Unemployment and Domestic Violence: Theory and

Evidence✩

Dan Anderberg∗, Helmut Rainer†, Jonathan Wadsworth‡, Tanya Wilson§

∗Royal Holloway College, University of London; Institute for Fiscal Studies†University of Munich; CESifo; Ifo Institute for Economic Research

‡Royal Holloway College, University of London; Centre for Economic Performance, LSE; IZA§Royal Holloway College, University of London

Abstract

While many commentators perceive unemployment to be a key risk factor for intimatepartner violence, the empirical evidence remains limited. We combine individual-leveldata from the British Crime Survey (BCS) with local labor market data to estimate theeffects of total and gender-specific unemployment rates on domestic violence. The anal-ysis uses the substantial variation in the increase in unemployment across areas, gender,and age-groups associated with the onset of the latest recession. Our main specificationlinks a woman’s risk of being abused to the unemployment rate among females and malesin her local area and age group. Our results suggest that male and female unemploymenthave opposite-signed effects on domestic violence: while female unemployment increasesthe risk abuse, unemployment among males has the opposite effect. The result is shownto be robust to the inclusion of a wide set of control and also remains when we instrumentfor male and female unemployment using shift-share indices of labor demand. We arguethat our findings are consistent with a theory of domestic violence in which (i) marriageprovides insurance against employment risk through the pooling of resources, and (ii)a woman does not know the violent predisposition of her partner but infers it from hisbehavior. When the male partner face a high risk of unemployment, a potentially abusivehusband strategically conceals his type as he has an economic incentive to avoid divorceand the associated loss of spousal insurance. However, when the female spouse faces ahigh risk of unemployment, her expected financial dependency on her partner promptsa husband with violent predisposition to reveal his abusive nature.

Keywords: Domestic violence, unemploymentJEL Classification: J12, D19.

✩The paper benefited from comments from seminar participants at the Universities of Dublin andLinz.

Email addresses: Dan.Anderberg@rhul.ac.uk (Dan Anderberg), rainer@ifo.de (Helmut Rainer),j.wadsworth@rhul.ac.uk (Jonathan Wadsworth), Tanya.Wilson.2011@live.rhul.ac.uk (TanyaWilson)

March 12, 2013

1. Introduction

During the past three global recessions, media coverage in many countries was repletewith reports suggesting that domestic violence increases with unemployment. In 1993,for example, the British daily newspaper The Independent cited a senior police officer assaying of the increase in domestic violence:

“With the problems in the country and unemployment being as high as itis and the associated financial problems, the pressures within family life arefar greater. That must exacerbate the problems and, sadly, the police serviceis now picking up the pieces of that increase.” (The Independent, March 9,1993)

In a 2008 interview for The Guardian, the then-attorney general Lady Scotland arguedthat domestic violence will spread as the recession deepens:

“When families go through difficulties, if someone loses their job, or they havefinancial problems, it can escalate stress, and lead to alcohol or drug abuse.Quite often violence can flow from that.”

And in 2012, an executive director of a Washington-based law enforcement think-tankexpressed his concerns about rising domestic violence rates in a USA Today article:

“You are dealing with households in which people have lost jobs or are infear of losing their jobs. That is an added stress that can push people to thebreaking point.”

All these accounts are based on the same underlying logic and suggest that high unem-ployment could provide the “trigger point” for violent situations in the home. However,from a research perspective, it is far from clear whether unemployment is the overwhelm-ing determinant of domestic violence that many commentators a priori expect it to be.Indeed, no clear theoretical framework has yet emerged for the study of this problem andthe evidence remains limited and inconclusive. With this paper, we aim to fill this gapby examining, theoretically and empirically, the impact of unemployment on domesticviolence.

A starting point for our theoretical model is the observation that the vast majority ofadults are not currently unemployed. Hence the model explores theoretically the impactof the risk of future unemployment on current violence behavior by males. The modeldepicts marriage as a non-market institution that can provide protection against unem-ployment risk through the pooling of resources. A male may or may not have a violentpredisposition and his wife infer his type from his behavior. In equilibrium, a male witha violent predisposition can either reveal or conceal his type and his incentives for doingso depend on each partners’ future earnings prospects as determined by unemploymentrisks and potential wages.

The key theoretical result is that an increased risk of male unemployment lowers theincidence of intimate partner violence, while a rising risk of female unemployment leadsto an increase in domestic abuse. When a male with a violent predisposition faces ahigh unemployment risk, he has an incentive to conceal his type as the wife would, givenhis low expected future earnings, have strong incentive to leave him if she were to learn

2

his violent nature. Conversely, when a wife faces a high unemployment risk, her lowexpected future earnings would make her less inclined to leave her partner even if shewere to learn that he has a violent nature. Anticipating this, a husband with a violentpredispostion will reveal his nature.

We build our empirical approach on the theoretical prediction that a woman’s riskof being abused depends on gender-specific unemployment risks. In particular, in theempirical analysis we relate a woman’s risk of being abused to the local unemploymentrate among males and females in her own age group. To this end, we combine individual-level data on intimate partner violence from the British Crime Survey (BCS) with locallabor market data at the Police Force Area (PFA) level from the Annual PopulationSurvey (APS). Our basic empirical strategy exploits the exceptional variation in the risein unemployment across PFAs, gender and age groups that occurred from around 2004and through the ongoing recession. We first use ordinary least squares regressions toestimate the effects of total and gender-specific unemployment rates on both physicaland non-physical abuse. The structure of our data allows us to control for observablesocioeconomic characteristics at the individual-level as well as observable economic anddemographic variables at the police-force area-level. In addition, we control for unob-servable time invariant area-level characteristics and national trends in the incidence ofabuse through the inclusion of area and time fixed effects. Finally, we run two-stage leastsquares regressions using gender-specific shift-share indices of labor demand as instru-ments for male and female unemployment.

Our empirical analysis points to two main insights. First, we find no evidence for theview that domestic violence increases with the total unemployment rate. This result par-allels findings in previous studies suggesting near zero effects of total unemployment ondomestic violence (Aizer, 2010; Iyengar, 2009). However, when we model the incidenceof domestic violence as a function of gender-specific unemployment rates—as suggestedby our theory—we obtain striking results. In particular, we find that male and femaleunemployment have distinct impacts on the incidence of intimate partner violence. Overour sample period, male unemployment rose from 4.6% to 8.6% while female unemploy-ment increased from 3.9% to 6.3%. Our empirical results suggest that the increase inmale unemployment reduces the incidence of domestic violence by up to 9.8%. But theincrease in female unemployment increases the incidence of domestic violence by up to11.6%. Thus, our results are strongly supportive of the predictions arising from the the-ory. Moreover, they also rationalize findings in previous studies of near zero effects oftotal unemployment on domestic violence. We perform a battery of robustness checkson our data and find that our results are robust to various alternative specifications.

The paper contributes to a small but growing economics literature on domestic vio-lence. These studies can be divided into three broad categories. The first examines therelationship between the relative economic status of women and their exposure domesticviolence. Aizer (2010) specifies and tests a simple intra-household model where (some)males have preferences for violence and where the partners bargain over the level of abuseand the allocation of consumption in the household.1 The model’s key hypothesis is thatincreasing a woman’s relative wage increases her bargaining power and monotonicallydecreases the level of violence by improving her outside option. Consistent with this

1Earlier studies that have also employed a household bargaining approach to analyze domestic violenceinclude Tauchen et al. (1991) and Farmer and Tiefenthaler (1997).

3

prediction, Aizer (2010) presents robust evidence that decreases in the gender wage gapreduce intimate partner violence against women.

The second type of study investigates the effects of public policy on domestic violence.Iyengar (2009) finds that mandatory arrest laws have the perverse effect of increasingintimate partner homicides. She suggests two potential channels for this increase inhomicides: decreased reporting by victims and increased reprisal by abusers. Aizer andDal Bó (2009) find that no-drop policies—which compel prosecutors to continue withprosecution even if a domestic violence victim expresses a desire to drop the chargesagainst the abuser—result in an increase in reporting. Additionally, they find that no-drop policies also result in a decrease in the number of men murdered by intimatessuggesting that some women in violent relationships move away from an extreme type ofcommitment device (i.e., murder of abuser) when a less costly one (i.e., prosecution ofabuser) is offered.

The third type of study argues that intimate partner violence represents expressivebehavior that is triggered by payoff-irrelevant emotional shocks. Card and Dahl (2011)specify a behavioral model in which negative emotional cues—benchmarked relative toa rationally expected reference point—make violent episodes between intimate partnersmore likely. They test this hypothesis using data on police reports of family violenceon Sundays during the professional football season in the US. Their result suggests thatupset losses by the home team (i.e., losses in games that the home team was predicted towin) lead to a significant increase in police reports of at-home male-on-female intimatepartner violence.

The remainder of the paper is organized as follows. Section 2 lays out a theoreticalframework as a vehicle for interpreting the empirical results. Section 3 describes the datathat we use. Section 4 outlines the methodology we employ to test the main ideas behindthe model and presents the results. Section 5 concludes.

2. Theoretical Background

In this section we present a simple “reputation-based” model of domestic violencewhere risk of future unemployment matters for the current abusive behaviour. Thecentral feature of the model is that the wife does not know her whether or not herhusband has a violent pre-disposition, but infers from his behaviour. A husband with aviolent predisposition will strategically choose whether to engage in violence (and therebyreveal his type) or not. The model falls within the general class of models of domesticviolence that assumes that some men have a preference for violent behavior. This is afeature that the model has in common with the standard “bargaining” model that hasbecome a standard work-horse for empirical work, most recently by Aizer (2010).

However, relative to the standard bargaining model, the current model depicts thehusband’s behavior is more forward-looking and strategic. In the standard standard bar-gaining model, the husband effectively reacts to the wife’s outside options as determinedby her current economic environment. In the current model, the husband chooses hiscurrent behavior in order to strategically manipulate the wife’s beliefs about his true na-ture and his incentives for doing so depend on each partner’s future earnings prospects.Future earnings prospects are determined by both unemployment risks and potentialwages. The model is thus unique in that it links current domestic violence behavior tothe risk of future unemployment. If such a link can be established empirically, it would

4

constitute a strong test of genuine forward looking and strategic behavior by males witha violent pre-disposition.

The current model is not unique in modeling forward looking behavior by prospectiveabusive males. In cue-triggered violence model by Card and Dahl (2011), the husbanddoes engage in forward looking behavior, but does so by choosing his exposure to “cues”which, if negative, could cause him to lose control; in particular, the husband does notact strategically with respect to his violence behavior. The only other model in which thehusband’s conduct with respect to violence is directly forward-looking is the “extortion”model by Bloch and Rao (2002), where violence is used by husbands in order to conveydissatisfaction with the marriage in order to extract transfers from the wife’s family.There are however few other similarities between their model and ours.

We present here a simple two-period version of the model that gives the basic in-tuition.2 We show that, under natural assumptions, a husband with a violent pre-disposition chooses to be violent when his wife faces low earnings prospects relative tohimself.

2.1. Setup

Consider a couple consisting of a husband m and a wife f . There are two periods,t = 1, 2. The husband is one of two possible types, θ ∈ {θ0, θ1}. Either he a violentpredisposition, θ1, or he does not, θ0. The wife has a prior belief g that the husbandis of type θ1. Partner j has an unemployment probability π

j ∈ (0, 1) for period t = 2.If employed, partner j earns wj . An unemployed individual has an income of b, whichwe interpret as an unemployment/welfare benefit and we assume that b < wj for eachpartner j.

The timing of the model is as follows. First the husband chooses whether or not tobe violent in period t = 1, a decision which we denote by v1 ∈ {0, 1}. Having observedv1 the wife updates her beliefs about the husband’s type to g̃. Given her updated beliefsshe then decides whether to continue the partnership or to terminate it, a decision whichwe denote by p ∈ {0, 1}. Then nature decides on employment outcomes according to πm

and πf . Finally, if the wife has decided to continue the partnership, the husband choosesagain whether or not to be violent, v2 ∈ {0, 1}.

There is an economic gain from being in a partnership. We model this is the simplestpossible way: if in a partnership at time t, the consumption of individual j is

cjt = c(yjt , y

−jt

)≡ yjt + αy

−jt , (1)

where yjt is either wj or b depending on the employment status of partner j and where

α ∈ (0, 1). If single, consumption is just the own income. Partner j obtains utility from

consumption uj(cjt

)where uj (·) is increasing and strictly concave. In addition, the wife

has a disutility q > 0 from violence. The husband has utility d > 0 from violence if andonly if he is of type θ1.

2The current model can be immediately extended to an infinite horizon with employment shocksthat are i.i.d. over time. Future work on the model will extend it to state-dependent unemploymentprobabilities.

5

The model is solved for a pure strategy perfect Bayesian equilibrium, where thewife updates her expectations using Bayes rule whenever applicable. Out-of-equilibriumbeliefs are discussed below.

In this simple two-period version of the model, the husband’s decision in the finalstage of the game is trivial: he chooses to be violent, v2 = 1, if and only if he has aviolent pre-disposition. Consider then the wife’s value of continuing or terminating thepartnership. The value of termining the partnership is

V0(πf

)≡

(1− πf

)uf

(c(wf , 0

))+ πfuf (c (b, 0)) (2)

The value of continuing the partnership depends on not only her own unemploymentprobability, but also on the husband’s unemployment probability and on her beliefs about

his type. Let V1

(πf , πm, f̃

)denote the expected value of continuing the partnership.

We have that

V1(πf , πm, g̃

)=

(1− πf

)(1− πm)uf

(c(wf , wm

))+ πf (1− πm)uf (c (b, wm))(3)

+(1− πf

)πmuf

(c(wf , b

))+ πfπmuf (c (b, b))− f̃ q

The wife continues the partnership if and only if V1(πf , πm, g̃

)≥ V0

(πf

). We now make

a set of assumptions. The first two assumptions effectively say that the wife’s toleranceof violence depends on her earnings prospects. The third assumption tells us that if thewife’s beliefs are unchanged, then she will want to continue relationship (and hence itjustifies why she has accepted to be in partnership with him in the first place).

Assumption 1. If the wife will be employed with certainty and she knows that he hasa violent pre-disposition, then she will choose to leave the husband irrespective of hisemployment probability: V1 (0, π

m, 1) < V0 (0).

Assumption 2. If the wife will be unemployed with certainty and the husband will beemployed with certainty, and she knows that he has a violent pre-disposition, then shewill not leave him: V1 (1, 0, 1) > V0 (1)

Assumption 3. The wife’s prior beliefs that the husband has a violent pre-dispositionare low enough that she will continue the relationship at any employment probability,V1

(πf , πm, g

)≥ V0

(πf

).

We further assume that the husband will not choose v1 = 1 to immediately trigger aseparation. This assumption along with Assumption 3 will be discussed in more detailin the context of generalizing the model below.

2.2. Equilibrium

Define πf (πm) as the highest unemployment probability for the wife at which shewill choose to continue the partnership when she knows that the husband has a violentpre-disposition. Hence πf (πm) is implicitly defined through,

V1(πf (πm) , πm, 1

)= V0

(πf

). (4)

This equation may fail to have a solution in the unit interval. In particular, notingthat the value of continuing the partnership (given g̃ = 1) is decreasing in πm it may

6

be that V1 (1, πm, 1) ≤ V0 (1) for π

m above some critical value πm. Intuitively, it maythat the wife, knowing that she will be unemployed with certainty, will not stay with aviolent man if his unemployment risk is sufficiently high. In that case, we define πf (πm)through (4) at πm < πm and define πf (πm) = 1 at πm ≥ πm. Hence πm is the highestunemployment risk for the husband at which the wife would continue the relationship ifshe will be unemployed with certainty and she knows that he has a violent pre-disposition.

The locus πf (πm) will play a critical role in determining the nature of the equilibriumand the following Lemma gives some of it’s key properties:

Lemma 1. Properties of the wife’s critical unemployment probability πf (·):

1. πf (πm) > 0 for any πm ∈ [0, 1]

2. πf (0) < 1 (which also implies that πm > 0 if it exists)

3. πf (πm) is continuously differentiable and strictly increasing in πm on the interval[0, πm) .

4. πf (πm) is also continuously differentiable in wf and wm, and is increasing in theformer and decreasing in the latter.

Proof. See AppendixThe πf (·) partitions the set of possible

(πm, πf

)profiles into two “regimes”

Regime 0: R0 ≡{(

πm, πf)|πf ≤ πf (πm)

}

Regime 1: R1 ≡{(

πm, πf)|πf > πf (πm)

}

By Assumptions 1 and 2 both regimes are non-empty.The equilibrium of the game depends on which regime the couple’s unemployment

risk profile(πm, πf

)falls within.

Proposition 1. Perfect Bayesian Equilibrium by unemployment risk profile regime inthe two-period setting:

• If(πm, πf

)∈ R1 then the following constitutes a PBE: The husband chooses v1 = 1

if and only if he is of type θ1. If the wife observes v1 = 1 she updates her beliefsto g̃ = 1 whereas if she observes observes v1 = 0 she updates her beliefs to g̃ = 0.In either case, she continues the partnership. Finally, the husband is again violentv2 = 1 if and only if he is of type θ1.

• If(πm, πf

)∈ R0 then the following constitutes a PBE: The husband chooses v1 = 0

irrespective of his type. If the wife observes v1 = 1 she updates her beliefs tog̃ = 1 and leaves her partner. If she observes observes v1 = 0 her beliefs remainunchanged, g̃ = g and she continues the relationship. Finally, the husband is violentv2 = 1 if and only if he is of type θ1.

To see that this describes PBEs, consider each case in turn, starting with regime1. In this case, the husband knows that the wife will not leave him even if she learnsthat he has a violent pre-disposition. Hence the husband, if he does have a violent pre-disposition, chooses to be violent at the initial stage, thus immediately revealing his type.As a husband without a violent pre-disposition will not be violent, the equilibrium is aseparting one and the wife’s belief updating follows Bayes’ rule as this applies at eitherv1 outcome. Her continuing of the partnership is rational by the definition of the regime.

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

πm

π fRegime 1

Regime 0

Figure 1: The critical locus πf (·) separatingRegime 0 (below the locus) and Regime 1 (abovethe locus).

Consider then regime 0. This is a pooling equilibrium where a husband with a violentpre-disposition conceals his type by refraining from violence at the initial stage. Key tothe equilibrium are the out-of-equilibrium beliefs (at v1 = 1 which according to theequilibrium shouldn’t happen).3 The equilibrium stipulates that if the wife neverthelessis exposed to violence in the initial period, then she will believe that the husband hasa violent pre-disposition and she will leave him (as, per definition of the regime, this isrational for her given her belief that he has a violent pre-disposition). Given that thewife would respond to violence in the initial period by terminating the relationship itis rational for the husband with a violent pre-disposition to conceal this by refrainingfrom violence at the initial stage. Note that the wife’s belief updating follows Bayes’rule in the case where v1 = 0 as this is action is chosen by husband’s of both types inequilibrium.

The following figure illustrate the case where wj = 4, b = 1, α = 1/2, q = 0.6 anduf (c) = ln (c).4

The solid line is the critical locus πf (·) . Regime 1 are unemployment risk profiles(πm, πf

)above the locus whereas regime 0 are profiles below the locus.

In this simple two-period model, a husband with a violent pre-disposition will alwaysbe violent in the final period. However, the difference lies in the violence behaviour inthe first period. Hence we use the the equilibrium incidence of violence in the first periodas an indicator of whether the partnership will be a violent one or not.5 The model thus

3The out-of-equilibrium beliefs seem natural and satisfy standard refinements such as the Choo-Krepsintuitive criterion.

4The implicit equation characterizing πf (πm) can easily be solved explicitly for purposes of plotting.5We show below that in a generalization of the model to infinite horizon, under the current assump-

tions, a husband with a violent pre-disposition will never violent in regime 0. Moving to an infinitehorizon model thus removes the “last period effect”.

8

Table 1: Demographic Characteristics of the Sample

Variable Mean Std. Dev. Variable Mean Std. Dev.

Age 38.93 11.67 Qual: NVQ 4-5 0.375 0.484

Ethnicity: White 0.927 0.259 Qual: NVQ 3 0.150 0.350

Ethnicity: Mixed 0.010 0.096 Qual: NVQ 2 0.250 0.430

Ethnicity: Asian 0.028 0.166 Qual: NVQ 1 0.052 0.220

Ethnicity: Black 0.023 0.150 Qual: None 0.141 0.340

Ethnicity: Other 0.011 0.107 Qual: Other 0.031 0.170

Religion: None 0.215 0.412 Married 0.453 0.497

Religion: Christian 0.738 0.439 Cohabit 0.120 0.320

Religion: Muslim 0.016 0.120 Separated 0.050 0.210

Religion: Hindu 0.010 0.090 Divorced 0.124 0.330

Religion: Sikh 0.004 0.060 Widowed 0.018 0.130

Religion: Jewish 0.003 0.050 Number of Kids 0.461 0.874

Religion: Buddhist 0.005 0.068 Poor Health 0.031 0.170

Religion: Other 0.007 0.086 Long-standing Illness 0.178 0.380

Number of Observations 93,401

predicts that the risk of domestic violence is positively related to the wife’s unemploymentrisk and negatively related to the husband’s unemployment risk. Similarly, since, byLemma 1, an increase in wf shrinks Regime 1 whereas an increase in wf expands Regime1, it follows that the risk of violence is negatively related to the female-male gender wagegap. This is of course the same prediction that is the hall-mark of the standard bargainingmodel.

3. Data and Descriptive Statistics

3.1. Domestic Violence Data from the British Crime Survey

The most reliable data on the extent of domestic violence in England and Wales comefrom the British Crime Survey (BCS). The BCS is a nationally representative, repeatedcross-sectional survey of the population resident in households in England and Wales. Itasks respondents about their experience as victims. Being a household survey, the BCSprovides a more accurate picture of the extent of domestic violence than police recordedstatistics, as not all violent episodes between intimate partners are reported to the police,let alone recorded. As will become apparent below, the BCS information on domesticviolence is based on a broad set of questions covering both physical and non-physicalabuse.

The BCS employs two different methods of data collection for measuring the extentand experience of domestic violence victimization. The first method, available from thesurvey’s inception in 1981, is based on face-to-face interviews. However, the unwilling-ness of respondents to reveal episodes of domestic violence to interviewers means thatthis method significantly underestimates the true extent of domestic violence. As analternative method to overcome non-disclosure, a self-completion module on intimatepersonal violence (IPV)—which respondents complete in private by responding to ques-tions on a laptop—has been included in the BCS on a comparable basis since the 2004/05wave. In 1996, an early trial of the self-completion module in the BCS revealed that the

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Table 2: Variable Definitions

Behavior Physical Abuse Non-Physical Abuse Any Abuse

Prevented from fair share of h-hold money x xStopped from seeing friends and relatives x xRepeatedly belittled you x xFrightened you, by threatening to hurt you xPushed you, held you down or slapped you x xKicked, bit, or hit you x xChoked or tried to strangle you x xThreatened you with a weapon x xUsed a weapon against you x xUsed other force against you x x

incidence of domestic violence was three times higher than that arising from the face-to-face interviews. In the 2008/09 wave, the self-completion prevalence estimate wasten times higher than the interview-based figure. Therefore, it is the questions in theself-completion module that will be considered here.

The self-completion module on IPV in the BCS is restricted to respondents aged 16to 59 and asks about their experience of domestic abuse, sexual assault and stalking. Ourmain analysis focuses on intimate partner violence experienced by women. Therefore,we restrict our sample to female respondents. We use the first seven waves of the BCSIPV module, and our sample therefore covers the period from April 2004 to March 2011.Table 1 presents the means of several demographic characteristics for our sample. Theaverage age of women in our study is 39 years; the majority of respondents is white;roughly 45% of women in the sample are married; and slightly less than two-thirds ofrespondents hold less than a university degree. Our final repeated cross-sectional samplecomprises roughly 93,000 observations.

In the existing BCS IPV module, respondents are presented with a list of behaviorsthat constitute domestic abuse and asked to choose which, if any, they had experiencedin the 12 months prior to the interview. Table 2 presents this list of abusive behaviorsand illustrates how we have constructed three binary variables that indicate whetherthere has been some form of domestic abuse against the respondent in the last year.The first, physical abuse, is a dummy variable indicating whether the respondent hadany physical force used against them by a current or former partner, such as pushing,hitting, kicking or strangling. The second, non-physical abuse, indicates whether therespondent was exposed to controlling behaviors or deprived of the means needed forindependence by a current or former partner. Finally, the variable any abuse indicateswhether the respondent was exposed to any form of abusive behaviors in the year priorto the survey.

In our sample, 2.9% of women report episodes of physical abuse in the past 12 months;3.8% declare having experienced non-physical abuse; and 5.8% of respondents indicatehaving experienced some form of abusive behavior. Figure 2 illustrates the extent towhich the incidence of physical abuse varies with the demographic characteristics of thesurvey respondents. First, it is evident that older women are less likely to report currentexperience of physical abuse than younger women. Second, as the level of educationincreases, the likelihood that a woman reports that she has experienced domestic violence

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0.0

1.0

2.0

3.0

4.0

5F

requ

ency

of F

orce

16−24 25−34 35−49 50−59

Age Group

0.0

2.0

4.0

6F

requ

ency

of F

orce

White Mixed Asian Black Other

Ethnicity

0.0

1.0

2.0

3.0

4F

requ

ency

of F

orce

OtherNo QualNVQ1 NVQ2 NVQ3 NVQ4+

Qualification

0.0

1.0

2.0

3.0

4F

requ

ency

of F

orce

None Christ Muslim Other

Religion

Figure 2: Incidence of physical abuse by demographiccharacteristics

in the past year decreases. Finally, the victimization differentials by religion and ethnicitydo not appear to be large.

3.2. Labor Market Data from the Annual Population Survey

We merge our individual-level data from the BCS IPV module with labor market datafrom the Annual Population Survey (APS). First conducted in 2004, the APS combinesresults from the Labour Force Survey (LFS) and the English, Welsh and Scottish LFSboosts. Datasets are produced quarterly, with each dataset containing 12 months ofdata. The data are available for Government Office Region (GOR) or by Police ForceArea (PFA). Our labor market data set is disaggregated by the PFAs in England andWales and covers the period from 2005 to 2011. There are 42 PFAs in our data set.6

Table 3 presents descriptive statistics for local unemployment and highlights consid-erable variation across PFAs and across time. Average total unemployment over theentire sample period was 5.4%. There was a of course sharp increase in unemploymentin conjunction with onset of the most recent recession in 2008 as shown in Figure 3.Importantly for us, however, this increase was far from uniform across areas, gender andage groups.

Our remaining explanatory variables at the PFA-level include the following economicindicators. First, for each PFA we construct measures of mean hourly real wages of

6There are 43 PFAs in England and Wales. However, the City of London PFA is a small policeforce which covers the “Square Mile” of the City of London. As this is small area enclosed in the manytimes larger Metropolitan PFA we merge the two. This leaves us with 42 PFAs. They are Avon andSomerset, Bedfordshire, Cambridgeshire, Cheshire, Cleveland, Cumbria, Derbyshire, Devon and Corn-wall, Dorset, Durham, Essex, Gloucestershire, Greater Manchester, Hampshire, Hertfordshire, Humber-side, Kent, Lancashire, Leicestershire, Lincolnshire, City of London and Metropolitan Police District,Merseyside, Norfolk, Northamptonshire, Northumbria, North Yorkshire, Nottinghamshire, South York-shire, Staffordshire, Suffolk, Surrey, Sussex, Thames Valley, Warwickshire, West Mercia, West Midlands,West Yorkshire, Wiltshire, Dyfed-Powys, Gwent, North Wales, and South Wales.

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Table 3: Summary Statistics for Main Explana-tory Variables

Variables Mean Std. Dev. Min Max

Total unemployment

aged 16-64 0.054 0.018 0.023 0.121

aged 16-24 0.141 0.042 0.045 0.258

aged 25-34 0.052 0.021 0.017 0.121

aged 35-49 0.035 0.014 0.011 0.097

aged 50-64 0.033 0.012 0.011 0.077

Male unemployment

aged 16-64 0.060 0.023 0.024 0.143

aged 16-24 0.162 0.051 0.062 0.307

aged 25-34 0.061 .026 0.016 0.156

aged 35-49 0.039 0.017 0.009 0.115

aged 50-64 0.043 0.017 0.014 0.102

Female unemployment

aged 16-64 0.048 0.015 0.016 0.095

aged 16-24 0.124 0.040 0.020 0.242

aged 25-34 0.058 0.021 0.021 0.129

aged 35-49 0.037 0.013 0.014 0.085

aged 50-64 0.033 0.013 0.009 0.093

Note. For all labor market variables we have 294 Observations,covering 42 PFAs over the period from 2005 to 2011. For furtherdescription of the data and data sources see the main text.

men and women as they have been identified as important determinants of domesticviolence in our theory and in previous studies (Aizer, 2010). Second, for each PFA wealso construct measures of the average unemployment rates in neighboring PFAs. Bothmeasures are included as additional regressors in some specifications.

Figure 4 plots the change in frequency of abuse against the change in aggregateunemployment between the 2004-6 and 2008-11. There is no clear visual relationshipbetween the local overall unemployment rate and incidence of domestic violence. Hencethere is no obvious simple relation whereby the areas that saw the largest increasesin unemployment also saw the largest increases in abuse. The following analysis willtherefore provide a more detailed analysis by considering gender-specific unemployment,allowing finer time periods and age-groups, and adding demographic controls.

4. Empirical Specification and Results

4.1. Baseline Specification

Our theory suggests that a reduced-form model of the probability of a woman beinga victim of domestic violence should include variables that proxy for own and partner’sunemployment risk. To do so, we link a woman’s exposure to domestic abuse to thelocal level of unemployment. We focus in particular on the levels of female and maleunemployment within the respondent’s own age-group as these are likely to be the mostrelevant proxies for the respondent’s own employment prospects as well as her (potential)partners. The age groups used in the analysis are those indicated in 3, that is 16-24,25-34, 35-49, and 50-64. As noted above, the respondents are asked about any abuse theymay have experience over the past 12 months and interviews take place in all months of

12

45

67

89

10U

nem

ploy

men

t Rat

e %

2004 2005 2006 2007 2008 2009 2010 2011year

Males Females

Figure 3: Unemployment rate for men and women aged16-64 in England and Wales

the year. The labor market data is provided in overlapping 12 month periods: January-December, April-March, July-June, October-September. Hence we define “periods” sothat t = 1 for the January 2004 to December 2004, t = 2 for April 2004 to March 2005,etc. This allows us to match closely the 12 month period for which the respondent isasked to report incidence of abuse to the relevant 12 month window for the labor marketdata.

Our basic model for the incidence of abuse against individual i in police-force area jin period t and within age group g is given by:

yijtg = βXijtg + γfUNEMPLfjtg + γ

mUNEMPLmjtg + λt + αj + εijtg (5)

whereXijtg includes basic demographic controls (age, ethnicity, religion) at the individual-

level. The variables UNEMPLfjtg and UNEMPLmjtg respectively represent the female

and male unemployment rates for i’s own age-group in police-force area j during periodt. The parameters λt and αj are fixed effects for time and police-force areas respectively,and pick up variation in the incidence of domestic abuse caused by national trends andby factors varying across areas but constant over time. Thus, our basic model identi-fies the impact of gender-specific unemployment on domestic abuse through within-areavariations from aggregate trends.

4.2. Baseline Results

Our basic results for the probability of being a victim of physical abuse are in Ta-ble 4.7 Column (1) gives the correlation between the incidence of physical abuse andthe total unemployment rate within own-age group. The estimated model includes basic

7Here, we use ordinary least squares (OLS) to estimate specification (5). However, estimates fromunreported probit regressions give nearly identical results.

13

−.0

2−

.01

0.0

1.0

2C

hang

e in

inci

denc

e of

phy

sica

l. ab

use

.005 .01 .015 .02 .025 .03Increase in unemployment rate between 2004−7 and 2008−11

Figure 4: Change in the unemployment rate amongstindividuals aged 16-64 and change in incidence of phys-ical abuse between 2004-7 and 2008-11 across PFAs inEngland and Wales.

individual-level controls as well as area effects and time effects. We see that the correla-tion is small and insignificant.8 This result parallels findings in previous studies (Aizer,2010; Iyengar, 2009) suggesting near zero effects of total unemployment on domesticviolence. Column (2) shows the correlation between between the incidence of physicalabuse and gender-specific unemployment rates within own-age group. On one side, thepoint estimate for female unemployment in the own age group is positive and statisticallysignificant. The magnitude of the coefficient suggests that a 1 percentage point increasein the own-age female unemployment rate causes an increase in the likelihood of beinga victim of physical abuse by 3.8%. However, we also see that the point estimate formale unemployment is negative and statistically significant, and the magnitude of thecoefficient indicates that a 1 percentage point increase in own-age male unemploymentcauses a decline in the risk of physical abuse by roughly 2%. Column (3) includes addi-tional individual-level controls and the point estimate for male unemployment increasesin size while coefficient on female unemployment remains unchanged. Controls for maleand female unemployment within other age-groups are added in column (4). We findthat male and female unemployment within own age-group still have opposite-signed ef-fects on the risk of physical abuse while unemployment among age-groups other than theown appear to have no significant impact. Column (5) shows that these correlations arerobust to the introduction of measures of the average unemployment rate in neighboringpolice-force areas (NAs). Our theory suggests that potential wages of men and womenmight also matter for the incidence of abuse. Therefore, we add measures for femaleand male mean hourly real wages within own-age group in column (6). Controlling forwage-effects in this way leaves the point estimates for male and female unemployment

8A regression on aggregate unemployment - across genders and age groups - suggests if anything anegative effect. However, the estimates lacks precision due to low variation in unemployment at such anaggregated level.

14

Table 4: Ordinary least squares regressions of physical abuse with respect to total and gender-specific police-force area unemployment rates

Specification (1) (2) (3) (4) (5) (6) (7)

Unemployment in own-age group 0.024 0.052(0.036) (0.039)

Female unemployment in own-age group 0.111*** 0.116*** 0.135*** 0.130*** 0.107***(0.032) (0.032) (0.043) (0.048) (0.034)

Male unemployment in own-age group -0.059* -0.069** -0.070* -0.071* -0.071**(0.030) (0.031) (0.037) (0.041) (0.031)

Female unemployment in other age groups -0.030(0.058)

Male unemployment in other age groups -0.033(0.061)

Female unemployment in own-age group in NAs -0.002(0.001)

Male unemployment in own-age group in NAs 0.001(0.001)

Diff in unemployment rate (F-M) in own-age group 0.088***(0.024)

Basic demographic controls yes yes yes yes yes yes yesAdditional demographic controls no no yes no no no noMale and female real hourly wages in own-age group no no no no no yes noArea and time fixed effects yes yes yes yes yes yes yes

Observations 92,682 87,867 87,846 75,826 87,867 87,624 87,867

Note.—Cluster-robust standard errors computed at the police-force area-level are shown in parentheses. The parameter estimates are thecoefficients from ordinary least squares regressions where the dependent variable is physical abuse. Basic demographic controls include age,religion and ethnicity. Additional demographic controls include qualification dummies and number of kids. *** Significant at 1%. ** Significantat 5%. * Significant at 1%.

largely unchanged. In column (7) we add the linear difference between male and femaleunemployment within own-age group as a regressor while additionally controlling forown-age total unemployment. The coefficient estimate is similar to the effects impliedby entering male and female unemployment as separate regressors.

Further specifications include (a) extended demographic controls such as marital sta-tus and health indicators which are likely to be in part endogenous to the violence out-come, (b) controls for the qualification distribution in the respondent’s own-age group,and (c) area-specific linear time trends in abuse. The results from these additional spec-ifications are presented in Table 7 in Appendix B. The finding regarding the impact ofgender-specific unemployment in the own age group is robust to these variations of themain specification.

Table 6 presents corresponding results for non-physical abuse. The results are strik-ingly similar to those for physical abuse. In Appendix B we provide corresponding resultsfor catch-all measure any abuse which again confirm the main results (see Table 8).

To summarize, we find no evidence for the view that total unemployment increasesdomestic abuse. Instead, our results suggest that male and female unemployment havedistinct impacts on the incidence of domestic abuse: increases in male unemploymentare associated with declines in domestic abuse while increases in female unemploymenthave the opposite effect. This finding is consistent with our model’s key prediction. Themagnitude of the estimated relationships imply (a) that a 4 percentage point increase inmale unemployment—as observed in England and Wales between 2005 and 2011—causesa decline in the incidence of domestic abuse of between 8.1% and 9.8%, and (b) that the

15

Table 5: Ordinary least squares regressions of non-physical abuse with respect to total andgender-specific police-force area unemployment rates

Specification (1) (2) (3) (4) (5) (6) (7)

Unemployment in own-age group 0.034 0.050(0.033) (0.037)

Female unemployment in own-age group 0.101** 0.106** 0.129** 0.117** 0.101**(0.043) (0.043) (0.055) (0.057) (0.046)

Male unemployment in own-age group -0.068* -0.078** -0.092** -0.072 -0.075*(0.039) (0.038) (0.043) (0.045) (0.041)

Female unemployment in other age groups 0.144**(0.069)

Male unemployment in other age groups -0.012(0.067)

Female unemployment in own-age group in NAs -0.001(0.001)

Male unemployment in own-age group in NAs 0.001(0.001)

Diff in unemployment rate (F-M) in own-age group 0.092**(0.037)

Basic demographic controls yes yes yes yes yes yes yesAdditional demographic controls no no yes no no no noMale and female real hourly wages in own-age group no no no no no yes noArea and time fixed effects yes yes yes yes yes yes yes

Observations 92,635 87,820 87,799 75,783 87,820 87,577 87,820

Note.— Cluster-robust standard errors computed at the police-force area-level are shown in parentheses. The parameter estimates arethe coefficients from ordinary least squares regressions where the dependent variable is non-physical abuse. Basic demographic controlsinclude age, religion and ethnicity. Basic demographic controls include age, religion and ethnicity. Additional demographic controls includequalification dummies and number of kids. *** Significant at 1%. ** Significant at 5%. * Significant at 1%.

2.5 percentage point increase in female unemployment over the sample period causes anincrease in the incidence of domestic abuse of between 9.5% and 11.6%.

4.3. Instrumental Variables

The analysis in the previous section assumes that the measured variation in unem-ployment is exogenous in the regressions reported. Though most of the variation inunemployment over time in our data is driven by the recession that started halfwaythrough our sample period we cannot rule out that omitted variables, and possibly si-multaneity, bias our OLS estimates of the impact of unemployment impacts on domesticabuse. As an example of a potential omitted variable could be the general level of crim-inal activity which could be driving both the local trends in abuse and also affect localeconomic activity.

To account for such issues, we build on a shift-share approach (see, e.g., Katz andMurphy, 1992; Bound and Holzer, 2000) to create instruments that capture exogenouschanges in the labor demand for males and females. More precisely, we construct gender-specific predicted unemployment rates based on pre-existing industrial composition ineach police-force area and the national unemployment rates by industry. Analytically,the industry-predicted unemployment for gender k in police-force area j in period t isgiven by

̂UNEMPLk,ind

jt =∑

z

χkj,z · UNEMPLkz,t

16

Table 6: Ordinary and two-stage least squares regressions of phys-ical abuse with respect to total and gender-specific police-force areaunemployment rates

Specification (1) (2) (3) (4)OLS 2SLS OLS 2SLS

Female unemployment in own-age group 0.111*** 0.574** 0.116*** 0.673***(0.032) (0.217) (0.032) (0.220)

Male unemployment in own-age group -0.059* -0.326** -0.069** -0.390**(0.030) (0.141) (0.031) (0.141)

Basic demographic controls yes yes yes yesAdditional demographic controls no no yes yesArea and time fixed effects yes yes yes yes

Observations 87,867 87,867 87,846 87,846

Note.— Cluster-robust standard errors computed at the police-force area-level are shown inparentheses. The parameter estimates are the coefficients from ordinary least squares (OLS) andtwo-stage least squares (2SLS) regressions where the dependent variable is physical abuse. Basicdemographic controls include age, religion and ethnicity. Basic demographic controls includeage, religion and ethnicity. Additional demographic controls include qualification dummies andnumber of kids. *** Significant at 1%. ** Significant at 5%. * Significant at 1%.

where χkj,z is the share of industry z among employed individuals of gender k in police-

force area j at baseline (year 2004), and UNEMPLkz,t is the national unemploymentrate in industry z in period t for gender k. Hence the industry-predicted local unemploy-ment rate for gender k in period t is a weighted average of the national industry-specificunemployment rates in period t where the weights reflect the initial local industry com-position.

The approach thus exploits that, over the sample period, some industries (e.g. theconstruction, banking and finance-, and manufacturing industries), saw substantiallylarger increases in unemployment than others and that PFAs differed at baseline interms of their industry composition.

The original gender-specific unemployment variables used in our basic empirical spec-ification are measured at the own-age group level. Since there are substantial and per-sistent differences in the level of unemployment across age-groups, we include age-groupdummies among the instrument set in order to account for this levels differences. Hencethe final instrument set used to instrument for the local female and male unemploymentrate in the age group assigned to woman i in the sample include the two gender-specificindustry-predicted unemployment rates

̂UNEMPLmale,ind

jt and ̂UNEMPLfemale,ind

jt

along with a set of age group dummies.Table 6 presents the 2SLS estimates of the gender-specific unemployment effects for

the incidence of physical abuse. We report only the relevant unemployment coefficients,and reproduce the OLS results from Table 4 for comparison. Like the OLS results,the 2SLS estimates suggest that increases in male unemployment cause the incidence ofdomestic violence to decline while increases in female unemployment have the oppositeeffect. Furthermore, in all specifications the 2SLS regressions yield coefficients that arelarger in magnitude than their OLS counterparts. We interpret this as evidence that our

17

OLS results do not overestimate the distinct impacts of male and female unemploymenton crime.9

5. Concluding Comments

This paper has examined the effect of unemployment in England and Wales on do-mestic abuse. The geographical variation in unemployment in these countries during themid-to-late 2000s provides an interesting context in which to look at domestic abuse.Our empirical approach is grounded in a theoretical model in which marriage providesinsurance against unemployment risk through the pooling of resources. The key the-oretical result is that an increased risk of male unemployment lowers the incidence ofintimate partner violence, while a rising risk of female unemployment leads to an in-crease in domestic abuse. We have demonstrated that this prediction accords well withevidence from the British Crime Survey matched to geographically disaggregated labormarket data. Our empirical results, in particular, suggest that a 1 percentage point in-crease in male unemployment causes a decline in the incidence of domestic abuse of upto 2.5 percent, while the same increase in female unemployment increases the incidenceof domestic abuse of up to 4.6 percent. Moreover, our results also rationalize findings inprevious studies of near zero effects of total unemployment on domestic violence.

Overall therefore, our theoretical model and empirical results go against the conven-tional wisdom that unemployment is a key determinant of domestic violence. Quite tocontrary, latent abusive males who have lost their job or are in fear of loosing their jobmay rationally abstain from abusive behaviors as they have an economic incentive toavoid divorce and the associated loss of spousal insurance. However, when women areat a high risk of unemployment, their dependency on spousal insurance may preventthem from using divorce as a credible threat. This in turn might prompt male part-ners with a predisposition for violence to reveal their abusive tendencies. Thus, highfemale unemployment leads to an elevated risk of intimate partner violence. From apolicy perspective, it is therefore conceivable that policies designed to enhance women’semployment security could be an important contributor to domestic violence reduction.

References

Aizer, A. (2010). The gender wage gap and domestic violence. American Economic Review, 100, 1847–1859.

— and Dal Bó, P. (2009). Love, hate and murder: Commitment devices in violent relationships. Journalof Public Economics, 93 (3), 412–428.

Bloch, F. and Rao, V. (2002). Terror as a bargaining instrument: A case study of dowry violence inrural India. American Economic Review, 92.

Bound, J. and Holzer, H. (2000). Demand shifts, population adjustments, and labor market outcomesduring the 1980s. Journal of Labor Economics, 18 (1), 20–54.

Card, D. and Dahl, G. (2011). Family violence and football: The effect of unexpected emotional cueson violent behavior. Quarterly Journal of Economics, 126 (1), 103–143.

Farmer, A. and Tiefenthaler, J. (1997). An economic analysis of domestic violence. Review of SocialEconomy, 55, 337–358.

9In unreported regressions, we used gender-specific occupation-predicted unemployment rates alongwith a set of age group dummies as instruments for local female and male unemployment rates. Weobtained similar, though less precisely estimated, coefficients on own-age male and female unemployment.

18

Iyengar, R. (2009). Does the certainty of arrest reduce domestic violence? evidence from mandatoryand recommended arrest laws. Journal of Public Economics, 93 (1), 85–98.

Katz, L. and Murphy, K. (1992). Changes in relative wages, 1963–1987: supply and demand factors.Quarterly Journal of Economics, 107 (1), 35–78.

Tauchen, H. V., Witte, A. D. and Long, S. K. (1991). Violence in the family: A non-random affair.International Economic Review, 32, 491–511.

19

Appendix A

Proof of Lemma 1. Part (1) follows directly from Assumption 1. Part (2) follows directly fromAssumption 2. On the interval [0, πm), πf (πm) is characterized by the implicit equation (4)which, by substitution can be written

0 =(

1− πf (πm))

uf(

c(

wf, 0))

+ πf (πm)uf (c (b, 0)) (6)

−

(

1− πf (πm))

(1− πm)uf(

c(

wf, w

m))

− πf (πm) (1− πm)uf (c (b, wm))

−

(

1− πf (πm))

πmuf(

c(

wf, b))

− πf (πm) πmuf (c (b, b)) + q

That πf (·) is continuous and differentiable in πm follows directly from the functional form.Totally differentiating with respect to πm yields

0 = −∂πf (πm)

∂πmA+B (πm) (7)

where

A ≡[

uf(

c(

wf , 0))

− uf (c (b, 0))]

− (1− πm)[

uf(

c(

wf , wm))

− uf (c (b, wm))]

−πm[

uf(

c(

wf , b))

− uf (c (b, b))]

> 0(8)

andB (πm) ≡

(

1− πf (πm)) [

uf(

c(

wf , wm))

− uf(

c(

wf , b))]

+πf (πm)[

uf (c (b, wm))− uf (c (b, b))]

> 0(9)

The sign of A follows from concavity of uf (·) which implies that the gain in utility from ownemployment is highest to the wife when she has no partner. Hence it follows that

∂πf (πm)

∂πm=

B (πm)

A> 0 (10)

Similarly, totally differentiating with respect to wf and wm yields that

∂πf (πm)

∂wf=

B(

wf)

A> 0 and

∂πf (πm)

∂wm=

B (wm)

A< 0 (11)

where

B(

wf)

≡

(

1− πf (πm)){

ufc

(

c(

wf, 0))

− (1− πm)ufc

(

c(

wf, w

m))

− πmufc

(

c(

wf, b))}

> 0

(12)(where the sign again follows by concavity of uf (·)) and

B (wm) ≡ −α (1− πm){(

1− πf (πm))

ufc

(

c(

wf, w

m))

+ πf (πm)ufc (c (b, wm))

}

< 0 (13)

Appendix B

20

Table 7: The Effect of Local Unemployment on Exposure to Physical Abuse: FurtherSpecifications

Specification (2) (8) (9) (10)

Female UE own age group 0.111*** 0.109*** 0.104*** 0.109***(0.032) (0.033) (0.034) (0.034)

Male UE own age group -0.059* -0.080** -0.063** -0.058*(0.030) (0.030) (0.031) (0.032)

Basic demographic controls yes yes yes yesAdditional demographic controls no yes no noExtended demographic controls no yes no noLocal qualifications composition no no yes noLocal linear trends no no no yesArea fixed effects yes yes yes yesPeriod fixed effects yes yes yes yes

Observations 87,867 87,758 87,867 87,867

Robust standard errors in parentheses*** p