a) Opeta
    - Kuvaile motivaatio / miksi.
    - Selitä teoreettinen viitekehys ja hypoteesit.
    - Esittele käytetty data.
    - Kuvaile käytetyt menetelmät.
    - Yhteenveto artikkelin tuloksista.
    - Tuo esiin artikkelin politiikkatiedot ja johtopäätökset.

b) Kriittistä arviointia
    - Kommentoi teorian/käsitteellistämisen ja analyysin välistä yhteyttä.
    - Arvioi käytetyn datan laatu.
    - Arvioi menetelmien sopivuus suhteessa tutkimuskysymykseen ja dataan.
    - Kritiikki empiria analyysin toteutuksesta.
    - Pohdi syy-seurausidentifiointistrategian pätevyyttä ja uskottavuutta.
    - Arvioi artikkelin selkeyttä ja viestintää.


Reducing Crime and Violence: Experimental Evidence
from Cognitive Behavioral Therapy in Liberia†
By Christopher Blattman, Julian C. Jamison, and Margaret Sheridan*
We show that a number of noncognitive skills and preferences, including
patience and identity, are malleable in adults, and that investments in
them reduce crime and violence. We recruited criminally engaged men
and randomized one-half to eight weeks of cognitive behavioral therapy
designed to foster self-regulation, patience, and a noncriminal identity
and lifestyle. We also randomized $200 grants. Cash alone and therapy
alone initially reduced crime and violence, but effects dissipated over
time. When cash followed therapy, crime and violence decreased
dramatically for at least a year. We hypothesize that cash reinforced
therapy’s impacts by prolonging learning-by-doing, lifestyle changes,
and self-investment. (JEL D12, D83, H23, I32, K42, O15, O17)
In many countries, poor young men exhibit high rates of violence, crime, and
other antisocial behaviors. In addition to their direct costs, crime and instability
hinder economic growth by reducing investment or diverting productive resources
to security. In fragile states, such men are also targets for mobilization into election
intimidation, rioting, and rebellion.1
1For example, poor urban young men were recently recruited into election violence in Sierra Leone (Christensen
and Utas 2008) and as mercenaries in Côte d’Ivoire (Blattman and Annan 2016).
*Blattman: Pearson Institute for the Study and Resolution of Global Conflicts and Harris School of Public
Policy, University of Chicago, 1155 E. 60th Street, Chicago, IL 60637 (e-mail: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>); Jamison:
The World Bank Global Insights Initiative, MC4-404, 1818 H Street NW, Washington, DC 20433 (e-mail: julison@
gmail.com); Sheridan: Clinical Psychology, University of North Carolina at Chapel Hill, 235 E. Cameron Street,
Office 248, Chapel Hill, NC 27599 (e-mail: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>). For implementation we thank Global
Communities and the Network for Empowerment and Progressive Initiatives (NEPI). For comments we thank
Thomas Abt, Jeannie Annan, Alex Coppock, Patricio Dalton, Stefano DellaVigna, Ruben Enikolopov, Roland Fryer,
Don Green, Jonas Hjort, Macartan Humphreys, Dean Karlan, Larry Katz, Shamus Khan, Winston Lin, Jens Ludwig,
Mattias Lundberg, Sendhil Mullainathan, Anandi Mani, Chris Muller, Suresh Naidu, Jonathan Pinckney, Vincent
Pons, Nancy Qian, Steve Radelet, Gautam Rao, Adam Reich, Alix Rule, Cyrus Samii, Rachel Strohm, Francesco
Trebbi, Eric Verhoogen, the referees, and participants at numerous conferences and seminars. This study was
funded by the National Science Foundation (SES-1317506), the World Bank’s Learning on Gender and Conflict in
Africa (LOGiCA) trust fund, the World Bank’s Italian Children and Youth (CHYAO) trust fund, the UK Department
for International Development (DFID) via the Institute for the Study of Labor (IZA), a Vanguard Charitable Trust,
the American People through the United States Agency for International Developments (USAID) DCHA/CMM
office, and the Robert Wood Johnson Health and Society Scholars Program at Harvard University (Cohort 5). The
contents of this study are the responsibility of authors and do not necessarily reflect the views of their employers,
funding agencies, or governments. Emma Tsui trained our qualitative researchers. Philip Blue, Natalie Carlson,
Samantha DeMartino, Camelia Dureng, Mathilde Emeriau, Yuequan Guo, Brittany Hill, Tricia Koroknay-Palicz,
Rebecca Littman, Ryan Luby, Ben Morse, Richard Peck, Patryk Perkowski, Colombine Peze-Heidsieck, Katherine
Rodrigues, Carmel Salhi, Joe St. Clair, Helen Smith, Gwendolyn Taylor, Abel Welwean, Prince Williams, Xing
Xia, Adam Xu, and John Zayzay provided research assistance through Innovations for Poverty Action. The authors
declare that they have no relevant or material financial interests that relate to the research described in this paper.
†Go to https://doi.org/10.1257/aer.20150503 to visit the article page for additional materials and author
disclosure statement(s).1166 THE AMERICAN ECONOMIC REVIEW april 2017
Two of the most common government responses are policing and job creation. Both
take the person as they are and try to change their incentives or simply incarcerate them
(Becker 1968; Draca and Machin 2015). This paper investigates an alternative: rehabilitation, or changing behavior by shaping people’s underlying skills and preferences.
A large literature has shown that a broad set of so-called noncognitive skills,
especially self-control, predict long-run economic performance and criminal activity.2 These skills respond to investment, especially in childhood (Cunha, Heckman,
and Schennach 2010). They are fostered by family, schools, and communities. There
is little evidence, however, on the returns to late-stage noncognitive investments,
and so it is unclear whether by adulthood self-investment or interventions can shape
noncognitive skills and hence behavior (Heckman and Kautz 2014; Hill et al. 2011).
It is also unclear what specific skills are both important and malleable.
To investigate, we recruited 999 of the highest-risk men in Liberia’s capital, generally aged 18 to 35. Most were engaged in part-time theft and drug dealing, and regularly had violent confrontations with each other, community members, and police.
We experimentally ran two interventions. One was an eight-week program of
group cognitive behavior therapy (CBT) called the STYL program (Sustainable
Transformation of Youth in Liberia). We assigned offers by lottery. Following this,
we held a second lottery for a $200 grant, about three months’ wages. The cash was
partly a measurement tool, to see if therapy affected economic decisions. The cash
was also a treatment, in the sense that it could stimulate legal self-employment,
and we included it to compare therapy to a rise in the returns to legal work.3
Experimentally, subjects either received offers of therapy alone, cash, therapy then
cash, or neither. To deliver both treatments cost about $530 per person.
CBT is a therapeutic approach that can be used to treat a range of harmful beliefs
and behaviors, including depression, anger, and impulsivity. First, CBT tries to
make people aware of and challenge harmful, automatic patterns of thinking or
behavior. Second, it tries to disrupt these patterns of thinking and to foster better ones by having people practice new skills and behaviors—learning by doing.
A Liberian nonprofit, the Network for Empowerment and Progressive Initiatives
(NEPI), designed and ran STYL. NEPI facilitators were themselves ex-combatants
or ex-criminals who graduated from prior NEPI programs.
Among noncognitive skills, NEPI designed STYL to focus on forward-looking
behavior and self-control. By self-control, psychologists and criminologists typically mean one’s short-term abilities to regulate emotions and to be resistant to
impulse, as well as more sustained abilities to be planful, persevering, and patient.
This concept has parallels to economic time preferences, and we measure them
in the manner of both fields. Becoming more self-controlled and forward-looking
are central components of many programs, from preschool to rehabilitation therapy.4 The curriculum focused on helping men foster skills of planning, goal-setting,
reflection, deliberate decision-making, and controlling emotions and impulses.
2For example, Nagin and Pogarsky (2004); Heckman, Stixrud, and Urzua (2006); Borghans et al. (2008).
3Evidence from East Africa suggests that the poor and unemployed are credit-constrained and have high returns
to cash (Haushofer and Shapiro 2016; Blattman, Fiala, and Martinez 2014; Blattman et al. 2016).
4For example, Gottfredson and Hirschi (1990) and Borghans et al. (2008). As an example of an intervention,
the famous Perry Preschool Program emphasized the ability of young children to plan tasks, to execute their plans,
and to review their work in social groups (Almlund et al. 2011).VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1167
The therapy also encouraged nonviolent, noncriminal behavior and lifestyles by
fostering a change in the men’s social identity. A premise of STYL was that the men
self-identified as outcasts and did not hold themselves to the standards of mainstream
society. The therapy tried to persuade the men that they could change who they were
and how they were perceived. NEPI facilitators modeled this identity change. They
walked the men through basic steps, such as changing their appearance, engaging
in normal social interactions, and behaving more cooperatively. They discouraged
drug use and association with bad peers. Therapy also required men to practice
going to supermarkets, banks, and other “normal” places.
Research in both psychology and economics supports the idea that social identity
and associated values influence behavior, and that both can change. This literature
treats values as direct utility benefits or penalties from acting in accordance with
or against a set of preferences (Bénabou and Tirole 2004; Almlund et al. 2011).
Akerlof and Kranton (2000) and Jolls, Sunstein, and Thaler (1998) both argue that
these preferences or values are tied to a person’s social identity, and that to some
extent people can change their perceived social category and with it values that
reward and penalize certain behaviors.
There are striking parallels between STYL and socialization into militaries, street
culture, gangs, and armed groups. Such groups use similar techniques (appearance
change, practice, modeling) to shape young men’s social identity and behavior
(Vigil 2003; Wood 2008; Maruna and Roy 2007). NEPI designed STYL to reverse
this process.
We surveyed the men beforehand, a few weeks after the interventions, and a
year later. Most had no fixed address, phone, or even name. Despite this mobility,
we reinterviewed 92.4 percent. We rely on self-reported data since (like most poor
and fragile states) there are no administrative data. We did not necessarily trust
self-reports, and so we attempted to recheck and validate behaviors such as stealing
through in-depth interviews with a subsample.
We approached roughly 1,500 high-risk men, and 999 agreed to enter the study.
Of those assigned to therapy, nearly all attended at least one day, and two-thirds
completed it. The higher-risk men were the most likely to finish.
Men who received therapy reduced their antisocial behaviors dramatically,
roughly 0.2 standard deviations compared to the control group. Within a few weeks
of therapy, for example, we observe large reductions in an index of behaviors,
including stealing and drug selling. With therapy alone, these effects diminished
after a year. When therapy was followed by cash, however, the reductions in an
index of all antisocial behaviors were lasting.
The therapy probably worked through many channels, and we see evidence of
improvement in two of the hypothesized channels: time preferences and noncriminal
identity/values, with time preference changes most persistent. There is also some evidence of improvements in positive self-regard, plus temporary changes in drug abuse
and noncriminal social networks. With therapy alone, a broad index of all these intermediary outcomes changes diminished after a year, just as we saw with antisocial behavior itself. When therapy was followed by cash, however, the overall changes in these
intermediary outcomes were lasting and fairly large: at least 0.25 standard deviations. If
we account for multiple comparisons, it becomes difficult to single out any one mechanism, but the largest and most statistically significant change is in time preferences.1168 THE AMERICAN ECONOMIC REVIEW april 2017
How was cash used? Regardless of therapy, little of the grant was spent on drugs
or “wasteful” things. Most funds were invested in business or saved. Cash led to a
short-term increase in an index of economic performance (including income, savings, employment, and investment), due largely to increased petty trading. After a
year, however, these gains disappeared, partly because most men were robbed regularly, irrespective of treatment.
The fact that the grant was crucial to sustaining therapy’s effects is our most
unexpected and important finding. As we find no sustained effect of cash on earnings, cash clearly did not raise the opportunity cost of antisocial behavior after a
year. Thus, economic performance does not explain the sustained effect of therapy
plus cash on crime and aggression. Drawing on qualitative interviews and psychological theory, we suggest that the short-term increase in income and legal employment helped to solidify therapy’s impact on noncognitive skills and preferences.
Specifically, for a few months after therapy, cash allowed men to project a new self,
to stave off homelessness and stealing, and practice the self-control and future orientation started by therapy. This hypothesis will be important to test in future research.
An obvious concern is our reliance on self-reported data. We argue that misreporting is unlikely to drive our results for two reasons. The first is the pattern of
treatment effects: 12–13-month impacts from therapy plus cash, but not from cash
or therapy only. Systematic measurement error would need to be correlated with
the “both” treatment arm only. This seems feasible but unlikely, especially given
the magnitudes of the impacts. To check further, we attempted to validate a subset of questions using intensive qualitative observation. The patterns suggest that,
if anything, the control group underreported sensitive behaviors such as stealing.
Hence, the treatment effects may actually underestimate therapy’s impacts. We also
learn that the control group reported fewer expenditures in the survey versus the
validation exercise, suggesting that some of the short-term economic gains from
cash may be illusory. These insights come with the caveat that they assume that data
collected though in depth interviews on a small subset of questions, with a focus on
trust-building, are more accurate than survey measures. It is better to think of the
validation as a confidence-building exercise rather than hard proof.
In addition to evaluating the pairing of an economic intervention with CBT, this
study addresses several gaps in the literature. One is the absence of evidence on behavior change outside the United States, especially in fragile states. Even within the United
States, however, there is limited evidence on adult behavior change. Most evaluations
of US-based crime and violence-reduction programs focus on education and employment interventions.5 Studies of CBT tend to be small-sample and nonexperimental
(Wilson, Bouffard, and Mackenzie 2005).6 But STYL’s impacts on adult antisocial
behavior are consistent with evidence from US adolescents and children, which show
that CBT programs in schools and correctional institutes reduce criminal recidivism.7
5Two US programs, Job Corps and ChalleNGe, are residential programs for at-risk youth that tackle noncognitive skills but focus on jobs and job training (Schochet, Burghardt, and McConnell 2008; Millenky et al. 2012).
6An exception is Little, Robinson, and Burnette (1994), who randomly assigned CBT to 1,381 general offenders in Tennessee. They found that re-arrest fell from 56 percent to 41 percent after five years. Our study adds to this
large-sample evidence.
7For evidence on children and adolescents, see Heckman and Kautz (2014) and Hill et al. (2011). Metaanalyses of adolescent and adult interventions in correctional institutes find that CBT-informed programs appear to
outperform alternate therapies (Andrews et al. 1990; Lipsey 2009).VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1169
Finally, few studies have measured noncognitive skill and preference changes
directly, and so our study strengthens arguments that they are malleable and contribute to antisocial behavior. If we adjust our p-values conservatively for multiple
comparisons, it is difficult to single out any one skill or preference change, though
there is suggestive evidence that time preferences, identity, social networks, and
mental health all improve. The malleability of social identity is consistent with
evidence from stigmatized Indian sex workers, where short courses of non-CBT
psychological therapy increased self-worth, reduced shame, and increased savings
and health-seeking behavior (Ghosal et al. 2015). The majority of this evidence,
however, comes from small, observational, unpublished studies, which, because of
a reliance on administrative data, seldom measure mechanisms directly.8 But three
recent randomized control trials among at-risk Chicago adolescents show that CBT
can help adolescents reduce automatic behaviors (such as violent retaliations to a
slight) by learning to override fast decision-making with conscious slow reflection
(Heller et al. 2013, 2015). The parallels between that program and STYL, in both
the curriculum and impacts, are striking.
It remains to be seen if STYL is replicable, but it is promising that it was adapted
from foreign therapies and developed its own facilitators from prior graduates,
enhancing scalability. Future work should test the approach in new contexts, compare CBT to other therapies (or a placebo), and reduce the reliance on self-reported
data.
I. Intervention and Experiment
Liberia’s capital, Monrovia, is home to one-third of the country’s 4.3 million people. There are few formal jobs. Most men aged 18 to 35 have limited employment
and earn money through a mix of agriculture, casual labor, or petty business. A few
turn to crime, which is becoming more violent and commonplace.
During the periods 1989–1996 and 1999–2003, two civil wars wracked Liberia.
They killed 10 percent of the population, displaced a majority, and recruited tens
of thousands into combat. Since 2003, however, Liberia has been at peace with the
help of a United Nations (UN) peacekeeping force. During our study period, 2009–
2012, the economy was growing 6 percent per year (Republic of Liberia 2012).
Nonetheless, in 2009, people aged 18 to 35 would have spent up to 15 years of their
childhood or adolescence in conflict, many robbed of the institutions that normally
fostered planfulness, emotional stability, and other noncognitive skills.
Marginalized young men were one of the government’s main concerns, especially
poorly integrated ex-combatants and other men involved in drugs and crime. Drug
and criminal networks are disorganized, but the government worried they could consolidate. They also worried about political violence. High-risk men had joined riots
and election violence in the past, and they were targets for mercenary recruitment
into the 2010–2011 war in Côte d’Ivoire.
8Of 20 studies identified by Wilson, Bouffard, and Mackenzie (2005) only 4 were experimental and 3 of these
had sample sizes of 100 or less. The observational studies were also small and were of mixed quality.1170 THE AMERICAN ECONOMIC REVIEW april 2017
A. Target Population and Recruitment
We set out to recruit 1,000 high-risk men: men actively involved in crime, interpersonal violence, and drugs, or who were poor and at risk of engaging in these
activities. With no administrative data on such men, we recruited them directly. We
selected five neighborhoods in Monrovia known for high rates of crime. These were
generally mixed-income residential areas with large markets, with populations of
roughly 100,000 each.
Recruiters were NEPI affiliates who were not involved in the interventions. NEPI
had extensive knowledge of these neighborhoods and connections to local leaders,
as well as a strong reputation that target men could verify. Recruiters had worked
closely with high-risk men before, and were themselves past graduates of a NEPI
program.
We charged the recruiters with finding men who were homeless, drug-using, disreputable in appearance, or present in locations known for crime, armed recruitment,
and violence. Community members could easily identify these spots and their denizens. Similarly, certain professions had strong reputations for crime.9 Appearance
was also a useful guide. For instance, recruiters looked for men with a dirty or
unkempt appearance, long hair, apparent intoxication, or a “tough” style of dress.
To minimize correlated outcomes and spillovers, we avoided recruiting close
associates. We instructed NEPI to approach just one out of every seven to ten
high-risk men whom they visually identified. Recruiters then described the therapy,
the allocation by lottery, and the baseline survey. They never mentioned cash grants.
Over several weeks, recruiters identified roughly 10,000 potentially high-risk men
and approached 1,500. Of these, about one-third refused interest in the therapy and
survey.10
In the end, 999 men agreed to enter the sample. We estimate that they represented
0.6 percent of all adult males in the neighborhoods, and about 12 percent of men
aged 18–35 and in the bottom decile of income (online Appendix A.2). Column 1 of
Table 1 describes this sample at baseline. On average the men were 25, had nearly 8
years of schooling, earned about $68 in the past month working 46 hours per week
(mainly in low skill labor and illicit work), and had $34 informally saved; 38 percent
were a former member of an armed group.
B. Interventions
Cash.—A nonprofit organization, Global Communities (GC), distributed the
cash. They ran a lottery, where winners received $200 cash and losers received a
9Location was especially important. Within each of the neighborhoods there were pockets of insecurity where
high-risk men were known to live or congregate: abandoned buildings, garbage dumps, drug-dealing spots, parking
lots, and homes for rootless young men run by ex-military commanders. Common professions included “car loaders” who have reputations for pickpocketing, or wheelbarrow and motorbike parking areas with reputations for drug
selling and crime. They avoided recruiting men known to be “bosses”—men who run homes or drug dens that cater
to petty criminals and low-level drug dealers.
10We do not have systematic data on refusers, but recruiters reported two main types: men who were poor but
were low-risk in that they did not appear to be involved in crime and violence; and high-risk men who said they were
too busy to take part in therapy because they had legal or illegal business to attend to.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1171
consolation prize of $10. There was minimal framing.11 GC held cash lotteries a
week after the end of therapy.
Therapy.—CBT is a short-term approach that tries to reduce self-destructive
beliefs or behaviors and promote positive ones. It does so in two ways. First, the
therapist tries to help the patient become more aware of their automatic thoughts:
inaccurate or negative thinking about themselves or others. Shifting automatic
thoughts allows them to respond to everyday situations in a more effective way. A
central principle of CBT, however, is that sustained changes in behavior or symptoms also come from actively practicing new behaviors, often starting with simple
tasks and, through repetition, positive reinforcement, and gradually increasing the
difficulty or complexity of the tasks, changing both behavior and thinking. This
practice happens in therapy but also as “homework” (Beck 2011).12
11See online Appendix B.4 for implementation details. Prior to the lottery, subjects were given about 15 minutes
of information on how to keep the money safe (e.g., depositing it with a bank) and examples of what they could use
it for (e.g., starting a small business or home improvement). But GC explicitly emphasized to subjects that the grant
was unconditional and they were free to do what they wished.
12CBT has been studied extensively and validated as a treatment for several of the behaviors targeted by
STYL: anger, aggression, criminality, and substance abuse (Saini 2009; Pearson et al. 2002; Wilson, Bouffard, and
Mackenzie 2005; Del Vecchio and O’Leary 2004).
Table 1—Baseline Summary Statistics and Test of Balance for Select Covariates
Test of randomization balance (N = 999)
Sample
Assigned
therapy
Assigned
cash
Assigned
both F-test
Mean Coeff. p-value Coeff. p-value Coeff. p-value p-value
(1) (2) (3) (4) (5) (6) (7) (8)
Baseline covariate
Age 25.40 −0.16 0.68 0.19 0.59 −0.18 0.68 0.18
Married or partnered 0.16 −0.03 0.65 −0.04 0.67 0.04 0.76 0.93
Number of children <15
in household
2.21 −0.58 0.07 −0.51 0.19 0.64 0.29 0.32
Years of schooling 7.72 −0.19 0.68 0.04 0.95 −0.01 0.99 0.55
Has any disabilities 0.08 0.04 0.29 0.00 1.00 −0.04 0.48 0.19
Ex-combatant 0.38 0.06 0.09 0.08 0.08 −0.07 0.11 0.12
Weekly cash earnings (US$) 17.02 −1.89 0.03 −4.85 0.03 5.48 0.00 0.02
Currently sleeping on street 0.24 −0.01 0.80 0.00 0.93 −0.02 0.75 0.26
Savings stock (US$) 33.75 −10.08 0.26 −12.84 0.31 15.69 0.31 0.53
Hours/week, illicit activities 13.55 1.21 0.68 −0.86 0.67 0.06 0.99 0.14
Hrs/week, agriculture 0.36 0.34 0.26 −0.10 0.35 0.13 0.84 0.01
Hrs/week, low-skill wage labor 19.39 0.54 0.88 1.24 0.73 −0.43 0.90 0.94
Hrs/week, in low-skill business 11.53 0.16 0.92 −1.53 0.60 5.76 0.13 0.50
Hrs/week, high-skill work 1.51 −0.05 0.91 0.94 0.03 0.11 0.85 0.01
Sells drugs 0.20 0.01 0.69 0.00 0.92 0.00 0.93 0.92
Uses marijuana daily 0.44 0.08 0.15 0.04 0.13 −0.09 0.22 0.34
Uses hard drugs daily 0.15 −0.04 0.21 0.02 0.52 0.01 0.90 0.37
Committed theft, past 2 weeks 0.53 0.05 0.51 0.01 0.61 −0.02 0.62 0.77
R2 0.16 0.12 0.35
p-value on F-statistic 0.50 0.44 0.50
Notes: We report a selection of covariates here, and all 57 covariates are reported in online Appendix A.1. Column 1
reports the sample mean. A small number of missing values are imputed at the median. Columns 2–7 report the
coefficients and p-values from ordinary least squares (OLS) regressions of each baseline covariate on three indicators, one for assignment to each treatment arm, controlling for block fixed effects. Column 8 reports the p-value
from a joint test of statistical significance of all three treatment indicators.1172 THE AMERICAN ECONOMIC REVIEW april 2017
Origins and Aims of STYL.—STYL grew out of the experiences of NEPI’s founders, but over time they integrated standard Western CBT practices, in part through
interactions with international organizations and experts.
The program combined group therapy with one-on-one counseling. Twenty men
met in groups three times a week, four hours at a time, led by two NEPI facilitators. On alternate days when groups did not meet, facilitators visited men at home
or work to provide advising and encouragement. NEPI offered no compensation
except lunch, since men who sacrificed four hours of work could not afford to eat.
NEPI designed the curriculum and approach to encourage two main kinds of
change. First, to foster future orientation over present-biased behavior, they taught
skills of self-control: to manage anger and emotions, reduce impulsivity, become
more conscientious and persevering, and become more planful and goal-oriented.13
While impulsivity and conscientiousness are often described as personality traits,
such traits appear to evolve over the life cycle and are affected by upbringing and
investment, so we follow Heckman and Kautz (2014) in considering them skills.
These skills of self-control may or may not be related to economic time preferences.
The measures and model we outline below treat time preferences and self-control as
distinct, and whether they covary is an empirical question.14
Second, NEPI tried to persuade men to adopt an anticriminal, antiviolent lifestyle, identity, and values, by helping them self-identify as a normal society member
rather than an outcast. A premise of STYL was that the security and respect associated with a mainstream identity were familiar and attractive to the men. So were the
values associated with a mainstream identity; it was no mystery that crime and drugs
were considered “bad.” But to someone who identified as an outcast, those norms
and values did not apply to outcasts like them, and a mainstream identity seemed
out of reach.
NEPI facilitators tried to persuade the men that this identity was attainable, and
that the men should at least try. Partly through exercising skills of self-control, and
partly by practice and exposure to new situations, the STYL curriculum walked men
through the process of change. The facilitators were an integral part of this intervention, because they modeled the change in skills and values. All were graduates of a
prior STYL-like program run by NEPI, and three-quarters were former street youth
or combatants.
There are parallels to interventions which show that aspirations—forwardlooking goals or targets—influence behavior and respond to intervention (Bernard
et al. 2014). There are also parallels to switching social identity.15
13Note that psychologists also use self-control to refer to abilities such as executive function (EF) and delay of
gratification (DoG), both of which are thought to lead to less impulsive decision-making and influence long-term
success (Mischel, Shoda, and Rodriguez 1989). Some evidence suggests that EF and DoG are distinct from our
character skills and are less malleable (Duckworth and Schulze 2011). We measured EF and DoG but they were not
the focus of the therapy and we did not hypothesize any change.
14The limited evidence comparing economic time preferences to psychological measures of self-control suggests correlations are positive but low, suggesting they may be distinct (Becker et al. 2012).
15Akerlof and Kranton (2000) review a wide social science literature. Relatedly, criminologists sometimes
refer to a similar process of knifing off from old social rules and behaviors, and associate these changes with significant turning points in life, such as marriage, a move, or a life-threatening experience (Maruna and Roy 2007).
This literature almost always ties successful knifing off to having a new script for the future. The STYL program
is effectively that script.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1173
STYL Curriculum and Approach.—The sessions employed a variety of techniques, from lectures and group discussions, to various forms of practice, including:
role-playing in class, homework that requires practicing tasks, exposure to real situations, and in-class processing of experiences of executing these tasks. Like many
CBT programs, these tasks began simply and got more difficult over time.16
In the first three weeks, facilitators encouraged men to try to maintain some new,
simple behaviors. This included getting a haircut and removing facial hair, wearing
shoes and pants instead of sandals and shorts, improving personal hygiene and the
cleanliness of their living area, and reducing substance abuse. These simple exercises in goal-setting and self-control also helped men start to operate within mainstream social norms.
In the middle weeks, facilitators encouraged men to engage with society in
planned and unaccustomed ways, akin to exposure therapy.17 For instance, homework included reintroducing themselves to their family, joining community sports,
and visiting banks, supermarkets, shops, and so forth. Men also studied successful
people in their community, and reached out to one as a mentor. Men then processed
their attempts as a group. Often homework was independent, but facilitators might
accompany the more troubled men.
Men also learned to manage emotion: practicing nonaggressive responses to
angry confrontations in class, and recognizing signs of angry reactions and learning to distract or calm oneself (walking away, doing other activities, or breathing
techniques).
In the last weeks, facilitators taught planning and goal setting. These lessons
included training on breaking down large goals into smaller accomplishable
subgoals, and then creating plans to accomplish them via concrete steps. For example, men would list subgoals of a plan; these were written on a paper in front of
the room, for all to see; the group critiqued them; and plans were rewritten. For
homework men would attempt planning in their own lives: how to feed their family
the next day; starting a garden; making a savings plan; reconciling with estranged
family; or starting a business. These assignments began easy and got more difficult.
This process of goal identification and planning is central to most forms of CBT,
especially for disruptive behavior disorders (Langberg et al. 2013).
Cost.—The cost of delivering both interventions was $530 per head: $189 for
CBT, $216 for the grant, and $125 for registration and administration.
C. Experimental Design
We used a 2 × 2 factorial design. The experiment proceeded as follows. First,
following the baseline survey, the respondent was assigned to an offer of therapy
16Online Appendix B.3 describes the curriculum in more detail. The full program manual is available at http://
chrisblattman.com/documents/policy/2015.STYL.Program.Manual.pdf.
17Therapy for patients with social phobia practices similar engagement (Ponniah and Hollon 2008). Besides
practice, subjects learn that social feedback is less negative than feared. By reengaging with society, participants
tested their negative beliefs about themselves.1174 THE AMERICAN ECONOMIC REVIEW april 2017
by drawing chips from an opaque bag.18 Therapy began one week later. About one
to two weeks after therapy ended, GC announced and held a private draw for $200
grants among the full sample, in blocks of roughly 50 men. Finally, a third organization (Innovations for Poverty Action) ran endline surveys 2 and 5 weeks, and then
12 and 13 months, after grants.
The sample were mistrustful of authority, and we randomized by individual draw
rather than computerized assignment to maximize transparency and staff safety.
Men in each block took turns drawing colored chips from a fabric bag.19
Balance.—This resulted in 25 percent assignment to cash only, 28 percent to
therapy only, 25 percent to both, and 22 percent to neither.20 Treatment is largely
balanced along covariates. Table 1 reports tests of balance for each treatment, for
selected covariates (see online Appendix A.1 for all, and for endline respondents
only). Of 57 covariates over 3 treatments, 14 (8.2 percent) have a difference with
p < 0.05, and within treatment arms the covariates are not jointly significant. There
are, however, large though not statistically significant baseline differences between
the control group and the treatment arm that received both therapy plus cash, many
in the direction of the both arms having better prerandomization outcomes: a random outcome that is in part a result of the smaller sample in these subgroups. To
reduce concerns that these imbalances cumulate and could influence endline results,
in online Appendix E.8 we create indexes of baseline data much like the endline
indexes and look for treatment effects (where these endline behaviors are available) and look for treatment effects. We find that no baseline effect is significant at
the 95 percent confidence level, and none of those that are marginally significant
(p < 0.10) are in the therapy plus cash treatment arm. Therefore, baseline imbalances are unlikely to be driving our results. However, we still control for baseline
covariates in our main empirical specification.
Compliance.—Both interventions had high compliance, in part due to NEPI’s
persuasive efforts and street credibility. Of men assigned to the grant, 98 percent
received it. Of men assigned to therapy, 5 percent attended none, another 5 percent
dropped out within the first three weeks, and two-thirds attended at least 80 percent
of all sessions (online Appendix A.4). Those who dropped out early had less schooling, less self-control, and were less likely to exhibit antisocial behaviors such as
substance abuse or stealing (online Appendix A.3). Thus, the highest-risk men seem
more likely to have attended over poorer, noncriminal men.
18The baseline survey was conducted in central locations, and these draws were systematized and supervised
by members of the research team. Only the subject and the research team knew the assigned treatment, however. It
was not a public draw.
19The order of selection was deliberately unsystematic but not randomized. The number of chips in the bag
generally exceeded the number of draws, partly to avoid a correlation between order of the draw and treatment
assignment probabilities, and partly to avoid having late-drawing men receive their status by default. See online
Appendix B.2 for full details.
20See online Appendix A.2 for details by phase and neighborhood. The excess therapy assignments is in part
chance, and is in part driven by two blocks where excess treatment chips were accidentally used. All regressions
include block fixed effects to account for this.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1175
Phased Implementation.—For logistical reasons we recruited, treated, and studied the men in three phases. A pilot phase recruited 100 men, to ensure that the
therapy and cash grant caused no harm, to assess statistical power, and to allow us
to refine experimental protocols. The pilot showed no indication of harm, and so we
scaled to a further 900 with only minor changes to the interventions and protocols
in two phases.21
II. Conceptual Framework
The interventions were designed to affect two main outcomes: economic performance and antisocial behaviors (including crime and aggression). To structure this
argument, we start with a model of occupational choice between legal and illegal
work (such as crime, mercenary work, or election thuggery).22 Later we consider
how such a model could be used to understand other forms of antisocial behavior.
We develop the formal model in online Appendix C and outline the structure and
results here.
CBT could affect these outcomes in many ways but, as outlined in the next section, we focused on and prespecified three intermediary outcomes: time preferences,
self-control, and the values associated with a mainstream social identity. Our simple
model examines comparative statics in all three. A change in time preferences is the
simplest to examine in a standard model, and we consider the effects of changes in
both the discount rate and time inconsistency. More broadly, forms of self-control
such as improved emotional regulation, planning, and conscientiousness could be
considered a form of human capital that affects productivity. Hence, we also consider what happens when we model self-control as a parameter individual production function. Finally, we introduce a change in criminal identity/values as a change
in intrinsic preferences over criminal occupations.23
Of course, the therapy is a multifaceted treatment that likely operates through
a number of other mechanisms (changed peers or family circumstances, mental
health, prosocial preferences) and affects other outcomes and behaviors that themselves are associated with crime (drug abuse or prosocial behavior). We examine
these empirically below, but focus the model on the mechanisms that are most in
line with NEPI’s design principles and theories, as well as the psychological theory
and evidence cited above.
Setup.—Suppose people can allocate their time between leisure l, legal work L b
such as petty business or labor, and illegal occupations Lc such as crime, mercenary
work, or election thuggery. We refer to these simply as business and crime.
We assume crime uses labor alone and pays a wage w, which may be uncertain.
This resembles the returns we observe to illegal work of the type available to our
21See online Appendix A.2 for neighborhood details. Online Appendix B includes the power calculations
behind our experimental design.
22It is rooted in models of occupational choice with capital infusions and adapted to illicit behavior, as in
Blattman and Annan (2016), in the tradition of many economic crime models (Draca and Machin 2015).
23Typically, models treat such preferences as fixed, or ignore them. We outline how exogenous changes in noncognitive abilities or preferences affect the comparative statics in an otherwise standard model.1176 THE AMERICAN ECONOMIC REVIEW april 2017
population in Liberia.24 In the budget function, crime also carries a punishment f
with probability ρ, and we assume this risk increases with the time devoted to crime.
Punishment could mean prosecution, mob justice, or social sanctions.
Business uses capital, yielding output F(θ, Ltb, Kt) where θ is individual ability
and K
t is capital inputs. People start with wealth in the form of a riskless asset, a0,
and save or borrow at interest rate r. Self-control skills are one element of θ, and
output increases in θ. For simplicity, we focus on the case where self-control skills
are inputs into business but not crime. This is the interesting and relevant case, since
otherwise investments in self-control skills will not affect occupational choice. We
did not assume this from the outset, recognizing that in principle STYL could teach
men to be more effective criminals. The pilot phase, however, suggested the opposite was true, so this is the most useful case to discuss.
People choose consumption, labor supply in each sector, and the amount of
wealth to invest in business (versus the safe asset) in order to maximize their utility subject to the constraint that consumption plus wealth is equal to total income
from business, crime, and the interest on investment. We allow people to be present-biased in the sense that they have a general intertemporal discount factor δ, but
can also be time-inconsistent with an extra factor denoted β < 1 that multiplies
all future periods relative to the present (the standard form of quasi-hyperbolic
time preferences).
Finally, people value consumption and leisure, but we also allow for a consumption value from conforming to one’s identity and values (Akerlof and Kranton 2000;
Bénabou and Tirole 2004).25 In this case, a person’s identity and associated values
can penalize criminal acts. We use σ to indicate a preference against crime, and we
put it in the utility function, U(c, l, σLc), to distinguish these internal preferences
from external punishments f.
We are interested in the effect of the interventions on criminal versus legal labor.
Therapy can potentially influence this occupational choice through noncognitive
skills θ, time preferences (δ or β), anticriminal values σ, or all of the above. Cash,
meanwhile, can influence occupational choice by increasing the assets available for
capital inputs into legal business.
Occupational Choice in the Absence of Interventions.—Where financial markets work well and where people are time consistent (β = 1), businesses are
at their optimal scale—they have borrowed until the marginal return to capital
is equal to r. Of course, the poor are typically credit-constrained. In this case
poor people are forced to invest in capital over time until they reach the same
optimal scale. The young and those who have experienced bad shocks will be
the furthest behind. As a result, crime is more likely to be chosen by men with
low business ability θ, the poor and credit-constrained, those with low disutility of crime, and the time-inconsistent. People may also choose both crime and
24Petty crime requires little capital; drug dealers typically work for a “boss” who owns the supply; and those
who leave town to work in illicit mining work as “mining boys” for capital-owning “miners” on short-term renewable contracts that pay a daily wage plus a payment tied to output. This is also why we assume below that self-control
skills are less important for success in criminal activities.
25We ignore the possibility, proposed by Bénabou and Tirole (2004), that ability is imperfectly known and
correlated with perceived self-image.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1177
business. Credit-constrained people with partial capital for business may still spend
some time in crime. Also, risk averse people may do both activities when returns
are uncertain.
Impacts of Cash.—If there are no credit constraints, cash windfalls will not affect
occupational choice. But if people are poor and credit-constrained, windfalls will
be partly invested in business. People involved in crime will shift to business, especially those with high business ability. Cash infusions will lead to a smaller increase
in business for time-inconsistent individuals, however, since they will choose to
consume more today.
Impacts of Therapy.—Therapy could increase σ, θ, β, or δ. These channels have
distinguishing predictions. Interventions that increase the disutility from crime,
σ, will reduce time devoted to it, but will have no effect on returns to business.
Interventions that increase noncognitive ability θ will induce more time and investment in business, and also reduce crime. With the presence of risk in both sectors
(and assuming risk aversion), interventions in θ will have relatively greater effects
in terms of pushing individuals away from crime, because an increase in θ now also
makes business relatively less risky. A rise in σ will also have a bigger effect when
there is uncertainty, because risk aversion will reinforce the rise in crime aversion
and further reduce hours in crime.
What if an intervention increases time consistency, β? This will increase business investment and earnings among the credit-constrained. If people become more
time-consistent, they will be more strongly influenced by the consequences of their
actions in terms of punishments, and will therefore reduce criminal labor (and
increase business labor) as well. Similar comparative statics come from an increase
in patience.
Cash and Therapy in Combination.—The model implies that both interventions
should lead to a larger decline than one alone simply because the effects are cumulative. Moreover, when people are credit-constrained and also receive cash, this simple model predicts that the effects of a change in σ or θ will be greater with cash than
without it. Thus, the interventions may be complementary and the total effect could
be greater than the sum of the parts. Note that this simple model does not allow cash
to have direct behavioral effects through practice of new behaviors or reinforcement
of therapy’s lessons.
Relevance of the Model for Aggression.—This model is most useful for thinking
about illegal acts that carry material rewards. Other violence does not earn a wage,
or does not have an opportunity cost of time. Nonetheless, we can cautiously use
the model to think about acts such as aggression. For instance, we can think of
some acts as having consumption value that is fleeting (the expressive pleasure of
anger or revenge) or persistent (deterring future slights). In this case, σ < 0. Like
crime, these acts carry a risk of punishment. If the criminal wage is zero, there is
still a trade-off between the consumption value today and the risk of punishment
tomorrow, and the main comparative statics of therapy are similar to the case of
crime: instilling values against violence (increasing σ) will reduce aggression; and1178 THE AMERICAN ECONOMIC REVIEW april 2017
increasing time consistency, β, also reduces aggression. Cash, however, will have
little deterrent effect on aggression.26
III. Data
We tried to survey each subject five times: (i) at baseline prior to the intervention;
(ii and iii) at short-run endline surveys 2 and 5 weeks after the grants; and (iv and v)
at two endline surveys 12 and 13 months after grants.27 We ran pairs of surveys to
reduce noise in outcomes with potentially low autocorrelation (such as earnings or
criminal activity). To measure baseline time preferences and abilities (such as executive function), following each survey the respondents also conducted 45 minutes of
incentivized games and tests.28
This sample was mobile and difficult to track. Roughly 40 percent changed locations between surveys, many changing sleeping places every few weeks or nights.
Just 30 percent had mobile phones. Most went by several aliases, and may have
been on the run. To minimize attrition, we collected extensive contact information
(all known addresses, plus at least five close contacts), and went to extreme effort
to locate each person, wherever they had moved, averaging three to four days of
searching per respondent per survey.
We collected data on 92.4 percent across all endline surveys. Attrition is relatively unsystematic: treatment arms had similar response rates (within 0.4 percent of
the control group) while a test of joint significance of all baseline covariates yields
p = 0.328.29
We collected longitudinal qualitative data to better understand the context,
intervention, and mechanisms. First, a Liberian research assistant acted as a
participant-observer during the Phase 1 therapy. Second, we interviewed facilitators for their impressions of the intervention and participants. Third, 3 Liberian
research assistants conducted semiscripted interviews, 14 pretreatment and 130
posttreatment, with 66 men in the sample.30 Interviews covered job satisfaction,
investments, economic challenges, plans, antisocial behaviors, and perceptions of
the interventions.
26In this simple case, there is no role for self-control skills, θ, in aggression. This is a drawback of adapting the
pecuniary crime model, since STYL explicitly teaches men skills to regulate their emotions in charged, automatic
situations. In some sense, then, STYL may not only change the underlying value of σ (the extent of one’s desire
not to engage in criminal activity) but also one’s ability to ensure that expressed actions conform to the underlying
preferences rather than succumbing to immediate temptation or anger. This is functionally equivalent to predictions
associated with a larger σ.
27The exception is the 100 men in the pilot, which had a single short-run survey 3 weeks after grants. Actual
survey times were, on average, 2.2, 5.7, 55.4, and 61.1 weeks after grants. Surveys were 90 minutes long and delivered verbally by enumerators in Liberian English on handheld computers.
28See online Appendix D for measurement details. Average winnings equaled about one-half-day’s wages.
29See online Appendix A.3 for tracking techniques, response rates by survey wave and treatment group, and
correlates of attrition. Of the 298 nonresponses (of 3,896), we (i) had no location information (75 percent); men
were mentally incapacitated (1 percent), died (8 percent, or 9 men), were in prison (12 percent), or refused (3 percent). Covariates associated with higher attrition include better mental health and income.
30Nineteen in control, 16 in therapy, 15 in cash, and 16 in therapy then cash. Sampling was purposeful, based
on variation in key baseline measures: economic success, crime, drug use, and present bias.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1179
A. Key Outcomes and Multiple Comparisons
After observing the pilot results, we decided to focus on five primary outcomes:
two ultimate outcomes—antisocial behavior and economic performance—and
three intermediary outcomes: economic time preferences, self-control skills, and
anticriminal identity/values. The study began before the advent of the social science registry, but we outlined these core hypotheses in a 2012 National Science
Foundation (NSF) proposal 1225697.31 The proposal does not have the level of
detail or precision as a pre-analysis plan, but it does describe our main hypotheses
and approach to measurement in some detail. The results we present hew closely to
the proposal, with only a small number of exceptions.32
Naturally, CBT could influence antisocial behavior through other mechanisms, such
as drug abuse, changed social networks, or mental health. While plausible, these were
not the primary focus of the therapy’s design, and as such we did not specify them as
core hypotheses in the NSF proposal. These secondary intermediary outcomes are
important and relevant, however, and we measured and report on them for this reason.
To organize and reduce the number of hypothesis tests, we combine related measures into mean effects summary indexes.33 We do so for the two ultimate outcomes
of interest plus an index of all intermediary outcomes (primary and secondary).
We classify intermediary outcomes into six families: time preferences, self-control
skills, identity/values, mental health, substance abuse, and social networks. The
first three families were prespecified, while the latter three families (the secondary outcomes) were determined ex post, based on perceived conceptual similarity.
Online Appendix D describes these measurement decisions.
Moreover, the tables present unadjusted standard errors as well as p-values
adjusted for multiple comparisons. We use the Westfall and Young (1993) free
step-down resampling method for the family-wise error rate (FWER), the probability that at least one of the true null hypotheses will be falsely rejected, using
randomization inference.34 Rather than simply adjust for comparisons across the
31See Blattman, Jamison, and Sheridan (2012), where the core hypotheses (and division into ultimate and
intermediary outcomes) are outlined in Sections 1 and 4.1, and the operationalization (and measurement) of key
outcomes in Section 4.4 of the proposal.
32These decisions are described in online Appendix D in detail. First, our final measure of antisocial behavior
(which we called crime and violence in the proposal) does not include political violence, because none occurred
before endline. Second, after the proposal but before data collection, we excluded executive function from our
measure of self-control, since we determined it was unlikely to be affected by the therapy. Third, after the analysis,
we expanded our measure identity and value change to include prosocial behaviors and appearance change, at the
suggestion of referees. These changes have only a modest effect on the results, as outlined in online Appendix E.1.
33We take averages of our outcome measures, coded to point in the same direction, akin to the approach by
Kling, Liebman, and Katz (2007). Note that the outcomes used to create the summary index may themselves be
composites of many survey questions, such as consumption (a composite of many goods) or an aggressive behavior
index (a composite of many types of aggressive behavior, a standard way that psychologists measure aggression).
We do so because it is typically the composite itself rather than its component survey questions that we have theoretical interest or priors. In most cases this is reflected in the survey design, where the survey questions in each composite measure comprise a separate survey section. Also, to create an index by averaging the component variables
would give more weight to outcomes that are typically measured with many different questions (such as aggressive
behavior) versus one that can be precisely measured with a small number of variables (such as drug selling), which
we find inappropriate. Nonetheless, online Appendix E.2 shows robustness to an index that averages all survey
questions rather than composite measures, or uses covariance weighting rather than mean effects.
34Other papers taking this approach include Kling, Liebman, and Katz (2007); Casey, Glennerster, and Miguel
(2012); and Anderson (2008). Using the Westfall-Young bootstrap and the Holm-adjusted Bonferroni methods
yields similar results.1180 THE AMERICAN ECONOMIC REVIEW april 2017
major family outcomes, we also adjust for the fact that we are estimating three
treatment effects (one for each arm). Thus, for our three main family indexes (economic behavior, antisocial behavior, and intermediary mechanisms) we report
p-values adjusted for nine comparisons in total. By reporting both the adjusted and
unadjusted statistical significance, readers can use the threshold appropriate to their
question and preferences. If interested in the specific hypothesis of the effect of CBT
on antisocial behavior, the unadjusted p-value is appropriate. If asking about the
effects of different treatment combinations on mechanisms, as in our NSF proposal,
the conservative adjusted p-values are more appropriate.
IV. Empirical Strategy and Estimation
We estimate intent-to-treat (ITT) effect on outcomes, Y, via the OLS regression
(1) Yij = τ1TherapyOnlyi + τ2CashOnlyi + τ3Cash&Therapyi + Xiλ + γj + εij ,
where TherapyOnly, CashOnly, and Cash&Therapy are indicators for random
assignment to treatment arms: therapy only, cash only, or both therapy and cash.
We control for a vector of baseline characteristics, X, and fixed effects for each of
the j randomization blocks, γj; Yij is the average of the two proximate survey rounds
(e.g., the two- and five-week surveys for short-term effects). To reduce sensitivity
to outliers, we top-code continuous variables at the ninety-ninth percentile. We test
sensitivity to alternative approaches in online Appendix E.2.
Self-Reported Data.—One threat to identification comes from systematic measurement error in self-reported data, especially measurement error correlated with
treatment status. In the absence of administrative data such as arrest records, we
developed a technique to validate select survey variables through intensive observation. Blattman et al. (2016) reports the approach in detail, and we summarize in
Section VI and online Appendix F.
Spillovers.—Another threat to identification comes from spillovers. Our recruiting strategies—working in large neighborhoods, recruiting less than 1 percent of
adult men in those areas, and less than 15 percent of high-risk men we could identify
on the street—were designed to reduce equilibrium effects such as a change in the
returns to illicit work. We do not have the data or research design, however, to confirm that these equilibrium effects were minimized.
Another potential spillover involves interactions within and between treatment
arms, especially therapy. For example, because of peer effects and the emphasis on
social norms, there could be positive spillovers from treating groups of friends. If
so, the coefficients on therapy would overestimate the effect of therapy in isolation.
Alternatively, to the extent that control subjects interact with and learn from treatment subjects, they may acquire some of the lessons, leading us to underestimate
therapy’s impact.
We designed recruitment to minimize such interaction bias, but could not eliminate it. We do not have detailed social network data for the full sample, but we did
trace social networks within the first two therapy groups. On average, each subjectVOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1181
was acquainted with 6 of the 43 others in therapy, and 30 percent reported 1 close
associate in therapy. Given randomization, we can assume similar relationships in
the other arms. Without systematic data on networks we cannot estimate spillovers,
and this is a weakness of our design. The two effects should cancel each other out
somewhat, but the extent is unknown.
Interpretation and Generalizability—Another point is that our sample is not
drawn from a well-defined population. This is a function of the setting—there is
no administrative record of high-risk men in Liberia (or in any low-income or fragile state). We recruited men in a relatively transparent, replicable fashion, but onethird declined to enter the study for reasons we cannot observe. Thus, the treatment
effects we estimate cannot be generalized to a defined population. This is not only
a constraint of the setting, but also the nature of a proof-of-concept trial, where we
have two promising but highly uncertain treatments—unconditional cash and CBT.
Thus, our study is akin to a medical efficacy trial, to determine whether the intervention produces the expected result under favorable circumstances.
V. Results
Figure 1 reports ITT estimates using equation (1) on the two ultimate outcomes
of interest and an index of all intermediary outcomes. Figure 2 reports ITT estimates
for the six intermediary outcome families. Both panels display regular 95 percent
confidence intervals as well as p-values unadjusted and adjusted for multiple comparisons, as outlined in Section IIIA. Figure 1 adjusts for 9 comparisons (3 arms
× 3 outcomes) and Figure 2 adjusts for 18.35
Broadly speaking, cash did not lead to a statistically significant or sustained
reduction in overall antisocial behaviors, but therapy did. In the short run, therapy led to large reductions, by 0.25 standard deviations with therapy alone and
0.31 standard deviations with therapy plus cash. This reduction in antisocial behaviors persisted, however, only when therapy was followed by cash: one year after
therapy, therapy alone led to a 0.08 standard deviation fall in antisocial behaviors
(not statistically significant) compared to a 0.25 standard deviation fall with therapy
plus cash (significant at the 1 percent with unadjusted p-values, and at the 5 percent level if adjusted). This difference between therapy and therapy plus cash after
12– 13 months is significant at the 5 percent level using unadjusted p-values.36
35These are calculated separately for the 2–5 week and 12–13 month surveys. Online Appendix E.1 also displays results if we ignore the ultimate/intermediary and primary/secondary distinctions, and simply adjust p-values
for 3 × 8 = 24 comparisons. In this case, the short-term conclusions are unaffected, but over 12–13 months, the
effect of cash plus therapy on antisocial behaviors has a p-value of 0.106. As seen in online Appendix E.2, these
effects are robust to a variety of specifications and attrition scenarios. We obtain similar results if we: pool the endlines rather than averaging them; construct summary indexes of all underlying survey questions rather than indexes
of the composite measures; or covariance weight rather than weight index components equally. We also show that
the results are robust to conservative attrition scenarios by substituting extreme values for missing outcomes.
36See online Appendix E.3 for formal tests of the difference between both therapy and cash to therapy or cash
alone. Online Appendix E.4 tests whether 2–5-week and 12–13-month impacts are equal. We cannot reject the
hypothesis that the effects of both therapy and cash are equal over time, but we can reject the equality of effects for
therapy alone.1182 THE AMERICAN ECONOMIC REVIEW april 2017
Figure 1. Program Impacts on the Two Ultimate Outcomes and an Index of All Intermediary Outcomes
(z-scores) with 95 Percent Confidence Intervals and Unadjusted/Adjusted p-values
Notes: The figure reports the effect of each treatment arm after 2–5 weeks and 12–13 months with 95 percent confidence intervals and two p-values, one unadjusted and one adjusted for 9 comparisons (3 arms and 3 outcomes)
using the Westfall-Young method. Treatment effects are estimated via OLS controlling for baseline covariates and
block fixed effects. Each summary index is the standardized mean of composite outcomes. Standard errors are
heteroskedastic-robust.
Figure 2. Program Impacts on Families of Intermediary Outcomes (z-scores) with 95 Percent
Confidence Intervals and Unadjusted/Adjusted p-values
Note: Estimated as in Figure 1, except that adjusted p-values account for 18 comparisons (3 arms and 6 outcomes).
Economic
Reduction in
antisocial
behaviors
All intermediary
outcomes
Cash only Therapy only
Impact by treatment arm, standard deviations
Both
−0.2 0 0.2 0.4 0.6 −0.2 0 0.2 0.4 0.6 −0.2 0 0.2 0.4 0.6
p = 0.984 / 1.000
p = 0.385 / 0.391
p = 0.175 / 0.681
p = 0.163 / 0.287
p = 0.985 / 1.000
p < 0.001 / <0.001 p = 0.073 / 0.199
p = 0.533 / 0.904
p < 0.001 /<0.001
p = 0.478 / 0.904
p = 0.005 / 0.026
p = 0.373 / 0.878
p = 0.001 / 0.004
p = 0.005 / 0.037
p < 0.001 / <0.001
p = 0.211 / 0.713 p = 0.003 / 0.028
p = 0.054 / 0.194
2–5 weeks 12–13 weeks
Social network
Reduction in
substance abuse
Self-regard
and mental health
Identity
and values
Self-control
Time
preferences
Cash only Therapy only
Impact by treatment arm, standard deviations
Both
−0.2 0 0.2 0.4 0.6 −0.2 0 0.2 0.4 0.6 −0.2 0 0.2 0.4 0.6
p = 0.632 / 0.992
p = 0.076 / 0.422
p = 0.407 / 0.833
p = 0.305 / 0.952
p = 0.707 / 0.992
p = 0.139 / 0.582
p = 0.426 / 0.962
p = 0.031 / 0.259
p = 0.794 / 0.992
p = 0.159 / 0.595
p = 0.476 / 0.833 p = 0.068 / 0.419
p = 0.303 / 0.952 p = 0.144 / 0.826
p = 0.080 / 0.631
p = 0.699 / 0.992
p = 0.724 / 0.992
p = 0.282 / 0.951
p = 0.018 / 0.178
p = 0.496 / 0.971
p < 0.001 / <0.001
p < 0.001 / 0.004
p = 0.211 / 0.904
p = 0.006 / 0.067
p = 0.011 / 0.154
p = 0.696 / 0.833
p = 0.047 / 0.464
p = 0.001 / 0.019
p = 0.006 / 0.067
p = 0.024 / 0.287
p = 0.335 / 0.952
p = 0.145 / 0.826
p < 0.001 / 0.006
p = 0.222 / 0.681
p = 0.043 / 0.319
p = 0.383 / 0.833
2–5 weeks 12–13 weeksVOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1183
We see a change of similar proportions of the intermediary outcomes in aggregate, and this too is only persistent in the group that received therapy plus cash.37
Individually, most of the intermediary outcomes shift in the expected direction,
and moderate over time. In the short run, only time preferences, mental health, and
social networks are statistically significant using the more conservative adjusted
p-values. After a year, no individual intermediary outcome is significant using the
adjusted p-values, although all (except substance abuse) are pointing in the expected
direction.
A. Antisocial Behaviors
We defined antisocial behaviors as disruptive or harmful acts toward others, such
as crime or aggression. Thus, the family excludes self-harm (e.g., drug abuse) or
acts by peers. Table 2 reports impacts on the components of the antisocial behavior index, for illustrative purposes. The table reports both unadjusted and adjusted
p-values. The adjusted p-values on the antisocial behaviors family index come
directly from Figure 1 (adjusted for the nine comparisons of three arms and three
outcomes). The adjusted p-values on the seven components reflects the 3 × 7 = 21
comparisons across all arms and components.
We constructed the seven component measures from sets of related survey questions, each typically from its own survey module. All are self-reported. In general,
the coefficients on therapy only or therapy plus cash are negative, and a majority are
statistically significant using unadjusted standard errors. We must interpret the individual point estimates with caution, since almost none of the individual components
are significant when we adjust for 24 comparisons, save aggressive behaviors.
•  Drug selling and other crime. In the short run, 17 percent of the control group
said they sold drugs, and they admitted to 2.6 acts of theft in the past two
weeks. A year later, 13.5 percent sold drugs and they reported 1.9 acts of theft.
Crime rates may fall because we are recruiting people in hard times, and there
is regression to the mean. With therapy, however, crime rates fell by almost
50 percent in the short run, and this fall persisted a year with therapy plus cash.
Online Appendix D describes specific crime measures. To give a crude sense of
magnitude, if we extrapolate results to the full year since baseline, therapy plus
cash led men to go from 66 to 40 crimes per person per year (online Appendix
E.6). Given the $530 intervention cost, this is roughly $21 per crime in the first
year, ignoring any ongoing impact on crime or other program benefits.
•  Fights. We also asked about nine types of verbal and physical altercations in
the past two weeks, including the frequency and severity of disputes with peers,
neighbors, leaders, or police. Here, as with all summary indexes in the paper,
37A natural question is whether the therapy is impactful for the most or least antisocial men. Online Appendix E.7
reports ITT regressions where we add an interaction between treatment arms and a standardized index for baseline
antisocial behavior, as well as initial future orientation and self-control. The therapy was impactful for the average
participant, but the greatest decline in antisocial behavior was among those with the highest antisocial behaviors and
the lowest levels of self-control and future orientation. These estimates must be taken with caution, since the heterogeneity analysis was not prespecified. But these were the only heterogeneity analyses run on antisocial behavior.1184 THE AMERICAN ECONOMIC REVIEW april 2017
we use the standardized mean effects of all nine survey questions.38 On average, men reported about one dispute in the past two weeks. None of the effects
are distinguishable from zero, and only the point estimates on therapy and cash
are negative.
38A main reason is because the measurement scales differ across component survey variables and the absolute valuer of the scales themselves are not meaningful (e.g., a frequency scale of 0–3, from never to often). We
standardize individual survey questions, average them, and standardize this composite to have mean zero and unit
standard deviation. Results are robust to alternate weighting and indexing approaches.
Table 2—Program Impacts on Antisocial Behaviors
ITT regression: (N = 947)
Therapy only Cash only Both
Control
p-value p-value p-value
mean ITT SE Unadj. Adj. ITT SE Unadj. Adj. ITT SE Unadj. Adj.
Outcome (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Panel A. 2–5 week impacts
Antisocial behaviors,
z-score
0.151 −0.249 [0.088] 0.005 0.026 −0.079 [0.091] 0.385 0.391 −0.308 [0.089] 0.001 0.004
Usually sells drugs 0.166 −0.077 [0.027] 0.005 0.082 −0.041 [0.029] 0.157 0.803 −0.076 [0.028] 0.006 0.099
# of thefts/robberies
in past 2 weeks
2.577 −0.841 [0.400] 0.036 0.359 −0.770 [0.409] 0.060 0.502 −1.236 [0.407] 0.002 0.045
Disputes and fights in
past 2 weeks, z-score
0.076 0.013 [0.092] 0.888 0.999 0.027 [0.091] 0.768 0.999 −0.132 [0.076] 0.084 0.596
Carries a weapon on
bodya
0.157 −0.086 [0.034] 0.013 0.167 −0.045 [0.037] 0.224 0.888 −0.093 [0.035] 0.007 0.115
Arrested in past
2 weeks
0.139 −0.011 [0.027] 0.674 0.999 0.006 [0.027] 0.819 0.999 −0.013 [0.029] 0.637 0.999
Aggressive behaviors,
z-score
0.102 −0.208 [0.081] 0.011 0.151 0.008 [0.085] 0.928 0.999 −0.196 [0.087] 0.024 0.275
Verbal/physical abuse
of partner, z-scorea
−0.035 −0.087 [0.111] 0.430 0.985 0.091 [0.114] 0.422 0.985 −0.032 [0.115] 0.777 0.999
Panel B. 12–13 month impacts
Antisocial behaviors,
z-score
0.032 −0.083 [0.093] 0.373 0.878 0.132 [0.097] 0.175 0.681 −0.247 [0.088] 0.005 0.037
Usually sells drugs 0.135 −0.034 [0.029] 0.249 0.909 0.035 [0.030] 0.244 0.909 −0.059 [0.029] 0.041 0.474
# of thefts/robberies
in past 2 weeks
1.839 0.073 [0.395] 0.855 0.997 0.352 [0.388] 0.365 0.943 −0.728 [0.363] 0.045 0.482
Disputes and fights in
past 2 weeks, z-score
−0.060 −0.026 [0.091] 0.772 0.997 0.100 [0.090] 0.267 0.909 −0.100 [0.077] 0.194 0.881
Carries a weapon on
bodya
0.148 −0.059 [0.031] 0.061 0.553 0.043 [0.035] 0.215 0.894 −0.066 [0.033] 0.049 0.490
Arrested in past
2 weeks
0.118 −0.006 [0.024] 0.811 0.997 0.007 [0.025] 0.793 0.997 −0.033 [0.024] 0.171 0.863
Aggressive behaviors,
z-score
0.188 −0.153 [0.110] 0.163 0.863 −0.043 [0.107] 0.685 0.997 −0.339 [0.109] 0.002 0.035
Verbal/physical abuse
of partner, z-scorea
−0.071 0.142 [0.100] 0.156 0.863 0.233 [0.113] 0.040 0.474 0.059 [0.104] 0.574 0.992
Notes: The table reports intent-to-treat estimates of the effect of each treatment arm after 2–5 weeks and
12–13 months, controlling for baseline covariates and block fixed effects. We focus on predefined composite measures, typically defined by survey module. For instance, thefts/robberies is the sum of 8 kinds of crimes; disputes/
fights is the standardized mean of 9 kinds of physical or verbal altercations with peers, community, and authorities; aggressive behaviors is the standardized mean of 19 possible types of aggression and hostility; and verbal and
physical abuse of partners is the standardized mean of 3 forms of verbal abuse of intimate partners plus one form
of physical abuse. (For the latter two cases, we report standardized indexes since the incidents are measured on a
0–3 frequency scale, and the absolute sum itself has no interpretation.) The overall summary index is the standardized mean of these seven composite outcomes, standardized. Heteroskedastic robust standard errors are reported
in brackets. Adjusted p-values use the Westfall-Young method to correct for multiple comparisons, as described in
Section IIIA. The overall family index is adjusted for nine comparisons, as in Figure 1. Within each endline round,
the component indexes are adjusted for 21 comparisons, for 3 arms and 7 outcomes. p-values less than 0.05 are
bolded.
aThese variables were not collected during every phase/round, so their regressions have a smaller sample size.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1185
•  Weapons. We asked men if they carried a weapon on their body for protection.
This was typically a knife, as guns were rare. After a year, 15 percent were carrying a weapon, and this fell about by about one-half with either therapy alone
or therapy plus cash.
•  Arrests. Fourteen percent of the control group reported an arrest in the two
weeks before the short-run endline, and 12 percent after a year. We did not see
a statistically significant decline in arrests, though after one year the coefficient on therapy plus cash represented decline of almost one-third, or about one
arrest per year.
•  Aggressive and hostile behaviors. We asked 19 questions about reactive and
proactive aggression, such as the frequency with which they yell, curse, bully
others, cheat, or lose their tempers.39 After a year, the index of all 19 questions
fell 0.15 standard deviations (not significant) with therapy alone and 0.34 with
both (significant at the 5 percent level with multiple comparison adjustment).
•  Intimate partner abuse. We have a crude measure of intimate partner abuse—
three questions on verbal abuse (e.g., cursing and yelling) and one on physical
abuse in the past two weeks. A standardized index of these measures fell little in
the short run with therapy, and after a year the coefficient are actually positive
(the only instance where therapy is positively correlated with violence).
•  Political violence. Given Monrovia’s history of mercenary recruitment, riots,
and election violence, we predicted the men would have opportunities for political violence. Indeed, shortly after the Phase 1 men received therapy, there was
a minor riot in the city.40 From then, however, Liberia entered one of the most
politically quiescent periods in recent history, and so we had no political violence to measure. This is the only prespecified outcome that we could not test
directly.
B. Economic Performance
Table 3 reports program impacts on an index of measures of economic performance: income, homelessness, savings, investment, and employment levels. In the
month after grants, general economic activity increased among those receiving cash
alone (0.66 standard deviations) or cash following therapy (0.58 standard deviations). But after a year the effects in all three arms have approached zero. The same
patterns hold if we look at income alone.41 We measured income in three ways:
(i) consumption in the past two weeks; (ii) estimated earnings in all activities in
the past two weeks; and (iii) an index of durable assets.42 The short-term rise in
39We used nine questions from a standard scale, adapted to Liberian English (Raine et al. 2006), and added ten
more locally relevant acts based on our qualitative interviews.
40The men in all three treatment arms were slightly less likely to participate or sympathize with the rioters, but
with a sample size of just 100 these effects were not significant.
41We focus on an overall index of economic performance to reduce concerns of cherry-picking, and because the
NSF proposal we use as a pre-analysis plan did not single out income as our primary metric.
42To obtain earnings, we first asked each respondent their gross and net earnings in the past four weeks across
25 economic activities (legal and illegal). This earnings measure could still be subject to recall and other biases, and
may inadequately capture home production. Thus, we also use two measure of permanent income. One is an index
of durable assets—a z-score constructed by taking the first principal component of 42 measures of land, housing
quality, and small and large household assets. We also conduct an abbreviated consumption module of short-term
food and nonfood consumption.1186 THE AMERICAN ECONOMIC REVIEW april 2017
consumption is significant at the 1 percent level, and the rise in assets at the 10 percent level, after adjusting for multiple comparisons. An overall index of all three
income measures is significant at the 1 percent level (not shown). Homelessness
also falls significantly as income rises, but there is no decline after a year.
Consumption and assets could rise simply from spending the grant. But this does
not explain the temporary earnings boost. Overall, the cash seems to have been
invested in petty business, and this accounts for the rise in short-run earnings. But
bad shocks, especially theft, meant these gains were fleeting.
To see this, we assessed grant spending in two ways. Using pictures of different
types of spending and plastic chips, we first asked grant recipients to indicate how
they used the grant. Table 4 reports self-reported allocations of the grant by treatment arm. We see little effect of the recent therapy on allocations. Little of the grant
seems to have been spent on drugs, alcohol, gambling, and prostitution. Even if men
underreport these expenses, we see no difference between cash recipients who did
and did not receive therapy.
We can also look at expenditure data, which included a range of business investments in the two weeks prior to the two- and five-week surveys. As reported in
Table 3, those who received only cash reported $56 more investment in each twoweek period. Thus, the total 5-week investment treatment effect is at least $112—just
Table 3—Program Impacts on Economic Performance
ITT regression: (N = 947)
Therapy only Cash only Both
Control
p-values p-values p-values
mean ITT SE Unadj. Adj. ITT SE Unadj. Adj. ITT SE Unadj. Adj.
Outcome (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Panel A. 2–5 week impacts
Economic
performance,
z-score
−0.312 0.155 [0.086] 0.073 0.199 0.658 [0.096] <0.001 <0.001 0.580 [0.096] <0.001 <0.001
Consumption,
past two
weeks, US$
44.394 10.683 [3.812] 0.005 0.066 24.040 [4.199] <0.001 <0.001 20.695 [3.553] <0.001 <0.001
Weekly
earnings
in past two
weeks, US$
14.410 1.351 [1.592] 0.396 0.907 4.128 [1.572] 0.009 0.106 3.036 [1.535] 0.048 0.383
Durable
consumption
assets,
z-score
−0.093 0.124 [0.094] 0.185 0.738 0.138 [0.094] 0.141 0.672 0.240 [0.097] 0.014 0.141
Savings stock
(US$)a
45.957 −1.652 [8.901] 0.853 0.981 16.342 [10.018] 0.103 0.591 18.131 [10.169] 0.075 0.503
Investment
in past two
weeks (US$)
18.444 6.748 [6.291] 0.284 0.849 55.750 [8.410] <0.001 <0.001 48.122 [7.797] <0.001 <0.001
Hours/week
of work in
past month
36.773 1.044 [2.879] 0.717 0.978 7.439 [2.907] 0.011 0.119 1.787 [2.966] 0.547 0.953
Homeless
in past two
weeks
0.202 0.004 [0.029] 0.882 0.981 −0.090 [0.029] 0.002 0.032 −0.100 [0.029] 0.001 0.010
Home robbed
or belongings
stolen in past
month
0.859 −0.011 [0.035] 0.759 −0.009 [0.036] 0.808 −0.012 [0.038] 0.752
(Continued)VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1187
ITT regression: (N = 947)
Therapy only Cash only Both
Control
p-values p-values p-values
mean ITT SE Unadj. Adj. ITT SE Unadj. Adj. ITT SE Unadj. Adj.
Outcome (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Panel B. 12–13 month impacts
Economic
performance,
z-score
−0.031 0.065 [0.104] 0.533 0.904 0.002 [0.101] 0.984 1.000 0.069 [0.098] 0.478 0.904
Consumption,
past two
weeks,US$
47.347 −1.870 [3.808] 0.623 0.999 −2.615 [3.605] 0.468 0.998 −5.005 [3.589] 0.164 0.934
Weekly
earnings
in past two
weeks, US$
17.595 0.764 [1.723] 0.658 0.999 1.341 [1.663] 0.420 0.998 0.436 [1.882] 0.817 0.999
Durable
consumption
assets,
z-scorea
0.009 0.143 [0.096] 0.138 0.904 −0.111 [0.097] 0.251 0.983 0.086 [0.096] 0.369 0.996
Savings stock
(US$)a
51.395 11.360 [11.042] 0.304 0.991 2.026 [10.245] 0.843 0.999 21.744 [11.352] 0.056 0.616
Value of
business
assets
(US$)a
26.121 3.116 [13.431] 0.817 0.999 19.391 [15.276] 0.205 0.964 13.548 [13.007] 0.298 0.991
Hours/week
of work in
past month
34.273 1.030 [2.550] 0.686 0.999 0.681 [2.525] 0.788 0.999 −1.416 [2.493] 0.570 0.999
Homeless
in past two
weeks
0.145 0.018 [0.030] 0.554 0.999 0.016 [0.029] 0.586 0.999 −0.020 [0.030] 0.497 0.999
Home robbed
or belongings
stolen in past
month
0.797 −0.037 [0.043] 0.386 −0.086 [0.042] 0.039 0.013 [0.042] 0.766
Notes: The table reports intent to treat estimates of the effect of each treatment arm controlling for baseline covariates
and block fixed effects. The income summary index is the standardized mean of three composite outcomes (themselves
first standardized). Heteroskedastic robust standard errors are reported in brackets. Adjusted p-values use the WestfallYoung method to correct for multiple comparisons, as described in Section IIIA. The overall family index is adjusted
for nine comparisons, as in Figure 1. Within each endline round, the component indexes are adjusted for 21 comparisons, for 3 arms, and 7 outcomes; p-values less than 0.05 are bolded.
aThese variables were not collected during every phase/round, so their regressions have a smaller sample size.
Table 3—Program Impacts on Economic Performance (Continued)
Table 4—Self-Reported Allocation of the Grant, by Expenditure Category
Treatment
arm
Difference
(N = 475)
Cash and therapy Cash only Coeff. p-value
Expenditure category (1) (2) (3) (4)
Living expenses (such as food, clothing, rent) 28% 26% 0.03 0.23
Durable assets 7% 6% −0.01 0.48
Drugs, alcohol, gambling, and sex 4% 4% 0.00 1.00
Gifts and transfers to others 11% 11% 0.00 0.97
Business investments and expenses 23% 24% −0.03 0.28
Savings and debt payments 20% 20% 0.01 0.56
Own health and education 8% 8% 0.01 0.68
Notes: Using pictures of different types of spending and plastic chips, grant recipients indicated how they used the
grant. Columns 1–2 report the means for each treatment arm. Columns 3–4 report the coefficients and p-values from
an OLS regression of the proportion spent on an indicator for assignment to therapy then cash controlling for block
fixed effects and baseline covariates.1188 THE AMERICAN ECONOMIC REVIEW april 2017
over one-half of the grant (significant at the 1 percent level using adjusted p-values). Meanwhile, the therapy only group resembled the control group in terms of
investment.
These short-run investments did not last. In the cash only group, the stock of
business assets after a year was only $19 greater than in the control group, not statistically significant. We also see no one-year difference in total work hours.43
What happened? From qualitative interviews, insecure property rights were a
major barrier to capital accumulation. A large number of men reported the theft
of all their assets, or all their wares, on a regular basis, by criminals or (for market
wares) the police.44 We added this question to the survey (though not as part of the
performance index). At each survey round, about 70 percent of the men reported a
house robbery and belongings stolen in the past month.45 This implies a robbery
every other month, at least. There is little difference by treatment status, suggesting
that men were not more likely to be targeted if they received cash. But they would
have had more to lose.
C. Indications of Noncognitive Skill and Preference Change
Tables 5 and 6 report treatment effects on our six intermediary outcome families
and their components. For the six family indexes, we report regular standard errors as
well as p-values adjusted for 18 comparisons (3 arms and 6 families), as in Figure 1
above. (Correcting for just the three primary outcomes yields qualitatively similar conclusions.) We also report and discuss the components of each index mainly
for illustrative purposes. We add p-values adjusted for multiple comparisons within
each family, so that a family with 4 components is adjusted for 12 comparisons.
Time Preferences.—We report a summary index of four measures of patience and
four of time inconsistency—akin to δ and β in our model. Specifically, we measured:
a set of incentivized trade-offs between modest amounts of money now versus in
two weeks, and again in two versus four weeks, that allow us to place men in seven
ordered bins of patience and time-inconsistency (for an average payout of $3, about
a day’s wages); a hypothetical (non-incentivized) version of the same trade-offs,
with higher stakes trade-offs; and self-reported assessments of time preferences. All
are described in online Appendix D.3.
In the short term, time preferences become more forward-looking for all treatment arms, though the result is largest (0.32 standard deviations) and statistically
significant only for therapy plus cash (at the 1 percent level even accounting for
multiple comparisons). After a year, the point estimates from therapy are positive—0.15 standard deviations for therapy alone and 0.21 standard deviations for
therapy plus cash. The latter is statistically significant using regular standard errors
43All three treatments caused individuals to substitute from illicit work to nonagricultural low-skill business in
the short term, but the effects were most pronounced and longer lasting for the group that received both cash and
therapy (see online Appendix E.8).
44In some cases this was theft by a friend, family member, or stranger. Also common was confiscation of wares
by the police. Some forms of market selling contravene official rules, often unenforced, but nonetheless giving
police opportunities to confiscate. Some confiscation is legitimate, some not.
45We do not include this outcome in the economic performance index as it’s not a measure of economic performance. Rather we report it in the table mainly for descriptive purposes.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1189
but, like all the family indexes, is not significant after a year after accounting for
multiple comparisons. Looking within the family index, point estimates are larger
and more precise for patience than time inconsistency.
Self-Control Skills.—We measured self-control skills using standard psychometric questionnaires for four constructs that psychologists associate with less
impulsive and more planful behavior.46 First, we looked at nine questions from
the Barrett Impulsiveness Scale (Spinella 2007), which assesses one’s inability to
46In addition to these four psychological scales, we also conducted tests of executive function—cognitive processes associated with inhibitory control, working memory, self-regulation, and planning, such as digit recall (see
online Appendix D.6). We did not hypothesize a change in executive function because these are thought to be abilities that solidify in early childhood (online Appendix D.8). As expected, there is no statistically significant change
from treatment. This is one instance where the NSF proposal says otherwise, but our assessment of the literature
changed between that proposal and data collection. Hence, we exclude executive function from the index. Including
it would not materially change our conclusions, since the self-control measure performs weakest of all the noncognitive skills. Online Appendix D.8 reports these robustness checks.
Table 5—Program Impacts on Time Preferences, Self Control Skills, and Identity/Values
ITT regression: (N = 947)
Therapy only Cash only Both
Control
p-value p-value p-value
mean ITT SE Unadj. Adj. ITT SE Unadj. Adj. ITT SE Unadj. Adj.
Outcome (z-score) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Panel A. 2–5 weeks
Forward-looking time
preferences
−0.202 0.179 [0.098] 0.068 0.419 0.071 [0.099] 0.476 0.833 0.318 [0.099] 0.001 0.019
Patience −0.093 0.187 [0.073] 0.010 0.046 0.116 [0.073] 0.114 0.280 0.267 [0.074] <0.001 0.002
Time inconsistency 0.008 −0.063 [0.074] 0.393 0.596 −0.009 [0.076] 0.903 0.902 −0.138 [0.075] 0.067 0.209
Self-control skills −0.037 0.085 [0.098] 0.383 0.833 −0.147 [0.104] 0.159 0.595 0.037 [0.096] 0.696 0.833
Impulsiveness −0.010 −0.011 [0.101] 0.915 0.995 0.180 [0.108] 0.094 0.619 0.104 [0.095] 0.275 0.919
Conscientiousness −0.077 0.109 [0.105] 0.301 0.919 0.046 [0.106] 0.664 0.995 0.163 [0.105] 0.120 0.684
Perseverance/GRIT −0.035 0.027 [0.099] 0.785 0.995 −0.130 [0.105] 0.217 0.873 0.042 [0.104] 0.686 0.995
Reward responsiveness −0.010 −0.071 [0.106] 0.503 0.980 0.107 [0.107] 0.321 0.919 0.013 [0.105] 0.901 0.995
Identity and values −0.169 0.192 [0.095] 0.043 0.319 0.199 [0.092] 0.031 0.259 0.268 [0.097] 0.006 0.067
Attitudes toward use
of violence
0.100 −0.206 [0.094] 0.028 0.123 −0.187 [0.096] 0.051 0.176 −0.180 [0.097] 0.065 0.176
Index of appearance −0.118 0.085 [0.081] 0.295 0.298 0.131 [0.081] 0.105 0.189 0.203 [0.080] 0.011 0.062
Panel B. 12–13 months
Forward-looking time
preferences
−0.149 0.149 [0.102] 0.144 0.826 0.105 [0.102] 0.303 0.952 0.209 [0.105] 0.047 0.464
Patience −0.240 0.170 [0.103] 0.097 0.350 0.145 [0.096] 0.132 0.386 0.258 [0.099] 0.009 0.048
Time inconsistency 0.129 −0.072 [0.083] 0.386 0.712 0.018 [0.087] 0.833 0.836 −0.059 [0.084] 0.480 0.712
Self-control skills −0.070 0.159 [0.090] 0.080 0.631 −0.025 [0.095] 0.794 0.992 0.244 [0.095] 0.011 0.154
Impulsiveness 0.082 −0.178 [0.096] 0.064 0.431 0.006 [0.098] 0.951 0.961 −0.212 [0.099] 0.032 0.265
Conscientiousness 0.018 −0.065 [0.097] 0.506 0.961 −0.028 [0.100] 0.779 0.961 0.044 [0.097] 0.648 0.961
Perseverance/GRIT −0.037 0.116 [0.099] 0.241 0.851 0.057 [0.099] 0.565 0.961 0.105 [0.103] 0.311 0.903
Reward responsiveness 0.072 −0.165 [0.102] 0.106 0.580 0.084 [0.100] 0.397 0.938 −0.242 [0.102] 0.018 0.177
Identity and values −0.021 0.013 [0.089] 0.882 0.992 −0.101 [0.089] 0.255 0.962 0.034 [0.090] 0.704 0.904
Anticriminal/
antiviolent values
0.070 −0.076 [0.088] 0.386 0.880 0.026 [0.088] 0.768 0.948 −0.177 [0.086] 0.040 0.279
Index of appearance 0.016 −0.102 [0.078] 0.191 0.750 −0.085 [0.077] 0.269 0.813 −0.109 [0.082] 0.182 0.750
Prosocial behavior 0.018 0.041 [0.088] 0.636 0.948 −0.075 [0.085] 0.378 0.880 −0.017 [0.090] 0.850 0.948
Notes: The table reports intent to treat estimates of the effect of each treatment arm controlling for baseline covariates and block fixed effects. We focus on predefined composite measures, typically defined by survey module.
The overall summary indexes are the standardized mean of its composite outcomes, standardized. Heteroskedastic
robust standard errors are reported in brackets. Adjusted p-values use the Westfall-Young method to correct for
multiple comparisons, as described in Section IIIA. The overall family index is adjusted for 18 comparisons, as in
Figure 2. Within each endline round, the component indexes are adjusted for three arms and the number of components within the family; p-values less than 0.05 are bolded.1190 THE AMERICAN ECONOMIC REVIEW april 2017
control thoughts and actions.47 Second, we used eight questions from the NEO-five
factor personality inventory to assess conscientiousness (Costa and McCrae 1997).
Topics included following societal rules and controlled, careful behavior. Third, we
took seven questions on perseverance from the GRIT scale (Duckworth and Quinn
2009), which captures the ability to press on in the face of difficulty. Finally, we
selected eight questions on reward responsiveness—whether they are motivated
47Examples include “I buy things on impulse” or “I say things without thinking.”
Table 6—Impacts on Mental Health, Substance Abuse, and Quality of Social Networks
ITT regression: (N = 947)
Therapy only Cash only Both
Control
p-value p-value p-value
mean ITT SE Unadj. Adj. ITT SE Unadj. Adj. ITT SE Unadj. Adj.
Outcome, z-score (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Panel A. 2–5 weeks
Positive
self-regard/
mental health
−0.168 0.111 [0.091] 0.222 0.681 0.137 [0.093] 0.139 0.582 0.343 [0.094] <0.001 0.004
Positive
self-regard
−0.179 0.121 [0.089] 0.178 0.419 0.151 [0.095] 0.111 0.338 0.346 [0.094] <0.001 0.001
Depression
and distress
0.095 −0.022 [0.099] 0.827 0.827 −0.063 [0.104] 0.546 0.766 −0.210 [0.101] 0.039 0.155
Substance abuse
(0–3 index)
1.378 −0.249 [0.070] <0.001 0.006 −0.060 [0.072] 0.407 0.833 −0.197 [0.071] 0.006 0.067
Usually drinks 0.671 −0.107 [0.041] 0.009 0.062 −0.023 [0.041] 0.575 0.928 −0.030 [0.040] 0.444 0.928
Usually uses
marijuana
0.495 −0.111 [0.037] 0.003 0.022 −0.017 [0.036] 0.640 0.928 −0.141 [0.036] <0.001 0.001
Usually takes
hard drugs
0.212 −0.030 [0.027] 0.271 0.815 −0.020 [0.030] 0.496 0.928 −0.024 [0.030] 0.438 0.928
Quality of social
network
−0.241 0.147 [0.062] 0.018 0.178 0.109 [0.061] 0.076 0.422 0.325 [0.062] <0.001 <0.001
Peer quality −0.160 0.207 [0.091] 0.023 0.190 0.014 [0.095] 0.881 0.996 0.235 [0.094] 0.013 0.120
Quality of family
relations
−0.192 0.106 [0.099] 0.286 0.874 0.131 [0.105] 0.212 0.812 0.307 [0.099] 0.002 0.024
Ex-commander
ties
−0.141 −0.011 [0.038] 0.776 0.996 0.013 [0.038] 0.730 0.996 −0.038 [0.038] 0.321 0.874
“Big men” ties −0.012 0.015 [0.104] 0.889 0.996 0.039 [0.107] 0.714 0.996 0.172 [0.107] 0.109 0.596
Panel B. 12–13 months
Positive
self-regard/
mental health
−0.050 0.032 [0.091] 0.724 0.992 −0.034 [0.091] 0.707 0.992 0.205 [0.090] 0.024 0.287
Positive
self-regard
−0.054 0.029 [0.093] 0.754 0.987 −0.035 [0.092] 0.709 0.987 0.222 [0.090] 0.014 0.072
Depression
and distress
0.022 −0.031 [0.093] 0.741 0.987 0.021 [0.094] 0.824 0.987 −0.113 [0.096] 0.238 0.676
Substance abuse
(0–3 index)
1.091 −0.065 [0.061] 0.282 0.951 0.063 [0.062] 0.305 0.952 −0.057 [0.060] 0.335 0.952
Usually drinks 0.766 −0.063 [0.042] 0.135 0.631 −0.047 [0.042] 0.267 0.813 −0.045 [0.043] 0.288 0.813
Usually uses
marijuana
0.490 −0.024 [0.035] 0.502 0.924 0.018 [0.035] 0.610 0.935 −0.042 [0.036] 0.238 0.812
Usually takes
hard drugs
0.196 −0.005 [0.031] 0.864 0.973 0.079 [0.033] 0.016 0.123 0.006 [0.031] 0.849 0.973
Quality of social
network
0.066 0.063 [0.092] 0.496 0.971 −0.044 [0.092] 0.632 0.992 0.139 [0.095] 0.145 0.826
Peer quality 0.040 0.011 [0.088] 0.898 0.999 −0.070 [0.089] 0.430 0.982 0.017 [0.090] 0.852 0.999
Quality of family
relations
−0.019 0.124 [0.099] 0.213 0.862 0.070 [0.100] 0.485 0.986 0.129 [0.097] 0.185 0.841
Ex-commander
ties
0.176 0.004 [0.076] 0.953 0.999 0.026 [0.078] 0.740 0.998 −0.139 [0.074] 0.060 0.458
“Big men” ties 0.120 0.001 [0.155] 0.997 0.999 −0.130 [0.152] 0.395 0.979 0.071 [0.160] 0.657 0.998
Note: See Table 5.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1191
by immediate, typically emotional rewards—from the Behavioral Inhibition/
Behavioral Activation Scale.48 We adapted the scales and questions to the context
and Liberian English. Online Appendix D lists all questions.
In the short term, respondents reported little change in self-control skills. In fact,
self-control was the only one of the six families that did not show a statistically
significant increase in the short run, using unadjusted p-values. In contrast, after a
year, both therapy and therapy plus cash are associated with increased self-control
of 0.16 and 0.24 standard deviations. The latter is statistically significant at the 5
percent level with regular standard errors but not with the adjustment for multiple
comparisons. Looking at the components of the self-control index, all point in the
expected direction, though the magnitudes and precision are greatest with impulsivity and reward responsiveness.
Time preferences enter into our theoretical model differently than self-control,
but an obvious question is whether they are distinct. The correlation between the
self-control and time preference summary indices is 0.33, significant at the 1 percent level. If we combine the time preference and self-control measures into a single
summary index, therapy alone and therapy plus cash both have statistically significant positive impacts of roughly 0.22 standard deviations after 2–5 weeks and 0.26
after 12–13 months (online Appendix D.8).
We must be cautious because all self-control scales are self-reported, and treated
men could simply be repeating back their lessons. There is some evidence this is not
so. We divide the 32 self-control questions into two indexes: questions with high
(44 percent) and low (56 percent) emphasis in the curriculum.49 Table 7 reports the
ITT estimates after a year. The effect of cash and therapy is at least as large for low
emphasis items.
Anticriminal Identity/Values.—Social identity and values are not straightforward
to measure, and we are not aware of existing models. Based on our qualitative work,
we developed three main measures, which we assemble into a single family index.
First, we attempted to measure values directly, using a set of 33 self-reported attitudes
toward the appropriate use crime and violence in the men’s own lives—indicators
of the degree to which they had internalized mainstream social norms.50 Second, at
the one-year endline we measured an index of prosocial behaviors, including group
memberships, group and community leadership, and contributions to local public
48Examples include “I will often do things for no other reason than that they might be fun” or “When I see an
opportunity for something I like I get excited right away.” Previous research has linked disruptions in and extremes
of reward motivation to substance abuse (Robinson and Berridge 2000).
49We rated each index component on a scale of 0 (not emphasized) to 4 (very emphasized). We then defined
low-emphasis components as those rated 0 or 1 and high emphasis components as those rated 2 or above. These
results are unchanged for using 1.5 or 2 as the emphasis cutoff.
50Our approach drew on the way social psychologists distinguish between social norms versus attitudes, but we
focus on the attitudes alone. Social norms capture what people think others do or should do in a particular situation,
and surveys sometimes then ask the same respondents what they believe is appropriate behavior (i.e., a norm deviation). Our questions are akin to these attitudinal questions. (We did not measure perceived norms because we did
not have the space, nor did we deem the measure a priority.) Specifically, we asked 11 questions on attitudes to the
use of violence to solve community or personal problems, such as mob killings of suspected thieves, or attacking
their unfaithful wife’s lover. We also asked 12 questions about their attitude toward participating in crime, including whether they would feel fine taking unwatched goods or stealing $100 from someone’s pocket. We also asked
about six hypothetical forms of political violence, including whether they discuss protesting with friends or making
trouble or conflict with the authorities.1192 THE AMERICAN ECONOMIC REVIEW april 2017
goods. (These are more of a behavior than a skill or preferences, but we hypothesized that it would be a reasonable proxy, allowing us to infer preferences from
behaviors.) Finally, the therapy encouraged men to change their appearance as part
of the identity change, and we asked survey enumerators to record their subjective
impressions: quality of dress, shoes, cleanliness, and smell.51
In the short run, the family index improves by roughly 0.2 standard deviations
in all treatment arms, significant at the 5 percent level with unadjusted p-values but
not with adjusted p-values. With therapy followed by cash, the identity/values index
rises 0.27 standard deviations, with p = 0.067 with our conservative adjustment.
After a year, these treatment effects attenuate somewhat, particularly the change in
appearance (which reverses in sign), and so the change is not statistically significant
at conventional levels. Among the three components, however, the largest change
appears to be in self-reported anticriminal and antiviolent values, and the impacts
are sustained after a year in magnitude (though not statistically significant).52
Positive Self-Regard/Mental Health.—Half our mental health family index is
positive self-regard. Poor self-regard has been linked with many aspects of negative behavior and counterproductive or extreme risk-seeking behavior (Coopersmith
1967). Some research (e.g., Judge et al. 2002) suggests self-regard is captured by
an interrelated set of psychological scales, including: (i) neuroticism, the tendency
to experience emotional instability or anxiety, assessed with eight questions from
the NEO-5 factor personality inventory (Costa and McCrae 1997); (ii) self-esteem,
assessed with eight questions such as, “I am able to do things as well as most other
people” or “I take a positive attitude toward myself”; and (iii) locus of control, the
51Unlike the other two measures, we did not prespecify appearance as a reflection of identity and values in the
NSF proposal. But it is difficult to see where else it belongs, and at the suggestion of referees we include it in this
family.
52As with self-control, we divide the 29 value questions into 2 indexes by high and low emphasis in the curriculum. Table 7 reports the ITT estimates after a year. The effect of cash and therapy is at least as low for the low
emphasis components.
Table 7—Program Impacts on Noncognitive Skills and Values According to Their Coverage
in the STYL Curriculum, 12–13 Month Only
ITT regression: (N = 947 subjects)
Control Therapy only Cash only Both
Outcome (number of question in index), mean ITT SE ITT SE ITT SE
z-score (1) (2) (3) (4) (5) (6) (7)
Summary index of self-control skills
Topics emphasized in curriculum (16) −0.092 0.169 [0.090] 0.026 [0.091] 0.170 [0.093]
Topics not emphasized in curriculum (16) −0.024 0.054 [0.098] −0.070 [0.101] 0.232 [0.099]
Summary index of anticriminal/
antiviolent valuesa
Topics emphasized in curriculum (8) 0.041 −0.085 [0.107] 0.012 [0.110] −0.116 [0.108]
Topics not emphasized in curriculum (21) 0.070 −0.062 [0.087] 0.028 [0.087] −0.184 [0.086]
Notes: The table reports intent to treat estimates of the effect of each treatment arm controlling for baseline covariates and block fixed effects. We have subdivided the summary indexes reported in Table 5 by their coverage of the
specific topics in the STYL curriculum.
aThese variables were not collected during every phase/round, so their regressions have a smaller sample size.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1193
extent to which individuals believe they versus fate control events affecting them,
measured using eight questions from a standard questionnaire (Sapp and Harrod
1993). Arguably related to positive self-regard, we also collected a classic happiness
measure, asking men to rank their subjective well-being in absolute terms and relative to others in their community.53
A second element of the mental health index is depression and distress. We
assessed 6 symptoms of depression and 12 symptoms of posttraumatic stress,
based on a locally adapted instrument used previously with ex-combatant populations in Liberia (Blattman and Annan 2016). We group this with positive
self-regard as a mental health family in the interests of minimizing the number of
families.
We treat positive self-regard/mental health as distinct from the anticriminal identity and values because, in principle, a positive self-image and criminal/outcast
social identity are compatible. This simply happens to be uncommon in Liberia,
where there is typically little social esteem associated with outcast/criminal social
category. Moreover, the main purpose of the anticriminal measure was not to capture the quality of the men’s self image, but rather their change in values, as illustrated in the theoretical model.
In the short run, a family index of these measures rises 0.34 standard deviations
from therapy plus cash (significant at the 1 percent level using multiple comparisons). After a year, these treatment effects attenuate and are significant with unadjusted p-values only. Examining components suggests that the effects are driven
most of all by the subjective well-being measure and self-esteem, although this does
not stand up to adjusted p-values.
Substance Abuse.—Therapy tried to equip participants with strategies to cut back
substance abuse, and while an obvious outcome of interest, it was not one we specified in advance of primary interest for two reasons: the program’s overwhelming
focus on antisocial behaviors, with drug use seen by NEPI as only a modest factor;
and the fact that systematic reviews of CBT do not find support for its effectiveness treating substance use disorder (Hofmann et al. 2012). Nonetheless, if the therapy decreased substance use, we could see both economic and antisocial behavior
change.
In the short run, reports of daily use of in the control group are 67 percent for
alcohol, 50 percent for marijuana, and 21 percent for hard drugs. An index of all
three indicators (0–3) fell 0.20, significant at the 10 percent level using adjusted
p-values. At the one-year endline, reports of daily use of in the control group are
roughly similar. An index of all three indicators (0–3) fell only 0.06 after a year as a
result of therapy and cash (not statistically significant).
Quality of Social Networks.—Finally, we also assessed risky social networks.54
We did not prespecify a change here, but over the course of the qualitative interviews,
53We asked about well-being, health, wealth, and power in absolute terms. We asked about wealth, respect,
power, and access to services in relative terms. Each used a picture of a ladder with ten rungs. The summary index
is the average of each ladder. Patterns are broadly similar across all ladders.
54In some settings, neighborhood changes would also indicate a change in risky behavior, but not in Liberia.
Most high-crime neighborhoods in Monrovia are mixed-income residential and market areas where high-risk men1194 THE AMERICAN ECONOMIC REVIEW april 2017
respondents repeatedly talked about changing peer groups to avoid crime, violence,
and drugs. We thus measured the traits, positive and negative, of men’s five closest peers.55 We also asked about closeness to and support received from family
members, former rebel commanders, and “big men” (intended to connote a criminal
boss). A summary index of positive social networks increased in the short run by
0.15 standard deviations with therapy and 0.33 standard deviations with therapy
plus cash (the latter significant at the 1 percent level with adjusted p-values). After
a year, the point estimates remain positive, but are about one-half as large and not
statistically significant.
D. Insights from Qualitative Interviews and Observation
One of the strongest impressions we gained from interviews was the importance
men attached to identity change, or what NEPI called transformation. Nearly all
of the subjects described feeling ostracized at baseline, and many reported that the
therapy pushed them to believe they could be someone better for the first time.
The facilitators played an important role here. The participants we interviewed
unanimously had admiration and praise for the facilitators, highlighting that their
backgrounds demanded respect and provided credibility, while their personal stories of change were encouraging. Beyond modeling the change in social identity,
men reported the facilitators were also sometimes the first people to treat them with
seriousness and respect, and this built their confidence to reintroduce themselves to
community members, or to expose themselves to banks and shops.
Attempts to behave normally, especially the exposure to new social situations,
seemed to reinforce skill and identity change. Many of the men failed in their plans,
or experienced stigma in their shop or bank visits. In group sessions, men discussed
what went wrong and why (such as poor decisions, or choice of dress). Men with
setbacks learned from and were encouraged by the positive experiences of others.
And facilitators sometimes observed men’s homework attempts and coached them
through difficulties.
Men’s appearance also transformed during therapy. The first day men arrived with
long or messy hair, facial hair, dirty or ripped clothing, wearing t-shirts with shorts
and sandals. Their demeanor was tough, and their appearance signaled outcast status. Haircuts were offered in week two, and many men took advantage, symbolizing
the change. Others showed up beforehand having gotten a haircut on their own.
Similarly, before the unit on hygiene, some men began arriving in pants, shoes, and
collared shirts. Typically a few men in each group resisted these changes. But seeing
the positive experiences of others, they too began to arrive more clean cut, trying out
the new identity. The survey results confirm a short-term change in appearance. The
absence of 12–13-month change is puzzling.
are a small minority. They live on the margins, often in abandoned areas within these neighborhoods (building sites,
forested groves, garbage dumps, and abandoned buildings). Men who turn their lives around do not need to move
neighborhoods, but rather stay where they are but move in different, more mainstream social circles, avoiding highrisk hangouts. Thus, we do not report on neighborhood movements.
55We ask men who their five closest peers are, by name, and then ask whether they hold any of 20 qualities
ranging from positive (they work hard, save, go to school) or negative (they steal, do drugs, get in fights).VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1195
A year later, therapy participants also described applying skills of self-regulation
in their lives. To avoid fights, they used new tactics: removing themselves from
emotionally charged situations, allowing space to process their feelings, and ignoring negative automatic thoughts in the favor of more controlled thinking. Related
were improved social and communication skills. Interviewees described how such
skills allowed them to engage with community members or in disputes and express
themselves without anger or violence.
Not only did the community regard them differently, many said, but troubled
young men began coming to them for advice and lessons learned from the therapy
once they saw the sudden and sustained change—another important source of reinforcement, and perhaps one reason we do not see a change in peer quality in the data.
VI. Can We Believe Our Self-Reported Data?
Self-reported data raise several worries, the most serious being measurement
error correlated with treatment. For instance, men who receive an antiviolence intervention might be more likely to tell us they are nonviolent, overestimating the estimated treatment effect of therapy.
This kind of bias is hard to square with the patterns of effects we observed.
Therapy followed by cash would have to induce systematic errors where therapy or
cash alone did not. Nonetheless, this is possible—for example if the largest misreporting were associated with larger past benefits. Thus, concerned that our survey
measure, y s, may be biased, we set out to intensively validate some measures, yv.
If yv is closer to the true behavior, y∗, this allows us to estimate the degree and direction of bias. We summarize the approach, empirical strategy, and results here, with
details in online Appendix F and Blattman et al. (2016).
A. Approach to Validation
Of more than 4,000 endline surveys, we randomly selected 7.3 percent and
retested answers to six survey-based measures with two-week recall periods. We
chose four potentially sensitive behaviors—marijuana use, thievery, gambling, and
homelessness. We also chose two everyday expenditures that could be subject to
recall bias or other error—paying to watch television in a video club, and paying
to charge a mobile phone. We chose these six because we wanted a diverse set of
behaviors with similar recall periods. We also wanted very specific behaviors (e.g.,
stealing rather than any crime, or marijuana rather than substance abuse). Finally,
we wanted outcomes that were a primary focus of the treatment (e.g., stealing) and
others that were not (gambling or expenditures).
We used intense qualitative work—in-depth participant observation, open-ended
questioning, and efforts to build relationships and trust—to try to elicit more truthful
answers. Over several days of trust-building and conversation, plus direct observation, we tried to elicit a direct admission or discussion of the behavior.
We selected and trained eight of the study’s most talented qualitative research
staff as validators, all Liberians. In the ten days following the survey, a validator
visited the respondent over four days, spending several hours each day in conversation and observation. Validators shadowed respondents as they went about their day,1196 THE AMERICAN ECONOMIC REVIEW april 2017
rather than conduct formal interviews. They raised target topics through indirect
questions while chatting.
Validators developed techniques to foster trusting relationships and to build rapport: becoming close to street leaders; eating meals with subjects; sharing personal
information (including similar acts they or their friends engaged in); and mirroring participants’ appearance and vernacular as appropriate. Validators would also
observe the respondent’s behavior from afar, as well as converse with peers and
family. The goal was to attain insider status, and thus reduce the chance of misreporting. The premise was that time, a focus on a small number of behaviors, and
trust/rapport building would mean that respondents were less willing, or feel less
able, to deceive a more familiar person, who also knows them better. Validators also
had the opportunity to clear up misunderstandings and get a more accurate assessment of the behaviors. By discussing sensitive behaviors openly, relating their own
experiences and that of friends, validators sought to dispel any notion that certain
answers are more desirable, or would result in any strategic gains.
Without knowing the respondent’s survey response, y s, the validators coded an
indicator of whether or not the respondent engaged in the behaviors in the two weeks
prior to the survey, yv. The authors reviewed the evidence and the coding for every
case. In general, we used a relatively high standard of evidence, only coding yv = 1
for a direct admission of the behavior or persuasive statements that they did not
engage in the behavior.56
If this technique simply reproduced the errors in the survey data, then the validation is little help. The key assumption is that four days of building trust and
gathering extensive information, regarding just six behaviors, reduced experimenter
demand and other biases correlated with treatment compared to responses during a
300-question, 90-minute questionnaire.
Nonetheless, yv is not free from error. Online Appendix F.1 reviews our approach
and its limitations in more detail. Many of these limitations—the requirement of a
direct admission, the disruption in people’s lives, errors in recall periods, or increased
social desirability bias from scrutiny—undoubtedly led to systematic errors in yv.
These errors, however, are not necessarily correlated with treatment. This is possible, for example, because validators could have learned men’s treatment status in
conversation, and this could have biased their coding. Nonetheless, we designed the
trust-building and evidentiary standards to minimize this risk.
B. Survey-Validation Differences
Of the 297 men we selected for validation, we found and validated 240 (81 percent).57 Table 8 reports the means of y s and yv in the full sample and each treatment arm, as well as the percentage of times the two measures agree; y s and yv are
56The validators only witnessed or received third-party evidence of the behavior in one-fifth of cases, but neither was considered sufficient evidence for a final coding. Both had to be followed by questions confirming that the
respondent also engaged in the behavior in the two weeks prior to the survey. In general, we used a relatively high
standard of evidence, only coding yv = 1 if the validator directly observed the behavior or the respondent directly
admitted it.
57Attrition was higher than the survey as we could not validate the behaviors of men who migrated across the
country. Attrition was not correlated with treatment or baseline covariates (Blattman et al. 2016).VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1197
identical about 80 percent of the time for sensitive measures and about 70 percent
of the time for expenditures. As expected, however, y_s < y_v: the average person
reported 1.21 sensitive behaviors and 1.09 expenditures in validation, and 1.12 sensitive behaviors and 0.82 expenditures in the survey.
With this sample, only the underreporting of expenditures is statistically significant. We report t-tests of the simple difference, yis − yiv, in online Appendix F.2.1,
as well as a discussion of patterns of under- and overreporting. Expenditure underreporting appears to be largest in the control group, possibly because they are trying
to appear more needy. Among sensitive behaviors, underreporting is generally less
than 10 percent of the survey means, and is only statistically significant in the case
Table 8—Comparison of Survey and Qualitative Validation Means at Endline
Potentially sensitive behaviors Expenditures
All
(0–4) Steal Marijuana Gamble Homeless
All
(0–2) Video Phone
All
(0–6)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Panel A. Full sample
Survey mean 1.12 0.22 0.48 0.18 0.23 0.82 0.42 0.39 1.93
(y s) (1.14) (0.42) (0.50) (0.39) (0.42) (0.73) (0.50) (0.49) (1.31)
Validation mean 1.21 0.20 0.51 0.29 0.21 1.09 0.61 0.48 2.30
(yv) (1.18) (0.40) (0.50) (0.45) (0.41) (0.74) (0.49) (0.50) (1.21)
Percent in agreement 79 85 72 82 62 82
Panel B. Control group
Survey mean 1.25 0.27 0.48 0.23 0.27 0.68 0.37 0.32 1.93
(y s) (1.31) (0.45) (0.50) (0.43) (0.45) (0.70) (0.49) (0.47) (1.44)
Validation mean 1.30 0.23 0.49 0.34 0.23 1.18 0.65 0.54 2.48
(yv) (1.23) (0.42) (0.50) (0.48) (0.42) (0.70) (0.48) (0.50) (1.21)
Percent in agreement 80 88 72 77 47 75
Panel C. Therapy only
Survey mean 1.06 0.19 0.48 0.17 0.22 0.81 0.41 0.41 1.87
(y s) (1.11) (0.39) (0.50) (0.38) (0.42) (0.75) (0.50) (0.50) (1.35)
Validation mean 1.09 0.17 0.48 0.24 0.20 0.98 0.54 0.44 2.07
(yv) (1.14) (0.38) (0.50) (0.43) (0.41) (0.76) (0.50) (0.50) (1.24)
Percent in agreement 80 89 74 80 72 81
Panel D. Cash only
Survey mean 1.03 0.21 0.49 0.13 0.21 0.77 0.37 0.40 1.81
(y s) (1.16) (0.41) (0.50) (0.34) (0.41) (0.71) (0.49) (0.49) (1.35)
Validation mean 1.32 0.23 0.53 0.33 0.24 1.00 0.55 0.45 2.32
(yv) (1.26) (0.42) (0.50) (0.47) (0.43) (0.81) (0.50) (0.50) (1.33)
Percent in agreement 76 82 74 90 56 85
Panel E. Therapy + cash
Survey mean 1.13 0.22 0.48 0.21 0.22 0.98 0.54 0.44 2.11
(y s) (0.98) (0.42) (0.50) (0.41) (0.42) (0.73) (0.50) (0.50) (1.11)
Validation mean 1.11 0.19 0.52 0.24 0.16 1.17 0.70 0.48 2.29
(yv) (1.11) (0.40) (0.50) (0.43) (0.37) (0.68) (0.46) (0.50) (1.05)
Percent in agreement 81 83 68 81 71 87
Observations 239 238 238 238 239 239 238 239 239
Notes: The table reports the means (standard deviations) of the survey and the qualitatively validated measures for
the full sample and by treatment arm. Percent in agreement is the percentage of respondents for whom the survey
indicator equals the qualitatively validated indicator.1198 THE AMERICAN ECONOMIC REVIEW april 2017
of gambling. This is mainly driven by the cash only arm, who may have been reluctant to report spending the grant this way.
C. Is Measurement Error Correlated with Treatment?
Empirical Strategy.—If we believe that the validation measure is closer to the
true behavior, then one way to test for bias in the survey-based treatment effects
is to take the difference yis − yiv, our proxy of measurement error for person i, and
regress it on treatment:
(2) yis − yiv = β0 + β1 Ti + μi .
If β1 < 0 for sensitive measures, then treated men were more likely to underreport
bad behaviors, and our survey-based treatment effects may overestimate the decline
in antisocial behaviors. And if β1 > 0 for expenditures, then treated men may have
overreported their expenditures more than the control group, and our survey-based
treatment effects may overestimate the short-run increase in income.
With a sample of 240, we estimate we are powered to detect average under- or
overreporting of at least 14 percent, and error correlated with treatment of 28 percent.58 Because of power concerns, we pay close attention to the sign, magnitude,
and confidence interval for β1.
Of course, the crucial assumption is that yv is closer to the true behavior. This
parallels the no liars and no design effects assumptions in list experiments. The
assumption cannot be tested directly, but can only be argued on context and the
quality of the approach.
We can also let misreporting vary by whether validation confirmed the behavior:
(3) yis = β̃0 + β̃1 Ti + β̃2yiv + β̃3(yiv × Ti) + μ̃i.
Equation (2) is simply the special case where β̃2 = 1 and β̃3 = 0.59 We are mainly
interested in whether β̃1 ≠ 0 and β̃1 + β̃3 ≠ 0. The disadvantage of this more
flexible form is statistical power, especially with three treatment arms.60 We are also
interested in correcting for the average bias in survey-based treatment effects, which
we get from β1 from equation (2). But the more flexible form provides insight into
58Our target sample of 297 was the maximum number of interviews we felt qualified validators could manage
logistically. We calculated minimum detectable effects (MDE) using a two-sided hypothesis test with 80 percent
power at a 0.05 significance level, using baseline and block controls when calculating the R2 statistic. We calculated
an MDE for both the 0–2 expenditures index and the 0–4 sensitive behaviors index. The expenditures index had a
mean of 0.82 in the survey and an MDE of 0.13 for general over- and underreporting, and 0.29 for a treatment effect
on misreporting. The sensitive behaviors index had a mean of 1.12 in the survey and an MDE of 0.2 for general
over- and underreporting, and 0.36 for any treatment effect on misreporting. We estimate that doubling the sample
size would have increased power by about one-third.
59Online Appendix F.1 derives and interprets these regressions in more detail.
60With 240 observations in total, each parameter is estimated off of roughly 30 observations, putting us on a
steep part of the power curve.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1199
the patterns of measurement error. For instance, if underreporting is concentrated
among men who commit crimes and were treated, then β̃1 + β̃3 < 0.61
Results for Sensitive Behaviors.—We estimate equations (2) and (3) in Table 9,
including block fixed effects.62 For sensitive behaviors, almost none of the coefficients on treatment indicators or interactions are statistically significant. We see
little evidence of the therapy inducing a desirability bias, and indeed the effects run
in the opposite direction.
Indeed, looking at the index of four sensitive measures (panel A, column 5), β1 is
actually greater than zero for therapy plus cash, implying that the impacts of therapy
plus cash are, if anything, larger than the survey data imply. Online Appendix F.3
displays these updated treatment effects. For example, using survey data alone,
the treatment effect (standard error) of therapy and cash on the sensitive behaviors
index is −0.4 (0.09), a 36 percent decrease. The results from panel A, column 5
suggest that the adjusted treatment effect should be −0.516 (0.194), significant at
the 1 percent level.
The results of the more flexible regression in panel B, column 5 show that
these averages conceal important heterogeneity. Treated men who we think
did not engage in the sensitive behaviors tend to overreport them (β̃1 Both > 0),
and treated men engaged in the sensitive behaviors seem to underreport them
(β̃1 Both + β̃3 Both < 0).
Results for Expenditures.—All treatment arms associated with a roughly
0.3 increase in our proxy for measurement error (panel A, column 8). There is
underreporting across all arms, but it is greatest in the control group. This could
have implications for one of our main findings, on income. Using survey data, the
treatment effect of cash only on the 2-item expenditure index is 0.08 (0.052), which
is consistent with the short-run increase in consumption we observed among cash
recipients. But adjusting for observed measurement error, the adjusted treatment
effect is −0.205 (0.143).
Interpretation.—Our qualitative work suggests two explanations. The men have
been members of a subculture where drugs, crime, and gambling are commonplace,
and admitting to the behaviors in a survey carries little stigma. Speculatively, therapy
may have accustomed men to talking about these behaviors or reduced stigma. As
for expenditures, control men may have acted strategically, trying to appear poorer
in the hopes they would be eligible for assistance. We discuss implications for our
conclusions in the following section.
61If we exclude the block fixed effects used for estimating ITT effect, as in equation (1), then β̃0 also contains
information: if men honestly report crime in the survey then β̃0 should be close to 0 and β̃2 should be close to 1,
while if there is a general desirability bias in the survey, then β̃0 + β̃2 < 1. See online Appendix F.2.2 for this
analysis. In general, estimates of β̃0 suggest that sensitive behaviors are 12–15 percent more likely to be reported
in the survey, possibly because of the validator’s fidelity to the two-week recall period and specific definitions, or a
general conservatism, but there is no evidence this survey overreporting is correlated with treatment, which is the
main purpose of the analysis.
62That is, in equations (2) and (3) we actually estimate β0j and β̃0j, which is necessary to identify treatment
effects when the probability of treatment assignment varies by block. The results without block fixed effects (not
shown) are qualitatively similar.1200 THE AMERICAN ECONOMIC REVIEW april 2017
VII. Discussion
A. Lessons from the Cash Transfer
One lesson is that these supposedly undisciplined men largely invested and saved
a grant. Even accounting for the underreporting we see in gambling and other expenditures, little of the grants seem to have been spent on temptation goods. While
Table 9—Estimates of the Correlation between Treatment and Measurement Error
Dependent variable: (N = 239)
y
s
− y
v, sensitive behaviors y s − yv, expenditures
Stealing Marijuana Gambling Homeless
All
(0–4)
Video
club
Phone
charging
All
(0–2)
Covariate (1) (2) (3) (4) (5) (6) (7) (8)
Panel A. Constrained, with block fixed effects (equation (2))
β0 (constant) −0.029 0.062 −0.109 0.093 0.015 −0.326 −0.194 −0.517
[0.087] [0.061] [0.093] [0.076] [0.177] [0.118] [0.095] [0.158]
β1
Therapy −0.019 0.015 0.025 −0.025 −0.004 0.170 0.174 0.339
[0.084] [0.057] [0.097] [0.091] [0.199] [0.102] [0.085] [0.132]
Cash −0.038 −0.042 −0.085 −0.077 −0.237 0.109 0.165 0.269
[0.088] [0.067] [0.090] [0.079] [0.195] [0.111] [0.078] [0.134]
Both −0.006 −0.024 0.077 0.031 0.079 0.127 0.181 0.304
[0.080] [0.062] [0.095] [0.089] [0.183] [0.103] [0.075] [0.115]
Dependent variable: (N = 239)
y
s, sensitive behaviors y s, expenditures
Stealing Marijuana Gambling Homeless
All
(0–4)
Video
club
Phone
charging
All
(0–2)
Covariate (1) (2) (3) (4) (5) (6) (7) (8)
Panel B. Unconstrained, with block fixed effects (equation (3))
̃β0
(constant) 0.301 0.098 0.231 0.283 0.976 0.367 0.047 0.048
[0.140] [0.092] [0.118] [0.129] [0.287] [0.130] [0.093] [0.208]
β̃1
Therapy −0.022 0.010 −0.009 −0.036 0.154 −0.190 0.100 −0.011
[0.070] [0.077] [0.074] [0.079] [0.228] [0.124] [0.072] [0.207]
Cash 0.003 0.025 −0.079 −0.138 −0.069 −0.072 0.040 0.089
[0.068] [0.081] [0.064] [0.070] [0.220] [0.139] [0.068] [0.219]
Both −0.013 0.025 0.064 −0.025 0.271 −0.113 0.041 −0.150
[0.064] [0.081] [0.076] [0.083] [0.241] [0.138] [0.064] [0.209]
̃β2
(yv) 0.496 0.735 0.315 0.405 0.677 0.038 0.504 0.328
[0.158] [0.096] [0.108] [0.158] [0.108] [0.123] [0.096] [0.129]
β̃3
Therapy × yv −0.166 −0.014 −0.131 −0.020 −0.210 0.434 0.079 0.222
[0.234] [0.125] [0.176] [0.220] [0.147] [0.169] [0.143] [0.169]
Cash × yv −0.232 −0.114 −0.063 0.286 −0.134 0.133 0.196 0.064
[0.208] [0.134] [0.148] [0.202] [0.144] [0.174] [0.137] [0.169]
Both × yv −0.064 −0.085 −0.241 0.066 −0.230 0.386 0.234 0.379
[0.214] [0.129] [0.173] [0.232] [0.137] [0.171] [0.130] [0.168]
p-value from test that β̃1 = β̃3 = 0
Therapy 0.671 0.991 0.682 0.867 0.345 0.035 0.161 0.098
Cash 0.511 0.648 0.299 0.115 0.307 0.744 0.134 0.380
Both 0.908 0.784 0.369 0.941 0.240 0.023 0.054 0.005
Notes: The table reports the degree and direction of bias in our treatment effects. In panel A, we assume that our
measurement error does not vary by whether or not the individual engages in the behavior, which allows for a simple way to use β2 to adjust our ITT estimates. In panel B, we relax this assumption and let the measurement error
vary by behavior and treatment arm at the cost of reduced statistical power.VOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1201
Evans and Popova (2017) see the same result in 19 other cash transfer programs, it’s
striking to see the same with this extreme group. Caution is also warranted, because
of the evidence that the control group underreported expenditures. But in the short
run, men seem to have used the cash for petty trade, earning returns to capital of at
least 26 percent.63
There is only weak evidence, however, that criminal activities fell as business
income rose. Those who received cash reduced their antisocial behaviors only 0.08
standard deviations (not significant) but reduced stealing by about 30 percent (significant at the 10 percent level with unadjusted p-values only). The direction of
effects, however, is consistent with rural ex-combatants in Liberia, who shifted away
from illicit activities when a much more intensive employment program raised their
farm productivity (Blattman and Annan 2016).
Any investments and income gains disappeared within a year, however, in part
due to poor property rights protections. The men’s homes and neighborhoods were
highly insecure. Extrapolating from reports of burglary and theft at each endline
(from Table 3), men in our sample experienced a theft or robbery roughly eight
times in the year after the grant. While treated men were no more likely to experience theft, they had more to lose, especially their savings and investment in nascent
businesses.
Nonetheless, the fact that cash was well used is important, since concerns about
temptation spending restrain political support for cash-based welfare programs. The
men received a few months’ worth of income, and basic consumption—especially
basic shelter and food—improved for about that length of time. This is important.
Future research should study how to sustain the economic effects of cash. It may
be that helping people relocate to better quality neighborhoods or enhance their personal security, or providing the information and means to gain necessary licenses or
protection from security forces, would reduce expropriation. Alternately, programs
can try to provide crude insurance. It is possible that regular cash transfers would
stimulate enterprise development more than the one-time transfer we study (Bianchi
and Bobba 2013; Karlan, Knight, and Udry 2015).
B. Lessons from Behavior Change
The interventions have extremely similar impacts on both antisocial behaviors
and an index of all noncognitive skill and preference measures, suggesting that noncognitive change, broadly speaking, was a major source of behavior change. The
impact of cash plus therapy on antisocial behaviors (0.31 standard deviations in two
to five weeks and 0.25 after a year) is mirrored by a change of similar magnitude in
noncognitive skills and preferences (0.43 standard deviations in two to five weeks
and 0.25 after a year: see Figure 1). Likewise, therapy alone significantly affects
antisocial behaviors and noncognitive changes within two to five weeks but not after
63For instance, the impact on earnings ($8.25 a month) represents a monthly return of 4.1 percent on the $200
grant, while the impact on nondurable consumption ($48 per month) represents a monthly return of 24 percent.
While there are reasons these figures might overstate returns, recall that men only invested about 60 percent in the
month after the grant, implying returns on actual investment are probably higher. Unfortunately we cannot say
whether the cash grant passed a cost-benefit test in private monetary returns alone.1202 THE AMERICAN ECONOMIC REVIEW april 2017
a year, and cash alone does not have a significant effect on either outcome in any
period.
Among the six noncognitive families we defined, all but self-control skills showed
a large and (with unadjusted standard errors) statistically significant decline after two
to five weeks as a result of therapy and cash. Forward-looking time preferences, mental health (particularly positive self-regard), social networks (particularly peer quality
and family relationships), and identity/values all show large and robust changes after
adjusting for multiple comparisons. After a year, it is difficult to single out any one
noncognitive skill or preference change as robust. Individually, the largest and most
precise changes are in forward-looking time preferences and self-control skills.
Nonetheless, if we were to merge time preferences and self-control skills into a
single index of future orientation, we see some evidence of sustained impacts as a
result of therapy plus cash. The combined index increased by 0.22 standard deviations after two to five weeks (adjusted p = 0.029), and by 0.26 standard deviations
after one year (adjusted p = 0.068). We must take this result with some caution,
partly because we prespecified them as distinct measures, and partly because we do
not see robust changes in short-term self-control. Nonetheless, a change in future
orientation would echo the effects of adolescent CBT programs in Chicago that target similar automatic behaviors (Heller et al. 2015).
We see less conclusive evidence on the least standard aspect of the therapy—the
focus on changing social identity and values. This family index increased 0.27 standard deviations after two to five weeks, significant at the 1 percent level with conventional standard errors but an adjusted p = 0.067. This index change driven by
significant changes in both appearance and anticriminal values. But effects moderate after a year, particularly for appearance (and with the addition of prosocial
behaviors), and become less precise.
These estimates contrast with men’s qualitative personal accounts, where identity change was paramount. Qualitatively, the changes in appearance, in community
regard, and the exposure to new places and situations seem to have been particularly
important. So was the identity of the NEPI facilitators, and the fact that they modeled this identity change. This change has a basis in the theory underlying CBT:
positive interactions challenged respondents’ negative beliefs about themselves,
and reinforced their identity as more responsible, mainstream members of society.
Possibly identity and values are difficult to measure, and so this remains an important area for further innovation and testing.
In psychology, efficacy trials such as this one are typically followed by further
trials that try to identify the “active ingredients,” by varying modules and methods.
This, plus more investment in measurement, seems like a fruitful area for research.
C. Understanding the Cash–Therapy Interaction
We did not expect that the effects of therapy would persist only when cash was
received as well. Our theory predicted that the two interventions should have a larger
effect only if cash raises earnings permanently, which was not the case.
Our qualitative evidence and psychological theory, however, suggest a hypothesis
for testing in future trials: that receiving cash was akin to an extension of therapy,
in that it provided more time for the men to practice independently and to reinforceVOL. 107 NO. 4 BLATTMAN ET AL.: REDUCING CRIME AND VIOLENCE 1203
their changed skills, identity, and behaviors. The therapy was brief—just eight
weeks long. It helped men change their intentions, identity, and behavior, and provided almost daily commitment and reinforcement. After eight weeks the men who
received therapy alone had to contend with their usual economic and peer pressures.
The grant, however, provided some men with the cash they needed to maintain their
new identity—to avoid homelessness, to feed themselves, and to continue to dress
well. They had no immediate financial need to return to crime.
The men could also do something consistent with their new identity and skills:
execute plans for a business. This was a source of practice and reinforcement of
their newfound skills and identity. It was also a form of performance, to themselves
as well as their family and neighbors, who could see the men engage in legitimate
business. Our qualitative interviews also suggested that the cash helped men to survive shocks. In this way, the grant may have parallels to booster sessions commonly
used in therapy. A small body of experimental research on CBT for aggression or
substance abuse indicates that follow-up therapy sessions weeks or months after the
intervention improve 12–13-month outcomes (e.g., Lochman 1992).
Caution is warranted. We cannot reject the hypothesis, for instance, that positive reinforcement from winning a grant was enough to reinforce therapy. In future
research, a comparison of extended therapy to shorter therapy plus cash would offer
a more direct test.
Nonetheless, high short-run returns to capital and sustained social spillovers
suggest that the combination of cash and therapy had promising returns. Since the
private returns to the grant were temporary, however, the cost effectiveness rides
mostly on the social benefits from roughly one fewer crime per week per person.
These social returns are unknown. If these social returns are greater than $20 or $25
per crime, however, the STYL program is a promising investment on basis of crime
reduction alone.
D. Generalizability
For several reasons this approach has promise beyond Liberia. First, the therapy
was adapted from US-based CBT programs, suggesting that adaptability to other
contexts is feasible. Second, we kept the intervention low-cost and created a publicly
available manual, curriculum, and training guidelines to ease adaptation and replication.64 Third, with time it should be possible to develop qualified and effective
facilitators in other countries, not least because there are established methods for
training counselors in CBT; general levels of education (and the number of social
workers) are greater in most other countries; and new facilitators should emerge
among graduates of the program, as with STYL.
The theory and results are also strikingly consistent with comparable US programs and best practice. The attention to noncognitive skill change and social identity, the targeting of the highest-risk men, as well as the nonresidential nature of the
therapy, correspond closely to best practice in criminal rehabilitation in US correctional institutions (Andrews et al. 1990; Lipsey 2009). The 40–50 percent falls
64This manual can be found at http://chrisblattman.com/documents/policy/2015.STYL.Program.Manual.pdf.1204 THE AMERICAN ECONOMIC REVIEW april 2017
in antisocial behaviors we observe are similar in proportion to the falls in arrests
documented in Tennessee and Chicago (Little, Robinson, and Burnette 1994; Heller
et al. 2015). Moreover, as in Chicago, the effects of therapy alone were temporary.
Other US work suggests that employment can be complementary to social and
emotional counseling (Heller 2014). In low-income countries, however, where most
employment programs will involve self-employment, property security and risk are
important scope considerations. Cash transfers in other poor countries have generally led to higher and more persistent incomes, in part because the gains are not
stolen. So the STYL program could arguably work even better in places with more
secure property rights.
There are limits to generalizability of course. For instance, there were no gangs
or armed groups vying for men in our sample. CBT-based approaches may be most
effective against disorganized, impulsive crime and violence rather than organized
crime. There is also selection onto the street, and a country which has experienced
many negative shocks (such as Liberia) might have more high-potential young men
who need only a little help to regress to the mean. On the other hand, our evidence
from dropouts suggests that the most antisocial men stay, and the program is most
effective with them. These limits are speculative without further testing, however,
and replication and experimentation seem more than warranted given the results of
these efficacy trials in Liberia, Chicago, and elsewhere.