Woman’s relative wage and intimate partner violence
in Mexico: A non-monotonic relationship∗
Aixa Garcı́a-Ramos†
This version: December 9, 2016
Abstract
It is estimated that around a third of Mexican women experience intimate partner
violence (IPV). This paper identifies the effect of a woman’s relative wage on the
incidence of physical, sexual, emotional and economic IPV. It goes beyond previous
studies by examining whether this effect is non-monotonic. To account for the
potential endogeneity of woman’s relative wage I exploit inter-state variation in
female industrial labour composition. My results indicate a significant U-shaped
impact of a woman’s relative wage on the incidence of the four types of IPV with
turning points just above the middle of the relative wage distribution. I also find
a U-shaped relationship between woman’s relative wage and her bargaining power,
which I propose as a possible explanation.
Keywords: Domestic violence, relative wage, bargaining power, non-monotonicity
JEL Classification: J12, J16
∗
I am grateful to Professor Anindya Banerjee and Dr Siddhartha Bandyopadhyay for support and
guidance throughout this project. I would also like to thank participants of the 21st SMYE, XIX AEM
and SSDEV 2016 for their helpful comments. I acknowledge the Economic and Social Research Council
for financing my PhD. The usual disclaimer applies.
†
PhD Candidate in Economics at the University of Birmingham. Department of Economics, University
of Birmingham, B15 2TT Birmingham, United Kingdom. Contact: [email protected]
Violence is part of a system of domination, but it is at the same time a measure of its
imperfection (Connell, 2005, p. 84)
1
Introduction
Intimate partner violence is the most common form of abuse against women. It is
estimated that around one third of women worldwide experience it, which is not limited
to any particular country, culture or social class. Moreover, it manifests itself in multiple
forms including physical, sexual, emotional and economic abuse, all of which rarely occur in isolation, but usually overlap (Gibson-Davis et al., 2005). This paper focuses on
the determinants of male-to-female IPV in the Mexican context. Exploiting inter-state
variation in female industrial labour composition, it specifically identifies the effect of
a woman’s relative wage on the incidence of physical, sexual, emotional and economic
IPV. Furthermore, it goes beyond previous papers by examining whether this effect is
non-monotonic.
According to ENDIREH (Encuesta Nacional sobre la Dinámica de las Relaciones en
los Hogares) [National Survey on the Dynamics of the Relationship within the Households], 29.48% of Mexican women experienced IPV in 2011 compared to 33.34% in 20061 .
Although there has been a decrease over time, the incidence of violence continuous to
be high. This overall figure hides significant differences across states2 and types of IPV
(see figure 1). Emotional abuse is the most common, followed by economic, physical and
sexual, respectively. In addition, IPV has dramatic direct and indirect physical, psychological and economic consequences for women3 . It also alters the welfare distribution in
the household and can have negative consequences on other family members including
children and elder members (Pollak, 2004; Aizer, 2010b; WHO, 2012). Moreover, it represents a large economic cost for society in terms of health care, social services, criminal
justice system and loss of economic output (Walby, 2009).
Both the high incidence rates and negative consequences make IPV a serious public
health problem, as well as a violation of human rights, which constitutes an obstacle
to women’s empowerment and overall human development (Agarwal and Panda, 2007).
In this context, it is crucial to understand the determinants of abuse, as well as the
1
Figures presented throughout the paper refer to my sample, which is comprised of married or cohabiting women aged 15 or older residing with their partners. I do not restrict the age because there is no
upper age limit for working in Mexico. The definition of IPV refers to the 12 months previous to the
survey and, unless otherwise specified, it excludes three violent items (see section 5.2.1 for details). If I
add them, the incidence rate increases to 39.36% in 2006 and 34.52% in 2011. Also, I use IPV, violence
and abuse interchangeably.
2
During my time span Mexico had 31 states and one Federal District, but for simplicity I refer to all
of them as states. Recently the Federal District has changed its name to Mexico City and has been made
equal to the rest of states in political and constitutional terms. The incidence rate of IPV ranges from
22.04% in Chiapas (Southern state) to 40.64% in Colima (Northwestern state).
3
17.68% of women experiencing IPV report physical injuries and 59.17% mental health problems.
8.42% have stopped working or lost money or possessions, and 11.51% have thought about committing
suicide of which 41.41% have attempted it.
2
Figure 1: Incidence rate of IPV by year
0.24
.25
0.24
0.22
Percentage
.2
0.18
.15
.1
0.08
0.06
0.05
.05
0.03
0
2006
Physical
2011
Sexual
Emotional
Economic
Notes: The horizontal axis refers to the year and the vertical axis to the percentage of women experiencing
each type of abuse.
nature of the relationship, in order to inform policy-makers about a potential successful
targeting of resources. Previous research has provided some insights in this line, but
it still presents several limitations, which this paper attempts to overcome. First, it
has primarily assumed the existence of a linear relationship between a woman’s economic
status and IPV4 . I go beyond this by examining the potential existence of a non-monotonic
relationship. Second, it has mainly focused on physical IPV, even when emotional and
economic abuse are significantly more common and can have longer-lasting consequences
(Mechanic et al., 2008). I examine four types of IPV, since each of them represents a
different dimension of violence and their underlying mechanisms can differ.
Third, previous papers using Mexican data have primarily examined the relationship
between a woman’s employment status and IPV. I depart from this approach in two ways.
Firstly, I use a different dimension of economic status: wage. While employment status
only provides information on whether the individual is working, her wage shows by how
much she contributes to the household income5 . Secondly, I use a relative measure of
woman’s economic status: the share of her wage over the couple’s total wage. It has
long been documented that it is not the absolute wage, but the difference between the
partners’ wages that matters in explaining IPV (Atkinson et al., 2005; Aizer, 2010a).
A final limitation of previous studies about Mexico is that they have failed to account
4
See Farmer and Tiefenthaler (1997), Angelucci (2008) and Anderberg and Rainer (2013) for an
exception in the economic literature, which will be discussed in the next section.
5
Another body of the Mexican literature has examined the effect of Oportunidades, which is a conditional cash transfer programme targeted at women, on the incidence of IPV. The comparison of my study
with this body of research provides some interesting insights into the differential impact of alternative
sources of income on IPV.
3
for the potential endogeneity of woman’s economic status6 . I overcome this by employing
an appropriate instrumental variable for the woman’s relative wage (inter-state variation
in female industrial labour composition) and estimating the model by Two-Stage Least
Squares (2SLS).
My sample is comprised of 102,599 married or cohabiting women aged 15 or older
residing with their partners, which is drawn from the last two waves of ENDIREH (2006
and 2011). This survey captures information on male-to-female IPV and a rich set of
partners, couple and household characteristics. Previous papers have mainly used data
from 2003 and 2006 ENDIREH waves, but given the availability of a recent wave, it is
important to examine the phenomenon of IPV in an updated context.
My results indicate that the effect of a woman’s relative wage on the incidence of IPV
need not be linear. Specifically, I find evidence for a U-shaped effect, which is robust to a
number of checks. That is, as the woman’s relative wage increases, the incidence of IPV
decreases up to a certain threshold of the relative wage distribution, which is between 0.55
and 0.67 depending on the type of violence. The difference across the four thresholds is
not significant. Beyond this point, an improvement in the woman’s relative wage increases
the incidence of IPV. However, the increase in IPV is likely to affect a limited number of
women, since only 8.25% of them have a relative wage greater than 0.5.
I also examine several potential explanations beyond this pattern. I find that, as
the woman’s relative wage improves, she curtails her bargaining power (proxied by her
decision-making power) up to a relative wage equal to 0.394 after which she increases
it. I suggest that a woman might curtail her bargaining power as a way of assuaging for
her improved relative wage and, thus, maintaining an equilibrium with traditional gender
norms. This might be her strategy to avoid a potential male backlash effect (i.e. a threat
to the man’s culturally prescribed dominance) that leads to IPV. The woman continues to
curtail her bargaining power up to a relative wage equal to 0.394 after which she claims
greater. This threshold is significantly different to the one for the four types of IPV.
By claiming greater power, she might be threatening the man’s dominant position in the
household, which could increase the probability that he uses IPV in order to restore power
and reassert dominance. This latter explanation is consistent with relative resource theory
and a male backlash effect, which have been widely used in the sociological literature for
explaining IPV.
Furthermore, the increase in the woman’s relative wage seems to have been accompanied by a reduction in household stress, which might have further decreased the incidence
of IPV, as well as an improvement in the woman’s gender beliefs, whose effect on IPV is
unclear. Also, I find no evidence that the increase in violence is driven by motives of rent
extraction.
The rest of the paper is structured as follows. Section two reviews the theoretical
6
To my knowledge, Villarreal (2007) is the only exception. I discuss this paper in the next section.
4
and empirical literature on the relationship between a woman’s economic status and IPV.
Building on this, section three brings in gender identity norms and examines how they
shape a woman’s relative wage and its relationship with violence. Section four presents
the identification strategy, while section five describes the data and variables. Section six
reports the baseline results and section seven conducts some robustness checks. Several
potential explanations are examined in section eight. Finally, section nine concludes and
discusses the limitations and implications of the study.
2
Literature Review
2.1
Economic theory
Economists have primarily analysed IPV using non-cooperative household bargaining
models in which the threat point (also called reservation utility7 ) is determined by the
threat of divorce8 . This threat point is the utility each partner would obtain outside of
the marriage, and she will not tolerate violence that lowers her utility below this point. In
this paper I focus on one particular factor that can impinge on the woman’s reservation
utility, namely her relative wage9 .
These models usually assume that a woman’s reservation utility is binding in equilibrium, which is consistent with IPV only being used as a direct source of gratification.
Their standard prediction is that, as the woman’s threat point improves, her partner
reduces IPV by raising her bargaining power. Conversely, when a woman’s reservation
utility is not binding, violence can have both an expressive and an instrumental motive,
and IPV can either increase or decrease as her reservation utility improves (Tauchen et al.,
1991; Farmer and Tiefenthaler, 1997; Aizer, 2010a)10 . Eswaran and Malhotra (2011) challenge the monotonic decreasing relationship between a woman’s threat point and IPV by
showing that, as the former improves, she might impose her preferences so much that it
can lead to a rise in the incidence of IPV in equilibrium, even if the frequency of it has
declined.
More recently, Anderberg and Rainer (2013) posit an inverted U-shaped relationship
between a woman’s potential relative wage and the incidence of economic abuse. The
idea is that when her relative wage is very low, her partner transfers money to her and
7
‘Threat point’ coincides with ‘reservation utility’ when the breakdown of the bargaining process
results in divorce (Pollak, 2005).
8
These external threat point models assume that the woman’s threat of divorce is credible, which
might not be the case in the context of developing countries. Lundberg and Pollak (1993) and Anderson
and Eswaran (2009) develop alternative models in which the breakdown of the bargaining process is
represented as a non-cooperative outcome within the marriage.
9
Other factors include educational attainment, unearned income, asset ownership, external support
from social services or relatives, as well as laws regarding marriage, divorce and inheritance (Agarwal,
1997; Fafchamps et al., 2009).
10
Expressive violence refers to violence being used as a release of frustration or anger, or as a source
of direct gratification, while instrumental violence is used to exert control over the woman, subdue her
or restore man’s power (Tauchen et al., 1991; Kimmel, 2002; Heath, 2014).
5
she voluntarily specialises in domestic work. This coincides with his desired outcome
and, therefore, there is no need to use violence. As her relative wage increases, she
enters the labour market, which contradicts his desired outcome. He tries to persuade
her to stay at home through monetary transfers and eventually direct sabotage of her
employment opportunities. However, his incentives to use sabotage vanish as her relative
wage approaches the top of the distribution, since her earnings become very important
for him and the household.
2.2
Sociological theories
The relationship between a woman’s economic status and IPV has also long been
studied in the sociological literature. One of the most often cited theories is relative
resource theory (Anderson, 1997; Macmillan and Gartner, 1999). It posits that violence
is an ultimate resource available for the man to restore power if he perceives that there
is an imbalance in access to resources (for example, wage, education, status, etc.) to his
detriment. Atkinson et al. (2005) expand this theory to include the man’s gender ideology.
They argue that traditional men link economic status to masculinity. Consequently, when
masculinity cannot be validated through economic status, they might restore to violence.
Conversely, men that hold a egalitarian ideology are not expected to do so.
Related to these two versions of resource theory is the existence of a male backlash
effect, which has been a termed previously used in the economic literature. A male
backlash effect occurs when the increase in the woman’s relative wage threatens the man’s
culturally prescribed dominant position (Connell, 2005). Consequently, he might use
IPV as an expression of his dissatisfaction with the woman’s improved economic position
and/or with the objective of reasserting his dominance (Kimmel, 2002; Chin, 2012; Heath,
2014).
2.3
Empirical work
In the empirical domain, evidence remains inconclusive regarding the sign, direction
and magnitude of the relationship between a woman’s reservation utility and IPV. Broadly
speaking, most studies in developed country (mainly United States, Canada and United
Kingdom) have found a negative association, regardless of whether endogeneity has been
taken into account. Examples of woman’s reservation utility shifters include her employment status (Gibson-Davis et al., 2005), her potential relative wage (Aizer, 2010a)11 ,
the gender-profile unemployment rate, (Anderberg et al., 2015)12 and the introduction of
11
Aizer (2010a) argues that it is not the current wage but the potential one that matters, since a
woman’s bargaining power is determined by her earnings at the threat point, which do not necessarily
coincide with those at the equilibrium point. This implies that an increase in the woman’s relative wage
in the labour market will reduce IPV, even if she is not currently working.
12
Anderberg et al. (2015) develop a dynamic game of incomplete information in which the husband can
be of two types: have a violent predisposition or be averse towards violence. Whether he reveals which
type he is depends on his and her unemployment risk.
6
unilateral divorce laws (Stevenson and Wolfers, 2006). Interestingly, Aizer (2010a) finds
that, as the woman’s potential relative wage increases, the incidence of IPV declines. Her
findings are consistent with the classical prediction of household bargaining models, while
not with a ‘male backlash effect’. However, she assumes a linear relationship, which might
hide heterogeneous impacts depending on the point of the relative wage distribution that
the woman is at.
More disagreement persists in developing countries. In the context of India, Eswaran
and Malhotra (2011) find a positive relationship between being working for pay and the
incidence of physical IPV13 , while Panda and Agarwal (2005) find a negative association
between a woman’s possession of assets and physical and psychological violence. However,
these studies fail to account for the endogeneity of woman’s economic status. Chin (2012)
overcomes this limitation using an instrumental variable approach. Her results show a
negative effect of being working for pay on the incidence of physical IPV.
Research using Mexican data flourished with the publication of the first wave of two
nationally representative surveys on IPV in 200314 being ENDIREH one of them. Employing the first wave of this survey, Frı́as and Angel (2013) estimate several logistic
regressions and find an insignificant association between a woman’s employment status
and the risk of physical abuse, which confirms previous findings by Frı́as and Angel (2012).
Analysing heterogeneous effects by age, Castro et al. (2008) find that women working for
pay in the age group between 30-34 are more likely to experience physical violence, while
they find no significant association for those in the age groups of 15-21 and 45-49.
A widespread limitation of these studies is that they do not account for the potential
endogeneity of woman’s economic status. This is accounted for in Villarreal (2007). He
employs a maximum likelihood method to estimate a multivariate probit model of two
simultaneous equations (physical abuse and employment). He uses three instruments
for employment (financial help from relatives, woman’s work orientation and number
of pre-school children living with her) and IPV (violence in the husband’s childhood,
length of current relationship and number of residents per room). However, some of these
instruments may not satisfy the exclusion restrictions15 .
Another strand of the Mexican empirical literature has examined the impact of Oportunidades, which is Mexico’s flagship antipoverty conditional cash transfer (CCT) programme, on the incidence of IPV. Bobonis et al. (2013) find that beneficiary women are
more likely to experience threats of violence with no associated physical abuse compared
to non-beneficiary ones. They suggest that this is driven by a motive of rent extraction. In
13
This is confirmed by Heath (2014) for a sample of 60 villages in Bangladesh.
Most research about IPV in Mexico has been done by sociologists. To the best of my knowledge,
only the works of Angelucci (2008) and Bobonis et al. (2013) have an economic background.
15
For instance, financial help from relatives can be correlated with unobserved factors, such as woman’s
health condition. Also, it can directly affect IPV if the man uses violence as an instrument to extract
these additional resources.
14
7
a related paper, Angelucci (2008) exploits the initial random implementation of the programme in rural villages to examine its impact on drunken violence. She finds differences
depending on the magnitude of the income transfer. Drunken violence declines by 37%
for households entitled to small transfers where husbands have completed primary school.
Conversely, it increases for households entitled to large transfers where husbands have no
education. She suggests that men without education might hold traditional beliefs and,
consequently, they might perceive income transfers targeted at women as a threat to their
dominant position.
Angelucci (2008)’s findings question the usually assumed linear relationship between
a woman’s economic status and IPV. Sociologists outside Mexico have also attempted to
shed some light on the non-monotonic nature of it by using dummy variables for different
levels of the man’s relative wage (Anderson, 1997) or by including the squared term of
it (Atkinson et al., 2005). For instance, Atkinson et al. (2005) find no support for nonmonotonicity in the United States.
3
Relative Wage, IPV and Gender Identity Norms in Mexico
The literature review has stressed the importance of taking into account the particular
context of the country under study. While in developed countries a theoretical model
predicting a decrease in IPV following an improvement in the woman’s reservation utility
might be suitable, it is not clear whether this is the case for developing countries (Chin,
2012). The reason lies on the importance of gender identity norms, which are likely to
shape both women and men’s behaviour.
Maldonado et al. (2005) conduct group interviews on partners of women beneficiaries
of Oportunidades in three rural communities of Mexico. The interviews revealed that,
in general, men think that they have to be the breadwinners, which they associate with
masculinity. When they do not fulfill this role, they feel threatened, since it questions
their culturally prescribed dominant position in the household, as well as gives women
economic freedom, which they can use to ‘rebel’ against male authority. Moreover, some
of the men think that major decisions have to be taken by them and they need to exert
control over their partners. Indeed, one of the main reasons for using IPV is the unequal
distribution of power between partners16 .
Given the potential conflict that working for pay, earning more than the man and
exerting power might have, women are likely to shape their behaviour in order to avoid
this conflict, which could otherwise lead to IPV. They might decide to stay out of the
labour force in order to fulfill the prescribed role of ‘housewives’. In my sample, 59.61%
of women only fulfill this role compared to 30.65% that work for pay.
If they work for pay, they might decide to earn less than their partner, as Bertrand
16
Although the interviews are specific of men with limited resources living in rural communities, their
findings are also likely to be relevant for other groups of men across Mexico (Frias, 2010).
8
et al. (2015) suggest in the context of United States. Figure 2 depicts the histogram of
woman’s relative wage after excluding the extreme values of zero and one. As can be seen,
the fraction of women drops once she starts earning more than her partner. According to
data from ENADIS 201017 , 22.6% of women agree with the statement that ‘men should
earn more than women’ and 24.0% of them think that, if the woman earns more, she
loses respect for him. Furthermore, in the cases in which women work for pay, they might
decide not to translate this into claims of power (Tichenor, 1999).
Figure 2: Distribution of woman’s relative wage
2006
2011
.3
Fraction
.2
.1
0
0
.5
1
0
.5
1
Woman's relative wage
density
kdensity
Graphs by year
Notes: The height of each bin (25 in total) represents the proportion of women with that specific
relative wage. The histogram is overlaid with a scaled kernel density estimate. The extreme values
zero and one are excluded.
Evidence above suggests that men might feel their dominant position threatened as
the woman’s relative wage improves, which could ultimately trigger IPV. However, this
might depend on the point of the relative wage distribution that the woman is at. For
instance, a man might feel threatened as soon as her partner starts earning a greater wage
than him, but not before, which predicts a non-monotonic relationship. Figure 3 depicts
the incidence rate of IPV by woman’s relative wage, which I have split into five bands.
Although this figure might be capturing the effect of a number of other factors, it suggests
a U-shaped relationship, at least for a positive relative wage. Furthermore, if women are
aware that improvements in their relative wage could be perceived as a threat by their
partner, they might attempt to assuage them by claiming lower power.
The evidence above also suggests that current and potential relative wages might
17
ENADIS (Encuesta Nacional sobre Discriminación en México) [National Survey on Discrimination
in Mexico].
9
Figure 3: Incidence rate of IPV by woman’s relative wage
.3
0.27
0.23
Percentage
0.22
0.20
0.26
0.26
0.23
0.23
0.22
.2
0.16
0.10
.1
0.06
0.04
0
0
Physical
0.06
0.05
0.05
0.04
(0-0.5)
0.5
Sexual
0.06
0.05
(0.5-1)
Emotional
0.07
1
Economic
Notes: The horizontal axis refers to five bands of the woman’s relative wage distribution and the
vertical axis to the percentage of women experiencing each type of abuse.
have a different impact on IPV. Men might feel their dominant position threatened by
the woman’s potential relative wage. However, if she decides not to work or earn a lower
income than her potential one, he might not feel threatened by her current relative wage18 .
Thus, it is important to provide evidence from both, since they might not necessarily work
in the same direction. In this paper I focus on current relative wages19 .
4
Identification Strategy
4.1
Estimation Procedure
In order to examine the effect of a woman’s relative wage on the incidence of IPV, I
pool the two survey years and estimate the following Linear Probability Model (LPM):
2
P (IP Vimt = 1) = β0 + δ1 RWimt + δ2 RWimt
+ β1 Ximt + αm + γt + εimt
(1)
where i refers to woman, m to municipality and t to survey year. IP Vimt is the incidence
2
of IPV, which will be explained later. RWimt is woman’s relative wage and RWimt
the
square of it. Thus, δ1 and δ2 are the parameters of interest. Ximt includes a vector of
observable characteristics at the individual, couple and household level, whereas αm are
municipality fixed effects for woman’s municipality of residence. They control for timeinvariant unobserved factors that might affect all women living in the same municipality,
such as historical and cultural factors, as well as access to health clinics, shelters and the
18
So far I have assumed that the threat of divorce is credible. However, if this is not the case (e.g. the
breakdown of the bargaining process is over minor decisions, couples live in very traditional areas of the
country or divorce is not easy), the relevant threat point might be better represented as internal to the
marriage. In this case the woman’s potential relative wage might be well proxied by her current one.
19
See Aizer (2010a) for an example of potential relative wages.
10
criminal justice system. They also controls for time-invariant differences in labour market
conditions across municipalities, which could directly affect IPV. I include municipality
instead of state fixed effects because there is high heterogeneity within states. γt are year
fixed effects, which capture aggregated time trends that might affect all women. εimt is
the error term. I use robust standard errors20 .
The parametric approach of including the first and second-order terms has been the
most common method to test for non-monotonicity (Lind and Mehlum, 2010; Kostyshak,
2015). However, as Lind and Mehlum (2010) argue, several studies have inadequately
concluded that there is a non-monotonic relationship by only checking that the sign and
significance of the two terms is as expected (in some cases the first term is even not
included) and that the turning point lies within the range of possible values. These
two aspects are necessary, but not sufficient, since this shape can also be reached if the
relationship is non-linear, but monotonic, such as exponential. This would occur if the
turning point is close to the limit of the range of values.
Given these limitations, Lind and Mehlum (2010) propose a test for the presence of a
U shape, which is based on the general framework developed by Sasabuchi (1980)21 . It
specifically tests the combined null hypothesis that the relationship is increasing at low
values of woman’s relative wage and/or decreasing at high values (i.e. it is monotone
or has an inverse U shape) against the alternative that it is decreasing at low values of
woman’s relative wage and increasing at high values (i.e. it has a U shape). This test
provides the necessary and sufficient conditions for the existence of a U shape, and will
be used in the empirical section.
The main concern in estimating the effect of a woman’s relative wage on the incidence
of IPV is the potential endogeneity of woman’s relative wage arising from unobserved
heterogeneity, measurement error and reverse causality. Unobserved heterogeneity arises if
unobserved factors that impinge on IPV are correlated with woman’s relative wage or any
other covariate. By controlling for municipality fixed effects I account for time-invariant
unobserved heterogeneity at the municipality level. However, there can still be concerns
if unobserved factors vary over time or across women within the same municipality. For
instance, the overall violent environment of Mexico has dramatically changed between
2006 and 2011. Since 2007 some regions of the country have experienced a boost in
violence accompanied by high impunity levels, which previous papers have found to be
related to the Operativos Conjuntos (Joint Operations)22 (Dell, 2015). This increase in
violence might be associated with a woman’s relative wage and directly affect IPV. Women
20
Results hold if I use robust standard errors clustered at the state level instead.
Implemented in Stata by the command utest. This test has been increasingly used in the empirical
literature (see, for instance, Samargandi et al. (2015)).
22
These operations consist on the deployment of military forces as part of the crackdown on drugs by
the Mexican government. They started in December 2006 and have expanded across several regions of
the country.
21
11
living in highly violent municipalities could have stopped working due to the insecurity.
Also, men living in these areas could be more prone to inflict IPV given the likely higher
tolerance for violence and associated higher levels of impunity. As a robustness check,
I will control for homicide rates in the municipality as a proxy for the overall violent
environment.
Examples of factors that can vary across women within the same municipality include
health condition and alcohol or drugs consumption, for which I do not have information in
the survey. Woman’s relative wage could be lower for women with poorer health condition
or with alcohol or drugs consumption problems. However, health condition is unlikely to
be a major problem, since only 0.13% of women report that they have a physical or mental
limitation that prevents them from working.
Regarding alcohol and drugs consumption, previous work has found evidence that
women that consume alcohol (and/or her partner does) are more likely to experience abuse
(Angelucci, 2008; Luca et al., 2015). Even if my survey does not capture information on
this, other surveys23 report that the consumption of alcohol and drugs is greater in urban
than in rural areas. Moreover, individual consumption is likely to depend on several
municipality specific factors, such as alcohol and drug prices, as well as the existence
of stores that sell alcohol and drug dealers that sell drugs. Thus, by controlling for
municipality fixed effects, as well as whether the household is located in an urban or rural
area, I reduce omitted variable concerns.
Measurement error is a common problem in self-reported data on IPV, which is likely
to be under-reported (Ellsberg et al., 2001). Under the classical measurement error assumptions, misreporting in the dependent variable only causes the variance to be larger
(Hausman, 2001). These assumptions would be violated if, for instance, the amount of
misreporting is correlated with the true value of IPV. However, there is no strong reason to
expect this is the case. First, ENDIREH interviews are conducted by trained women, who
stress the confidentiality of the responses. Second, interviews are conducted in private
with the woman alone, which reduces the pressure from the presence of other members.
Third, the questionnaire asks multiple questions for each of the four types of abuse.
The main endogeneity concern is the potential reverse causality of IPV on woman’s
relative wage. Although this is expected to be more important for women that have
already experienced abuse, it is also true for those that have not, since they can, to
some extent, infer whether their partner has a violent predisposition from his behaviour.
All of them could decide not to work in the first instance, not to accept promotions at
work, obtain a lower paid job or even stop working if they think that any of these could
cause violence. Also, the man could directly sabotage the woman’s wage prospects by
forbidding her to work, which has a direct effect on her relative wage. Furthermore, any
type of violence can negatively impinge on her productivity, self-esteem, as well as cause
23
See Encuesta Nacional de Adicciones [National Survey of Addictions].
12
mental health problems that in turn reduce her wage (WHO, 2012; Anderberg and Rainer,
2013).
In order to deal with the potential endogeneity of woman’s relative wage, I use an
instrumental variable approach and estimate my model using 2SLS. The instrument is
based on inter-state variation in female industrial labour composition and will be explained
in detail in the next section. I estimate a LPM with instrumental variables, as done in
Eswaran and Malhotra (2011) and Chin (2012)24 .
4.2
Instrumental Variable
I propose to exploit inter-state variation in female industrial labour composition to
instrument for individual woman’s relative wage. Specifically, the instrumental variable,
e
γjst
, is the proportion of female workers E in industry j in state s in year t for a given
Ee
e
= P Ejste . Thus, the instrument varies by industry, state, time
educational level e: γjst
jst
and education category25 .
ENDIREH survey differentiates between several industries, which I group into nine:
public sector (only white-collar jobs), private companies (only white-collar jobs), retail
trade, schools (includes teachers, as well as administrative and other support staff in either public or private schools), factories, taller 26 , paid work at home (e.g. hairdressers,
product sellers, teachers, cleaners, carers, etc.), agriculture (includes agriculture and livestock farming) and other. Regarding the education categories, I differentiate across three,
namely primary or less, secondary and higher education (they will be explained in the
next section).
The motivation for using this instrumental variable is based on the fact that, for a
given industry, year and educational level, woman’s relative wage is, on average, greater
in states in which the proportion of women working in that specific industry is larger
compared to states in which it is lower. For instance, taking the group of women with
primary or less education working in factories, those living in Baja California (a state with
a large proportion of women working in factories) have, on average, a higher relative wage
than those living in a state with a lower proportion of women working in this industry.
One possible explanation for this positive association relies on the differential demand
24
An alternative would have been to estimate a probit model with a control function approach. Each
of these methods has both advantages and disadvantages. One of the main flaws of the control function
estimator is that it requires the first-stage regression to be correctly specified, while the 2SLS estimator
only requires that the instrumental variable is sufficiently correlated with the endogeneous regressor and
uncorrelated with the error term. Moreover, the control function estimator is more efficient, but less
robust than the 2SLS estimator (Lewbel et al., 2012), but given my large sample size I am not very
worried about efficiency. In addition, since I include municipality fixed effects, estimating a probit model
would be very computationally demanding. Also, given the small within-municipality cell size in some
cases (only two observations in 10 municipalities), the model might fail to converge (Bobonis et al., 2013).
25
It is important to differentiate by education category because the proportion of female workers across
industries significantly varies by this variable. For instance, 25% of women with higher education work
in the public sector compared to 0.8% with primary or less.
26
Small workplaces where the manufacturing process is done by hand.
13
for female workers by industry across states. If, for a given year and educational level, an
industry demands relatively more women in one state than in another, it is also expected to
pay relatively more in order to attract more women, everything else equal. Consequently,
for a given man’s wage, her relative wage is likely to be higher27 . Another explanation
refers to man’s attitudes towards female work. When a woman is employed in an industry
which is relatively more female intensive, her partner might associate this with work
security and decide to work relatively less and, consequently, earn less. One limitation of
my data is that I do not know the man’s industry of work, which could provide interesting
insights into these two aspects.
I can directly test for the strength of this relationship by running the first-stage regression of 2SLS. Assuming a linear relationship between the woman’s relative wage and the
incidence of IPV, I find that my instrument is significantly positively correlated with the
endogenous variable at the 1% level. Specifically, a 0.1 point increase in the proportion of
female workers is associated with a 0.005 increase in the woman’s relative wage. In order
to estimate equation 1, I use this instrumental variable and the square of it. The joint F
statistic28 value is 21.56, which suggests no weak instrument-related problems.
In order for the instrumental variable to be valid, it needs to fulfill two additional conditions, namely being uncorrelated with unobserved factors and only affect IPV through
its effect on the woman’s relative wage. These two conditions cannot be tested and constitute my identifying assumptions. They would be satisfied if the regional variation in
female industrial labour composition, which is in turn the reflection of the location of
industries across states, is not driven by supply factors29 , but by demand ones. This is
presumably the case.
For instance, the location of factories can respond to the closeness to Mexico-US border
and to benefits provided by governments at the time in which they set up (they started
growing in the 1960s). It can also respond to exogenous shocks, such as the growth
of China’s manufacturing sector or the global financial crisis. In the case of agriculture,
whether it is more developed in one state or another is presumably driven by natural (type
of soil, climate, etc.) and historical factors, as well as commercial trades and government
agriculture-related laws. Similarly, private companies are located in Mexico City and
Quintana Roo because the first is the capital of the country and the second is a large
touristic destination. The fact that Quintana Roo has developed as a touristic centre is
presumably due to its large coast in the Yucatan peninsula, which was seen as a potential
for beach tourism development in the past. Also, the location of the mining industry,
27
The fact that there are differences in wages across states for the same industry, as well as across
industries in the same state, can be problematic if women sort into industries that pay more or move into
states where her industry of work pays more. However, this is only likely to be a problem when using
panel data, as has been widely acknowledged in the literature (Aizer, 2010a; Anderberg et al., 2015).
28
Refers to the Kleibergen-Paap rk Wald F statistic, but for simplicity I call it F statistic.
29
Supply factors would reflect workers’ characteristics, which are likely to be correlated with unobserved
ones.
14
which is included under the industry ‘other’, is determined by the availability of natural
resources. The Southern states of Tabasco and Campeche have the vast majority of oil
production sites.
Furthermore, one could argue that the state level industrial labour composition reflects
a woman’s potential labour opportunities, which can directly affect IPV (Aizer, 2010a;
Anderberg et al., 2015). This would violate the second untestable condition. However,
women are expected to respond to labour market opportunities determined at a lower
geographical level, such as municipality. Given that I control for municipality fixed effects,
I account for time-invariant factors that determine differences in labour market conditions
across municipalities, which are the ones likely to be associated with IPV.
In order to construct my instrument I first collapse the ENDIREH data by woman’s
industry of work, state, year and education category, and then compute the proportion of
women working in industry j in state s in year t for a given educational level e. I match
my instrument with individual women based on these four variables and include specific
controls for them. I consider women not working as an additional industrial category,
which is driven by the observation that states with a higher relative wage also have a
higher proportion of working women. The specific instrument for this group refers to the
proportion of women working in state s in year t for a given educational level e.
Before using woman’s industry of work I need to make a few adjustments. Wage
data comes from the demographic section of the survey and refers to the job the woman
had in the week before the survey was conducted. Conversely, information on woman’s
industry of work comes from the public abuse section of the survey, which asks about
whether she experienced abuse in her current job or any other had during the last year.
Thus, it can occur that the industry a woman reports is not the same as the one she is
currently working in and, therefore, the one her wage comes from. In both sections I have
information on woman’s occupation category30 . In order to minimise this problem, I drop
observation where woman’s occupation between the two sources differ.
5
Data and Variables
5.1
ENDIREH Survey
I use data from the last two waves of ENDIREH, which were conducted in 2006 and
201131 . They are independent repeated cross-sections. ENDIREH is a national (urban
and rural) and state level representative survey, whose target population are women 15 or
older. They are grouped into three categories according to their marital status: married or
cohabiting, ever married or cohabiting (divorced, separated or widowed) and single (never
married or cohabiting). In this paper I restrict my sample to those married or cohabiting,
30
Includes four categories: non-working, employee, employer and self-employed.
So far three waves of this survey have been released being the first one in 2003. I do not employ
ENDIREH 2003 because the sample is more restricted and the questions included differ with respect to
those in the other two years.
31
15
which make a total of 83,159 and 87,169 women in 2006 and 2011, respectively. Data
were collected through direct interviews conducted by women between 9 October and 3
November of 2006 and 3 October and 11 November of 2011. Participants were interviewed
in private and were guaranteed confidentiality (INEGI, 2006, 2011).
ENDIREH contains information on the incidence, frequency and severity of male-tofemale IPV throughout the relationship and in the last 12 months, dwelling characteristics,
sociodemographic and economic variables of all household members, marital history, violence in childhood, violence inflicted on the children, conflict between partners, availability
of resources, decision-making process, woman’s freedom, woman’s attitudes towards gender roles, social resources, share of household work, public abuse (at school, work and
community) and specific information for women 60 or older.
5.2
5.2.1
Variables
Dependent variable
The dependent variable captures the incidence of IPV, which refers to whether the
woman has been abused. Given the multidimensionality of violence, the survey contains
information on 30 violent items32 that capture four types of abuse: physical, sexual,
emotional and economic. Table 1 presents the complete list of them and their incidence
rates in 2006 and 2011. I exclude from my analysis the violent event ‘has your partner
stopped talking to you?’, since this question can be interpreted with a large degree of
subjectivity due to the way it is formulated33 .
Before constructing the dependent variable I need to reduce the 29 remaining violent
items into a smaller number of dimensions. For this purpose I use factor analysis (see the
appendix for details). Based on its output, I exclude two violent items and classify the
remaining 27 into four dimensions, which I term physical34 , sexual, emotional and economic abuse. Next, I construct the dependent variable for each of these four dimensions.
It is defined as a dichotomous variable equal to one if the woman has experienced IPV in
the 12 months previous to when the survey was conducted and zero otherwise.
5.2.2
Explanatory variable
The explanatory variable of interest is woman’s relative wage, which is measured as
the share of woman’s wage over the couple’s total wage. This variable can range between
zero and one. Table 2 presents the relative wage distribution split into five bands. As
said before, it is highly skewed to the right, since around 70% of women do not work for
pay. Income is given in Mexican Pesos (1 British Pound is approximately 23.79 Mexican
32
I use violent item, violent event and violent act interchangeably.
For instance, the woman might interpret that stop talking for a few hours as a consequence of an
argument is emotional abuse, but it is far from clear that it is. Conversely, if stop talking is used as a
punishment or to make the woman feel guilty, then it can be considered emotional abuse.
34
Includes both physical violent items and serious threats.
33
16
Table 1: Incidence rate of IPV by violent item and year
2006
2011
Mean
Std.
Dev.
Mean
Std.
Dev.
Physical IPV
pushed you or pulled your hair?
tied you up?
kicked you?
thrown any object at you?
beaten you with his hands or any object?
tried to hang or choke you?
assaulted you with a knife or blade?
fired a weapon at you?
0.074
0.002
0.020
0.030
0.053
0.010
0.004
0.001
0.262
0.040
0.140
0.172
0.225
0.100
0.066
0.033
0.037
0.001
0.008
0.015
0.035
0.007
0.002
0.000
0.189
0.031
0.091
0.123
0.185
0.081
0.047
0.018
Sexual IPV
demanded you to have sexual relations?
forced you to do sexual things that you do not like?
used physical strength to force you to have sexual relations?
0.050
0.015
0.016
0.219
0.121
0.126
0.025
0.009
0.010
0.157
0.095
0.100
0.068
0.097
0.072
0.059
0.252
0.296
0.259
0.235
0.076
0.097
0.069
0.053
0.265
0.296
0.253
0.224
0.067
0.250
0.060
0.237
0.029
0.024
0.022
0.005
0.015
0.167
0.153
0.146
0.073
0.123
0.023
0.021
0.022
0.004
0.013
0.149
0.144
0.147
0.066
0.113
0.031
0.174
0.026
0.159
0.202
0.402
0.173
0.378
0.110
0.312
0.087
0.282
0.073
0.260
0.068
0.252
0.042
0.063
0.008
0.083
0.200
0.242
0.090
0.275
0.037
0.049
0.008
0.056
0.189
0.215
0.089
0.230
During the last year, has your husband or partner...
Emotional IPV
ashamed, underestimated or humiliated you?
ignored or not shown you affection?
said you cheat on him?
made you feel fear?
threatened to leave you, hurt you, take your children away
or kick you out?
locked you in, forbidden you from going out or being visited?
turned your children or relatives against you?
stalked or spied on you?
threatened you with a weapon?
threatened to kill you, himself or the children?
destroyed, thrown away or hidden things belonging to you or
the household?
sopped talking to you?
got angry because household chores are not done or not like
he wants?
Economic IPV
been stingy with the household expenses, even though he
has money?
not given you the upkeep or threatened you to not giving it?
spent money needed for the household?
appropriated or taken money or possessions from you?
forbidden you to work or study?
Notes: ‘Std. Dev.’ stands for standard deviation.
Pesos). It is top-coded, such as the upper bound is 999,998 in 2006 and 96,000 in 2011.
This has two main implications. First, it can introduce a censoring problem if the number
of top-coding cases is large, but this is not the case in my sample. It only affects 41 women
17
and 336 men in 2006 and 0 in 2011 (0.37% of the sample).
Table 2: Distribution of woman’s relative wage
2006
2011
Woman’s relative wage
Frequency
Percentage
Frequency
Percentage
0
(0-0.5)
0.5
(0.5-1)
1
37,097
8,324
2,234
2,893
1,078
71.86
16.12
4.33
5.6
2.09
35,312
8,505
2,660
3,171
1,325
69.28
16.69
5.22
6.22
2.6
Total
51,626
100
50,973
100
Second, it can lead to a peak in a relative wage equal to 0.5, since any woman in
a couple where both partners have an income above the upper bound will have this
value of the relative wage. I deal with this latter issue by excluding couples where both
partners earn top-coded incomes, as done in Bertrand et al. (2015). This drops very few
observations. After this adjustment, there is still a large peak at a relative wage equal to
0.5 in both years (see figure 2, which was presented in section 3). This could be due to
the fact that the wage question is answered by the target woman in 2006, while by any
member of the household aged 15 or older who knows about the sociodemographic and
economic characteristics of all the household members in 2011. Specially in this latter
case, it could be that if the respondent does not know the exact wage of either of the
partners, she could report the same amount for both, which would introduce measurement
error bias.
In order to examine this for 2011, I create an indicator of having a relative wage equal
to 0.5 and regress it on a dichotomous variable equal to one if the respondent is the
eligible woman and zero otherwise. After controlling for municipality fixed effects and a
wide set of covariates35 , I find that the probability of having a relative wage equal to 0.5
is lower when the eligible woman is the respondent. However, it is only significant at the
10% level. Moreover, the baseline results, which will be presented later, are very similar
when I control for whether the woman is the respondent. As an additional robustness
check, I will exclude the observations in which the relative wage is in the middle of the
distribution.
5.2.3
Covariates
I also include a set of control variables at the individual (both partners’ age, both partners’ indigenous background, both partners’ educational attainment, woman’s experience
of violence in her childhood and woman’s industry of work), couple (couple’s total wage,
35
The ones explained in the next subsection.
18
marital status, length of the relationship and extended family) and household (urban
residence and socio-economic status (SES) index) level. All data comes from ENDIREH.
Age is given in years and ranges from 15 onwards. I measure whether the woman or
her partner are part of an indigenous group with a dichotomous variable equal to one if the
woman (man) speaks an indigenous language and zero otherwise. Educational attainment
is measured as the maximum level of education reached, which I group into three categories, namely primary education (completed primary or a lower educational attainment),
secondary education (finished secondary education, A levels or technical vocational training) and higher education (finished higher technical vocational training, undergraduate
or postgraduate studies). Woman’s experience of violence in her childhood is a dichotomous variable equal to one if the woman experienced either physical or emotional abuse
before the age of 13 in her family of origin and zero otherwise. There is a large body of
literature in favour of the inter-generational transmission of IPV (Pollak, 2004; Franklin
and Kercher, 2012). Woman’s industry of work is a categorical variable that includes the
nine industries explained in the previous section, as well as a ‘baseline’ industry, which
includes women non-working for pay. This controls for differences between working and
non-working women36 .
Couple’s total wage is the sum of both partners wage expressed in Mexican pesos. They
have been converted into real terms using the National Consumer Price Index. Marital
status is a dichotomous variable equal to one if the woman is married and zero if she is
cohabiting. The length of the relationship is the number of years the couple has been
living together. Extended family is equal to one if the couple lives with other members
and zero if they live alone. Urban residence is measured as a dichotomous variable equal
to one if the household is located in an urban area and zero if in a rural. Mexican surveys
and censuses define a rural area as that with less than 2,500 inhabitants. I include a SES
index, which captures the household living standard (see details in the appendix). Table
3 presents the descriptive statistics of all the variables.
5.3
Sample
I restrict my sample to married or cohabiting women residing with their partners.
I exclude d1ivorced, separated, widowed and single women because I am interested in
within couple dynamics. Moreover, I do not have the same information than for married
or cohabiting women regarding IPV, as well as no information about their partner’s
characteristics including wage. I further restrict the sample to women working as employee
or not working, which excludes those that are employers or self-employed. The reason
is that I do not have data on their industry of work, which I need for constructing my
instrument. In addition, since my instrument is driven by inter-state variation in the
36
Results are very similar if, instead of the woman’s industry of work, I use an indicator of whether
the woman is non-working, as done in Bertrand et al. (2015).
19
Table 3: Descriptive statistics
Physical IPV
Sexual IPV
Emotional IPV
Economic IPV
Woman’s relative wage
Age woman
Age man
Indigenous woman
Indigenous man
Primary education woman
Secondary education woman
Higher education woman
Primary education man
Secondary education man
Higher education man
Violence in childhood
Industry: Non working
Industry: Public sector
Industry: Private company
Industry: Trade
Industry: School
Industry: Factory
Industry: Taller
Industry: House
Industry: Agriculture
Industry: Other
Couple’s total wage
Marital status
Length relationship
Extended family
Location
SES low
SES middle
SES high
N
Mean
Std. Dev.
Min
Max
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
102,599
0.061
0.041
0.228
0.203
0.143
38.019
41.030
0.061
0.064
0.373
0.440
0.187
0.361
0.431
0.208
0.418
0.706
0.080
0.060
0.048
0.029
0.018
0.006
0.038
0.006
0.009
12,136
0.753
16.522
0.910
0.804
0.416
0.398
0.187
0.240
0.198
0.419
0.402
0.246
11.987
12.575
0.239
0.246
0.484
0.496
0.390
0.480
0.495
0.406
0.493
0.456
0.271
0.238
0.213
0.168
0.133
0.080
0.190
0.077
0.096
73,111
0.431
11.971
0.287
0.397
0.493
0.489
0.390
0.000
0.000
0.000
0.000
0.000
15.000
13.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
4.793
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
1.000
92.000
97.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1,246,235
1.000
75.000
1.000
1.000
1.000
1.000
1.000
Notes: ‘N’ stands for number of observations, ‘Std. Dev.’ for standard deviation, ‘Min’ for
minimum value and ‘Max’ for maximum value.
demand for workers, employer and self-employed women are not likely to be affected by
changes of it.
I also drop observations in which none of the partners has earned income from work,
since I am interested in how they react to wage differences between them37 . I also remove
all observations with any single missing value in a variable. Finally, I drop municipalities
with only one woman in my sample, since I am using within-municipality variation for
estimation. After all these adjustments, the sample is reduced to 102,599 women (51,626
in 2006 and 50,973 in 2011).
37
Following this argument, I should also remove couples where both partners earn the same income.
However, I include them, since men might react to a relative wage equal to 0.5 given that his partner is
near to overpass his wage.
20
6
Baseline results
Table 4 reports the first and second-order coefficients of woman’s relative wage based
on the estimation of equation 1. Column I presents the OLS estimates and column II the
corresponding 2SLS. Both point in the same direction. Column III restricts the sample to
women with a relative wage greater than zero. The reason is that, given the large number
of women not working for pay (around 70% of the sample), one might wonder whether
my results are driven by this subgroup. I only present the OLS estimates because the
instrument is weekly correlated with the woman’s relative wage. However, the fact that
specifications I and II point in the same direction, provides confidence that this might
also be the case for this subset of the sample38 .
Focusing on my preferred specification (column II), the first-order coefficient of the
woman’s relative wage is negative, while the second-order positive, and both are statistically significant at conventional levels39 . This is true for the four types of abuse. Thus,
the incidence of IPV declines as the woman’s relative wage increases up to a turning point
after which it increases40 .
The first three rows of table 5 report the value, standard error, and 95% Fieller
confidence interval of the turning points. The values are 0.549, 0.662, 0.625 and 0.604 for
physical, sexual, emotional and economic abuse, respectively, which lie within the range
of possible values and are not close to the extremes of the data range. Furthermore, these
thresholds are not significantly different from each other. The last row of table 5 reports
the p-value associated with the overall test for the U shape proposed by Lind and Mehlum
(2010). In the four cases I reject the null hypothesis that the relationship is monotone
and/or follows an inverted U shape, at least at the 5% significance level. In short, all this
evidence provides support for the existence of a U-shaped relationship.
Furthermore, table 6 presents the marginal effect of the woman’s relative wage on
the incidence of IPV, as well as the sample mean of IPV, at five representative values
of the relative wage distribution, namely 0, 0.25, 0.5, 0.75 and 1. The greatest effect is
seen at both extremes, which provides further support for the U-shaped relationship. For
instance, when the woman’s relative wage is equal to zero, an increase of 0.1 in her relative
wage is expected to decrease the probability of physical abuse by 0.185, while when her
relative wage is equal to one it is expected to increase it by 0.152. Given that the average
value of physical abuse when the woman’s relative wage is zero and one is 0.060 and 0.093,
respectively, these numbers are quite large in economic terms. However, the increase in
IPV is expected to affect a minority of women, since only 6.54% of them have a relative
38
Indeed, the estimates from 2SLS are in line with those in column II, even after conducting tests
robust to weak instruments (i.e. Anderson-Rubin Wald test and Stock-Wright LM S statistic).
39
The significance of the first-order coefficient is not necessary. Also, its interpretation differs from
that in a linear regression. It indicates the marginal effect when a woman’s relative wage is equal to zero
(Lind and Mehlum, 2010).
40
The fact that IPV increases indicates that women are below their threat point.
21
Table 4: Effect of woman’s relative wage on IPV – Main specification
I
II
III
Relative wage>0
All
OLS
2SLS
OLS
-0.058*
(0.034)
0.073***
(0.028)
-1.854**
(0.883)
1.687**
(0.657)
-0.068*
(0.034)
0.078***
(0.029)
-0.039
(0.024)
0.038*
(0.021)
-1.727**
(0.734)
1.304**
(0.546)
-0.026
(0.026)
0.025
(0.022)
-0.279***
(0.056)
0.215***
(0.045)
-6.212***
(1.749)
4.974***
(1.332)
-0.277***
(0.058)
0.204***
(0.047)
-0.249***
(0.055)
0.198***
(0.045)
-3.110**
(1.528)
2.576**
(1.169)
-0.223***
(0.057)
0.169***
(0.046)
102,599
102,599
30,190
Panel A: Physical
Relative wage
Relative wage sq
Panel B: Sexual
Relative wage
Relative wage sq
Panel C: Emotional
Relative wage
Relative wage sq
Panel D: Economic
Relative wage
Relative wage sq
Observations
Notes: OLS and 2SLS estimates reported. Robust standard errors in parentheses. Dependent variable
is the incidence of IPV. All specifications control for both partners’ age, both partners’ indigenous background, both partners’ educational attainment, woman’s experience of violence in her childhood, woman’s
industry of work, couple’s total wage, marital status, length of the relationship, whether the couple lives
alone, urban residence and SES index, as well as municipality and year fixed effects. ***significant at 1%
level, **at 5%, *at 10%.
wage above 0.549. Similar comments can be made for the other three types of abuse.
In sum, there is strong evidence in favour of a U-shaped effect of the woman’s relative wage on the incidence of the four types of IPV. Compared to previous studies, my
estimates are consistent with Angelucci (2008) findings for drunken violence in the context of the CCT programme Oportunidades. They are also in line with the estimates in
Farmer and Tiefenthaler (1997) using non-wage income for two small samples in Nebraska
and North Carolina (United States). However, these samples are not random and their
estimates are likely to suffer from endogeneity. Conversely, my results stand in sharp
contrast to the theoretical prediction of Anderberg and Rainer (2013) for the incidence
of economic abuse. They propose an inverted U shape, while I find the opposite. One
22
Table 5: Effect of woman’s relative wage on IPV – Turning point and U test
Threshold: Value
Threshold: SE
Threshold: 95% CI
U test: P>|t|
I
II
III
IV
Physical
Sexual
Emotional
Economic
0.549
(0.068)
[0.142, 0.654]
0.662
(0.058)
[0.472, 0.873]
0.625
(0.034)
[0.537, 0.697]
0.604
(0.058)
[0.171, 0.746]
0.018
0.015
0.000
0.021
Notes: Standard errors calculated based on the delta method. ‘SE’ stands for standard error and ‘CI’
for (Fieller) confidence interval. U test refers to the test for the presence of a U shape proposed by Lind
and Mehlum (2010).
Table 6: Effect of woman’s relative wage on IPV – Marginal effects
I
II
III
IV
V
0
0.25
0.5
0.75
1
Relative wage
-1.854**
(0.883)
-1.010*
(0.565)
-0.167
(0.274)
0.677***
(0.217)
1.521***
(0.485)
Mean of IPV
0.060
0.033
0.045
0.051
0.093
Relative wage
-1.727**
(0.734)
-1.076**
(0.471)
-0.424*
(0.230)
0.228
(0.182)
0.880**
(0.403)
Mean of IPV
0.039
0.038
0.029
0.026
0.060
Relative wage
-6.212***
(1.749)
-3.725***
(1.103)
-1.238**
(0.512)
1.249***
(0.441)
3.736***
(1.007)
Mean of IPV
0.219
0.261
0.197
0.103
0.259
Relative wage
-3.110**
(1.528)
-1.822*
(0.960)
-0.534
(0.437)
0.754**
(0.380)
2.042**
(0.883)
Mean of IPV
0.201
0.260
0.162
0.179
0.209
Observations
102,599
102,599
102,599
102,599
102,599
Panel A: Physical
Panel B: Sexual
Panel C: Emotional
Panel D: Economic
Notes: The first row reports marginal effects, while the second the average value of IPV in the sample.
Standard errors calculated based on the delta method in parentheses. Dependent variable is the incidence
of IPV. Columns I-V refer to five different values of woman’s relative wage (0, 0.25, 0.5, 0.75 and 1).
All specifications control for both partners’ age, both partners’ indigenous background, both partners’
educational attainment, woman’s experience of violence in her childhood, woman’s industry of work,
couple’s total wage, marital status, length of the relationship, whether the couple lives alone, urban
residence and SES index, as well as municipality and year fixed effects. ***significant at 1% level, **at
5%, *at 10%.
23
reason could lie in the importance of traditional gender norms in Mexico, which do not
favour the existence of a reduction in the incidence of IPV for high levels of the woman’s
relative wage.
7
Robustness Checks
In this section I test for the robustness of my results to adding additional control
variables, as well as changing the definition of the sample and dependent variable. All
tests are reported in table 7. The first robustness check consists of controlling for the
overall violent environment in the municipality (column I). I proxy for it by the male’s
homicide rate, which is measured as the number of intentional homicides per 100,000
inhabitants. Data for homicides come from death certificates and for population from
CONAPO (Consejo Nacional de Población) [National Council of Population]. I exclude
female homicides because this would also include intimate partner homicides, which are
likely to be endogenous (i.e. woman’s relative wage can directly affect female homicides).
Second, I add a dichotomous variable equal to one if the state had introduced unilateral
and no-fault divorce by the time of the survey (column II). Only two states introduced
this type of divorce during my time span, namely Mexico City in 2008 and Hidalgo in
2011. The introduction of this type of divorce reduces the cost of dissolving the marriage,
which could affect the probability that the man inflicts abuse, as previous studies have
shown (Stevenson and Wolfers, 2006; Brassiolo, 2016).
Third, I add the square of woman’s age (column III). Previous studies have found
that the incidence of IPV is greater when woman’s age is between the mid-20 and mid30, which suggests an inverted U-shaped relationship between age and IPV (Castro et al.,
2008). Thus, excluding this variable could be contaminating the non-monotonic estimates
of interest. The fourth robustness check consists of excluding women with a relative wage
equal to 0.5 (column IV). The reason is that I observe a peak in the middle of the relative
wage distribution, which could be driven by measurement error of the wages.
Fifth, I restrict the sample to women aged 15-65 (column V), which represents the
working age population in many countries. A sixth robustness test consists of modifying
the dependent variable (column VI). Based on factor analysis I had decided to drop two
violent items, namely ‘has your partner pushed you or pulled your hair?’ (classified as
physical abuse) and ‘has your partner appropriated or taken money or possessions from
you?’ (classified as economic abuse). I add these two items in order to check that my
results are not sensitive to include them.
Overall, estimates in table 7 confirm the baseline results in terms of the sign, magnitude
and significance of the coefficients. Furthermore, the thresholds are similar to the ones
reported in table 5. Finally, I check that the U-shaped effect is not driven by the extreme
values of the woman’s relative wage (zero and one). For this purpose I conduct the U test
restricting the data range to [0.1, 0.9]. Main conclusions remain unchanged.
24
Table 7: Effect of woman’s relative wage on IPV – Robustness checks
I
II
III
IV
V
VI
Homicide
rate
Unilateral
divorce
Age sq
RW=0.5
Age<=65
Factor
analysis
-1.837**
(0.882)
1.677**
(0.657)
-1.829**
(0.878)
1.668**
(0.652)
-1.796**
(0.878)
1.638**
(0.653)
-2.611**
(1.040)
2.293***
(0.784)
-1.922**
(0.892)
1.743***
(0.662)
-2.342**
(1.015)
1.884**
(0.759)
-1.742**
(0.735)
1.313**
(0.546)
-1.769**
(0.733)
1.336**
(0.544)
-1.649**
(0.730)
1.236**
(0.542)
-1.639**
(0.822)
1.292**
(0.618)
-1.639**
(0.737)
1.232**
(0.545)
-1.727**
(0.734)
1.304**
(0.546)
-6.270***
(1.743)
5.019***
(1.325)
-6.133***
(1.739)
4.906***
(1.323)
-6.610***
(2.000)
5.338***
(1.531)
-6.177***
(1.756)
4.954***
(1.335)
-6.212***
(1.749)
4.974***
(1.332)
-3.149**
(1.528)
2.601**
(1.169)
-3.345**
(1.528)
2.757**
(1.167)
-3.043**
(1.521)
2.519**
(1.162)
-3.736**
(1.748)
3.101**
(1.343)
-2.976*
(1.532)
2.483**
(1.170)
-3.112**
(1.528)
2.558**
(1.169)
102,599
102,599
102,599
97,703
100,532
102,599
Panel A: Physical
Relative wage
Relative wage sq
Panel B: Sexual
Relative wage
Relative wage sq
Panel C: Emotional
Relative wage
Relative wage sq
-6.238***
(1.750)
4.991***
(1.333)
Panel D: Economic
Relative wage
Relative wage sq
Observations
Notes: 2SLS estimates reported. Robust standard errors in parentheses. Dependent variable is the
incidence of IPV. All specifications control for both partners’ age, both partners’ indigenous background,
both partners’ educational attainment, woman’s experience of violence in her childhood, woman’s industry
of work, couple’s total wage, marital status, length of the relationship, whether the couple lives alone,
urban residence and SES index, as well as municipality and year fixed effects. ***significant at 1% level,
**at 5%, *at 10%.
8
Potential explanations
The previous section has shown the existence of a U-shaped relationship between a
woman’s relative wage and the incidence of IPV. This could be driven by several mechanisms, which this section attempts to shed some light on.
8.1
Couple’s total wage versus threat to man’s dominance
An improvement in the woman’s relative wage can directly impinge on IPV through
two opposite channels. First, by reducing household stress. The idea is that an increase
in the woman’s relative wage is accompanied by a raise in the couple’s total wage41 , which
41
This assumes that both partners’ can benefit from woman’s earnings and that the man’s earnings
have not decreased or have not done so to a larger extent.
25
might reduce household stress due to economic motives. This could in turn reduce the
incidence of IPV if it is associated with economic stress42 . Second, IPV can increase if the
improvement in the woman’s relative wage is perceived as a threat to the man’s dominant
position, which is consistent with a male backlash story.
Which of the two channels dominates might depend on the point of the relative wage
distribution in which the woman is at. It could be that, for low levels of her relative wage,
the increase in the couple’s total wage more than compensates the threat to the man’s
dominant position, which reduces the probability that he restores to IPV. Conversely, as
the woman’s relative wage increases, the threat to the man’s dominant position might
overcome the rise in the couple’s total wage, which would lead to an increase in the
incidence of IPV. This explanation is based on the theoretical argument proposed by
Angelucci (2008).
I cannot test for this channel with the available data. At most, I can test for whether
IPV declines as the couple’s total wage increases. In particular, I look at the covariate
‘couple’s total wage’, which has been included in all the previous regressions. I find that
an increase in the couple’s total wage is associated with a significant decline in IPV, which
is consistent with the reduction in household stress hypothesis. Whether this reduction
offsets the threat to the man’s dominant position cannot be known. Ideally, I would
like to have information on the man’s perception about the woman’s improved economic
condition in order to be able to compare the size of both effects.
8.2
Bargaining power
The previous explanation refers to a channel by which men directly react to the
woman’s relative wage. However, it could be that it is not only her relative wage that
matters, but also her bargaining power. External threat-point models predict an improvement in the woman’s bargaining power as her relative wage does. In order to examine
this, I estimate the same model as in equation 1, but with woman’s bargaining power as
an outcome. I proxy for it using her power in the decision-making process. Estimates are
reported in columns I and II of table 8. Column I assumes a linear relationship, while
column II allows for non-monotonicity. It seems that the impact is non-monotonic. Focusing on column II, I find a U-shaped relationship with a turning point at 0.394. This
is confirmed at the 10% significance level (p-value: 0.0563) when I use the test proposed
by Lind and Mehlum (2010). However, only 18.35% of women have a relative wage above
this point, which suggests that most of them are likely to curtail their bargaining power
as their relative wage improves.
Furthermore, table 9 reports the marginal effect at five representative values of the
woman’s relative wage. Looking at the extreme values, an increase of 0.1 in the woman’s
42
This argument is related to stress frustration theory, which posits that households with fewer resources
will experience higher levels of violence due to stress (Benson et al., 2003).
26
relative wage is expected to decrease her bargaining power by 0.107 when her relative
wage is equal to zero, while increase it by 0.164 when it is equal to one. Compared
to the average value of the index in the two extremes (0.610 and 0.694, respectively),
these numbers are quite low in economic terms, which contrasts with the findings for the
incidence of IPV.
In short, women curtail their bargaining power up to a relative wage equal to 0.394
after which they increase it. One possible explanation, which has already been outlined
in section 3, is related to gender identity norms. In particular, the woman might curtail
her bargaining power as a strategy to compensate for the increase in her relative wage in
order to maintain an equilibrium with traditional gender norms43 . Similar explanations
have been proposed in the context of woman’s autonomy in India (Eswaran and Malhotra,
2011) and housework share in United States (Bertrand et al., 2015). The existence of a
threshold could be due to the fact that, once she approaches her partner’s wage, she
might feel sufficiently empowered in economic terms to claim greater power and allocate
resources more in accordance with her preferences.
The curtailment of bargaining power is a priori not in line with the classical prediction
of household bargaining models. This is likely due to the fact that, under these models,
the woman’s bargaining power is determined by her relative wage at the threat point.
In this paper, I have been looking at the woman’s current relative wage and how this
impinges on the observed bargaining power, which does not necessarily coincide with the
unobserved one determined at the threat point. It could be that this latter has increased
along the relative wage distribution.
How the U-shaped relationship between relative wage and bargaining power relates to
that between relative wage and IPV cannot be determined with precision. The reason is
that whether a change in bargaining power is the cause or the consequence of IPV cannot
be known with the available data. Moreover, it could also be unrelated to IPV. Even
though, the fact that the threshold for the woman’s bargaining power (i.e. 0.394) occurs
at an earlier stage than those for the incidence of IPV (i.e. between 0.55 and 0.67)44 ,
suggests that the change in bargaining power might precede that in IPV, and provides a
plausible explanation. It could be that, by curtailing her bargaining power, the woman is
not challenging her partner’s culturally prescribed dominant position and, consequently,
the probability that he uses IPV declines. Conversely, once she starts claiming greater
power, she might be threatening his dominant position, which increases the likelihood
that he uses IPV as an expression of disagreement and/or in order to restore power
and reassert dominance. Broadly speaking, the increase in IPV is consistent with relative
43
One can interpret this curtailment of bargaining power as an allocation of resources more in accordance with her partner’s preferences. This interpretation assumes that the woman manages the income
of the household and decides on the allocation of resources, which is the most common way of household
financial management in Mexico.
44
The difference is significant for the four types of IPV.
27
resource theory and a male backlash effect. Moreover, it is also consistent with qualitative
evidence found in Maldonado et al. (2005).
8.3
Gender beliefs
A third potential explanation refers to a change in the woman’s gender attitudes. In
order to examine this, I estimate the impact of the woman’s relative wage on an index
that captures eleven questions related to her opinion about gender roles (details about
its construction can be found in the appendix). Estimates are reported in columns III
and IV of table 8. As can be seen, the relationship is better represented as linear, since
the second order-term is insignificant and the turning point lies outside of the range of
possible values. Thus, focusing on column III, an increase of 0.1 in the woman’s relative
wage is predicted to significantly raise the index by 0.041. This is a quite low number
compared to the average value of it in my sample (0.90).
Overall, it seems that the increase in the woman’s relative wage has been accompanied
by a move towards more progressive gender beliefs. However, as in the case of bargaining
power, its association with IPV is unclear. The reason is that I cannot disentangle whether
the change in gender beliefs precedes, follows or is completely unrelated with that in IPV.
If it precedes it, it could be that, for low levels of the woman’s relative wage, gender beliefs
do not represent a threat to the man’s dominant position, but they do for relatively higher
levels.
Table 8: Effect of woman’s relative wage on bargaining power/gender beliefs/economic
IPV item – Mechanisms
I
II
Bargaining power
Relative wage
IV
Gender beliefs
V
VI
Economic IPV item (income extraction)
Linear
Non-linear
Linear
Non-linear
Linear
Non-linear
0.770***
(0.148)
-1.066
(0.672)
1.352***
(0.513)
0.413***
(0.086)
0.932**
(0.439)
-0.381
(0.325)
0.051
(0.069)
-0.499
(0.340)
0.404
(0.254)
101,474
101,474
102,588
102,588
102,599
102,599
Relative wage sq
Observations
III
Notes: 2SLS estimates reported. Robust standard errors in parentheses. Dependent variable in columns
I and II is an index of woman’s decision-making power, in columns III and IV an index of woman’s gender
beliefs and in columns V and VI an indicator variable of whether the woman’s partner has appropriated
or taken money or possessions from her. All specifications control for both partners’ age, both partners’
indigenous background, both partners’ educational attainment, woman’s experience of violence in her
childhood, woman’s industry of work, couple’s total wage, marital status, length of the relationship,
whether the couple lives alone, urban residence and SES index, as well as municipality and year fixed
effects. ***significant at 1% level, **at 5%, *at 10%.
8.4
Income extraction
The increase in the incidence of IPV could also be driven by motives of rent extraction
(Bobonis et al., 2013). That is, the man uses IPV in order to extract the additional
28
Table 9: Effect of woman’s relative wage on her bargaining power – Marginal effects
I
II
III
IV
V
0
0.25
0.5
0.75
1
Relative wage
-1.066
(0.672)
-0.390
(0.423)
0.286
(0.196)
0.963***
(0.172)
1.639***
(0.391)
Mean of DMI
0.610
0.691
0.686
0.732
0.694
Observations
101,474
101,474
101,474
101,474
101,474
Notes: The first row reports marginal effects, while the second the average value of the woman’s decisionmaking index in the sample. Standard errors calculated based on the delta method in parentheses.
Dependent variable is the woman’s decision-making power index. ‘DMI’ stands for decision-making
index. Columns I-V refer to five different values of woman’s relative wage (0, 0.25, 0.5, 0.75 and 1).
All specifications control for both partners’ age, both partners’ indigenous background, both partners’
educational attainment, woman’s experience of violence in her childhood, woman’s industry of work,
couple’s total wage, marital status, length of the relationship, whether the couple lives alone, urban
residence and SES index, as well as municipality and year fixed effects. ***significant at 1% level, **at
5%, *at 10%.
wage earned by the woman. This is considered a type of economic abuse in the survey,
which is captured through the question ‘has your partner appropriated or taken money
or possessions from you?’45 . Only 0.81% of women report this is the case, which suggests
that it is unlikely to be a significant motivation for violence. I also estimate the effect of
the woman’s relative wage on this single violent item (see columns V and VI). I find that
the impact is insignificant, which provides further support against this mechanism.
9
Conclusion
The association between a woman’s economic status and IPV has been of interest
in the economics literature for almost three decades. However, previous research still
presents several limitations, which this paper has attempted to overcome. Exploiting
inter-state variation in female industrial labour composition and controlling for a wide
set of variables at the individual, couple and household level, as well as municipality
and year fixed effects, this paper has identified the effect of a woman’s relative wage on
the incidence of physical, sexual, emotional and economic abuse. It has gone beyond
previous studies by using a relative measure of woman’s economic status, accounting for
its endogeneity and examining the potential existence of a non-monotonic relationship.
I have found a U-shaped effect of the woman’s relative wage on the incidence of the
four types of abuse. That is, as the woman’s relative wage increases, the incidence of
IPV decreases up to a turning point, which occurs at a relative wage between 0.55 and
0.67, after which it increases. I have found support for several potential explanations.
The negative slope could be driven by a voluntary curtailment of the woman’s bargaining
45
This violent item has been excluded from the baseline regression based on the factor analysis output,
but it is included in the robustness check section, which confirms the baseline results.
29
power in order to maintain an equilibrium with traditional gender norms, which decreases
the probability that her partner uses IPV. Conversely, the positive slope could be driven
by an increase of the woman’s bargaining power, which is perceived as a threat by her
partner, who turns to the use of IPV in order to restore power and reassert dominance.
In addition, the increase in the woman’s relative wage seems to have been accompanied
by a reduction in household stress and a move towards more progressive gender beliefs.
Although my results are robust to a number of checks (i.e. adding additional covariates,
modifying the sample and changing the definition of the dependent variable), they have
to be interpreted carefully given the cross-sectional nature of the data. This does not
allow me to control for unobserved individual factors that could be contaminating the
relationship between a woman’s relative wage and IPV.
The findings in this paper shed some light on what type of intervention programmes to
prevent IPV could work. In particular, economically empowering women can significantly
decrease the incidence of IPV, at least up to a relative wage lower than her partner’s. A
programme in this line is Seguro contra la violencia familiar (Insurance against family
violence), which was implemented in Mexico City for the first time in 2009. One of its
goals is to provide an income transfer to the victim to make her economically independent
from the abuser. These types of initiatives can be useful to help women to leave a violent
relationship and start a ‘new’ life. However, they can also trap them into violence if
they decide to stay. The reason is that they assume that women stay with the abuser
because they are economically dependent. However, economic factors might not be the
real underlying reason. Only 2.21% of abused women in my sample report this is the case.
Most of them point out that violence is not that important, which suggests the need to
increase awareness about all types of IPV and their negative consequences.
As can be seen, programmes based on primarily empowering women in economic
terms are only likely to have a limited impact on reducing IPV, and can also have perverse
consequences on it. Moreover, they will not necessarily strengthen the woman’s bargaining
position in the household, since she might decide to curtail her bargaining power, which
I have shown to occur for a relative wage below 0.394. This could have further negative
consequences on children. Previous research has found that mothers tend to invest more
on children than fathers do, as well as allocate more resources to daughters than sons
(Reggio, 2011; Duflo, 2012).
In short, the evidence above suggests the need for programmes aimed at not only economically empowering women, but also changing the unequal gender power structures and
how they are perceived by both partners, since this constitutes the root of the problem.
These types of programmes should have both a preventive and post-violence perspective.
From a preventive perspective, IMIFAP (Institute for the Research on Family and Population) implements workshops aimed at changing socially prescribed gender roles, as well
as offering alternative ways of conflict resolution between women and men by recognising
30
each other’s rights as individuals46 . However, even if there has been a growing number of
intervention programmes to tackle IPV, there is still little evidence about the effectiveness
of them. This constitutes one of the main areas of development for future research, which
would shed some light on ‘what works’ to reduce IPV, as well as set some best practices
in the field.
46
For instance, one of the participants of IMIFAP workshops said ‘I learned that we as women have
rights to choose what we want, to do it or not, to decide about the number of kids, as well as our sexual
relations with our husbands’ (Pick et al., 2006, p. 271).
31
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36
Appendix. Construction of variables
Incidence of IPV: Factor analysis
Using factor analysis to reduce the number of violent items into a smaller set of
dimensions has been increasingly employed with Mexican data (see, for example, ValdezSantiago et al. (2013)). For doing so, I pool the two cross-sectional samples and drop
observations with missing values in any of the violent events. As said before, the item
‘has your partner stopped talking to you?’ has been removed. I use iterated principal
factor as the extraction method and oblique rotation (specifically, promax) for rotating
the factors47 .
The crucial question before rotation is how many factors keep for it. Several criteria
have been adopted being the most widely used the rule of thumb of retaining those with
an eigenvalue greater than 1. Applying this rule, I would retain only the two firsts.
However, this does not make theoretical sense, since a priori I would have expected at
least four conceptually different dimensions of abuse48 . Given this, I compare the results
obtained from retaining two, three and four factors and conclude that the one employing
three factors performs better. Factor 1 has an eigenvalue of 7.33 and explains 53.41% of
the variance in the data, factor 2 of 1.49 and explains 10.86%, and factor 3 of 0.92 and
explains 6.70%. These three factors explain in total 70.97% of the variance.
The output from factor analysis is shown in table 10. The three columns correspond
to the three factors retained, while the factor loadings are the correlations between each
of these three factors and the 29 violent events. The higher the factor loading, the more
relevant is the item for defining the factor. Most emotional and economic abuse items
have higher loadings in factor 1, most physical abuse items and serious threats in factor
2 and all sexual abuse ones in factor 3. It is not surprising that serious threats have been
grouped together with physical abuse events, since they can be considered to be similar
in terms of their severity.
I decide to exclude items that have ambiguous loadings (defined as loadings above
0.3 in more than one factor) and those with loadings below 0.3. This affects one item
in each case. The factor loadings of the events retained are marked in bold. A question
arose on whether to keep items with a very large uniqueness. The uniqueness refers to the
proportion of variance not explained by the common factors (Rencher and Christensen,
2012). Some authors argue that above 0.6 is a very large value, but this is not a feasible
threshold in my sample, since most of the items are above 0.6.
47
The factor loadings are rotated in order to obtain a clearer pattern of the data structure. Two main
types of rotation methods are available. First, orthogonal methods (varimax, quartimax, equamax),
which do not permit the factors to be correlated. Second, oblique rotation (promax, direct oblimin,
quartimin), which produce factors that are correlated. Within the second type, promax has been the
most often employed method (Rencher and Christensen, 2012).
48
One for physical, sexual, emotional and economic abuse; or, alternatively, one for severe physical,
non-severe physical and emotional, sexual, and economic abuse.
37
Regarding the economic abuse events, despite they are classified together with most
emotional ones, I consider them as a different dimension since they are conceptually
different. After all these adjustments, I end up with four dimensions that I have termed
physical (9 items), sexual (3 items), emotional (10 items) and economic (5 items) abuse.
This makes a total of 27 items. Their joint Cronbach’s alpha is 0.882 and the individual
0.723 for physical, 0.701 for sexual, 0.815 for emotional and 0.690 for economic abuse. The
Cronbach’s alpha is a measure of internal consistency, that is, the extent to which the
items included in the scale measure the same concept. In most social science applications,
a reliability coefficient higher than 0.70 is considered acceptable.
Index of woman’s decision-making power
The index of woman’s decision-making power is constructed based on eleven questions
referring to who decides most of the times whether the woman can work or study, go out
of home, buy things for herself, participate in community activities, how to spend her
own money, how to spend the household money, decisions regarding children, move house
or town, when to have sexual relations, if contraception methods are used and who uses
them. I score each of them as two if the woman makes the decision alone, one if both
partners make it and zero if the man makes it alone.
However, it could be that the woman reports that she makes the decision alone, but
she is influenced by her partner or by what she thinks her partner would like to see as
a result. Also, it could be that if her partner disagrees it is him who actually has the
final say. I have information on whether he complains if she makes the decision alone,
but not on what occurs if he does. Even though, the fact that only 9.76% of women
report that he complains provides confidence for the idea that a woman’s decision taken
alone is respected. An additional problem refers to the response ‘both partners’. This
could hide unequal relations in the participation of each member in the decision-making
process. The woman could just share her opinion, but it might be her partner who has
the final say (Casique and Castro, 2012). Two additional response options where given
in the questionnaire: ‘other people’ and ‘not applicable’. I have classified the first one as
zero, while the second is explained below.
I conduct factor analysis and retain the three first factors for rotation, which have an
eigenvalue of 3.40, 1.10 and 0.58, and explain 58.39%, 18.99% and 9.97% of the variance,
respectively. The results are shown in table 11. Factor 1 has higher loadings in decisions
related to woman’s personal activities and household expenses (six items), factor 2 in
reproductive and major decisions (three items) and factor 3 in decisions regarding contraceptive methods (two items). I construct three individual indices, which are the sum
of the score given to each decision within each dimension. The first index ranges between
0 and 12, the second between 0 and 6 and the third between 0 and 4.
In order to deal with ‘not applicable’ responses, I adjust the index by the number of
38
Table 10: Factor loadings and uniqueness of violent items
Factor 1 Factor 2 Factor 3
Uniqueness
During the last year, has your husband or partner...
pushed you or pulled your hair?
tied you up?
kicked you?
thrown any object at you?
beaten you with his hands or any object?
tried to hang or choke you?
assaulted you with a knife or blade?
fired a weapon at you?
0.350
-0.071
0.120
0.232
0.299
0.024
-0.182
-0.103
0.407
0.348
0.551
0.447
0.467
0.503
0.639
0.331
-0.080
-0.002
0.001
-0.026
-0.063
0.045
0.046
0.017
0.621
0.900
0.614
0.657
0.594
0.709
0.657
0.911
demanded you to have sexual relations?
forced you to do sexual things that you do not like?
used physical strength to force you to have sexual
relations?
0.175
-0.016
-0.039
-0.006
0.607
0.665
0.514
0.573
-0.064
0.055
0.774
0.411
ashamed, underestimated or humiliated you?
ignored or not shown you affection?
said you cheat on him?
made you feel fear?
threatened to leave you, hurt you, take your children
away or kick you out?
locked you in, forbidden you from going out or being
visited?
turned your children or relatives against you?
stalked or spied on you?
threatened you with a weapon?
threatened to kill you, himself or the children?
destroyed, thrown away or hidden things belonging to
you or the household?
got angry because household chores are not done or
not like he wants?
0.580
0.655
0.532
0.524
0.093
-0.007
0.084
0.223
-0.043
-0.053
0.011
-0.008
0.629
0.610
0.657
0.562
0.554
0.134
-0.024
0.616
0.350
0.183
0.074
0.732
0.394
0.319
-0.106
0.152
0.125
0.155
0.619
0.403
0.066
0.104
0.042
0.076
0.739
0.762
0.652
0.705
0.340
0.296
0.019
0.681
0.496
-0.051
-0.006
0.781
complained about how you spend money?
been stingy with the household expenses, even
though he has money?
not given you the upkeep or threatened you to not
giving it?
spent money needed for the household?
appropriated or taken money or possessions from
you?
forbidden you to work or study?
0.544
-0.082
-0.008
0.747
0.645
-0.144
0.046
0.631
0.595
-0.068
0.073
0.635
0.577
-0.054
0.048
0.666
0.231
0.095
0.119
0.860
0.343
0.026
0.054
0.848
applicable answers. Concretely, once the sum is obtained I compute the equivalent value
for the number of applicable questions. In this regard, I am not comparing a woman
who makes decisions in the eleven categories with a woman to whom only, say, five are
applicable. By proceeding in this way I am minimising the loss of observations. I have
39
Table 11: Factor loadings of variables related to woman’s decision-making power
Factor 1 Factor 2 Factor 3
Uniqueness
Who decides most of the times...
if you can work or study?
if you can go out of your home?
what to do with your own money?
if you can buy things for yourself?
if you can participate in community activities?
how to spend the household money?
about the decisions regarding your children?
about moving house or town?
when to have sex?
if contraception methods are used?
who has to use contraception methods?
0.524
0.660
0.706
0.763
0.684
0.489
0.057
-0.066
0.015
-0.009
0.019
0.097
0.028
-0.052
-0.106
0.021
0.130
0.516
0.793
0.428
-0.032
-0.027
-0.031
-0.031
0.011
0.032
-0.006
0.015
0.028
-0.072
0.180
0.922
0.705
0.673
0.557
0.535
0.482
0.519
0.667
0.683
0.469
0.704
0.182
0.510
applied the same strategy in the case of missing values. Therefore, I only exclude women
that have missing values in all of the decisions. Since I am interested in obtaining a single
index, I aggregate the three of them. First, I re-scale them to range between 0 and 1.
Second, I multiply each individual index by the proportion of variation that it explains
from the total of variation explained, as done in Casique and Castro (2012). Finally, I
sum the three indices. The Crobanch’s alpha including all decisions is 0.797, which is
acceptable. For each individual dimension it is 0.785, 0.556 and 0.755, respectively.
Index of woman’s attitudes towards gender roles
The index of woman’s attitudes towards gender roles groups a total of 11 questions:
a woman has the same capacity than a man to earn money, a woman has to obey her
partner in everything he orders, a woman has the right to choose her friends, the man has
to be responsible for all the household expenses, it is woman’s obligation to have sexual
relations with her partner, he has the right to batter her, women and men have the same
rights to make decisions, women and men have the same freedom, women have the right
to defend themselves and report any abuse, women have the right to choose about their
lives and women have the right to live a life free of violence. I classify the response to each
question as one if it is more close to a non-traditional ideology and zero if to a traditional.
In order to reduce these eleven questions into a single index, as well as examine whether
they represent the same underlying concept, I conduct factor analysis (similar to what
I did for the violent items). I retain the two first factors for rotation, which have an
eigenvalue of 2.10 and 0.63, and explain 55.73% and 16.60% of the variance in the data,
respectively. Table 12 reports the factor loadings and uniqueness. Due to ambiguous
loadings I decide to exclude two items, which are the ones not highlighted in bold. Factor
40
1 has higher loadings in opinions that refer to general attitudes towards gender roles (five
items), while factor 2 in opinions that concern spouses relationship (four items).
Table 12: Factor loadings of variables related to woman’s gender attitudes
Factor 1 Factor 2
a good wife has to obey her husband in everything he orders
a woman can choose her friends
a man has to be responsible for all the family expenses
a woman has the same capacity than a man to earn money
it is woman’s obligation to have sex with her partner
the man has the right to batter his wife
women and men have the same rights to make decisions
women and men have the same freedom
women have the right to defend themselves and report any
abuse
women have the right to choose about their own life
women have the right to live a life free of violence
Uniqueness
-0.035
0.151
-0.075
0.205
0.001
0.067
0.442
0.418
0.728
0.234
0.422
0.341
0.371
0.204
0.127
0.253
0.495
0.887
0.838
0.771
0.862
0.940
0.732
0.653
0.489
-0.125
0.808
0.603
0.469
-0.049
-0.059
0.664
0.804
Based on these two dimensions I construct two individual indices, which are the sum of
the opinions in each of them. Thus, the first index ranges between 0 and 5 and the second
between 0 and 4. Since I am interested in obtaining a single index, I aggregate both. I
construct the single index as explained before. A higher index means that the woman
holds less traditional beliefs, while a lower that she has an opinion more in accordance to
a traditional ideology. The overall Crobanch’s alpha is 0.631, while the individuals 0.567
and 0.535, respectively. These low numbers suggest that the variables are not capturing
the same underlying concept. Despite of this, I proceed to aggregate them in a single
index49 .
SES index
SES indices have been increasingly employed in the literature to measure the standard
of living of the households as an alternative to income and consumption or expenditure
measures (Falkingham and Namazie, 2002; Vyas and Kumaranayake, 2006). To construct
the SES index I follow two steps. First, I select the variables. Second, I compute the index
as a weighted sum of them. In order to obtain the weights I use Principal Component
Analysis (PCA), as in Filmer and Pritchett (2001) and McKenzie (2005). I have data
on access to utilities and infrastructure (type of floor material, water source, drainage
system and number of rooms for sleeping), durable assets ownership (radio, TV, video
cassette recorder, telephone line, fridge, washing machine, water boiler, automobile or
truck, house and land) and number of residents. I use the number of residents and the
49
All my results hold if I include each variable separately in the regressions instead.
41
number of rooms for sleeping to construct a crowding index, which is measured as the
ratio of the former to the latter.
Four main issues arise at this point. First, categorical variables with more than two
categories are not appropriate, since they are converted into a continuous scale with no
meaning. In order to avoid this, I recode them into binary variables. Table 13 presents the
descriptive statistics of the 26 variables included. The more they vary across households
and the more correlated they are, the better PCA works (McKenzie, 2005). Second, when
the frequency of two similar variables is low, I decide to exclude them (piped water from
public tap, piped water from another dwelling, tank water, pipe to ravine and pipe to
river).
Table 13: Descriptive statistics of SES variables
type of floor: earth
type of floor: cement
type of floor: wood
water source: piped water from inside the house
water source: piped water from outside the
house
water source: piped water from public tap
water source: piped water from another
dwelling
water source: tank water
water source: well, river, stream, lake
drainage system: sewer
drainage system: septic tank
drainage system: pipe to ravine
drainage system: pipe to river, lake, see
drainage system: no drainage
assets: radio
assets: tv
assets: videocassette
assets: computer
assets: telephone
assets: fridge
assets: washing machine
assets: water boiler
assets: automobile
assets: house
assets: land
assets: crowding index
N
Mean
Std.
Dev.
Min
Max
120,635
120,635
120,635
120,635
0.051
0.545
0.404
0.791
0.220
0.498
0.491
0.407
0.000
0.000
0.000
0.000
1.000
1.000
1.000
1.000
120,635
0.128
0.334
0.000
1.000
120,635
0.008
0.089
0.000
1.000
120,635
0.009
0.096
0.000
1.000
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
120,635
0.011
0.053
0.742
0.193
0.009
0.003
0.053
0.814
0.962
0.614
0.312
0.488
0.879
0.738
0.454
0.456
0.692
0.181
2.522
0.105
0.223
0.437
0.394
0.095
0.058
0.224
0.389
0.191
0.487
0.463
0.500
0.326
0.440
0.498
0.498
0.462
0.385
1.358
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.200
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
18.000
Notes: ‘N’ stands for number of observations, ‘Std. Dev.’ for standard deviation, ‘Min’ for
minimum value and ‘Max’ for maximum value.
Third, because every household lies within one of the variables under the categories
‘type of floor’, ‘source of water’ and ‘drainage system’, I have the problem of perfect linear
42
combinations. I have already removed at least one variable in the categories ‘source of
water’ and ‘drainage system’, but I need to exclude one within ‘type of floor’. I decide to
remove the variable ‘earth’ because it is the one less frequent and with less variation. The
resulting total number of variables has been reduced from 26 to 20 (19 binary variables
and one continuous). Finally, I exclude all women with missing values in at least one of
the variables.
The second step in order to construct the SES index is to assign weights to each of
the selected variables by using PCA. As in factor analysis, I need to choose the number of
principal components to retain for rotation. The common practice in the SES literature
has been to retain only the first one on the empirical basis that it provides a good estimation of the socio-economic status of the household (Filmer and Pritchett, 2001; McKenzie,
2005). This is the approach adopted here. The eigenvalue of the first component is 5.14
and it explains 25.72% of the variation in the data. This is a low number, but it is in line
with other studies on the topic (Vyas and Kumaranayake, 2006). The loadings resulting
from the first principal component are shown in table 14. In general, a variable with a
positive weight is associated with a higher SES, while a variable with a negative weight
with a lower one. The sign of the loadings is as one might expect.
Table 14: PCA loadings of SES variables
Loadings
type of floor: cement
type of floor: wood
water source: piped water from inside the house
water source: piped water from outside the house
water source: well, river, stream, lake
drainage system: sewer
drainage system: septic tank
drainage system: no drainage
assets: radio
assets: tv
assets: videocassette
assets: computer
assets: telephone
assets: fridge
assets: washing machine
assets: water boiler
assets: automobile
assets: house
assets: land
assets: crowding index
-0.224
0.287
0.300
-0.212
-0.165
0.275
-0.192
-0.176
0.138
0.166
0.242
0.262
0.275
0.237
0.252
0.289
0.231
0.044
-0.035
-0.230
In addition, I compute the Kayser-Meyer-Olkin measure of sampling adequacy, which
ranges between zero and one. A low value means that the variables have too little in
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common to justify the use of PCA. A criterion commonly used is that all variables need
to have a value greater than 0.5. In my case, I obtain an overall number of 0.664, while
individuals’ range between 0.321 and 0.941 of which only four are below 0.5. Furthermore,
the Cronbach’s alpha is 0.798, which suggests that the index has a relatively good internal
consistency.
Using the loadings from the first principal component I construct the SES index for
each household. Concretely, I multiply each variable by its weight and sum across them.
I classify the households into three groups based on their SES value, namely the lowest
40% as ‘low’, the highest 20% as ‘high’ and the rest as ‘middle’ (Filmer and Pritchett,
2001). The corresponding descriptive statistics are presented in table 15.
Table 15: Descriptive statistics of SES index
Classification
N
Mean
SES low (40%)
SES middle (40%)
SES high (20%)
48,279
48,238
24,118
-2.321
0.923
2.801
Total
120,635
1.800
Notes: ‘N’ stands for number of observations.
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