost of trial, negotiating andaccepting a plea generates a better outcome. Note also that the first-best outcome requiresthat a prosecutor of high quality be retained since V (H) > V (L). Also, if the prosecutor islow quality, then a new one should be selected, since EV > V (L).

2.2 The model with asymmetric information

Consider the principal-agent problem that arises when the principal (in this case the votingpublic or, with ideological heterogeneity, the median voter) wants to maximize total welfare,but does not know the quality of the prosecutor or of the evidence in each particular case.With perfect information only high-quality prosecutors are retained. However, with a bonusfor being retained, the prosecutor’s payoff does not coincide with the median voter’s. Thus,both types of prosecutor may try to signal that they are of the high type. We are interestedin understanding how such signals distort outcomes in the criminal justice system.

Suppose that the benefit received by the prosecutor from a particular case is eitheru(pq(θ)s)− c or u(g(θ)). Her total expected utility, Uq , then aggregates the expected bene-fits derived in each case. Assume that the expected utility of a randomly selected case equalsthe average utility generated over the prosecutor’s term. Also, let b > 0 denote a retentionbonus, which the prosecutor receives if and only if she is reelected. One may think of thebonus as future wages earned, but it could also represent the future gains for an altruisticprosecutor who, if retained, gains utility from prosecuting more cases.7 Thus, if a prosecutortakes a case to trial if and only if θ ≥ θq , then

Uq =∫ θq

θ=0u(g(θ)

)dF(θ) +

∫ θM

θ=θq

[u(pq(θ)s

) − c]dF(θ) + b (2)

if retained and Uq − b if not retained. To simplify the analysis we assume, absent compen-sation, that the preferences of the prosecutor are related directly to welfare, u(P ) = αw(P )

for α > 0. Thus, the environment may be best thought of as addressing the decision makingof the chief prosecutor who is directly accountable to the voting public.

6With the assumption that pq(θ)s and g(θ) are continuous functions and c is bounded from above, the inter-mediate value theorem guarantees that these thresholds (and all thresholds derived throughout the analysis)exist.7This setup is similar to the incumbent-challenger models of political agency, which consider the reelectionmotives of political leaders (Besley 2006). It is possible to make b type-dependent as well, i.e., a betterprosecutor could also enjoy higher utility in period 2 because society’s welfare is higher. As it does notchange the primary predictions of the model, such complications are not presented.

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We model the situation as a signaling game. Prosecutors signal their quality by takinga certain number of cases to trial. Voters observe the outcomes, namely the sentences ob-tained, in trial, and then decide whether to retain the prosecutor. If the prosecutor is notretained, a randomly chosen challenger takes the incumbent’s place. We will analyze theperfect Bayesian equilibria of this game.

3 Prosecutor evaluation

The question becomes how the prosecutor’s behavior responds to the way in which shebelieves voters will evaluate her. The voters, who do not have access to information abouther true type, may use a number of evaluation criteria. One possibility is that prosecutorscan signal to voters their quality by getting heavy sentences at trial. Thus, we analyze aparticular metric of quality, namely aggregate sentence lengths obtained. Evidence has beenpresented that this is one of the primary measurements used to assess incumbent prosecutorsin media coverage of elections (Wright 2009). Boylan (2005) provides evidence suggestingthat this is the quantity that prosecutors try to maximize.

Accordingly, suppose that voters assess the quality of the prosecutor by the lengths ofthe sentences she obtains. Sanctions may be achieved either through plea bargaining or bycourtroom victories. Hence, the aggregate sanction imposed over the course of her term canbe used to assess her quality.

As described above, for each trial an independent draw of θ is taken from the distributionfunction F . The prosecutor selects a threshold value of θ , denoted θq , for her term. If θ > θ̄q

the case is taken to trial; if θ < θ̄q a plea bargain results. This prosecutor’s choice arisesbecause the difference between the expected sanction from trial and the plea bargain growsas the quality and quantity of evidence improves. The threshold she chooses may depend onher quality. The expected sanction generated is

Sq =∫ θq

θ=0g(θ)dF (θ) +

∫ θM

θ=θq

pq(θ)s dF (θ). (3)

Since it is assumed that a large number of cases are decided in a term, the expected sanctionof a randomly selected case represents the actual aggregate sentence when the number ofcases is normalized to unity.

The voters observe the aggregate sanction achieved and decide whether to retain or re-place the incumbent. If the aggregate sentence length exceeds a threshold, then the prosecu-tor is retained. If the threshold is not achieved, then we assume that with probability z theprosecutor is replaced. This allows for uncertainty in the process and can be a summary mea-sure of the likelihood of a suitable replacement being found. For example, z may representthe probability that the opposing party organizes a campaign to replace the incumbent. Thus,the parameter z serves as a measure of the competitiveness of the electoral environment.

3.1 Separating equilibria

Consider, first, separating equilibria. In such outcomes voters keep the prosecutor if S

matches or exceeds Se, in the belief that a prosecutor whose aggregate sanction is greaterthan or equal to Se is type H and those below are type L. Which values of Se are voters ableto obtain in a separating equilibrium?

Public Choice (2014) 161:141–156 147

As a useful point of reference, consider the expected aggregate sanction that arises in thefirst-best outcome. When θH = θ̃H

S̃q =∫ θ̃q

θ=0g(θ)dF (θ) +

∫ θM

θ=θ̃q

pq(θ)s dF (θ). (4)

Since a prosecutor of high quality wins at trial with a higher probability and takes more casesto trial, it is straightforward to verify that S̃H > S̃L. Thus, sentence length is a reasonablemetric to use to distinguish between the types.

If the benefit to being retained is relatively small, then a low-quality prosecutor is unin-terested in increasing the number of cases she takes to trial so as to mimic a high-qualityprosecutor. This self separation occurs if the probability of being replaced is small. If theprobability of a low-quality prosecutor being replaced is small, then she will select SL = S̃L

and take her chances on Election Day. Therefore, there exists a threshold z defined by

z ≡ 1

b

∫ θ̃L

θ̃H

[u(g(θ)

) − u(pL(θ)s

) − c]dF(θ), (5)

where if z < z, then self separation occurs.

Lemma 1 If z < z, then the unique equilibrium has SL = S̃L and SH = S̃H .

Therefore, consider the environment where z ≥ z so that both types are willing to achieveS̃H to be retained, since the benefit to being retained is sufficiently high and the probabilityof being replaced is not too low. It follows that in the separating equilibrium Se must begreater than S̃H . One also finds, since for a fixed b the prosecutor’s utility is proportional towelfare, that a high-quality prosecutor is interested, if she remains in her job, in achievingthe shortest sentence length that secures her retention, or rather, S = Se. Thus, in a sepa-rating equilibrium a threshold θH = θe

H , below θ̃H , is set so that SH = Se . Consequently,an excessively aggressive aggregate sentence length is obtained by trying in court some de-fendants who in the first-best outcome would have received a plea offer. For this aggressivepolicy to yield an equilibrium she must be willing to push for these harsher sanctions. Ifshe is not willing, then the best choice for her is S = S̃H . Therefore, selecting θq = θe

H ispreferable if

∫ θeq

0u(g(θ)

)dF(θ) +

∫ θM

θeq

[u(pq(θ)s

) − c]dF(θ) + b

≥∫ θ̃q

0u(g(θ)

)dF(θ) +

∫ θM

θ̃q

[u(pq(θ)s

) − c]dF(θ) + (1 − z)b, (6)

where q = H . There exists a threshold level of θ , denoted φq , where (6) holds with equality.Hence, if θe

H ≥ φH , then the high-quality prosecutor is willing to take enough cases to courtto remain in her position. The cutoff value φq corresponds to a level of S denoted by Sφ

q

(> S̃q ).Now consider the incentives of the prosecutor of low quality. If she does not attempt

to increase S to Se , then she will not be retained. Since her preferences are proportionalto welfare it follows that if she chooses not to achieve Se she selects S = S̃L by adoptingθL = θ̃L. Alternatively, she may choose to misrepresent her type by taking more cases to

148 Public Choice (2014) 161:141–156

court and plea bargaining less. Since she is less successful in court, even more cases mustbe tried than if she were more skilled. Thus, to obtain Se, her threshold, θL, must be set ata value less than θe

H . As before, denote the value of θ that generates SL = Se by θeL. For Se

to be supported as a separating equilibrium, (6) must fail for θq = θeq when q = L. Thus,

a low-quality prosecutor is unwilling to mimic the high-quality prosecutor if θeL < φL, or

rather if Se > Sφ

L .Combining these two results, if Se > S̃H a separating equilibrium exists so long as Se ∈

(Sφ

L,Sφ

H ]. Since pH (θ) > pL(θ) ∀θ if a low-quality prosecutor is willing to achieve Se, sotoo is a high-quality one. Therefore, S

φ

H > Sφ

L and the interval is non-empty.

Proposition 1 There exists a range of aggregate sanctions, (Sφ

L,Sφ

H ], in which separatingequilibria exist.

As a consequence of Proposition 1, the incentives of a prosecutor encourage one of highquality to engage in an excessive number of trials. This increase in tried cases must beso large that a low-quality prosecutor would be unwilling to plea bargain so few cases.Interestingly, in these equilibria it is the low-quality prosecutor who selects the welfare-maximizing number of trials.

3.2 Pooling equilibria

In a pooling equilibrium both types achieve an aggregate sentence length Se and the votersbelieve that a prosecutor who achieves Se or above is type H with probability γ and type L

with probability 1 − γ . What outcomes can arise in such an equilibrium?First, Se ≥ S̃H since if this were not true a high-quality prosecutor would prefer to deviate

to S̃H .Second, for any outcome to be a pooling equilibrium each agent must be unwilling to

deviate. Since the outcome results in a greater aggregate sanction than what the prosecutorprefers, regardless of her type, only deviations that will lower aggregate sentences need beconsidered. If the voters believe that any such deviations are from low-quality prosecutors,then they will choose not to retain one who makes the choice. Thus, a pooling equilibriumrequires that deviating to S̃q , the most preferred deviation, results in a lower utility thanachieving Se and being retained. Again, this requires that (6) hold when θe

q is the cutoffvalue of θ that if used obtains S = Se. Hence, a prosecutor of quality q is unwilling to poolwith the other type if θe

q < φq . As a result, Se can be supported as a pooling equilibrium onlyif Se ≤ Sφ

q ∀q .Finally, since pH (θ) > pL(θ) ∀θ , any aggregate sentence length that a low-quality pros-

ecutor is willing to achieve to be retained, a high-quality one is also willing to obtain, so asto remain in the position. Thus, S

φ

H > Sφ

L . Additionally, since it is assumed that b ≥ b (sothat self separation is not an outcome), it must be that S

φ

L > S̃H . As a result, the interval[S̃H , S

φ

L] is non-empty and describes the set of pooling equilibria.Alternatively, if Se > S̃H , then it may be reasonable for voters to believe that a deviation

to S̃H comes from a high-quality prosecutor. With these beliefs no pooling equilibrium otherthan Se = S̃H exists. As is typical in signaling models, the size of the set of pooling equilibriadepends on beliefs regarding the likelihood of deviations to non-equilibrium outcomes.

Proposition 2 If it is believed that a deviation to a shorter aggregate sentence length comesfrom a low-quality prosecutor, then there exists a range of aggregate sanctions, [S̃H , S

φ

L],

Public Choice (2014) 161:141–156 149

in which pooling equilibria exist. If such deviations are believed to have to come from ahigh-quality prosecutor, then the unique pooling equilibrium is S̃H .

As a consequence of Proposition 2, a prosecutor of low quality is encouraged to engage inan excessive number of trials in order to raise the lengths of her obtained sentences. Interest-ingly, if the voter believes that shorter aggregate sentence lengths come from a low-qualityprosecutor, then there exist equilibria in which both types of prosecutors engage in insuffi-cient plea bargaining. Alternatively, with more reasonable beliefs, high-quality prosecutorsengage in the first-best number of prosecutions.

3.3 Testable predictions

Our theoretical model leads to several predictions that can be used as a test of the theory.First, consider cases that go to trial. As shown in Lemma 1, if reelection pressures are weak,then the first-best outcome is selected by the prosecutor, regardless of her type. Proposi-tions 1 and 2 illustrate that in all separating and pooling equilibria the number of cases takento trial is larger than what would be selected in the first-best outcome. Thus,

Result 1 Propositions 1 and 2 establish that if reelection pressures are strong, i.e., z > z,there is an increase in the number of cases arising from trials and a decrease in the numberof cases plea bargained.

As an immediate consequence, the number of convictions adjusts:

Result 2 If reelection pressures are strong, then there is an increase in the number of con-victions arising from trials increases and a decrease in the number arising from plea bar-gaining. Consequently, the proportion of total convictions coming from trial increases.

As the number of trials increases, the total number of jury trial convictions must also in-crease, while the total number of plea bargains decreases. Thus, the ratio of the two mustalso adjust. This distortion reduces the average sentence obtained in a year:

Result 3 An immediate consequence of Eq. (6) is that if reelection pressures are strong,then the average sentence obtained in jury trials declines, as does the average number ofguilty pleas.

More marginal cases, with lower expected sanctions, are taken to trial when the prosecutorexpands her use of the courtroom. This reduces the average sentences obtained in trial eventhough the aggregate sentences obtained increases.

4 Data

We now examine whether the effects predicted by the theory arise in practice. Data oncrime convictions and prosecutor elections in North Carolina are collected. While there are100 counties in the state, there are only 43 prosecutorial districts. More heavily populatedcounties comprise entire districts. Less-populated counties are grouped together into a singleprosecutorial district. Each district has one chief public prosecutor (known as the districtattorney) who is elected by voters to serve a four-year term. Each district attorney has a staff

150 Public Choice (2014) 161:141–156

of assistant district attorneys. The number of assistant district attorneys varies across thedistricts.

Data for felony prosecutions are collected from the North Carolina Sentencing and Pol-icy Advisory Commission. Each year the Commission publishes the Structured SentencingStatistical Report for Felonies and Misdemeanors.8 The Report provides data about convic-tions obtained for the fiscal year running from June 1 to July 30 of the following year. Dataare collected for the 1997–1998 fiscal year to the 2008–2009 fiscal year. Only felony con-victions are considered here. In each year a variety of information is available. First, in eachdistrict the total number of convictions obtained on the basis of guilty pleas and jury trialsis reported. Guilty pleas are obtained primarily through plea bargaining. The variable jurymeasures the proportion of total convictions obtained in jury trials in a district for a year.

The primary testable prediction of the theoretical model is that when reelection pressuresare strong prosecutors take more cases to trial and plea bargain less so that more convictionsarise from the courtroom relative to plea bargains. Thus, the hypothesis is that jury is dis-torted by elections. Measuring jury convictions as a proportion of total convictions controlsfor scale effects from districts differing in the total number of crimes committed.

We also have information regarding the type of punishment obtained. The type is de-termined by the most serious sentence an offender received. Each sanction falls into oneof three types: community, intermediate, and active punishments. Community punishmentsinclude community service, outpatient drug or alcohol treatment, and fines. Examples of in-termediate punishments in North Carolina include house arrest with electronic monitoring,residence in a substance abuse facility, or drug treatment court. Active sanctions are thosethat result in incarceration.

North Carolina uses sentencing guidelines to determine minimum and maximum sanc-tions. District attorneys complete a worksheet to calculate a score, which measures the num-ber and severity of past offenses. Given the offense the individual is convicted of and his/herprior record score, the guidelines provide for a range of acceptable sanctions. Judges may,though, choose to deviate from these bounds. North Carolina is unique in that the sentenc-ing guidelines also lay out a range of acceptable sanctions for both aggravated and mitigatedpunishments.9 If a mitigated or aggravated sanction is selected, the judge is required to pro-vide his/her reason. Additionally, defendants may appeal rulings on these ‘plus’ and ‘minus’factors.

The data also contain information about the duration of the active punishments. Foreach district for each year the average maximum sanction received by those convicted isreported.10

All of these sanction variables serve as measurements of the particular distribution ofcases and crimes that arise in a district over the course of the year. The seriousness of theoffenses committed in a year affects the decision to go to trial, independent of reelectionmotivations.

Socioeconomic variables are created. Population data are collected from the North Car-olina Office of State Budget and Management.11 Annual county-level population estimates

8www.nccourts.org/Courts/CRS/Councils/spac/Publication/Statistical/Annual.9In fact, the National Center for State Courts ranks North Carolina as having the most “mandatory” of sen-tencing guidelines in the country.10The size of the gap between the minimum and maximum sanction is fixed by the sentencing guidelines.Thus, only the maximum is considered here.11www.osbm.state.nc.us/ncosbm/facts_and_figures/socioeconomic_data/population_estimates/county_estimates.shtm provides the data and a description of the estimation procedures used.

Public Choice (2014) 161:141–156 151

are provided. Hence, the population density and the proportion of a district’s population thatis between the ages of 16 and 24 are recorded. The Office also reports data on the numberof males and the number of whites in each county in each year. Finally, district-level labormarket data are collected from the Employment Securities Commission of North Carolina.The unemployment rate is used as a control for economic opportunities and the opportunitycost of crime.12

In four cases, the composition of the district changed during our sample period. While inno circumstance was a county split between multiple districts, in four cases a prosecutorialdistrict that contained multiple districts was split into two districts. In the fiscal years 1997–1998 to 2005–2006 there were 39 districts. In 2005–2006 two districts were split so that thenthere were 41 in the state. Again in 2008–2009 two districts divided. Thus, for the 2008–2009 fiscal year there were 43 prosecutorial districts. Consequently, our sample contains476 observations.

Finally, election data are collected from the North Carolina’s State Board of Elections.13

We create three dummy variables to capture election outcomes. The dummy variable CIequals one if in a district in that year an incumbent runs for reelection and has a challenger(either in the primary or the general election).14 CI equals zero if there is no election, ifthe incumbent is not running for reelection, or if the incumbent running for reelection isunchallenged. While DAs serve four-year terms, elections are staggered so that some occurevery biennium. Second, reelect equals one if it is observed in the year before an incumbentruns for reelection. Reelect equals zero if it is not the year before an election or if it is theyear before an election but the incumbent does not run for reelection the following year.Third, the dummy variable never equals one if the district has not had either a contestedprimary election or a contested general election in any election cycle between 1998 and2008. A value of zero for never occurs if during an election in any year of the sample periodthe voters in the district had a choice between candidates.

Competitive prosecutorial elections are somewhat common in North Carolina. In 22.4 %of the elections there was a contest in the general election, while in 24.8 % of the electionsthere was a contest in a primary election. As a result, the mean value of reelect is 0.2668,CI is 0.0259, and never is 0.2668.

These three variables are used to measure reelection pressures and serve as proxies forz in the theoretical model. It is posited that in the year before a reelection campaign anincumbent, if she does adjust her behavior to the election cycle, will respond more in thatyear than in the previous two years. In the year of the election the presence of a challengerallows for the possibility of not being retained and, thus, compared to years without anelection or without challengers, the theory predicts an increase in the use of the courtroomrelative to the use of plea bargaining. Therefore, reelect captures the motivation to remainin office, while CI measures the pressure applied directly by a challenger. We assume thatvoters focus more heavily on the prosecutor’s performance in the year prior to and in theyear of the election. This is a common assumption in the literature on political business

12Labor market data are obtained from www.nces.com. The labor force participation rate is statistically in-significant in the results and, hence, is omitted. Furthermore, minor issues arising from the merging of thedata, such as aggregating county-level data into district-level observations and public financing issues arediscussed in detail in an online appendix (http://faculty.sbu.edu/bmccannon/supplement.htm).13www.sboe.state.nc.us.14The conviction data cover fiscal years beginning on July 1. Since the general election is in November ofthe calendar year, the election is recorded in our data as being in the same year if the general election fallswithin the fiscal year. This implies, though, that the primaries occur in the prior year within the dataset.

152 Public Choice (2014) 161:141–156

cycles (Rogoff 1990; Rogoff and Siebert 1988). Additionally, we predict that the set ofequilibria involve outcomes where there is less of use of the courts when z, the probability ofbeing removed from office by the voters if the incumbent underperforms, is small. Further,we posit that if a district has not experienced a single contested election over the entiresample period in either primary or general elections, then the incumbent prosecutors ofthese districts face lower zs than prosecutors in districts with electoral contests. Hence,never captures environments where electoral concerns are minimal.

5 Results

When an incumbent runs for reelection, the data indicate that there is an increase in thenumber of trial convictions per year of 7.2 % and an increase in jury trial convictions relativeto total convictions per year of 18.3 %. When she faces a challenger, additional increases of13.0 % and 23.7 %, respectively, arise. An incumbent who is immune from such pressuresprosecutes less (11.6 % and 23.8 %). A more rigorous econometric test is needed, though,to verify that such effects actually do occur.

The variables reelect and CI measure the existence of a reelection campaign and thepresence of a challenger, respectively. Jointly, these identify the potential distortions causedby retention motivations. CI is obviously collinear with never since for the former to equalone it is required that the latter equals zero. Hence, separate specifications are estimated toidentify the impact of the election variables on the use of jury trials. Note also that sincethe bulk of the elections occur in the 1998–2002–2006 cycle, CI and reelect are highlycorrelated with the year fixed effects. Finally, the variable never is a district-level controland, hence, never and district fixed effects cannot be entered simultaneously in a regressionspecification. Consequently, the econometric models to be estimated are

jurydt = α0 + α1CIdt + α2reelectdt + α3Sdt + α4Xdt + α5Dd + ε

jurydt = β0 + β1neverd + β2Sdt + β3Xdt + β4Yt + εdt ,(7)

where S is the sanction variables, X comprises the socioeconomic controls, and D and Y

are district and year fixed effects, respectively.Theory predicts that α1 > 0 and α2 > 0, along with β1 < 0. Table 1 presents results of

tests of the corresponding null hypotheses.The coefficients of CI and reelect are positive and statistically significant. If an incum-

bent is going to run for reelection and is interested in increasing her perceived “toughness”,then she increases the number of convictions that come from jury trials in the prior year. Thisis in order to gain attention in the news, appear hawkish to voters, obtain greater sanctions,deter challengers in the general election, and to be successful in the primary. If in the year ofreelection a challenger enters, then she increases the number of convictions obtained fromjury trials relative to the number of convictions obtained from guilty pleas. This representsat the mean a 9.7 % increase in the year before the reelection and an additional 14.7 %increase if in the reelection year a challenger enters the race. Put another way, the averagenumber of jury convictions in a district in a year in North Carolina is 17.66 and the averagenumber of guilty pleas is 700.92. Thus, there are 1.80 more convictions from jury trials inthe year before the reelection and 2.73 more convictions in the year of the contested race,for a total of 4.53 more jury trial convictions.

The coefficient of never is negative and statistically significant. Thus, a district that hasnot had an election contest between 1997 and 2009 experiences fewer jury trials. This cor-responds to a 10.8 % decrease in the number of jury trial convictions.

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Table 1 Effect of the election of prosecutors (dep. var. = jury, N = 476)

I II

CI 0.0038** (0.0018)

reelect 0.0025** (0.0011)

never −0.0028** (0.0012)

Population density −2.0 × 10−6 (1.6 × 10−5) 1.0 × 10−5 *** (0.0000)

Unemployment rate 0.0211 (0.0295) 0.2466*** (0.0778)

Average maximum sanction 0.0004*** (0.0001) 0.0005*** (0.0001)

% of convictions with anaggravated sentence

0.0341** (0.0159) 0.0048 (0.0136)

% of convictions with a mitigatedsentence

−0.0159** (0.0067) −0.0070 (0.0074)

% of convictions with anintermediate sanction

−0.0254** (0.0104) −0.0334*** (0.0101)

% of convictions with a communitypunishment

−0.0243* (0.0142) −0.0178 (0.0131)

% of population that is male −1.1489*** (0.3932) −0.3050*** (0.0593)

% of population that is white −0.0148 (0.1103) 0.0181*** (0.0053)

% of population between 16 and 24 −0.1047 (0.1291) 0.0079 (0.0273)

Year controls No Yes

District controls Yes No

adj R2 0.588 0.370

* 10% level; ** 5% level; *** 1% level. Heteroscedasticity-robust standard errors are reported

The sanction control variables are, for the most part, statistically significant and have thepredicted signs. The socio-economic variables help explain the use of jury trials to obtainconvictions as well.

Robustness checks including alternative specifications and standard errors, along withother election-related variables, are conducted and available in an online supplement.

One important concern is the potential endogeneity problem of the choice of a challengerto enter the race. Suppose a challenger runs because the incumbent is more aggressivelyprosecuting cases (resulting in more acquittals or a greater use of public funds, for exam-ple). The results presented in Table 1, then, may arise not from reelection concerns, butrather from the choices made by the challenger. To address this concern we adopt an IVapproach as a robustness check. To capture election pressure we need a variable correlatedwith CI but uncorrelated with jury. Specifically, an instrument is needed that measures thecompetitiveness of the political offices in an area. We consider judicial contests at the courtof first instance for felony cases (known as the Superior Court) as an instrument. The stateis divided into superior-court districts, which (roughly) coincide with prosecutorial districts.These judicial districts are organized into eight judicial divisions. Superior Court judges ro-tate between districts within their divisions. A term in office is eight years long and electionsfor those judges are staggered within the judicial division. In the year in which an incum-bent prosecutor of a district is running for reelection, judge equals the proportion of SuperiorCourt judicial seats that are contested within the division that contains the district. In yearsin districts without reelection campaigns for the DA position, judge equals zero. Correla-tion between prosecutor and judicial contests could arise because of public dissatisfactionwith elected officials, or because they are both more likely to occur in a relatively politically

154 Public Choice (2014) 161:141–156

Table 2 Two-stage least squaresresults (dep. var. = jury;N = 476)

* 10% level; ** 5% level; ***1% level

I II

CI 0.0121* (0.0069) 0.0119* (0.0070)

reelect 0.0028** (0.0014) 0.0028** (0.0014)

never −0.0019 (0.0015)

Controls:

socio-economic Yes Yes

sanction Yes Yes

adj R2 0.318 0.320

Table 3 Effect of prosecutor elections on sentencing (N = 476)

dep. var. % of convictionswith communitypunishment

% of convictionswith communitypunishment

averagemaximumsanction

CI −0.0213** (0.0098) −2.640* (1.3575)

reeleect 0.03383*** (0.0070) 1.4316* (0.7805)

never 0.0248*** (0.0054)

District controls No No Yes

Year controls No Yes No

adj R2 0.129 0.290 0.439

* 10% level; ** 5% level; *** 1% level. Heteroscedasticity-robust standard errors are reported

active, contested area. Our variable satisfies the criteria of an instrument, i.e., it is highlycorrelated with prosecutor contests, but not with jury. Table 2 presents the two-stage leastsquares estimates.

The significantly positive coefficients of CI and reelect show that our main results areconfirmed when entry is instrumented by judicial contests.

Another prediction of the theoretical model is that the average sentence length a prose-cutor achieves in jury trials in a year should tend to decline when reelection pressures arerelatively strong. We propose two ways to identify this effect. First, if the prosecutor is try-ing to appear tough and achieve longer incarcerations, then a reduction in the percentage ofconvictions that result in community punishments as the most serious sanction should occur.Since community punishments are expected to arise more frequently from plea bargainingthan from trial cases, a decrease in the percentage of convictions resulting in communitypunishments is evidence that prosecutors are pursuing relatively more severe sanctions. Sec-ond, if relatively more cases are being taken to court, and fewer are being plea bargained,then the marginal cases tried in court are those with the weakest evidence. Such cases willresult in lower expected sanctions than those tried regardless of the retention motivation,ceteris paribus. Thus, our model predicts that the average sanction obtained should decreasewith reelection pressures. Table 3 presents the corresponding results.15

15The first two columns include all socio-economic controls. The third and fourth columns include the sen-tencing guideline controls along with population density. The specification with never as the independentvariable and the average maximum sanction as the dependent variable is omitted because of never’s statisti-cal insignificance.

Public Choice (2014) 161:141–156 155

The predictions of the theoretical model are supported by these results. If a district hasan incumbent running for reelection, and a challenger has entered the race, the number ofconvictions that result in community punishments falls by 10.5 %. Similarly, the changein the average maximum sanction declines by 6.2 % in the year of a contested election, aswould be predicted if prosecutors increase the number of cases they take to trial by includingthose likely to receive lower than average sanctions.

Never having had a contested election increases the proportion of convictions that havecommunity punishments, while it does not have a statistically significant effect on the sanc-tions for active punishments (jail sentence). Interestingly, in the year before a reelectioncampaign, there is a greater reliance on community punishments, and there is an increase inthe average maximum active sanction. This could be explained, for example, by a forward-looking incumbent dispensing with weaker cases in the year prior to the reelection, by en-couraging pleas to community punishments so that in the reelection year resources can bedevoted to securing tougher sentences through trials (as would happen in Rasmusen et al.2009 framework).

Thus, it seems that the contested incumbent influences which cases go to trial. Given thestrict sentencing guidelines used in North Carolina, the result implies that prosecutors mustexpand the scope of cases prosecuted at trial and are unable to push for harsher sentences.

6 Conclusion

In the United States, prosecutors exercise a significant amount of discretion. Understandinghow direct elections affect prosecutors’ choices is crucial to developing the best mecha-nisms for implementing the preferences of the majority voting. There is a serious void inthe analysis of the decisions of prosecutors. In this paper we analyze prosecutors’ decisionsto take cases to trial or plea bargain. We investigate how the desire to win reelection affectsthis decision. We develop a theoretical model of asymmetric information to explore howtrial outcomes can be used as a signal to the voting public. The theory predicts that whenreelection pressures are strong, prosecutors increase the number of cases taken to trial andplea bargain less. The empirical evidence is consistent with the theory.

One might be concerned with the role of the judges, since their electoral cycles are cor-related with prosecutor electoral cycles. However, prosecutors (and not judges) make thedecision to take cases to jury trials. Thus, one would not expect the increase in the numberof jury trials prior to the prosecutorial election to be a result of judges’ reelection pres-sures. One can argue that there is an indirect effect if a judge, running for reelection, leviedenhanced punishments. That may cause prosecutors to revise their expectations about the ex-pected sanction if they took the case to trial and, consequently, induce prosecutors to avoidtrials and accept plea bargaining yielding tougher sentences. Our model suggests that is pre-cisely what will happen. Plea bargain outcomes adjust as defense attorneys anticipate thisbehavior and accept tougher sentences in plea bargains. Consequently, the incentive to takecases to trials should not change. Further, our empirical analysis shows that the judge vari-able is uncorrelated with the proportion of convictions that arise from trials. This suggeststhat the results are not influenced by judicial retention concerns.

The results strongly suggest that election pressures affect decisions. Whether this is sub-optimal depends on whether alternative explanations can be ruled out. For instance, reten-tion concerns may induce additional effort. If prosecutors are motivated to work harder, theaverage sanction achieved in trial presumably would increase, as a result of the additional re-sources expended on preparing cases. Hence, the result that the average maximum sanction

156 Public Choice (2014) 161:141–156

obtained decreases works against this alternative explanation. This suggests that reelectionconcerns do not necessarily induce greater effort, but rather that incumbents strategicallytake more cases to trial in order to be retained and thus weaker cases make their way to trial,causing the average sentence to drop.

Acknowledgements We thank the chief editor, associate editor, three anonymous referees, Juste Abramovaite,Toke Aidt, Ralph Bailey, Anindya Banerjee, Marco Barassi, Matthew Cole, Allin Cottrell, ValentinaDimitrova-Grajzl, Rob Elliott, Rosa Ferrer, Amanda Griffith, Andy Hanssen, Toby Kendall, Clare Leaver,Tito Pietra, Peter Postl, and seminar participants at the University of Birmingham, the University of Sassari,St. Bonaventure University, and the Workshop on Law, Economics, and Institutions for their comments andsuggestions.

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