Conflict in divorce disputes: the determinants of pretrial settlement

Conflict in divorce disputes: the determinants ofpretrial settlement

Amy Farmera,*, Jill Tiefenthalerb

aDepartment of Economics, University of Arkansas, Fayetteville, AR 72701, USAbDepartment of Economics, Colgate University, Hamilton, NY 13346, USA

Accepted 31 May 2000

Abstract

As a result of the increasing divorce rate over the past decades and the growing burden on the U.S.court system, many states have stressed mediation as an alternative to a court judgment. Whatdetermines whether divorcing couples reach a bilateral settlement or resort to use of the courts? In thispaper, we summarize the predictions from the theoretical literature on settlement failure and considerwhat these theories suggest in the application of divorce. The predictions of the models are empiricallytested using the Stanford Child Custody Study. Estimation of the determinants of going to courtindicates that none of the existing models on settlement failure adequately predict the determinants ofusing the courts. The results do, however, suggest that private information may play an important rolein court usage. In addition, the results provide some interesting implications for policy-makersinterested in decreasing the number of divorce cases that go to court. Attorney representation and theman’s income increase the likelihood of going to court while the woman’s education, the time lagbetween separation and divorce, and home ownership all decrease the likelihood of going to court.© 2001 Elsevier Science Inc. All rights reserved.

1. Introduction

One of the most important U.S. demographic trends of the latter half of the 20th centuryhas been the dramatic increase in the divorce rate. This trend has generated an increased needfor contracts to divide marital property, determine custody and visitation, and specify childsupport and spousal support payments. While many couples negotiate these contracts

* Corresponding author. Tel.:11-501-575-6093; fax:11-501-575-3241.E-mail addresses:[email protected] (A. Farmer), [email protected] (J. Tiefenthaler).

International Review of Law and Economics 21 (2001) 157–180

0144-8188/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved.PII: S0144-8188(01)00054-0

themselves, some divorce disputes require mediation while many end up in the courts to beresolved by judges.

The level of conflict in divorce cases is important for several reasons. First, the increasingburden on the court system resulting from an increasing divorce rate is costly to localgovernments and, therefore, taxpayers. Second, the escalation of conflict leads to greatertime, financial, and psychological costs to both parties, adding to the decrease in welfare thatresults from the process of splitting the household. These costs can be particularly importantfor women, who are shown to suffer more financially as a result of divorce (see, for example,Duncan & Hoffman [1985]). In addition, Peters et al. [1993] suggest that when divorcecontracts are self-negotiated, the probability that the payer makes the payments increases,thus reducing the future role of the courts as court enforcement is not required.

In response to these concerns, many states have pushed divorce mediation as an alterna-tive. For example, California has encouraged a wide variety of both private and publicmediation efforts. In the Los Angeles conciliation court, 55% of disputed custody casesreached mutual agreements savings the courts between $990,000 and $1,140,000 (see Mason[1994]). In addition, federal legislation, the 1988 Family Support Act, was passed requiringall states to adopt child support guidelines for courts to use in determining child supportobligations. In addition to moving toward a child support assurance system, another potentialbenefit of such legislation could be a decrease in the number of disputes going to court, asthe expected child support award is obvious to both parties. However, because awards are notcompletely explained by guideline variables, there is a margin for disagreement and,therefore, in some cases, conflict.1

The bargaining literature offers some insight into why pretrial settlement may not occureven when it is in the interest of both parties to avoid the costs of going to court. Players withsimilar information concerning the expected outcome of a mediated settlement should bewilling to reach an agreement. Even when informational asymmetries exist, Shavell [1989]argues that the informed party has the incentive to reveal her information. For example, it isin an injured plaintiff’s interest to credibly communicate the extent of her injury (perhaps bybeing examined by the defendant’s doctor). In light of the expected jury award for such aninjury, a bargain could be struck that avoids the cost and uncertainty of trial.

Given the incentives to settle, how can the large number of cases that do use the courtsbe explained? One explanation is that informational asymmetries may exist that cannot becredibly communicated to the uninformed party.2 For example, Farmer and Pecorino [1994]point out that risk preferences are not readily observable. Even if it is in an individual’sinterest to communicate their risk preference, it may be difficult to do so credibly. A secondclass of models proposes that players may make errors in their assessments of the likelyoutcome. These errors could be the result of simple mistakes (Hicks [1963]), excessiveoptimism (Shavell [1982]), or random errors generated from a distribution (Priest & Klein

1 In California, for example, Maccoby and Mnookin [1992] find that the guideline variables only explained36% of the variation in award levels.

2 See Bebchuk [1984, 1988], Reinganum and Wilde [1986] and Spier [1992], for example, for additionalasymmetric information models. See Cooter and Rubinfeld [1989] and Farmer and Pecorino [1995] for surveys.

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[1984]). If players with identical information err in their assessments of this information,bargaining could be impeded.3

While the literature on settlement failure is rich in theoretical models explaining why civillitigation often leads to court usage, there is little empirical work that tests these models. Inthis paper, we examine the usefulness of existing theories of settlement failure in explainingconflict in divorce disputes. We begin by reviewing the two dominant theories of settlementfailure and then clearly spell out the empirical implications of each of these models. Whilethe theoretical literature does not address the empirical predictions of the models, we proposefactors relevant to divorce that might relate to both information and optimism, and we offerpredictions as to how these factors might affect settlement. By comparing the predictions ofthe two models, we then identify empirical tests to distinguish between these two competingtheories. Given that divorce potentially involves highly emotional aspects as well as finan-cial, we discuss how the inclusion of spite might affect our results. Finally, we estimate thedeterminants of settlement failure and the impact on the magnitude of the final award usingthe Stanford Child Custody Study. While the empirical work is a rare test of the theoreticalmodels on settlement failure in general, empirically estimating the determinants of settlementfailure in divorce disputes specifically also provides useful information for policy-makersinterested in understanding the determinants of conflict in divorce negotiations.

2. Literature review

2.1. Theoretical models of settlement failure

Before examining the two dominant theoretical explanations for settlement failure, theinstitutional arrangements of the court system in divorce cases must be examined. Forexpositional ease, we denote the payer to be the man and the recipient to be the woman.4 Inmaking an award, the judge considers the state guidelines that result in an expected awardof aG whereG is a vector of guideline variables anda is a vector of weights.5 However, thisexpected payment may shift in favor of one party in any particular dispute. For example, ifone player has an attorney and the other does not, the player with the advantage get thejudgment shifted away from the guidelines in his favor. These variables might also includeeducation, race, and previous divorces.6 Variables that impact the judgment but are notguideline variables are denoted by P and are hereafter referred to as power variables. These

3 Note that these erroneous beliefs could be the result of findings that individuals process identical informationdifferently in favor of themselves. See Babcock (1995) for experimental evidence.

4 It is really the payer and payee that we are considering. For the empirical work, it is this differentiation thatmatters, not man versus woman.

5 Our data are only from California so the differences in guidelines across states do not matter for this study.6 These variables are observed by both parties. For example, both parties may realize that the payer has a

superior attorney and should accounted for this in determining the expected award. Settlement should beunaffected.

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power variables affect the award via a vector of weightsb. The judgment,J, should the casego to court is

J 5 aG 1 bP 1 « (1)

wheree represents a random error in the final decision.If a, G, b and Pare common knowledge, then we expect both parties to arrive at identical

estimates of the expected court verdict. Consequently they should be able to reach anagreement, saving both of them the cost of proceeding to court. The possible range for thesettlement (S) is

S[ @aG 1 bP 2 CW, aG 1 bP 1 CM# (2)

where CW and CM are the woman’s (recipient) and the man’s (payer) court costs, respec-tively. Any agreement within this range is a Pareto improvement over proceeding to court.Why then do people ever go to court? Below we discuss three competing theories that offerexplanations.

2.1.1. Asymmetric information modelsFirst, consider the possibility of asymmetric information. If one party has private infor-

mation that might impact the outcome of a trial, he may not be willing to settle in the samerange as the other party. However, Shavell [1989] argues that when such asymmetries exist,the party with favorable information has the incentive to reveal her information prior to goingto court at which point she would reveal the information anyway.7 As information isrevealed, a bargain could be struck that avoids the cost and uncertainty of trial. In the divorceapplication, if one party, for example, knows that the other had an affair and, therefore,power (P) has shifted in her favor, revealing this knowledge prior to paying court costs isoptimal.

What if the information cannot be revealed prior to going to court? Asymmetries mightexist that cannot be credibly communicate despite the desire to do so. For example, Farmerand Pecorino [1994] point out that risk preferences are unobservable and difficult to crediblyreveal. A risk neutral player would like to reveal his unwillingness to pay a risk premium inorder to prevent any attempts at extracting a surplus. However, since risk aversion cannot beverified in pretrial discovery, a risk averse player may attempt to bluff and the asymmetrywill likely not be resolved. The asymmetry may prevent settlement despite the presence ofcourt costs.

What is the impact of the existence of private information on conflict resolution indivorce? Any analysis requires an assumption of which party is privately informed, andwhich party has the power to make an offer. Suppose that the woman has private informationand the man is uninformed as to how her information will affect the outcome in court. Denotea type 1 recipient as a woman with private information that circumstances favor her (i.e., she

7 A party with unfavorable information would be revealed by her silence.


has an excellent attorney without a well-established reputation)8 and a type 2 player as awoman with private information that circumstances do not favor her. The man would ratherbargain with a type 2 woman, but he has no way of determining her type without going tocourt. A type 1 recipient is only willing to settle for the expected value of a trial outcomegiven her private information less her court costs (CW). Define x to be the amount she iswilling to accept where x5 E(trial type 1)2 CW. However, a type 2 woman is willing toaccept a lower offer, y, which is her expected value of proceeding to trial conditional on herinformation less her court costs (y5 E(trial type 2)2 CW).

Screening models where the uninformed party (the man in this example) makes an offerare characterized by sorting behavior in which the man must choose whether to make a softoffer that all recipients accept or whether to play tough and risk rejection and court.9 Atypical screening model implies that the man makes a soft offer (denoted x) rather than atough offer (denoted y) iff

x , p~E~trial utype1! 1 CM! 1 ~1 2 p! y. (3)

The left-hand-side of (3) is the soft offer, x, that is accepted with probability one. Theright-hand-side is the expected payout when making a tough offer y that is rejected withprobability p, the probability that the woman is a type 1 recipient. If rejected, trial results, andthe man expects to pay court costs (CM) plus the expected value of a trial outcome given thathe will be facing a type 1 player (i.e., the award is the larger of the two possibilities).However, with probability 1-p, the payer faces a type 2 opponent who accepts the offer ofy, and trial is avoided. If (3) holds, the payer offers x; if it fails, the offer is y and trials resultwith probability p.

What do general asymmetric information models such as this predict regarding thedeterminants of conflict escalation in divorce disputes? Does the result change if it is the manwho has private information, or if it is the woman who has the power to make a final offer?Does it matter if the asymmetry is two-sided?

First, in order for conflict to escalate to trial, asymmetric information models require thatprivate information exists but cannot be credibly revealed. If all information relevant to theexpected award is observable to both parties, pretrial settlement should occur somewherewithin the bargaining range. Modeling assumptions (such as who makes the offer and whohas the information) affect the prediction on where settlement occurs within this range, butregardless of the magnitude of the expected award (regardless ofa, G, b and P), partiesshould settle.

While settlement is expected when parties have the same information, these modelspredict that the existence of private information that cannot be revealed, increases thelikelihood that the case goes to court. For example, if one or both players hire well-knownattorneys, information concerning attorney quality should not lead to settlement failure. Thecouple should strike a bargain that accounts for the relative quality of their attorneys, and

8 Note that we are assuming that this information cannot be credibly revealed since all players would like topretend that they have an excellent but unknown attorney.

9 See Bebchuk (1984) for a classic example of a screening model.


avoid court costs. However, if the woman hires an unknown attorney that she knows to beof high quality, she has private information that can’t be credibly revealed and the case mightgo to court. (Unless the quality of the attorney introduces a new informational asymmetry,we might otherwise expect attorneys to facilitate information revelation and thus to minimizeconflict.)10

In more general terms, these models predict that variables that suggest the existence ofprivate information may increase the likelihood of conflict. On the other hand, any variablethat facilitates information revelation should decrease court usage. These predictions areexpected regardless of what form the asymmetry takes, and what magnitude of payment isexpected.

Finally, court is less likely as the right-hand-side of Eq. (3) increases. Men are more likelyto make the higher offer, x, and avoid court when (1) the probability (p) that the woman istype 1 increases, and (2) as the difference between x and y decreases. While the first case isobvious, the second may require additional explanation. It is not only the existence of privateinformation that matters, but the difference between what a type 1 and a type 2 woman willaccept (x-y) is also important. The difference between x and y is determined by howpowerful the private information is. If the difference between types (the difference betweenx and y in Eq. (3)) is large, then (3) is less likely to hold. In other words, the man is morelikely to make a tough offer that will be rejected by women with favorable private infor-mation. As applied to divorce, suppose that only the woman knows the quality of herattorney. If attorney quality has a big impact on what the woman would receive in court, thenthe difference between x and y will be great. In this case, the man may find it advantageousto take a chance and play hardball, hoping to extract a large surplus and risking court costsif his gamble fails. However, if the judge isn’t likely to be swayed by attorney arguments,her private information is less important (x-y is small) and the man’s incentive to play toughis diminished. Thus, if risk aversion is a source of private information, these models predictthat an increased variance in the trial outcome increases the risk premium (x-y) and thereforeincreases the likelihood of trial.

It is important to reiterate that it is only private information that is a source of conflict inthe asymmetric information model. Thus,variables that are observable to both partiesshould have no effect on the level of conflict.So, while the guideline (G) and power (P)variables determine the award, if these variables are observable to both parties they shouldnot influence the decision to go to court. Specifically, these models predict that guidelinevariable such as income and the number of children should not escalate conflict unless theyotherwise have an impact on private information. This finding is true regardless of thestructure of the model. These models also predict that higher court costs raise the likelihoodof settlement. From (3) this is obvious, but it is important to note that this prediction is robustto the model’s structure.

Finally, asymmetric information models predict that the level of conflict may influence the

10 Attorneys present additional issues such a principle-agent relationship. An attorney might want to pursuecourt for reputation purposes. See Farmer and Pecorino (1998). Also attorney usage may be a choice made bysomeone who has a preferenceex antefor litigation.


final award. However, predicting whether the award rises or falls as conflict escalatesdepends upon the modeling assumptions. For example, the screening model presented in (3)implies that conflict escalates when a tough offer is made to a type 1 woman who rejects theoffer. Since it is players with private information that favors their position who reach a higherlevel of conflict, we expect that those who go to court receive higher awards.11 Dependingon the structure of the asymmetry, court usage might have any number of effects on the finalaward.

2.1.2. Optimism modelsA second class of models (hereafter referred to as optimism models) propose that simple

mistakes, excessive optimism or random errors distort a player’s expectation of her awardand impede settlement. Egocentric bargaining models in which players process identicalinformation to favor themselves, fit this class of models as well. (See Hastorf & Cantril[1954] and Messick & Sentis [1979].) If a mistake is made, the bargaining range representedin (2) could disappear. Clearly, the greater mistake relative to the important variables, themore likely this will happen.

What do optimism models predict to be determinants of conflict? Do these predictionsdiffer from those generated by asymmetric information models? Ultimately, if the mistake(deviation of one party’s expectation from the true expected award) is large enough todominate the cost of proceeding to court, an individual will believe that it is in his bestinterest to go to court. Thus, willingness to settle depends upon the relative magnitudes ofmistakes and court costs. As court costs rise, mistakes are less likely to generate conflict; ittakes a bigger mistake (a greater degree of optimism or pessimism) to offset higher courtcosts. On the other hand, as the magnitude of the mistake rises, we expect a higherprobability of court usage.

Let us offer an example to illustrate this relationship. Assume a situation where theexpectation of the judgment is $100, and both players pay $10 if the case goes to court. Ifthere is no “optimism,” the couple settles within the range of mutually beneficial settlement($90- $110). However, now assume that the woman is optimistic about her court award andexpects a judgment of $130. Her optimism eliminates the bargaining range as the most thatthe man is willing to pay is still $110 but now the least that she will accept is $120. Thiscouple will proceed to court as a result of her optimism. However, if court costs increase to$20, the man’s best offer increases to $120 and the woman’s minimum acceptance falls to$110. The bargaining range (now $110–$120) is restored and court is avoided despite thewoman’s optimism.

When might we expect mistakes to be large relative to court costs? One possibility is thatmistakes may be larger in cases where incomes are large and there is a wider range ofpossible outcomes. In the example above, if the expected award is multiplied by a factor of

11 Note that players with unfavorable information cannot benefit by simply going to court. Their informationwill be revealed once they are there. It is not the act of going to court that raises the award, but rather an outcomeof the game that those with better cases may end up in court.


10 to $1000, the woman’s mistake of 30% would now imply that she will expect $1300. Inthis case, court costs would have to be greater than $150 for the bargaining range to exist.

Third, if players are risk averse, settlement is encouraged. For a given mistake a more riskaverse person is less likely to refuse to settle than one who is less risk averse. In addition,the greater the variance of the trial outcome, the more risk aversion will matter, and thereforethe more it can serve to reduce conflict. Note that in an asymmetric information model, riskaversion should have no impact unless it is private information. If it is, then an increase inthe risk premium (via an increase in risk aversion or trial variance) escalates conflict. (See2.1.1.).12

Fourth, since errors are random, observable variables that affect the level of the awardshould not impact the likelihood that optimism exists; consequently, as with asymmetricinformation, observable variables should not affect conflict escalation. Finally, the presenceof attorneys should minimize conflict. Although attorneys may err, they are less likely to bebeset with optimism or plagued with egocentric feelings toward a particular case.

In terms of the level of the award, optimism models predict that those who make mistakesfare worse than those who do not. The intuition is simple: making a mistake either leads oneto settle for a disadvantageous amount or to pursue court (and pay costs) when it is notnecessary. If we presume that both parties are equally prone to optimism, then neither sideshould systematically win or lose in court, and the final award should be independent ofconflict.

2.1.3. A model of spiteThe theoretical models outlined above suggest that settlement should occur as long as a

Pareto improving range exists. This range exists unless something, such as asymmetricinformation or optimism, interferes. However, divorce is a unique situation in which theparties may not simply be concerned about their own economic welfare. Given the highlyemotional element present in these cases, players may receive utility or disutility from thefinal position of the other party. If one party feels spite for the other, they may be willing toaccept a lower payoff (or incur additional costs) to decrease the payoff of the other. Thus,although players will settle if they can reach a Pareto improvement, if we consider utilityrather simply dollars, an improvement from one player’s perspective may involve lower finalwealth for both parties.

Thus, we may find that the leading theories of bargaining failure do not completely explaincourt usage in divorce cases. Empirically, the existence of spite simply implies greater courtusage across the board. Whether or not the existence of spite significantly increases courtusage could be tested by examining whether or not divorce and other emotionally chargedcases reach court more often. However, even if spite plays a role in the likelihood of divorcesettlement, this does not eliminate the role of other settlement factors; it simply suggests thatcourt usage should be everywhere shifted up. While we cannot test the effect of spite on courtusage in this paper, we do recognize that if emotion plays an important role in divorce cases,

12 We can view risk as raising the cost of court or lowering the benefits. Either way, the affect is the same.


the models outlined in the previous two sections will not explain all of the variation in courtusage.

2.2. Comparing the predictions of the models

Both asymmetric information and optimism models predict that while observable guide-line and power variable impact the expected award, they do not impact the level of conflict.In addition, both models predict that higher court costs should decrease conflict.

The presence of attorneys decreases conflict in optimism models, but the effect isuncertain in asymmetric information models. Because attorneys are more objective than theplayers in the conflict, their presence should decrease the possibility of simply makingmistakes. However, in the asymmetric information models, while attorneys may decrease theinformation gap and facilitate settlement, their presence might also introduce asymmetricinformation (in terms of attorney quality) and, therefore, complicate settlement.

Asymmetric information models predict that the existence of private information mightimpede settlement. This of course is not a factor in optimism models. Not only does thepresence of private information impede settlement, but the nature of this information can beimportant. For example, as the probability that the informed player has information thatfavors her position rises (in the screening model represented in Eq. (2), the higher the valueof (p)), the likelihood of settlement rises. In addition, the magnitude of the private informa-tion can impact bargaining. Recall from (3) that the difference between x and y matters. Interms of the implications for divorce disputes, this difference might be the range of attorneyquality, the range of risk aversion or the range of psychic costs involved in dragging out theprocess. Note that for any of these factors to impact settlement, they must be unknown to theother party. Of course, in practice it is difficult for an econometrician to know either theexistence of or the magnitude of private information in order to test whether it has an impacton conflict.

Recall that if the asymmetry in the information model takes the form of knowledge of risktype, then, the greater the difference between the level of risk aversion of type 1 and type 2women, the greater the man’s benefit from playing tough; therefore, conflict increases.(There is more to be gained by extracting the willingness to avoid trial.) Recall also that asthe variance of a court award rises this model suggests an increase in conflict. While thereis no private information in the optimism models, risk aversion does deter the usage of courtin these models because, like court costs, risk aversion makes trial more expensive. Note thatin these latter models, risk aversion need not be private information to have an impact.

In addition to risk aversion itself, the variance of the trial award affects conflict inasymmetric information models differently than in optimism models. As with the level ofrisk aversion, if the variance of trial awards increases, the certainty equivalent falls (repre-sented by y in (2)). As a result, playing tough becomes more attractive in the asymmetricinformation models, and conflict escalates. However, for a given level of risk aversion, as thevariance of the expected award increases, trials become more costly in the optimism models.Therefore, the variance of the expected award decreases the likelihood of conflict in thesemodels.

Consequently, optimism models suggest that both risk aversion and trial variance decrease


conflict, while asymmetric information models suggest the opposite. However, in the latterclass of models, risk aversion must be private information to have an impact. For the caseof divorce, this is unlikely to be true since the players know each other much better than ina typical settlement dispute. Therefore, risk aversion is likely to be common knowledge andthus have no impact in on conflict. Empirically, a test of whether risk aversion and trialvariance play a role in conflict escalation may provide insight into which factors maycontribute to bargaining failure.

Finally, in the asymmetric information model, the level of conflict affects the amount ofthe award. The direction of this impact is sensitive to the modeling assumptions, but the factthat strategic behavior might affect the magnitude of the award offers an interesting predic-tion. The optimism models do not predict this effect; optimism models imply that anydeviation from the expected award, as determined by the guideline and power variables, israndom.

The predictions generated from the two dominant models are summarized in Table 1.

2.3. Empirical evidence

The empirical literature on bargaining failure is quite limited. While we know of nostudies that attempt to test the predictions of both models outlined here, a few studies test aparticular prediction of one theory. In the context of salary disputes, several studies test theprediction that the level of conflict increases the amount of the award. For example, Auld[1981] finds that the salaries of Canadian public sector employees differ depending onwhether the dispute was negotiated or arbitrated. However, Delaney [1983] finds that thelevel of conflict does not affect teachers’ salaries. Considering a more strategic set-up,McConnell [1989] finds that in union negotiations, the outcome is consistent with a strategicsorting model with asymmetric information. Card [1990] finds no such impact, but concludesthat a more complex model with two-sided asymmetric information could be consistent withthis finding. Finally, Farmer et al. [2000] attempt to differentiate between optimism and

Table 1Summary of the predictions of the models

Variables AsymmetricInformation Models

Optimism Models

Effect on likelihood of going to court:Observable guideline and power variables No effect No effectCourt costs — —Presence of attorneys ? —Presence of private information 1 No effectMagnitude of private information: 1 No effect(Difference between x and y in our example)Degree of risk aversion No effect (unless unknown) —Variance of trial award 1 (if private info exists) —Effect on the amount of the award:Conflict Level Significant effect No effect

(sign depends on model)


asymmetric information in the context of baseball salary arbitration. They find evidence insupport of the optimism hypothesis, but cannot conclusively determine the cause of bar-gaining failure.

The optimism model (Priest & Klein, 1984) predicts that because mistakes are random,cases that go to court are random. In litigation where liability is the issue, this implies a 50%win rate for both sides. Several papers examine this issue, both experimentally and empir-ically. The results are mixed. While experimentally Stanley and Coursey [1990] and Thomas[1995] find support for the 50% win rate, a number of empirical papers (see Baxter [1980]and Wittman [1985] among others) find deviations from this rate.

This brief review of the empirical work on bargaining failure indicates the need for furtherempirical study. While the theoretical literature on settlement failure is substantial, there islittle empirical work that tests the predictions of the models. In addition, no theoretical orempirical work has previously been done to specifically explain bargaining failure in divorcecases.13

3. Data and empirical specification

The theoretical predictions of the models are tested using the Stanford Child CustodyStudy which is a three wave, longitudinal study of 1124 families who filed for divorce in twoCalifornia counties (San Mateo and Santa Clara Counties) between September, 1984 andApril, 1985. Eligibility for the study required that the couple had children who would remainminors throughout the course of the study. Three telephone interviews were conducted withthe parents over a three-year period and additional information was added from court records.While the study has four principal areas of inquiry, the one most relevant to this study is thefocus on the legal process leading to settlement. The data include information on how thecouple worked out their differences on custody, child support, visitation, and the division ofthe family home as well as the terms of all awards which were reached by the end of thestudy. The data set also includes demographic information including income and employ-ment data. While the data set includes 1124 observations, because of attrition and missingincome and employment data, there are only 838 cases which include all the informationneeded to estimate the conflict equation. See Maccoby and Mnookin [1992] for a detaileddiscussion of the Stanford Child Custody Study.

3.1. Determinants of court usage

A major goal of this research is to estimate the determinants of couples using the courtsto resolve divorce disputes. Empirically, the level of conflict is measured as either (0) theman and woman worked out an agreement on their own or in negotiations through theirlawyers or a mediator or (1) the case went to court. For the empirical work presented in

13 Note that much has been done to determine the level of divorce awards, but none include a study of howbargaining behavior might impact this award. In addition, no studies exist that examine the level of conflict itself.


Section 4, the dependent variable is equal to 1 if a judge heard the couple’s case because theydisagreed over any aspect of the divorce settlement (child support, spousal support, childcustody, property distribution or visitation).14 In the majority of cases, the couple worked outthe terms of the settlement on their own. However, 37.8% (317 of 838 cases) of the caseswere heard by a judge at some time before the divorce was final. It should be noted that in96 of the 838 cases, the divorce was not yet final at the end of the study. For these cases, thedependent variable is whether or not they had used the courts by the end of the study.Fifty-three percentage (51 of 96 cases) of the cases that weren’t settled by the end of thestudy had used the courts compared with 36% of those who had finalized their divorces.However, it is possible that some of the unsettled cases that did not go to court by the endof the study (45 unsettled cases did not use the courts by the last interview) did eventuallyend up in court. As a result, the level of dispute may be underestimated.

What determines whether a divorcing couple negotiates a contract themselves or uses thecourts to get a judgment? Table 1 (in Section 2) summarizes the general predictions that arisefrom each of the theoretical models and, therefore, provides a guide in outlining the variablesfor the empirical model of the determinants of court usage in divorce disputes.

The first prediction in Table 1 is that both models suggest that observable demographicvariables that impact the settlement or award should not affect the likelihood that a case goesto court. These variables, which include both the guideline (G) and power (P) variables, areincluded as regressors in the conflict equations; however, these observable variables areexpected to be insignificant in determining conflict. In California, the child support guidelineat the time of the data collection is that the award should depend on the income earned byeach parent, the number of children, and the amount of time the children spend with eachparent. The spousal support guideline provides that the spousal support award should dependon the incomes of both parties, the duration of the marriage, and their assets.15 Incomes ofthe man and the woman, the number of children, and the couple’s assets are included in theconflict equation. The incomes of the man and the woman are measured as monthly earningsconverted by the original investigators to an annual equivalent, the number of children is thenumber of children parented by the divorcing couple, and assets are proxied by whether ornot the couple owns a home. The time the children will spend with each parent is notincluded in the court equation because custody may be a source of conflict and would,therefore, be endogenous. The duration of the marriage is available but we don’t include itbecause doing so would require dropping approximately 200 cases due to missing data.

The power differential is proxied by several variables. First, having an attorney might givean individual an advantage in a divorce dispute. Therefore, two variables are included in theregression representing (1) whether or not the man is represented by an attorney, and (2)whether or not the woman is represented by an attorney. Whether or not each party was

14 It is not clear in the data whether the case was heard by the judge but settled by the couple or whether thejudge actually rendered a decision. However, in our models it is the use of court that matters. Going to court islikely to resolve any asymmetries and clear up any mistakes even if the judge doesn’t actually decide the award.Court costs will also be incurred. Thus, settlement resulting from court are treated the same as cases where thejudge actually determines the award.

15 See Maccoby and Mnookin [1992] for further discussion of the guidelines.


previously married is also included as an indicator of power. If the party has gonethrough the divorce process before, it is likely that their knowledge of the process and,therefore, their power is greater. Bargaining power may also depend on which partywants out of the marriage. The party that doesn’t want the divorce is likely to have morepower. Two variables are added to proxy for power— one indicating that only the manwants a divorce and another indicating that only the woman wants a divorce. The omittedcategory is that both individuals want the divorce. Other potential indicators of power arethe races and education levels of the man and the woman. Two dummy variables areincluded as regressors to indicate whether or not the man and the woman are racialminorities and two dummy variables are included to indicate whether or not the man andthe woman have college degrees. Recall that none of these variables are predicted toimpact conflict.

Both models also predict that higher court costs should discourage conflict. Because theStanford Child Custody Study includes data only on divorcing couples in California (andonly in two counties), different states’ divorce laws and court costs cannot be used as proxiesfor the costs of continuing the dispute. However, the data set includes information onwhether or nor each party is involved in another relationship. Being involved in anotherrelationship may suggest that the individual would like the process to end quickly and,therefore, has higher costs to continuing the dispute. Clearly, these variables do not offer anexact test of the impact of court costs on conflict.

Table 1 indicates that the presence of attorneys has an ambiguous impact on conflict in theasymmetric information model and an unambiguously negative impact in the optimismmodels. Therefore, attorney representation for the man and the woman are included asregressors in the court equation. However, as mentioned above, attorney representation mayalso proxy for power.

The fourth prediction, in the asymmetric information model only, is that an increase in thepayer’s expectation that the recipient has favorable private information (p) decreases thelikelihood that the case goes to court. The payer’s expectations are, obviously, unobservable.However, the payer might base his expectations about his ex-spouse on the behavior of otherwomen. Therefore, in a national data set, the percentage of cases that have gone to court inthe past might be a good proxy for p. Unfortunately, the Stanford Child Custody Study isdrawn from only two counties in California and, as result, there isn’t enough variation toinclude this measure as a proxy for p. Similarly, the existence and range (x - y) of privateinformation are also unobservable. However, both the likelihood that private informationexists and the range of this private information are likely to decrease if the couple has beenseparated for a long time before filing for divorce. As the process unfolds, information hastime to be revealed, and conflict should decline. Consequently, the number of days betweenthe couple’s separation and the first filing for divorce is included in the regression as a proxyfor the existence and range of private information.

The intuitive notion that the payee’s risk aversion decreases her willingness to pursueconflict is predicted in the optimism models. We proxy risk aversion with income, thenumber of children, and whether the recipient has job security. According to the optimismmodels, if income and job security are proxies for decreasing risk aversion, these variablesshould increase the likelihood of conflict. However, an increase in the number of children is


likely to increase risk aversion and, therefore, decrease conflict. While these variables arelikely to be good proxies for risk aversion, they are all likely to be known to both parties. Asa result, they are not good proxies forunknown risk preferences; recall only privateinformation is relevant in the information models. Because all variables observed by theresearcher are also likely to be observed (or credibly revealed) by both parties, we cannot testfor the prediction generated by the asymmetric information model that asymmetries over riskaversion might impede settlement.

Although the variance of trial awards is related to conflict via risk aversion, the predictionis somewhat different. In the optimism models, as the variance of the trial outcome rises, riskaverse recipients are more likely to settle. However, in the asymmetric information models,the opposite result is expected if the level of risk aversion is unknown; if risk preferences arecommon knowledge, we would expect no effect on conflict. Empirically, the variance of thecourt awards is likely to differ across states, courts, income groups, and family sizes. Strictstate guidelines and judges that stick to the established guidelines will decrease the varianceof court awards. Because the Stanford data set includes couples from only two counties inCalifornia, there is no variation resulting from differing state guidelines and little variationin courts. Unfortunately, although the Stanford Child Custody data set does include data fromtwo counties, the data set does not include a county identifier. Therefore, we can’t directlytest the effects of variance in court awards on the likelihood of using the courts with thesedata.

Table 2 presents the sample means and standard deviations for all variables included in theestimation of the court equation.

Table 2Means and standard deviations of variables in court equation (N5 858)

Mean Standard Deviation

Court (05 no/1 5 yes) 0.378 0.485Man’s income 34,833.91 27,189.91Woman’s income 15,753.14 13,204.33Number of children 1.64 0.742Couple owned a home 0.604 0.489Man represented by an attorney 0.773 0.419Woman represented by an attorney 0.628 0.484Man divorced before 0.195 0.396Woman divorced before 0.172 0.377Only man wants divorce 0.261 0.440Only woman wants divorce 0.516 0.500Man is a minority 0.251 0.434Woman is a minority 0.228 0.420Man has college degree 0.230 0.421Woman has college degree 0.345 0.476Man involved in relationship 0.527 0.500Woman involved in relationship 0.548 0.498Number of days between separation and filing 103.30 112.34Man has job security 0.541 0.499Woman has job security 0.575 0.495


3.2. Determinants of the award

What do the models predict about the determinants of the amount of the award? Obvi-ously, regardless of the model, the guideline (G) variables used by the court, and anyvariables that affect the power (P) differential between the two parties should explain mostof the differences in awards across cases. In addition, asymmetric information models predictthat the level of conflict may impact the expected award; the specification of the modeldetermines what that impact might be. The optimism models offer no predictions concerningthe magnitude of the award that cannot be explained by guideline and power variables.

Award equations are estimated for both child support and spousal support awards inSection 4. The dependent variables, the amounts of the child support and the spousal supportawards, were collected from court records and are included for all couples who had finalizedtheir divorces. Due to further data limitations, the number of observations included in theseestimations are reduced to 760 and 651 for child support and spousal support, respectively.The guideline and power variables are measured the same as those included in the conflictequation. However, rather than including the variables for the man and the woman, the dataare coded as payer and payee according to who made payments if payments were awardedand who sought payments if payments were not awarded. In addition, the guideline variablesomitted from the conflict equation are included in the award equations. The duration of themarriage, included in the spousal support award equation, is measured as the number of daysbetween the wedding and the divorce petition filing. The time the child spends with eachparent, included in the child support award equation, is proxied by whether or not the couplehas joint physical custody (the alternative is that the custodial parent, the payee, has solephysical custody). The level of conflict included in the right-hand-side of the child support(spousal support) award equation is a dummy variable which equals 1 if the couple went tocourt specifically over child support(spousal support) and 0 if the couple came to anagreement without using the courts.

Table 3 presents means and standard deviations of variables not previously included.While the Stanford Child Custody Study includes many of the needed variables, it does

have some limitations for our purposes. First, it contains cases from only two counties inCalifornia and therefore there is insufficient variation in court costs. Further, a national dataset would allow for testing the effects of different state guidelines, court costs, and variancein court awards on the level of conflict. Another limitation of the data set is that the study

Table 3Means and standard deviations of monthly child and spousal support awards

Child Support Spousal SupportMean Standard Deviation Mean Standard Deviation

Monthly award - all observations 322.63 268.71 167.02 431.86Monthly award - zeros excluded 382.54 249.28 503.70 555.36Court dummy 0.162 0.369 0.077 0.266Duration of marriage (days) — — 3612.9 2133.6Joint custody 0.209 0.407 — —Number of observations 760 651


was completed before all divorces in the sample were finalized. This could bias the resultssince the cases that took the most time are likely to be those with the greatest level of conflict.

4. Results

4.1. The determinants of going to court

The dependent variable in the conflict equation is coded as 0 or 1 where 0 indicates thatthe couple came to an agreement without using the courts and 1 indicates that the couplewent to court. Consequently, the probit technique is the appropriate method for estimation.Table 4 presents the results. The probit coefficients are followed by the t-statistics inparenthesis; partial derivatives, computed at the means of the independent variables, are alsopresented. The results from two different specifications of the model are presented. The firstspecification includes all independent variables discussed in Section 3. The second specifi-cation omits the dummy variables indicating lawyer representation because of the possibility

Table 4Court Equation Probit Results (N5 838)

Coefficient(T-statistic)

PartialDerivative

Coefficient(T-statistic)

PartialDerivative

Constant 20.723** (23.347) 20.271 0.016 (0.085) 0.006Guideline Variables:

Man’s income 0.003* (1.652) 0.001 0.004 (2.234)** 0.002Woman’s income 20.005 (21.249) 20.002 20.004 (21.175) 20.002Number of children 0.065 (1.025) 0.025 0.053 (0.849) 0.020Couple owned a home 20.385 (23.479)** 20.144 20.184 (21.771)* 20.070

Power Variables:Man divorced before 0.063 (0.488) 0.024 0.030 (0.243) 0.011Woman divorced before 0.103 (0.763) 0.039 0.122 (0.935) 0.046Only man wants divorce 20.234 (21.641)* 20.088 20.145 (20.992) 20.055Only woman wants divorce 20.072 (20.526) 20.027 20.003 (20.022) 20.002Man is a minority 0.019 (0.164) 0.008 20.014 (20.126) 20.005Woman is a minority 0.032 (0.259) 0.012 0.023 (0.190) 0.009Man has college degree 0.126 (1.013) 0.047 0.146 (1.205) 0.055Woman has college degree 20.286 (22.527)** 20.107 20.227 (22.062)** 20.086

Attorney Variables:Man represented by attorney 0.548 (5.070)** 0.205Woman represented by attorney 0.686 (5.377)** 0.257

Cost Variables:Man involved in relationship 20.039 (20.356) 20.015 20.011 (20.107) 20.004Woman involved in relationship 20.027 (20.239) 20.010 20.093 (20.854) 20.035

Asymmetric Information Variable:# days between separation and

filing for divorce20.001 (22.269)** 20.0003 20.001 (23.348)** 20.0005

Risk Aversion Variables:Man has job security 20.216 (22.228)** 20.081 20.170 (21.822)* 20.064Woman has job security 20.070 (20.716) 20.026 20.099 (21.042) 20.038

Percent correctly predicted 67% 63%


that these variables are endogenous. These variables will be endogenous if unobservablesthat affect the likelihood of a couple going to court also affect the likelihood that either ofthem hires an attorney.

Both models outlined in Section 2 predict that guideline and power variables should notaffect the likelihood of a case going to court. As the results in Table 4 indicate, most of theguideline and power variables do not directly impact the likelihood of going to court.However, the higher a man’s income the more likely the case goes to court. This is consistentwith the optimism models. As income increases so does the award and, therefore, a givenpercentage error is more likely to dominate court costs and impede settlement.16 While theasymmetric information model predicts that guideline variables should not affect courtusage, the model does predict that the variance of the outcome may increase the likelihoodof going to court if private information exists; in the absence of such asymmetries, varianceshould have no impact.

If the payer’s income is high, there is more variance in the expected outcome and theprobability of going to court increases. While this result is consistent with both models, if thisstory is accurate, we would also expect the number of children to increase the variance of theexpected outcome and, therefore, have a positive effect on court usage. While the effect ofthe number of children is positive, it is not statistically significant. However, the insignificantresult is not surprising given the low variance in the number of children across families. (Allcouples in the study have children.) Similarly, a rise in the woman’s income and hereducational attainment might be expected to lower the variance of the award since the rangeof possible awards would be less likely to include higher amounts. Both variables have theexpected signs, however, the woman’s education is significant but her income is not. Perhapsjudges rely more on potential income than actual income at the time of the divorce. If thisis the case, the woman’s education rather than her income lowers the award and, therefore,the variance.17 Following the same argument, there is less uncertainty about the award if boththe man and the woman have job security. Therefore, we expect that job security shoulddecrease conflict. The results support this prediction as both the man’s and the woman’s jobsecurity lower the probability of going to court. However, only the man’s job security issignificant.

In the optimism models, the presence of attorneys is expected to decrease conflict.However, in the asymmetric information, the effect is uncertain (attorneys may decrease theexisting information gap but also might introduce new uncertainty on attorney quality). Theresults from the first specification in Table 4 indicate a strong positive and significant effectof attorney representation on the likelihood of going to court. These results are contrary tothe optimism model but are consistent with one interpretation of the asymmetric information

16 Income may have other effects in addition to increasing the potential error. Higher incomes may imply a greatervariance of the award, thereby increasing the incentive for a risk averse woman to settle. However, this assumes thelevel of risk aversion to be constant, which is unlikely as income rises. Thus, it is impossible to sort out the impact ofincome on variance and risk aversion. Also, income is only a weak proxy for both of these variables, so we have chosennot to complicate the discussion with these issues.

17 Home ownership is difficult to analyze since it is a joint asset, and our predictions on men’s and women’sincome are opposite.


models. Of course the effect could result from omitted variable bias (unobservable variableswhich make a person more likely to hire a lawyer also make him more likely to go to court).However, omitting attorney representation does not produce dramatically different results.Another possible explanation for the positive result is the principal-agent problem. Byincreasing conflict, attorneys can increase their fees (attorney fees in divorce cases are basedon attorney time not contingency) even if it is not in the best interests of the clients.

Both models predict that higher costs of going to court will result in less court usage. Theonly proxies available in the data set are whether the man and woman are involved in newrelationships (and may want to speed up the process). While these proxies for court costs dohave negative effects on the likelihood of going to court, the estimates are insignificant.Future work that examines the effects of actual court costs across court systems would bevaluable.

The number of days between the separation and filing for divorce is included as a proxyfor the presence of asymmetric information. The longer a couple has been separated, themore likely it is that both postseparation incomes and custody and visitation preferences canbe credibly revealed. Therefore, a long separation both decreases the likelihood that asym-metric information exists and decreases the uncertainty about incomes and custody arrange-ments that can increase a judge’s discretion. Therefore, a long separation is likely toencourage settlement. Our findings support this prediction. Since the optimism modelpredicts no such effect, it is not supported by this test. The length of time between separationand filing for divorce might also be a proxy for spite. Couples that hate each other are morelikely to file for divorce right away in order to end the marriage as quickly as possible andalso more likely to go to court.

4.2. The determinants of the award

Our example of an asymmetric information model in Eq. (3) predicts that it is women withstronger cases who proceed to court. Of course different structures of the model may predictthe opposite. However, the optimism models suggest no effect of conflict on the award level.Thus, any such impact suggests that strategic play induced by asymmetric information mayplay a role in bargaining failure. To test this prediction, child support and spousal supportaward equations were estimated. Because many payees receive no child support or spousalsupport award there is left censoring at zero in the dependent variable; consequently, theaward equations are estimated using Tobit techniques. The samples are smaller in theseregressions because the awards that are not finalized by the end of the study must be omittedfrom the sample. As previously discussed, this omission is a source of potential bias and mayresult in underestimation of the effect of the level of conflict on the award. Additionalobservations were dropped due to missing data on independent variables. The results arepresented in the Appendix.

Going to court has a significantly positive effect on the child support and spousal supportawards; it raises the awards by $36.32 and $96.09 per month, respectively. These resultssupport the asymmetric information model that implies that court only happens as a conse-quence of strategic play. It is consistent with our example in which the payer makes a toughoffer that is rejected by the payee. She rejects the offer because she has private information


that she will fare well in court. Note that the optimism model predicts no effect of court usageon the award.

5. Conclusions

Low conflict dispute resolution in divorce cases is desirable in order to minimize the coststo the family and the state and because agreements reached outside of court are more likelyto be honored by both parties. Despite the beneficial effects of minimizing legal conflict indivorces, little research exists on what determines the use of the courts. In this paper, weexamine the well-developed theoretical literature on bargaining, formalize the empiricalpredictions of these models and determine what these models have to offer for understandingcourt usage in divorce cases.

What does our research say about the usefulness of the theoretical models in understand-ing settlement failure in the divorce application? First, in comparing the predictions of thetwo dominant models of settlement failure—asymmetric information and optimism mod-els—there are few predictions that both differ across the two models and are testable. Themodels offer identical predictions on the effects of observable guideline and power variables,court costs, and the variance of the award on the likelihood of going to court. While themodels have different predictions on the effects of the existence of private information andthe magnitude of this information, it is unlikely that a researcher observes information thatis unobservable to one of the parties. That leaves only two variables—attorney representationand the effect of the conflict on the amount of the award—that have different predictionsacross the models and are directly testable. Our results on these variables support theasymmetric information model over the optimism model. Contrary to the prediction of theoptimism model (but not the asymmetric information model), attorney representation in-creases conflict; however, this result is suspect since that attorney representation may beendogenous. The level of conflict is found to positively impact the award. Again, this resultis consistent with the asymmetric information but not optimism.

Despite this evidence supporting the asymmetric information model over the optimismmodel, our conclusion from both the theoretical application and empirical results is that bothof these models help to explain settlement failure in divorce cases yet neither fully capturesthe story. The result that an increase in the variance of the award (proxied by income,women’s education, and job security) increases conflict is an important result that isconsistent with both models. The result that women who go to court get higher awards is hardto explain in any other way than the existence of asymmetric information. While the resultson attorney representation and the time lag between separation and filing for divorce areconsistent with the asymmetric information model, there are alternative explanations forthese results that are equally persuasive. The divorce problem is complex, particularly dueto the high emotional aspect that generates nonfinancial incentives. Spite is likely to be animportant source of settlement failure in divorce cases (and other types of litigation as well)and is an alternative explanation for both of these results. In addition, the result that attorneyrepresentation increases court usage could be explained by the principal-agent problem. Inany case, our research questions the empirical usefulness of the theoretical literature on


settlement failure. It generates few testable predictions. While our application of the theoryfocuses strictly on divorce cases, the paucity of empirical tests of this literature in generalsuggests that the problem extends beyond the divorce application.

In addition to applying the theoretical literature on settlement failure to the divorcescenario, another goal of our research is to empirically examine the determinants of conflictin divorce disputes for public policy purposes. There are public policy concerns over thelarge costs of divorce conflict to both the state and the families involved. What do ourempirical results tell us about court usage in divorce cases? First, the results suggest that themore uncertainty about the outcome, the more likely a couple goes to court. The variance ofthe award is likely to increase with the man’s income, the woman’s income and education,and the number of children. Our results provide some support for a link between thesevariables and the probability of court. In addition, we also find that the man’s job securitysignificantly affects conflict. Job insecurity would increase the uncertainty about the likelyaward and, therefore, increase conflict. Moreover, in comparing the award estimations withthe conflict estimations, it is apparent that the same variables tend to matter in both equations.While the theory does not directly predict this result, the finding is consistent with the notionthat any variable that influences the final award generates uncertainty. To the extent thatjudges have discretion and the outcome may be influenced by any number of things thatcould cause informational asymmetries, these variables would be expected (according toasymmetric information models in general) to generate conflict.

While our results suggest that uncertainty about the court award increases the likelihoodof going to court, we need more work in this area. An excellent test of this hypothesis wouldbe to compare the effect of judicial discretion across states on the number of cases that goto court. If the result holds up, policies and laws that decrease the uncertainty about the awardwould decrease the burden on the courts. Uncertainty would fall significantly if child andfamily support guidelines were more concrete and judges had less discretion in these cases.For example, if child support were simply determined by a formula (comprised of theguideline variables) there would no uncertainty about the court award and even the cases ofthe highest awards would have no incentive to go to court. Of course, there are other benefitsof judicial discretion that are not present in this study, so our finding offers a cost which mustbe weighed against those benefits.

Our results also indicate that the presence of lawyers is strongly correlated with going tocourt. If this relationship results from simple correlation (people who want lawyers also wantto go to court) then it is not important from a policy perspective. However, if, in fact, lawyersencourage use of the courts, this is an extremely important result from a policy perspective.Why might lawyers encourage use of the courts? As the asymmetric information modelindicates, they may increase uncertainty (due to asymmetric information about their quality).Another explanation is that there is a principal-agent problem. Clients want their lawyers tosettle but it is in the lawyers’ best interests to continue the dispute (in divorce cases lawyersare paid hourly rather than on contingency). The results presented here don’t settle the issuebut they do highlight that the relationship between lawyers and going to court is anotherimportant area for further study.

The time between separation and filing for divorce is found to decrease the likelihood ofusing the courts. The interpretation we provided is that a long separation allows time for the


couple to credibly reveal information and work out their disputes on matters of custody,visitation, and property without using the courts. This result suggests that longer waitingperiods might decrease the number of couples who go to court to settle disputes. Whileresearch shows (see Sweezy & Tiefenthaler [1996]) that waiting periods have no impact onthe likelihood of divorce, we know of no other studies which examine the effect of waitingperiods on the likelihood of going to court. However, if the time to filing is simply a proxyfor the amount of animosity between the couple, waiting won’t have any effect on courtusage.

The result that women with higher education are less likely to go to court is noteworthyfor policy-makers. One obvious interpretation of this result is that men know that they can’ttake advantage of educated women so they offer a responsible, expected award. If this is true,it is important to recognize that uneducated women are receiving lower than expectedawards. The award equations for spousal support and child support (see Appendix) indicatethat this is the case as women with college degrees receive significantly higher awardsholding the guideline variables constant. This is an equity issue that policy-makers need toaddress.

Finally, both theoretical models of settlement failure predict that increasing the costs ofcourt usage may decrease the burdens on the court. Our analysis offers a very limited test ofthis hypothesis due to data limitations. A better test would require using a national data setwhere the actual court costs are observed. Future work showing a relationship between policyvariables such as court costs (money and time) and the likelihood of going to court wouldbe important.

Appendix

Table 5 presents the results from the child support and spousal support award estima-tions.18 The coefficient,bj 5 (E(y*)/Xj), is followed by the t-statistic in parenthesis.

In addition to the court variable, guideline and power variables are predicted to affectthe amount of the awards. As expected, the number of children and the payer’s incomesignificantly increase the child support award while the payee’s income significantlydecreases the award. Joint physical custody also significantly decreases the award. If thepayer has an attorney and the payee does not the award decreases by $204.52 permonth.19 The payee being represented by an attorney when the payer is not representedis not significant. Another source of power is understanding the divorce process; payees

18 Another econometric issue is the possible endogeneity of the conflict level. Unobservables that affect the levelof conflict may also affect the award conditional on the level of conflict. To address this issue, 2SLS estimation wasundertaken with the predicted level of conflict substituted for the actual level in the award equations. The 2SLS resultsindicate that the point estimate on conflict in both equations increases but the standard errors also increase resulting inthe coefficient becoming insignificant. The increased standard error is not surprising given the relatively low predictivepower of the first-stage conflict equations. Note that all other coefficients changed by only small amounts. Conse-quently, if there is endogeneity, it is not affecting these estimates.

19 In Tobit models the coefficient is the effect of the independent variable on the latent variable (y*) while the


who have had previous divorces receive significantly higher child support awards.Bargaining power may also result if one individual wants the divorce and the other doesnot. The child support results show the expected signs. If only the payer wants out theaward is higher while if only the payee wants out the award is lower. However, neitherresult is significant. Other demographic variables that significantly impact the childsupport award are education and religion. If the payee has a college degree, the awardsignificantly increases. If the payer is Protestant the award falls but if the payee isProtestant the award increases.

The spousal support guidelines include the incomes of the payer and payee, duration of themarriage, and whether the couple owns a home. As expected, the payer’s income and theduration of the marriage significantly increase the award while the payee’s income signifi-cantly decreases the award. If the payer has an attorney and the payee doesn’t, the payeereceives approximately $110.79 less per month in spousal support. In addition, if only thepayee wants out of the marriage she appears to have less bargaining power as she receivesapproximately $68.52 less per month. Again, payees with college degrees receive signifi-

marginal effect, the more interesting effect for policy purposes, is the effect of the independent variable on theobserved dependent variable (y). See Maddala [1983] for discussion and derivation.

Table 5Award Amount Tobit Results

Child Support Spousal Support

Constant 20.12 (0.57) 2535.00** (23.46)Court 40.15* (1.72) 409.55** (4.15)Payer’s income (thousands of dollars) 4.34** (12.12) 14.92** (11.16)Payee’s income (thousands of dollars) 22.44** (23.37) 226.75** (28.62)Joint custody 2126.02** (25.67)Number of children 139.79** (11.51)Duration of marriage (days) 0.041** (2.41)Couple owned a home 111.45 (1.34)Attorney: payer only 2226.09** (26.21) 2472.20** (22.88)Attorney: payee only 210.40 (20.50) 240.10 (20.47)Payer divorced before 243.57* (21.79) 46.96 (0.51)Payee divorced before 54.70** (2.20) 238.72 (20.40)Only payer wants divorce 22.56 (0.83) 2115.74 (20.92)Only payee wants divorce 238.03 (21.58) 2292.05** (22.37)Payer has college degree 9.94 (0.42) 130.20 (1.57)Payee has college degree 50.01** (2.41) 191.70** (2.54)Payer is a minority 233.72 (21.55) 2150.95* (21.71)Payee is a minority 228.02 (21.25) 123.93 (1.38)Payer is Catholic 213.30 (20.56) 2173.51* (21.94)Payee is Catholic 28.43 (1.16) 153.01 1.63Payer is Protestant 263.94** (22.61) 2268.55** (22.87)Payee is Protestant 45.27* (1.82) 124.88 (1.29)Payer is Jewish 22.44 (20.03) 2239.49 (21.06)Payee is Jewish 36.23 (0.51) 342.56 (1.59)


cantly greater awards. Payers that are minorities and payers that are Protestants pay signif-icantly lower awards.

Acknowledgments

The authors thank Kevin Rask, Bob Turner, the participants of the Colgate-HamiltonEconomics Seminar Series, and anonymous referees for helpful comments. Any errors areour own.

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Conflict in divorce disputes: the determinants of pretrial settlement

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