Cristina Clerici's presentation slides during the conference "Economic and Social Context of Domestic Violence".

1. Unemployment and Intra-Household Dynamics: the Effect of
Male Job Loss on Intimate Partner Violence in Uganda
Cristina Clerici1
Stefano Tripodi2
May 11, 2022
1
Stockholm School of Economics, cristina.clerici@phdstudent.hhs.se
2
Copenhagen Business School, st.eco@cbs.dk

2. Introduction
I Households in low-income countries are sensitive to economic shocks
I Economic shocks may affect intra-household dynamics and Intimate
Partner Violence (IPV)
I 243 million women and girls experienced IPV in 2019 (UNDP);
low-income countries more affected (in African region prevalence is
33%, WHO)
I IPV impacts women’s health and labor market outcomes (Sabia et al.,
2013), as well as children outcomes (Aizer, 2011; Rawlings & Siddique,
2014)
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3. This paper
I What is the effect of husband’s job disruption on the
incidence of IPV among female food vendors in Uganda?
I Sample of urban and working women → does economic empowerment
insure them against the consequences of a negative economic shock?
Comparison with DHS 2016
I Data collected through a phone survey with 809 respondents in
November 2020
I Identification: we use the COVID-19 containment measures as a source
of exogenous variation in male employment status, while keeping
female employment status constant
I Physical violence (including both beating and sexual abuse) 4.9 pp
higher in Affected group (45% over Non-affected group mean)
I Effect is immediate, but temporary
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4. Literature on IPV and economic conditions
I Impact of aggregate-level shocks (Aizer, 2010; Anderberg et al., 2016;
Tur-Prats, 2019; Ericsson, 2020)
I Impact of cash transfers (Pronyk et al., 2006; Angelucci, 2008; Hidrobo
& Fernald, 2013; Haushofer et al., 2019; Peterman et al., 2021)
I Few natural experiments for an individual-level shock in developing
countries (Bhalotra et al., 2021)
I We look at the impact of an individual negative economic
shock (husband’s unemployment) on IPV exploiting a natural
experiment
I We have a sample of urban and economically empowered
women
Framework IPV & COVID-19
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5. IPV in Uganda
I High acceptance of IPV by women (DHS, 2016)
I About 50% of women aged 15-49 ever experienced violence by an
intimate partner (UNDP, 2020)
I In 2020, 17,664 cases reported to Police while 13,693 reported in 2019
(Uganda Police, 2020)
I “By April 17, 2020: In Uganda alone, Police had registered 328
domestic violence related cases during the period of a one month
nationwide lockdown...” (Daily Monitor)
I “Anecdotal data from UN Women partners indicates violence against
women between March and September increased by 50 per cent.” (Daily
Monitor)
DHS 2016
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6. COVID-19 lockdown in Uganda and timeline
I Severe lockdown from March 2020 until beginning of June 2020, with
Stringency Index > 80 (Oxford COVID-19 Government Response
Tracker)
I Only essential services could operate: food markets, medical, veterinary,
telephones, door-to-door delivery, banks, private security companies, cleaning,
garbage collection, fire-brigade, petrol stations, water departments and some
Kampala Capital City Authority (KCCA) and Uganda Revenue Authority (URA)
services.
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7. Sampling frame
I Step 1: survey with 35 market Chairpersons in Kampala, Mukono and
Wakiso districts to get a list of all female food vendors
I Step 2: survey with food vendors to assess eligibility: 2,962 reached,
950 (32%) met criteria
a. Selling food items (including charcoal/firewood)
b. Being older than 18
c. Married or cohabiting with a man before lockdown
d. Worked in market during lockdown
e. Being the sole user of a mobile phone
I Non-eligible women: mostly not married/not cohabiting (1,167) or not
working during lockdown (397) Reasons not working
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8. Data collection: women survey
I Sample of 809 women
I Questions on husband’s employment, IPV and few individual
characteristics
I Questions about IPV episodes are taken from DHS and refer to
lockdown period (April-May 2020), but also before and after
IPV questions
I Validate measures of violence using list experiment (Kotsadam &
Villanger, 2020)
I Rigid survey protocol to ensure safety and confidentiality Protocol
I Main outcomes: any violence, any physical violence (includes both acts
of beating and sexual abuse), any emotional violence
Attrition IPV questions by phone
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9. Identification
I Only some job occupations were restricted during the COVID-19
lockdown (April and May) ⇒ variation in husband’s employment status
I All respondents worked as food vendors in April and May ⇒ no
variation in wife’s employment status
I Compare two groups of women:
I Non-affected: husbands allowed to work during the lockdown (e.g., food
vendors, farmers, etc.)
I Affected: husbands could not work during the lockdown (e.g.,
motorcycle drivers, construction workers, etc.)
I Assumptions:
A.1 No anticipation effect → restrictions unexpected and effective
immediately
A.2 Decision to work not correlated with husband’s employment status
during lockdown Selection
A.3 Husband selection into an “essential” occupation exogenous with respect
to IPV → verified when looking at pre-lockdown occurrences of violence
across groups Physical violence Emotional violence
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10. Husband occupation and assignment to groups
The figure shows the division of respondents into Affected and Non-affected groups based on
their husbands’ employment sector.
Balance checks
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11. Empirical strategy
Main specification:
yimt = β0 + β1aim + β2Wim + β3Xim + β4yimt−1 + agei + δm + εimt (1)
where
I yimt and yimt−1 = dummies for woman i in market m experiencing
violence during and before the lockdown
I aim = husband’s sector not allowed to operate during lockdown, i.e.
woman is affected
I Wim and Xim = wife’s and husband’s characteristics
I δm and agei = market and age fixed effects
⇒ Coefficient β1 identifies Intention To Treat (ITT) effect
IV specification Compliance
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12. IPV during lockdown
0
.1
.2
.3
.4
.5
Physical violence Emotional violence Any violence
The figure shows the prevalence of different macro-types of violence during the lockdown
period. 95% confidence bands on top of bars.
IPV during lockdown (II)
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13. Effect of male temporary job loss on IPV
+45%
+2% +3%
-.1
-.05
0
.05
.1
Physical Emotional Any
Lockdown
+23%
-9% -9%
-.1
-.05
0
.05
.1
Physical Emotional Any
Post-lockdown
Main controls All controls 90% CI
The figure shows point estimates for the OLS model and 90% confidence bands. Percentages
refer to change with respect to Non-affected women average.
List experiment Still married No missing imputation
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14. Who suffers the most?
OLS model. Robust standard errors clustered at the market level in parentheses, p-values in
square brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.
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15. Possible mechanisms
I Suggestive evidence of economic channel
I Short-term effect → a shift in norms would have longer-term effects
I Look at husband’s contribution to household expenses: bigger decrease
forAffected group
I Suggestive evidence against exposure theory (no sleeping in the market
as copying mechanism) and stress theory (no increase in husband’s
alcohol consumption)
I No difference in support (money, food, medicines) received from
government and/or NGOs
I Increase in physical and sexual abuse consistent with instrumental
violence and male backlash theory
Other effects
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16. Conclusions
I We examine the causal effect of male job loss on IPV by exploiting the
COVID-19 containment measures
I Physical IPV increases by 4.9 pp as a consequence of male job loss and
this effect is short-term
I The violence observed is “new” violence
I Unemployment shocks are quite common in Uganda: about 90% of
labor force engage in informal jobs with very low protection (ILO,
2017)
I No rigorous examination of mechanisms, but suggestive evidence of an
economic channel
I In the presence of negative economic shocks, need programs to identify
vulnerable women as well as to reintegrate men in the labor market
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17. Thank you!
Please feel free to send your questions and comments to
cristina.clerici@phdstudent.hhs.se.
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18. Appendix
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19. Framework
Based on Baranov et al., 2020
IPV & economic conditions
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20. IPV & COVID-19
I Studies in developed countries use police calls, crime data, calls to
hotline services and hospital data → only most serious cases reported
I Studies in developing countries rely mostly on of survey data → often
lack of clear identification
I In US evidence of increase in IPV (Leslie & Wilson, 2020; Mohler et
al., 2020; Ashby, 2020; Sanga & McCrary 2020; Hsu & Henke, 2020;
Miller et al., 2020; Gasangi et al., 2020; Davis et al., 2020) and of no
change (Campedelli et al., 2020; Payne & Morgan, 2020; Piquero et
al., 2020)
I Evidence of increase in IPV in UK (Ivandic et al., 2020; Anderberg et
al., 2020), Spain (Arenas-Arroyo et al., 2020), Romania (Socea et al.,
2020) and in some European countries (Berniell & Facchini, 2020)
IPV & economic conditions
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21. IPV & COVID-19 (II)
I Descriptive evidence of an increase in IPV in Jordan (Abuhammad,
2020; Aolymat, 2021), Iran (Fereidooni et al., 2021), Iraq (Mahmood
et al., 2021), South Africa (Zsilavecz et al., 2020), Indonesia (Halim et
al., 2020)
I Evidence of an increase in Bangladesh (Rashid et al., 2020; Hamadani
et al., 2020) and India (Ravindran & Shah, 2020; Pattojoshi et al.,
2020)
I Evidence of an increase in Mexico (Silverio-Murillo & de la Miyar,
2020), Peru (Agüero, 2020) and Argentina (Gibbons et al., 2020; M.
Perez-Vincent & Carreras)
I Evidence of an increase in Japan (Takaku & Yokoyama, 2020) and
China (Qin et al., 2020; Dai et al., 2021)
I Descriptive evidence of increase in perceived prevalence of violence in
Uganda (Mahmud & Riley, 2020) and in experienced violence in Kenya
(Pinchoff et al., 2021; Egger et al., 2021)
IPV & economic conditions
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22. 2016 DHS data on IPV in Uganda
Variable Mean St. Dev.
Panel A: all episodes
Emotional Violence 0.34 0.47
Humiliated 0.17 0.38
Threatened 0.14 0.35
Insulted 0.27 0.45
Less-severe physical Violence 0.28 0.45
Pushed/shook/thrown objects 0.15 0.35
Slapped 0.25 0.43
Punched/hit 0.12 0.32
Twisted arm/pulled hair 0.09 0.29
Severe physical Violence 0.13 0.33
Kicked/dragged 0.12 0.32
Strangled/burnt 0.04 0.18
Threated with weapon 0.03 0.17
Sexual Violence 0.16 0.37
Forced sex 0.15 0.36
Forced sexual acts 0.03 0.18
Panel B: aggregates
Any violence 0.42 0.49
Any EMOTIONAL violence 0.31 0.46
Any PHYS/SEX violence 0.32 0.47
Observations 956
Summary statistics using 2016 DHS data. Note: Sample of 956 working women in urban areas,
married and above 18 years of age.
IPV in Uganda
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23. Sample description and comparison with DHS 2016
The Table shows sample averages for selected variables comparing the collected data with the
2016 DHS data. The DHS sample includes only married women older than 18. In our sample,
all variables refer to the pre-lockdown period (i.e., before April 2020).
This paper
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24. Reasons for not working reported by respondents
0
.1
.2
.3
.4
Impossible to sleep in mkt. Impossible to reach mkt.
Lack of customers Lack of capital
Family issues Lockdown
Left town Sickness
Fear of COVID-19 Other
Self-reported reasons for not working during lockdown.
Sampling frame
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25. Selection of respondents by husband’s status
Identification
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26. Attrition
Women survey
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27. Survey questions on IPV
Women survey
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28. Survey protocol
To ensure respondents’ safety and confidentiality:
I Include in the eligible sample only women who can access a private
phone number
I Only female enumerators in the field staff
I Field staff trained on how to ask sensitive questions
I Not possible to infer the topic of the conversation by listening to the
phone call
I Respondents called during working hours
I Respondents reminded they can end the survey at any point and refuse
to answer any question
I Respondents referred to support organizations in case they ask for
advise
Women survey
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29. Collecting IPV information using a phone survey
I No risk of coverage bias, since all food vendors have access to a phone
I Wrong reporting of phone numbers or imperfect recall by market
chairmen can be considered random
I Collecting data by phone does not compromise quality and reliability
in health and innovation surveys (De Leeuw, 2004; Mahfoud et al.,
2015; Nandi & Platt, 2016)
I No evidence of recalling bias in similar settings (Egger et al., 2021)
I Victims of violence more willing to participate to a phone survey than
non-victims, unless they cohabited with their partner (McNutt & Lee,
2000)
I Majority of people asked about interpersonal violence by phone willing
to answer and only few report discomfort or fear (Black et al., 2006)
Women survey
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30. Husband’s occupation and physical violence
-.5
0
.5
1
1.5
Share
Farmer Food vendor Boda-boda driver
Taxi driver Other driver Non-food vendor
Doctor/nurse Restaurant Teacher
Construction worker Manual worker Unemployed
Employee Professional Pastor
Broker/Businessman/Middle man
Emotional violence
The figure shows the prevalence of emotional violence conditional on husband’s job sector.
Standard errors clustered at the market level. 95% confidence bands on top of bars.
Identification
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31. Husband’s occupation and emotional violence
-.1
0
.1
.2
.3
.4
Share
Farmer Food vendor Boda-boda driver
Taxi driver Other driver Non-food vendor
Doctor/nurse Restaurant Teacher
Construction worker Manual worker Unemployed
Employee Professional Pastor
Broker/Businessman/Middle man
Physical violence
The figure shows the prevalence of physical violence (broadly defined as beating and sexual
abuse) conditional on husband’s job sector. Standard errors clustered at the market level. 95%
confidence bands on top of bars.
Identification
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32. Descriptives and groups balance (I)
Assignment to groups
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33. Descriptives and groups balance (II)
Respondent’s income is in ’000 Ugandan Shillings.
Assignment to groups
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34. Descriptives and groups balance (III)
The table shows summary statistics and mean differences for selected variables across groups.
Affected - Non-affected differences and p-values are obtained by regressing each variable on a
group indicator, controlling for market fixed effects. F -stat is the F -statistic of a joint
significance test that all predictors do not predict Affected status. Robust standard errors for
individual regressions clustered at the market level are in parentheses in column (4). Standard
errors for the F -statistic regression are heteroscedasticity-consistent.
Assignment to groups
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35. IV specification
I Use enactment of government restrictions aim as instrument for actual
husband employment status during lockdown, eim → assumption:
Government measures affect IPV only through husband’s occupation
I First stage:
eimt = β0 +β1aim +β2Wim +β3Xim +β4yimt−1 +agei +δm +ηimt (2)
I Second stage:
yimt = γ0 + γ1êim + γ2Wim + γ3Xim + γ4yimt−1 + agei + δm + εimt (3)
⇒ Coefficient γ1 identifies Local Average Treatment Effect (LATE)
Empirical strategy
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36. Compliance
This table shows the number of husbands who decided to work or to not work during the
lockdown, when their sector was affected or non-affected.
Empirical Strategy
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37. IPV during lockdown (II)
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.4
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The figure shows the prevalence of different types of violence during the lockdown period. 95%
confidence bands on top of bars.
IPV during lockdown
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38. Robustness: married sample
OLS and 2SLS models. Robust standard errors clustered at the market level in parentheses,
p-values in square brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.
Main Results
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39. Robustness: no imputation of missing values
OLS and 2SLS models. Robust standard errors clustered at the market level in parentheses,
p-values in square brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.
Main Results
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40. List experiment
I Indirect way to ask sensitive questions
I Control list: 3 neutral statements, Treatment list: 3 neutral statements
+ IPV statement
I Random assignment to list stratified by group
I Respondent asked to indicate how many statements she agrees with →
IPV Prevalence = mean(statements T) - mean(statements C)
I IPV statement: “My husband slapped me, twisted my arm or pulled my
hair.”
Main Results
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41. List experiment text
Now I would like to read you 4 statements. The statements will be about some
situations you might have experienced during the period of the COVID-19
lockdown and curfew (April and May). Some of them will be true, some of them
will not be true. After I read all of them, I will ask you to tell me how many of
these statements are true for you. I do not want to know which ones are true, but
just how many. Now I will give you a suggestion on how to do it: after I read
each statement, I would like you to lift a finger if the statement is true, and do
not lift a finger if the statement is not true. In this way, you can count with your
fingers how many statements are true. DO NOT tell me each time you lift or do
not lift a finger. At the end, I will just ask you how many fingers you have lifted:
please just give me the number and do not tell me which statements are true. Is
this clear to you? If respondent says no, explain again.
I will now read the statements. Please, listen carefully:
1. I attended a religious service, but not for a special occasion like a wedding or
a funeral
2. I kept working at my usual job
3. I travelled back to my village
4. My husband slapped me, twisted my arm or pulled my hair (ONLY FOR
TREATMENT GROUP)
Main Results
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42. List experiment results...puzzling
The figure shows the prevalence of specific actions of IPV (slapping, arm-twisting and
hair-pulling) in the Affected and Non-affected groups using two different methods: direct
questions and list experiment. 95% confidence bands on top of bars.
Main Results
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43. List experiment results: regression table
OLS and 2SLS models. Robust standard errors clustered at the market level in parentheses,
p-values in square brackets. * p < 0.10, ** p < 0.05, *** p < 0.01. The outcomes are binary
variables indicating the occurrence of physical abuse in terms of slapping, hair pulling or arm
twisting, elicited using the list experiment technique and direct questioning.
Main Results
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44. Other effects
OLS and 2SLS models. Robust standard errors clustered at the market level in parentheses,
p-values in square brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.
Possible mechanisms
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