# Education and domestic violence: Evidence from a natural experiment in Turkey

Mustafa Özer, Jan Fidrmuc, Mehmet Ali Eryurt

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Mustafa Özer, Jan Fidrmuc, Mehmet Ali Eryurt. Education and domestic violence: Evidence from a natural experiment in Turkey. Kyklos, 2023, 76 (3), pp.436-460. 10.1111/kykl.12334. hal-04127758

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# Education and domestic violence: Evidence from a natural experiment in Turkey

Mustafa Özer<fnref n="1" /> | Jan Fidrmuc<fnref n="2" />,<fnref n="3" />,<fnref n="4" /> | Mehmet Ali Eryurt<fnref n="5" />

## Correspondence

Jan Fidrmuc, Lille Économie Management (LEM), Université de Lille, CNRS, IESEG School of Management, UMR 9221, F-59000 Lille, France.

Email: jan.fidrmuc@gmail.com; jan.fidrmuc@univ-lille.fr

## Abstract

We utilize a natural experiment, an education reform increasing compulsory schooling from 5 to 8 years in Turkey, to obtain endogeneity-robust estimates of the effect of male education on the incidence of domestic violence against women. We find that husband's education lowers the probability of physical, emotional, and economic violence. Schooling lowers also the likelihood of having an arranged marriage and makes men less inclined to engage in various socially unacceptable behaviors. We show that these findings are very robust to alternative regression specifications and restricted sample estimation. Finally, we argue that assortative mating implies that the educational outcomes of the two spouses are correlated. Our findings are robust to accounting for the husbands' and wives' education jointly. Moreover, when we separate the two effects, we show that the favorable effect of education can be attributed causally to men's education rather than to the education of their wives.

## 1 | INTRODUCTION

Even though most countries formally criminalize violence against women, the enforcement of such rules is often lacking and women remain on the receiving end of abuse. A report by the World Health Organization (WHO, 2013) observes that 35% of women around the world have been abused physically and/or sexually. Worryingly, women are most at risk of experiencing abuse in their own homes: The husband or intimate partner is the most common

<footnote n="1">¹Faculty of Economics and Administrative Sciences, Kilis Yedi Aralik University, Kilis, Turkey</footnote>

<footnote n="2">²Lille Économie Management (LEM), Université de Lille, Lille, France</footnote>

<footnote n="3">³BOFIT, Bank of Finland, Helsinki, Finland</footnote>

<footnote n="4">⁴PRIGO University, Haviřov, Czechia</footnote>

<footnote n="5">⁵Hacettepe University Institute of Population Studies, Ankara, Turkey</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
© 2023 The Authors. Kyklos published by John Wiley & Sons Ltd.</footnote>
perpetrator of violence against women (Garcia-Moreno et al., 2006; Heise et al., 1999). The current COVID-19 pandemic, and the lockdowns and increased reliance on working from home that it caused have brought about a further increase in the incidence of domestic violence (Berniell & Facchini, 2021; Leslie & Wilson, 2020).

In this paper, we study the determinants of spousal violence against women in the context of a developing country, Turkey. We are particularly interested in the role of education. Exploring the impact of education, however, is not straightforward. Anecdotally, the incidence of domestic violence appears negatively correlated with men's education in Turkey (see descriptive figures and discussion below). However, both education and the propensity to engage in domestic violence can be caused a third and unobserved variable (e.g., culture or social norms): Thus, the relationship may be subject to an endogeneity bias. Moreover, most studies on the relationship between domestic violence and education observe only the educational outcomes of women. This is because information on domestic violence is typically collected by means of surveying female respondents: The surveys therefore contain detailed information on the abused wives but limited or no information on their husbands or intimate partners. The downside of this approach is that such studies consider the effect of education of the victim rather than the effect of education of the perpetrator of domestic violence. Finally, couples often entail spouses with similar characteristics, including their educational attainments. Because the spouses' educational outcomes are correlated, it is difficult to disentangle the effect of male and female education on the incidence of abuse. These issues make assessing the effect of education a rather complex undertaking.

Our study contributes to the literature in several respects. First, to the best of our knowledge, ours is the first study to find a significant endogeneity-robust effects of male education on women's experience of domestic violence. The previous literature typically considers the effect of female education. The data that we use, the 2014 National Research on Domestic Violence against Women (NRDVW) survey, are unique in that they contain detailed information on the husbands of the (female) respondents, which allows us to focus on the effect of male education on the propensity to engage in domestic violence and other abusive or unreasonable behaviors. To get around the problem of endogeneity bias, we exploit a natural experiment as an instrument for schooling: a 3-year exogenous variation in schooling induced by a compulsory education reform (henceforth, CER) implemented in Turkey in 1997. The reform increased the obligatory education from 5 to 8 years for the affected cohort (those born in or after 1987).

Second, besides considering domestic violence, we also investigate the effect of male education on other *marital ills* such as having an arranged or consanguineous marriage (the latter is a marriage between spouses who are related to each other by blood) or on whether the husband engages in controlling or socially unacceptable behavior vis-à-vis the wife. Such outcomes and behavioral patterns can have serious implications for the women's quality of life.

Finally, we recognize that educational outcomes of the two spouses are related: Even if they completed their education before concluding their marriage, the wide-spread preference for assortative mating (marriage between individuals with similar characteristics) implies that the two spouses' educational outcomes are correlated. We address this issue in two ways: by considering the husband's and wife's education jointly and by separating the effects of male and female education. We believe our study is the first to explicitly address the joint effect of male and female education or to credibly separate the effects of male education from female education.

To preview our results, we find that the husband's education reduces the incidence of most sorts of domestic violence, including economic, emotional, and physical violence. Schooling also lowers the likelihood that the marriage was concluded against the woman's will and reduces the incidence of socially unacceptable behavior such as drinking, gambling, and drug abuse. We also find that women whose mothers or whose husbands' mothers experienced domestic violence are more likely to suffer violence themselves. These results are very robust to changes in regression specification. Besides education, the changes that we observe could also be attributed to lower acceptance of domestic violence among the younger individuals. However, by performing falsification tests, we show that the lower incidence of domestic violence is only observed for the cohort affected by the reform; no similar changes can be found for earlier cohorts. Our findings remain unchanged when we construct our indexes of domestic
violence by using principal component analysis (PCA) or when we omit control variables that could potentially be affected by education (in further tests, we show that the impact of the education reform on these variables was in fact not significant). Similarly, considering male and female education jointly yields results similar to our benchmark findings. Finally, we also present result where we separate the effect of male education from female education, because the incidence of domestic violence could reflect the education of either spouse. To this effect, we create a subsample of males, all of whom have wives affected by the reform, so that the only difference between the treatment and control groups is the husband's exposure to the reform. This exercise also yields results similar to the baseline estimation, so that we can causally attribute our finding of a favorable effect on domestic violence to men's education.

The study proceeds as follows. Section 2 contains the literature review. Section 3 describes the CER and explains why we believe that it constitutes a valid instrument for education. Sections 4 and 5 describe the data and empirical strategy, respectively. Section 6 presents and discusses the results. Lastly, Section 7 concludes.

2 | LITERATURE REVIEW

2.1 | Effect of education on domestic violence

Domestic violence against women remains rather common in developed and developing countries alike. Correspondingly, there is considerable body of studies on the determinants of domestic violence, including those that consider the role played by education. However, most of the existing literature predominantly investigates the effect of female education. The focus on women's rather than men's education is largely due to data availability, as most such studies rely on reports of domestic violence by female survey respondents. Simister and Makowiec (2008) find that education reduces the incidence, and acceptance, of domestic violence among Indian women. Sen (1999) compares the relative contributions of female employment and education and suggests that the latter is more effective in protecting women against abuse. Vyas and Watts (2009), Rani and Bonu (2009), and Simister (2013) confirm the favorable role of female education in broader multi-country contexts. Gibbons et al. (2021) consider the increase in the incidence of intimate-partner violence during the COVID-19-related lockdown in Argentina. They find that educated women were less likely to experience violence overall and that university education was associated with a lower frequency of domestic violence during lockdown.

Female education was also found to have favorable outcomes on other outcomes. Osili and Long (2008) for Nigeria and Breierova and Duflo (2004) find that educated women have lower fertility rates. Mocan and Cannonier (2012) find that schooling improves women's attitudes towards risky health behaviors and reduces their tolerance of violence. Women's education also decreases the desired number of children and raises the usage of modern contraception methods (Mocan & Cannonier, 2012; Samarakoon & Parinduri, 2015). On the other hand, there seems to be no relationship between women's education and their authority in decision-making (except savings), ownership of assets (apart from jewelry and household appliances), and participation in the community (Samarakoon & Parinduri, 2015).

Studies investigating the effect of female education on spousal violence using credible instruments are rare. An exception is a recent study by Erten and Keskin (2018) who deal with the endogeneity of schooling by using the same education reform in Turkey employed in the present paper. Applying Regression Discontinuity Design to the 2008 wave of NRDVW, an earlier wave of the same survey that we use, they find that female education increases the incidence of psychological abuse and partner's controlling behavior, whereas it has no impact on physical violence, partner and marriage characteristics, or women's attitudes to domestic violence. Erten and Keskin attribute the adverse effect of female education on the incidence of domestic violence to the increased benefits that accrue from education to women and to the ensuing struggle between the two spouses about sharing these benefits. They also point out that having more education may make women less tolerant of domestic violence, which in turn leads
to a greater inclination by women to report having been subjected to abuse; they find limited evidence in favor of this channel, however.<fnref n="1" /> In a follow-up paper, Erten and Keskin (2022) explore an additional mechanism: They argue that education helped increase the women's awareness of new laws criminalizing domestic violence and fostering gender equality in Turkey; however, this increased legal awareness does not seem to protect women from becoming victims of domestic violence.

Relatively, few studies examine the effects of male education on spousal violence against women, despite the fact that it is men who are most frequently perpetrators of domestic violence. The reason for this lack of studies is data availability: Most information on domestic violence is collected from women so data on women's education are usually readily available whereas their husbands' education is not always reported. Nevertheless, a systematic review of the previous studies in middle- and low-income countries reveals that if the partner has at least secondary education, the risk of physical and sexual violence against women from their partners drops significantly (Vyas & Watts, 2009). Two recent studies, Alkan et al. (2021) and Alkan and Tekmanlı (2021), consider the determinants of economic and sexual violence, respectively, in Turkey. Using the 2008 and 2014 waves of NRDVW, they find that both the husband's and wife's education are inversely correlated with the incidence of domestic violence. However, these analyses do not account for the possible endogeneity of education.

Erten and Keskin (2018) consider also the education of male partners and, rather surprisingly, conclude that male education has no effect on the incidence of domestic violence. They argue that this is because the CER had no impact on the men's education as most men were completing the junior high school (JHS) already before the reform. Their finding of no effect of the CER on male schooling contrasts with the well-established result in the literature that the reform has had a significant and positive effect on Turkish males (see Akyol & Kırdar, 2021; Aydemir & Kırdar, 2017; Aydemir et al., 2019; Baltagi et al., 2019; Cesur & Mocan, 2018; Dursun & Cesur, 2016; Dursun et al., 2018; Kırdar et al., 2010, 2012, 2016, 2018; Torun, 2018).

Another and perhaps more plausible reason for Erten and Keskin's insignificant finding could be the small number of males affected by the reform in their analysis: In our exploratory analysis of the 2008 NRDVW wave (which is the one that they use), we found only 51 individuals who were affected by the CER and were married. This is because the CER affected those born between 1987 and 1991 who were aged 17–21 in 2008 and 23–27 in 2014 (the former is the wave used by Erten and Keskin, and the latter is the wave that we use). Hence, many such men were likely to be still unmarried in 2008, which means no information on domestic violence is available for them in the data. For this reason, we use the 2014 wave, which does not suffer from this problem.

An additional and potentially crucial issue when studying the causal effect of male or female education on domestic violence is that the husband and wife are often similar in age. Thus, if one spouse has been affected by the educational reform, the other spouse is likely to have been affected by the same reform too. Indeed, it is possible that the impact of the reform on the incidence of domestic violence may (partially or mostly) take place in the marriage market. Given the general preference for assortative mating, women who were affected by the reform may end up marrying more educated husbands (who are less prone to abuse their wives) than the women in the control group. It therefore becomes impossible to clearly attribute the observed effect on domestic violence to the increased education of the husband or the wife, thus violating the exclusion restriction. Erten and Keskin (2018) do not address or discuss the possibility of education affecting domestic violence through the marriage-market channel because of assortative mating, and neither do (to the best of our knowledge) other contributions on this topic (except Akyol & Kırdar, 2021, who also raise other criticism of Erten and Keskin's work). To deal with this issue, we also consider the joint effect of male and female education. Subsequently, we consider a subsample based only on men, whether affected by the CER (treatment group) or not (control group), whose wives were all exposed to the reform. The latter allows us to observe variation in the husbands' CER exposure while holding their wives' treatment status constant.

<footnote n="1">¹Akyol and Kırdar (2021), however, question Erten and Keskin's findings and present alternative evidence that suggests that female education has no effect on domestic violence in Turkey.</footnote>
## 2.2 | Other determinants

According to the “cycle of violence” hypothesis, personal history of childhood abuse increases the likelihood of experiencing or engaging in violence in adulthood (Abrahams & Jewkes, 2005; Flake, 2005; Jewkes et al., 2002; Martin et al., 2002; Naved & Persson, 2005; Rivera-Rivera et al., 2003; Yüksel-Kaptanoğlu et al., 2012).

There is a correlation between power dynamics among couples and the difference in educational attainment and ages of married couples. In the context of Nepal, Adhikari and Tamang (2010) find the age difference between husband and wife to be a significant factor of violence against the wife. On the other hand, the findings are inconclusive about the effect of the education gap. When women have more education than their partner, they are more likely to experience spousal violence in India (Ackerson et al., 2008), whereas Hindin et al. (2008) report no significant association for Bangladesh.

It has been also found that violence rises when women are isolated from their biological family and close friends (Ellsberg et al., 1999; Heise, 1998). Those who can obtain support from their family members are less likely to face spousal violence (Clark et al., 2010; Naved & Persson, 2005). Moreover, in India, Egypt, and Peru, women who come from the higher end of the wealth spectrum are more protected than women living in lower economic conditions (Kishor & Johnson, 2004).

There is an ambiguous relationship between violence and the employment status of women. Rao (1997), for example, suggests that the personal income of a woman has a noticeable negative influence on physical violence from her partner. Nevertheless, Krishnan et al. (2010) find that low-income women in Bangalore who were employed are more likely to experience domestic violence than women who were unemployed. Additionally, property ownership may offer women a choice outside of marriage and a security against labor market shocks. Panda and Agarwal (2005), in the Indian context, use regression control strategies and find that if a woman's ownership of land increases, the authority of women to make important decisions rises, and violence against women decreases. The behaviors of men might also be different towards their spouse because of differences in cultural values between urban and rural areas. For instance, in the Middle East, women who live in rural areas are at a higher risk of violence than those in urban places (Boy & Kulczycki, 2008).

## 3 | INSTITUTIONAL BACKGROUND

Public education has been provided free of charge in Turkey since the foundation of the Republic in October 1923. Until August 1997, compulsory education was 5 years. The CER of that year increased this to 8 years: covering both the primary school (grades 1-5) and the lower secondary school (grades 6-8, often also referred to as junior high school; henceforth, we use junior high school and JHS interchangeably to denote this level). The CER has had little effect on the quality of education: According to Dulger (2004), the national-education curriculum introduced in 1968 has remained in effect also after 1997 with only minor alteration because of the time constraint during reform implementation. Instead, the Ministry of National Education of Turkey mainly focused on ensuring sufficient school capacity to accommodate the increased enrolment. Thus, new classrooms were built in existing schools, new teachers were hired, and old schools were renovated: 81,500 new classrooms were added to existing schools during 1997-2002, which amounts to around 30% capacity increase (Dulger, 2004; World Bank, 2005). After the reform, the gross school enrolment rate in grades 1-8 increased sharply (Figure 1).

The CER was motivated largely by political considerations. Following the 1995 election, Turkey received a government dominated by the Welfare Party, an Islamist political party; this was an unprecedented development for Turkey, as the post-Ottoman Turkish Republic was conceived as a strictly secular state. In February 1997, the National Security Council, a constitutional body largely controlled by the Turkish Army, accused the government of undermining the secular character of Turkey and forced it to resign. The same session of the Council called for compulsory education to be increased from 5 to 8 years. The reform was introduced so as to prevent children from
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** Gross enrolment rate in 8-year primary school education by academic year calculated as the number of students in grades 1–8 divided by the relevant population at that age group (i.e., aged 6–13). Enrolment rates during the 1990–1997 period (prior to the compulsory education reform) are calculated by adding the sum of the students in the primary school and junior high school. Source: Own calculation based on Ministry of National Education statistical data for 1990/1991 to 2003/2004 school years. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

enrolling in religious junior high schools: Only schools following the state-approved curriculum counted towards the 8 years of compulsory education; this was intended to decrease the odds of further Islamization in the future. For further discussion of the CER and its background, see Dulger (2004) and Özer et al. (2018).

The amendment in the education law went into effect in the 1997–1998 academic years, just months after the approval of the law. According to Turkish law, school enrolment is determined according to calendar years. That means that children born in 1987 were bound by the reform, as this is the cohort completing their 5-year elementary school education in 1997. Children born in 1986 and before were not affected. However, some of those who were born in 1986 might have been affected by the reform as the implementation of the age threshold was not strict. Because of this reason, we drop the 1986 birth cohort from our analysis.

4 | DATA

The first nationally representative survey on domestic abuse against women, entitled the National Research on Domestic Violence against Women (NRDVW), was carried out in 2008. The second wave was conducted in 2014. Our paper draws on the 2014 wave of this survey.<fnref n="2" /> The WHO's ethical and safety guidelines were used in every phase of the data collection to ensure the safety and dignity of the interviewers and the interviewed women (WHO Department of Gender and Women's Health, 2001). For example, instead of the word violence in the title of the survey, the fieldwork used the name “Turkey Women and Family Survey” so as not to frame the respondents' answers. Altogether, 8960 women aged 15–59 were chosen for the face-to-face interviews by means of the Kish

<footnote n="2">²The inclusion of the earlier wave would increase the number of observations only marginally: We found only 51 males affected by the reform and married among the 10,388 observations in this wave. Because the number of observations is so small, the 2008 wave was not included in the study. Erten and Keskin (2018), whose analysis utilizes the 2008 wave, indeed find no significant effect of education on males, which we believe to be largely attributable to this low number of observations.</footnote>
method.<fnref n="3" /> All interviews started after confirming the consent of the respondent, so that the survey eventually comprised 7642 women. Women who have ever had at least one partner answered a question related to whether a specific type of violence (physical, sexual, emotional, or economic) has ever been inflicted on them by their partners. If the answer was affirmative, further questions addressed the frequency of the abuse. Other survey questions focused on various types of unwelcome or inappropriate behavior of spouses or the nature of the marriage (such as having an arranged marriage or being married to a relative; these do not necessarily imply the incidence of domestic violence, but they may affect the women's quality of life). The questions used are standard for such surveys and have been used by the WHO previously (see Garcia-Moreno et al., 2006). Women also answered questions about their educational and family background, marriages, and how their marriages were formed.

We combine the questions on the incidence and frequency of each type of abuse into a single variable ranging from 0 (no experience of violence) to 8 (suffering frequent abuse).<fnref n="4" /> All variables are rescaled so that they range from 0 (never experienced domestic violence) to 1 (most frequent incidence). The survey distinguished between the following types of abuse:

**Physical violence:** Husband or intimate partner (i) slapped wife or threw something that could cause injury at her; (ii) pulled her hair; (iii) punched her or hit her with things that could hurt her; (iv) kicked, dragged, or beat her; (v) burned or choked her; and (vi) threatened her with a gun, knife, or any other weapons or actually used it. To form the index, these indicators are added up, adjusted for frequency (between 0 and 8) and divided by the maximum possible value (in this case, 6 * 8 = 48). The normalized index therefore ranges between 0 (no experience of violence) and 1 (the highest frequent experience of aggregated physical violence).

**Sexual abuse:** 3 indicators were used, reflecting whether the woman (i) was ever forced to have sexual intercourse; (ii) had sexual intercourse because of the fear of her husband; and (iii) was forced to participate involuntarily in a sexual act with her husband/intimate partner that she finds humiliating and degrading. Again, the sum is rescaled to range between 0 and 1 (with three indicators, the sum was divided by 3 * 8 = 24).

**Economic abuse:** 3 questions capture whether the husband (i) prevented the woman from working or made her quit her job, (ii) refused to give her money for household expenditures even though he had money, and (iii) took her income without her permission. The resulting index is again rescaled to range from 0 to 1 (by dividing it by 24).

**Emotional abuse:** The husband (i) insulted, (ii) humiliated, (iii) scared, or (iv) threatened to hurt the woman. The resulting index again ranges from 0 to 1 by dividing the sum of indicators by 32.

**Overall abuse index:** This index collects all 16 violence-related indicators into a single overall index, showing the incidence and frequency of all types of violence experienced by the woman from her spouse during her lifetime. Again, the index ranges from 0 to 1.

**Controlling behavior index:** A set of nine binary variables, which take the value of 1 if the woman reports that she experienced a particular controlling behavior from her husband and 0 otherwise, are used to construct the index. These are her husband (i) keeps her away from her friends, (ii) restricts her contact with her birth family or close relatives, (iii) always insists on knowing where she is, (iv) overlooks her or shows little interest in her, (v) gets annoyed when she speaks with other men, (vi) is frequently suspicious that she is unfaithful, (vii) expects her to seek his permission to go to health-care service providers, (viii) requires her to dress as he

<footnote n="3">³If there are more than one eligible woman in the households for the interview, the Kish method enables an unbiased random selection of one woman (Kish, 1949).</footnote>

<footnote n="4">⁴That is, if for a given type of abuse we have *n* individual indicators $v_i$, the index takes the form $\sum_{i=1}^{n} v_i/8n$, with each indicator $v_i$ taking values between 0 and 8 depending on the intensity of abuse. As a robustness check, we also constructed indexes by means of PCA (see Table A9 in Appendix S2).</footnote>
<table><caption>TABLE 1 Violence against women by spouse's educational attainment.</caption><thead><tr><th>Spouse's education</th><th>Overall</th><th>Economic</th><th>Emotional</th><th>Physical</th><th>Sexual</th></tr></thead><tbody><tr><td>No education/primary incomplete</td><td>0.595</td><td>0.317</td><td>0.405</td><td>0.366</td><td>0.121</td></tr><tr><td>8 years of primary school complete</td><td>0.500</td><td>0.248</td><td>0.357</td><td>0.205</td><td>0.062</td></tr><tr><td>Secondary school or higher</td><td>0.471</td><td>0.218</td><td>0.349</td><td>0.175</td><td>0.055</td></tr></tbody></table>

Note: Each domestic violence index ranges between 0 (no experience of domestic violence) and 1 (frequent victim of domestic violence).

Source: NRDVW 2014.

asks, and (ix) interferes with her use of social media (e.g., Twitter or Facebook). To build the index, variables are added up. The resulting value is divided by the maximum frequency, 9. Hence, the index ranges from 0 to 1.

**Socially unacceptable behavior index:** A set of five binary variables, which take the value of 1 if the woman reports a particular type of behavior by her husband and 0 otherwise, are used to build the socially unacceptable behavior index. These variables reflect whether her husband (i) often drinks alcohol, (ii) frequently gambles, (iii) uses drugs, (iv) argues with other men including engaging in physical violence, or (v) cheats on her. Again, these are added up and rescaled so that the index ranges from 0 to 1.

**Arranged marriage:** A dummy variable that equals to 1 if the woman did not want the marriage and 0 otherwise.

**Consanguineous marriage:** A dummy variable that equals to 1 if the woman and her husband are closely related by blood and 0 otherwise. In Turkey, consanguineous marriage usually involves two cousins, uncle and niece, or aunt and nephew.

**Bride price:** A dummy variable that equals to 1 if the husband's family paid a bride price to her family and 0 otherwise. Bride price refers to a monetary or wealth transfer from the groom (or his family) to the bride's family at the time the marriage is concluded or immediately prior to it (and as such, it is the opposite of a dowry).

In Table 1, we report the incidence of domestic violence according to the education of the husband. The table suggests that a large share of Turkish women suffer from domestic violence. A clear pattern of incidence rates falling with husband's education emerges.<fnref n="5" /> As argued above, however, this could also be driven by education and propensity being both caused by a third, unobserved, variable such as culture. Therefore, it is important to determine the causal relationship between education and domestic violence.

Tables 2 and 3 present the summary statistics for the dependent and independent variables, respectively (further details are in Appendix S1). Note that although the respondents were always women, they were also asked a number of questions about their husbands, including the husband's age and education. The CER affected individuals born in 1987 and subsequent years. We therefore divide the respondents into the pre-reform cohort (those whose husband's year of birth is between 1981 and 1985) and the post-reform cohort (those whose husband's year of birth is from 1987 to 1991). Recall that we omit respondents with husbands who were born in 1986, as those could be either affected or unaffected by the reform. Furthermore, by construction, all observations concern respondents who were married (at the time of the survey or in the past), as the domestic violence questions were not collected from respondents who were never married.

<footnote n="5">⁵Recall that the violence indexes combine incidence and intensity: A value of 0.5, for example, could be interpreted as implying that 50% of Turkish women experience this particular kind of violence regularly or that a larger share experience violence on less frequent basis.</footnote>
<table><caption>TABLE 2 Descriptive statistics: Dependent variables.</caption><thead><tr><th rowspan="2">A. Dependent variables</th><th colspan="2">81-91 cohort</th><th colspan="2">81-85 cohort</th><th colspan="2">87-91 cohort</th></tr><tr><th>Obs.</th><th>Mean</th><th>Obs.</th><th>Mean</th><th>Obs.</th><th>Mean</th></tr></thead><tbody><tr><td>Life time overall abuse</td><td>1138</td><td>0.541</td><td>805</td><td>0.556</td><td>333</td><td>0.501</td></tr><tr><td>Life time economic abuse</td><td>1158</td><td>0.280</td><td>818</td><td>0.281</td><td>340</td><td>0.278</td></tr><tr><td>Life time emotional abuse</td><td>1161</td><td>0.386</td><td>818</td><td>0.401</td><td>343</td><td>0.349</td></tr><tr><td>Life time physical violence</td><td>1157</td><td>0.177</td><td>815</td><td>0.201</td><td>342</td><td>0.115</td></tr><tr><td>Life time sexual violence</td><td>1168</td><td>0.082</td><td>822</td><td>0.088</td><td>346</td><td>0.059</td></tr><tr><td>Arranged marriage</td><td>1169</td><td>0.376</td><td>823</td><td>0.400</td><td>346</td><td>0.315</td></tr><tr><td>Consanguineous marriage (blood relation between husband and wife)</td><td>1166</td><td>0.230</td><td>822</td><td>0.204</td><td>344</td><td>0.297</td></tr><tr><td>Husband paid bride money</td><td>1168</td><td>0.124</td><td>822</td><td>0.115</td><td>346</td><td>0.147</td></tr><tr><td>Controlling behavior</td><td>1159</td><td>0.836</td><td>814</td><td>0.827</td><td>345</td><td>0.859</td></tr><tr><td>Socially unacceptable behavior</td><td>1166</td><td>0.222</td><td>822</td><td>0.244</td><td>344</td><td>0.165</td></tr></tbody></table>

Note: Domestic violence, controlling behavior, and socially unacceptable behavior indexes range between 0 (no experience of the pattern in question) and 1 (frequent victim of the behavior or violence). The remaining variables take values of 0 (condition does not apply) or 1 (condition applies).

Source: NRDVW 2014.

The descriptive statistics again show that most Turkish women have experience some form of domestic abuse. The incidence of domestic violence, furthermore, appears higher for the women whose husbands were not affected by the CER. Most women are also routinely subjected to controlling behavior or socially unacceptable behavior. Finally, a large share of Turkish women find themselves in an arranged or consanguineous marriage.

Looking at educational attainments, we seen that although the pre- and post-reform cohorts differ little with respect to the number of years of education, men who were exposed to the reform are more likely to have completed lower secondary school (junior high school, JHS): The difference in completion rate is 12 percentage points. Because of this, we use the JHS completion as our measure of husband's education. Reassuringly, the pre- and post-reform cohorts differ little from each other with respect to other characteristics.

## 5 | EMPIRICAL FRAMEWORK

The link between education and its non-market returns is captured by Equation (1), where Y stands for the unfavorable non-market outcome of interest. This can be domestic violence and abuse, socially intolerable behavior, controlling behavior, or marriage characteristics, such arranged or consanguineous marriage or whether a bride price was paid.

$$Y_i^{OLS} = \sigma + \theta X_i + \beta S_i + \varepsilon_i \quad (1)$$

$S_i$ represents the schooling of husband $i$ measured by completing the junior high school (i.e., completing 8 years of education). $X_i$ comprises the linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence.<fnref n="6" />

<footnote n="6">⁶We explore the robustness of our results to alternative regression specifications in Section 6.4.</footnote>
<table><caption>TABLE 3 Descriptive statistics: Independent variables.</caption><thead><tr><th rowspan="2">B. Independent variables</th><th colspan="2">81-91 cohort</th><th colspan="2">81-85 cohort</th><th colspan="2">87-91 cohort</th></tr><tr><th>Obs.</th><th>Mean</th><th>Obs.</th><th>Mean</th><th>Obs.</th><th>Mean</th></tr></thead><tbody><tr><td>Completing junior high school</td><td>1159</td><td>0.667</td><td>818</td><td>0.632</td><td>341</td><td>0.754</td></tr><tr><td>Years of education completed</td><td>1159</td><td>8.713</td><td>818</td><td>8.623</td><td>341</td><td>8.880</td></tr><tr><td>Husband's mother experienced spouse violence</td><td>1168</td><td></td><td>822</td><td></td><td>346</td><td></td></tr><tr><td>No</td><td></td><td>0.511</td><td></td><td>0.506</td><td></td><td>0.524</td></tr><tr><td>Yes</td><td></td><td>0.254</td><td></td><td>0.259</td><td></td><td>0.240</td></tr><tr><td>Does not know</td><td></td><td>0.235</td><td></td><td>0.235</td><td></td><td>0.236</td></tr><tr><td>Husband experienced physical violence from his family during his childhood</td><td>1166</td><td></td><td>820</td><td></td><td>346</td><td></td></tr><tr><td>No</td><td></td><td>0.604</td><td></td><td>0.594</td><td></td><td>0.628</td></tr><tr><td>Yes</td><td></td><td>0.217</td><td></td><td>0.225</td><td></td><td>0.197</td></tr><tr><td>Does not know</td><td></td><td>0.179</td><td></td><td>0.181</td><td></td><td>0.175</td></tr><tr><td>Woman's mother experienced violence from her spouse</td><td>1169</td><td></td><td>823</td><td></td><td>346</td><td></td></tr><tr><td>No</td><td></td><td>0.657</td><td></td><td>0.643</td><td></td><td>0.689</td></tr><tr><td>Yes</td><td></td><td>0.302</td><td></td><td>0.322</td><td></td><td>0.254</td></tr><tr><td>Does not know</td><td></td><td>0.041</td><td></td><td>0.035</td><td></td><td>0.057</td></tr><tr><td>Wealth index of the family</td><td>1169</td><td>0.593</td><td>823</td><td>0.614</td><td>346</td><td>0.528</td></tr><tr><td>Different ethnicity of husband versus wife</td><td>1165</td><td>0.055</td><td>820</td><td>0.054</td><td>345</td><td>0.062</td></tr><tr><td>Educational difference</td><td>1159</td><td></td><td>792</td><td></td><td>341</td><td></td></tr><tr><td>Husband has more education</td><td></td><td>0.445</td><td></td><td>0.436</td><td></td><td>0.469</td></tr><tr><td>No difference in education</td><td></td><td>0.314</td><td></td><td>0.323</td><td></td><td>0.286</td></tr><tr><td>Woman has more education</td><td></td><td>0.242</td><td></td><td>0.241</td><td></td><td>0.245</td></tr><tr><td>Age difference</td><td>1169</td><td></td><td>823</td><td></td><td>346</td><td></td></tr><tr><td>Same age or man 1 year older</td><td></td><td>0.359</td><td></td><td>0.343</td><td></td><td>0.391</td></tr><tr><td>Woman older than man</td><td></td><td>0.095</td><td></td><td>0.091</td><td></td><td>0.101</td></tr><tr><td>Man older than woman 2/4 years</td><td></td><td>0.276</td><td></td><td>0.276</td><td></td><td>0.300</td></tr><tr><td>Man older than woman 5 years or more</td><td></td><td>0.269</td><td></td><td>0.290</td><td></td><td>0.208</td></tr><tr><td>Husband's ethnicity</td><td>1166</td><td></td><td>821</td><td></td><td>345</td><td></td></tr><tr><td>Turkish</td><td></td><td>0.778</td><td></td><td>0.788</td><td></td><td>0.756</td></tr><tr><td>Kurdish</td><td></td><td>0.170</td><td></td><td>0.159</td><td></td><td>0.191</td></tr><tr><td>Other</td><td></td><td>0.052</td><td></td><td>0.053</td><td></td><td>0.053</td></tr><tr><td>Employed woman</td><td>1169</td><td>0.262</td><td>822</td><td>0.282</td><td>346</td><td>0.208</td></tr><tr><td>Unemployed husband</td><td>1169</td><td>0.065</td><td>823</td><td>0.062</td><td>346</td><td>0.070</td></tr><tr><td>Asset ownership of woman</td><td>1168</td><td>0.238</td><td>822</td><td>0.258</td><td>346</td><td>0.186</td></tr><tr><td>Woman family members live far away from her</td><td>1169</td><td>0.318</td><td>823</td><td>0.324</td><td>346</td><td>0.304</td></tr><tr><td>Woman can count on family support in case of a need</td><td>1168</td><td></td><td>823</td><td></td><td>345</td><td></td></tr><tr><td>Yes</td><td></td><td>0.810</td><td></td><td>0.805</td><td></td><td>0.821</td></tr><tr><td>No</td><td></td><td>0.164</td><td></td><td>0.167</td><td></td><td>0.159</td></tr><tr><td>Does not know</td><td></td><td>0.026</td><td></td><td>0.029</td><td></td><td>0.020</td></tr><tr><td>Lives in rural area</td><td>1169</td><td>0.201</td><td>823</td><td>0.178</td><td>346</td><td>0.257</td></tr></tbody></table>

Note: Italics denote the reference category.

Source: NRDVW 2014.
To deal with the potential endogeneity bias in the relationship between domestic violence and education, we exploit the 3-year exogenous variation in schooling attainments across cohorts induced by the timing of the CER as an instrument for education. A valid instrument should have no direct effects on the outcome of interest other than its impact through the endogenous variable. Our instrument satisfies this condition. First, the CER was motivated by political events, which were unrelated to the outcomes considered in this study. Second, factors typically causing endogeneity of schooling, such as innate ability and other background characteristics, are unlikely to be related to the birth year. For all these reasons, we are confident that the reform satisfies the above-mentioned validity condition.<fnref n="7" />

The first stage of the two-stage least squares (2SLS) estimation then is given by Equation (2):

$$ \hat{S}_i = \gamma + \rho X_i + \sigma T_i + \mu_i \quad (2) $$

where $\hat{S}_i$ is the predicted value of men's schooling, which we can measure, alternatively, by the number of years of education and by completing the junior high school (i.e., 8 years of education); $X_i$ is the set of control variables discussed above; and $T_i$ is a dummy variable equal to 1 for men born in or after 1987 and 0 for those who were born before 1986. Hence, men aged 23–27 in 2014 constitute the treated group, and men aged 29–33 form the control group.<fnref n="8" />

Next, we can obtain an unbiased estimate of the effect of education on our outcomes of interest by estimating Equation (3):

$$ Y_i^{2SLS} = \varphi + \delta X_i + \gamma \hat{S}_i + \varepsilon_i \quad (3) $$

where $\hat{S}_i$ indicates the predicted value of schooling, as given by Equation (2), and $Y_i^{2SLS}$ shows the outcome of interest. The remaining explanatory variables and clustering of robust standard errors are the same as in Equation (2).

Finally, by replacing $\hat{S}_i$ in Equation (2) with the outcome of interests, we can obtain the reduced form (RF) estimates:

$$ Y_i^{rf} = \beta + \mu X_i + \varphi T_i + e_i \quad (4) $$

where $\varphi$ indicates the RF effect of the CER. The RF estimates measure the change in the outcome of interest induced by the exposure to the CER. Note that both the first stage and the second stage are estimated by linear regression rather than by probit or logit even though the outcome variables, endogenous variables, and instrument are all discrete. This is because, as argued by Angrist (1991, 2001) and Angrist and Pischke (2009), linear estimation is preferable to estimate marginal effects when both the dependent variable and the instrument are binary variables (see also Clark & Roayer, 2013; Dursun et al., 2018; Jürges et al., 2011; Silles, 2009; Xie & Mo, 2014).

# 6 | REGRESSION RESULTS AND DISCUSSION

## 6.1 | First-stage results

Table 4 shows the effect of the CER on alternative measures of educational attainment, which serve as the first stage of our 2SLS estimates. The F statistic, testing the validity of the excluded instrument, is very high for

<footnote n="7">⁷As we discuss below, assortative mating implies that the two spouses' educational outcomes tend to be correlated, which could be a violation of the exclusion restriction (see also Akyol & Kırdar, 2021). We address this issue in Sections 6.6 and 6.7, where we consider the spouses' educational outcomes jointly and separate male and female education, respectively.</footnote>

<footnote n="8">⁸To test the validity of the treatment and control groups, we estimate Equation (2) by probit regression. To do this, the T dummy is replaced by dummies representing the years of birth. Table A1 in Appendix S2 reports the coefficients of these dummies. The coefficients are individually and jointly significant for the treatment birth years 1987–1991 and individually and jointly insignificant for the control group born 1982–1985 (with 1981 serving as a reference year; recall that men born in 1986 are dropped from the analysis). This confirms that the CER indeed has had a strong and statistically significant impact on the educational attainments of Turkish men.</footnote>
<table><caption>TABLE 4 The effect of CER on education (first stage).</caption><thead><tr><th></th><th>HGC<br>(1)</th><th>JHS<br>(2)</th><th>SHS<br>(3)</th><th>UEDC<br>(4)</th></tr></thead><tbody><tr><td>Instrument</td><td>0.408*<br>(0.218)</td><td>0.178***<br>(0.025)</td><td>0.018<br>(0.033)</td><td>0.018<br>(0.026)</td></tr><tr><td>F statistics</td><td>3.51</td><td>51.52</td><td>0.28</td><td>0.49</td></tr><tr><td>Observations</td><td>1150</td><td>1150</td><td>1150</td><td>1150</td></tr></tbody></table>

Note: Robust standard errors clustered at the region of residence by-birth year are reported in parentheses. All regressions control for linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence. The instrument is the husband's exposure to the CER.

Abbreviations: CER, compulsory education reform; HGC, highest grade completed; JHS, junior high school; SHS, senior high school; UEDC, university education degree completed.

***p < .01, **p < .05, and *p < .1.

completing 8 years of education (JHS) whereas it is relatively low for the years of education. It is insignificant for completing senior high school (SHS) and obtaining a university degree (UEDC): This is not surprising, as the CER had no direct effect on these higher stages of education. These results are important because if the value of *F* statistics is <10, the weak instrument problem arises (Staiger & Stock, 1997). For this reason, we use completing 8 years of education as the sole measure of schooling in this study.

The reform has had a considerable effect on the probability of completing 8 years of schooling for the treated individuals. Specifically, column (2) in Table 3 suggests that the probability of completing at least 8 years of compulsory education increases by 17.8 percentage points, which corresponds to a 26.7% jump in the share of those completing 8 years of education (from 66.6% before the reform).

## 6.2 | Second-stage results

### 6.2.1 | Effect of education on violence

Before presenting the ordinary least squares (OLS) and 2SLS estimates of the effects of men's schooling on spousal violence against women, we explore the effect of the CER on engaging in violent behavior against one's spouse, that is, the RF estimates (second row of Table 5). The coefficients of the instrument in RF regressions are significant for overall, economic, emotional, and physical violence but not for sexual violence. Overall, the RF estimates suggest that the effect of education on domestic violence is significant: The men who were affected by the CER are approximately one tenth less likely to subject their wives to domestic violence than the men in the control group.<fnref n="9" />

The first and third rows of Table 5 display the OLS and 2SLS results for the returns to schooling, respectively. The OLS estimates of the return to completing 8 years of education are negative but not statistically significant. In contrast, the 2SLS estimates are significantly negative for every category of abuse except sexual violence, and the coefficients are always larger than the OLS ones. This suggests that the OLS estimates are indeed biased upwards, towards zero. We can only speculate about the source of this bias. One possibility is omitted variables, such as cultural and social norms. For example, socially conserve men may be more likely to see women as subservient to men

<footnote n="9">⁹The overall abuse index is 0.54, whereas the individual sub-indexes range from 0.18 in case of physical violence to 0.39 for emotional abuse (see Table 2; note that the effect is not significant for sexual violence).</footnote>
<table><caption>TABLE 5 The effect of husband's education on domestic violenceª.</caption><thead><tr><th></th><th>Overall<br>(1)</th><th>Economic<br>(2)</th><th>Emotional<br>(3)</th><th>Physical<br>(4)</th><th>Sexual<br>(5)</th></tr></thead><tbody><tr><td>OLS</td><td>-0.016<br>(0.014)</td><td>-0.018<br>(0.013)</td><td>-0.005<br>(0.020)</td><td>-0.014<br>(0.012)</td><td>-0.019*<br>(0.010)</td></tr><tr><td>RF</td><td>-0.043***<br>(0.011)</td><td>-0.022**<br>(0.009)</td><td>-0.059***<br>(0.015)</td><td>-0.033***<br>(0.010)</td><td>-0.004<br>(0.008)</td></tr><tr><td>IV</td><td>-0.231***<br>(0.063)</td><td>-0.118**<br>(0.048)</td><td>-0.325***<br>(0.089)</td><td>-0.187***<br>(0.064)</td><td>-0.495<br>(1.005)</td></tr><tr><td>Observations</td><td>1120</td><td>1140</td><td>1142</td><td>1138</td><td>1149</td></tr><tr><th></th><th>Arranged<br>marriage<br>(6)</th><th>Consanguine<br>marriage<br>(7)</th><th>Bride price<br>paid<br>(8)</th><th>Controlling<br>behavior<br>(9)</th><th>Socially<br>unacceptable<br>behavior<br>(10)</th></tr><tr><td>OLS</td><td>-0.190***<br>(0.036)</td><td>-0.100***<br>(0.035)</td><td>-0.052*<br>(0.030)</td><td>-0.028<br>(0.020)</td><td>0.005<br>(0.010)</td></tr><tr><td>RF</td><td>-0.118***<br>(0.039)</td><td>0.070<br>(0.042)</td><td>0.007<br>(0.023)</td><td>-0.015<br>(0.015)</td><td>-0.033**<br>(0.013)</td></tr><tr><td>IV</td><td>-0.665***<br>(0.210)</td><td>0.390<br>(0.260)</td><td>0.039<br>(0.124)</td><td>-0.082<br>(0.083)</td><td>-0.189**<br>(0.077)</td></tr><tr><td>Observations</td><td>1150</td><td>1148</td><td>1149</td><td>1140</td><td>1147</td></tr></tbody></table>

Note: Robust standard errors clustered at the region of residence by-birth year are reported in parentheses. All regressions control for linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence.

Education is measured with JHS completion, and the first-stage results are reported in column (2) of Table 4.

Abbreviations: 2SLS, two-stage least squares; IV, instrumental variable; JHS, junior high school; OLS, ordinary least squares; RF, reduced form.

ªStata does not report R² for 2SLS regressions, as this statistic has no statistical meaning in the context of 2SLS/IV. Because of this, and to save on space, we do not report R² in our results tables. Nevertheless, the full versions of Tables 4 and 5 can be found as Tables A16 and A17 in Appendix S2.

***p < .01, **p < .05, and *p < .1.

and also expect children to be obedient and deferential to their parents. Then, it may be that men brought up in conservative families stay in full-time education longer than other children, while being more likely to abuse their wives or behave in socially unacceptable ways. Another possibility is measurement error. Turkey is a large and heterogeneous country. Therefore, the quality of education may vary across regions. Then, the same formal qualifications, such as JHS diploma, may have different implications for human-capital acquisition in different regions.

The effects of education are large: Completing junior high school lowers the incidence of overall spousal violence by 23%. Considering the sub-components, the negative effect is driven by economic, physical, and especially emotional abuse. In contrast, the impact of education on sexual violence, though negative and large, is not significant.<fnref n="10" />

<footnote n="10">¹⁰A plausible reason for the absence of an effect on sexual violence may be the fact that sexual violence is less common than the other types of abuse: Its mean is 0.08. As there are only around 350 individuals in the treatment group, many of whom would have completed the JHS even in the absence of the reform, the potential impact on the incidence of sexual violence is necessarily very small. Therefore, we abstain from making strong conclusions pertaining to sexual abuse.</footnote>
## 6.2.2 | Education effect on marriage characteristics and on controlling and socially unacceptable behavior

The lower panel of Table 5 presents the effects of husbands' education on marriage characteristics and on controlling and socially unacceptable behavior. The RF estimates indicate that the reform caused a statistically significant drop in the share of women who had an arranged marriage. However, the instrument has no significant impact on the incidence of paying bride money to the wife's family and having a consanguineous marriage. Similarly, the 2SLS estimates suggest that completing 8 years of formal schooling by the husband leads to a 67% reduction in the prevalence of having an arranged marriage, whereas the effect on the other characteristics is insignificant. The OLS results also point to a negative effect on arranged marriage, although the magnitude is substantially smaller, and also to negative effects on the probability of a blood relation between the spouses and on paying the bride price.

The last two columns of the lower panel of Table 5 present the effects of education on engaging in controlling behavior against the wife and on socially unacceptable behavior of men. The OLS estimates in row 1 show no statistically significant correlation between the husband's education and these outcomes. However, the 2SLS estimates in row 3 indicate that completing 8 years of schooling decreases the incidence of socially unacceptable behavior by 19%, whereas it has no impact on controlling behavior. The RF estimates, similarly, show a negative effect of the reform on socially unacceptable behavior.

## 6.3 | Effects of other explanatory variables

The regressions include a number of explanatory variables, which are omitted from Table 5 and from subsequent tables to save space. However, these can play important roles as determinants of domestic violence: The control variables capture the nature of the relationship between the two spouses and history of domestic violence in either the husband's or wife's family. Table 6 shows the impacts of these other factors. These correspond to the 2SLS regressions reported as the third row of Table 5.

Personal abuse history of both spouses clearly plays an important role for the outcomes examined: The incidence of domestic violence and the frequency of controlling behavior are positively correlated with history of abuse experienced by the husband's or wife's mothers or by the husband himself. Hence, domestic violence, on either the husband's or wife's side, can have significant repercussions also in future generations.

The effects of the ethnicity of husband, a proxy for the cultural environment in which he was brought up, indicate that men who are of Kurdish or other ethnicity are more likely to pay bride money than Turkish men. On the other hand, the incidence of socially unacceptable behavior is lower for Kurdish and other-ethnicity husbands than Turkish men, and men who are neither Turkish nor Kurdish are less likely to engage in every aspect of domestic violence.

Differences between the two spouses in education or age have a bearing for several marriage characteristics; this may reflect the effect of age and education on the power dynamics within the couple. Women who are more educated than their husbands are significantly less likely to experience domestic violence, with this pattern being observed across all types of domestic violence. A similar effect is observed for women who are older than their husbands, but the advantage that this appears to give them is only marginally significant. A gap in educational attainment (in either direction) is associated with lower probability of having an arranged marriage. On the other hand, women who are older than their husbands are more likely to find themselves in an arranged marriage. Finally, when the two spouses are of different ethnicity, the incidence of an arranged marriage and of paying bride money both fall, possibly because marriages across ethnic lines tend to occur among individuals who are less bound by traditional rules and conventions.

Interestingly, the incidence of controlling behavior tends to rise when the wife is employed or when the husband is unemployed, suggesting that such disparity between the spouses in resources increases the potential for conflict between them. In contrast, women who own assets in their own name seem more protected from domestic violence
<table><caption>TABLE 6 Effects of other control variables.</caption><thead><tr><th>Variables</th><th>Overall</th><th>Economic</th><th>Emotional</th><th>Physical</th><th>Sexual</th><th>Arranged marriage</th><th>Consanguine marriage</th><th>Husband paid a bride price</th><th>Controlling behavior</th><th>Socially unacceptable behavior</th></tr></thead><tbody><tr><td>Husband's mother was abused</td><td>0.040***<br>(0.013)</td><td>0.049***<br>(0.017)</td><td>0.061***<br>(0.019)</td><td>0.010<br>(0.011)</td><td>0.445<br>(0.281)</td><td>0.028<br>(0.033)</td><td>0.110**<br>(0.045)</td><td>0.012<br>(0.020)</td><td>0.047**<br>(0.021)</td><td>0.001<br>(0.013)</td></tr><tr><td>Husband does not know if mother was abused</td><td>-0.021*<br>(0.011)</td><td>-0.005<br>(0.011)</td><td>-0.028<br>(0.018)</td><td>-0.020**<br>(0.008)</td><td>-0.170<br>(0.160)</td><td>-0.073<br>(0.044)</td><td>-0.035<br>(0.032)</td><td>0.066***<br>(0.021)</td><td>0.010<br>(0.020)</td><td>-0.013<br>(0.012)</td></tr><tr><td>Husband was abused</td><td>0.037***<br>(0.012)</td><td>-0.001<br>(0.009)</td><td>0.061***<br>(0.015)</td><td>0.031***<br>(0.012)</td><td>0.674*<br>(0.292)</td><td>-0.023<br>(0.033)</td><td>-0.015<br>(0.030)</td><td>-0.019<br>(0.022)</td><td>0.039**<br>(0.016)</td><td>0.030**<br>(0.012)</td></tr><tr><td>Does not know if husband was abused</td><td>0.025<br>(0.019)</td><td>0.008<br>(0.012)</td><td>0.050*<br>(0.030)</td><td>0.019<br>(0.014)</td><td>0.016<br>(0.236)</td><td>0.027<br>(0.053)</td><td>-0.007<br>(0.050)</td><td>0.011<br>(0.025)</td><td>0.026<br>(0.019)</td><td>-0.012<br>(0.013)</td></tr><tr><td>Wife's mother was abused</td><td>0.041***<br>(0.009)</td><td>0.016**<br>(0.008)</td><td>0.051***<br>(0.015)</td><td>0.033***<br>(0.009)</td><td>0.318<br>(0.219)</td><td>0.055<br>(0.046)</td><td>-0.011<br>(0.035)</td><td>-0.003<br>(0.017)</td><td>0.058***<br>(0.016)</td><td>0.018*<br>(0.010)</td></tr><tr><td>Does not know if mother was abused</td><td>0.018<br>(0.015)</td><td>-0.031**<br>(0.016)</td><td>-0.051**<br>(0.026)</td><td>-0.016*<br>(0.016)</td><td>-0.261<br>(0.352)</td><td>-0.096<br>(0.086)</td><td>-0.088<br>(0.064)</td><td>-0.039<br>(0.038)</td><td>0.029<br>(0.029)</td><td>0.059***<br>(0.020)</td></tr><tr><td>Woman has more education</td><td>-0.034***<br>(0.012)</td><td>-0.031**<br>(0.012)</td><td>-0.051**<br>(0.021)</td><td>-0.016*<br>(0.009)</td><td>0.013<br>(0.328)</td><td>-0.222***<br>(0.049)</td><td>0.044<br>(0.051)</td><td>-0.005<br>(0.030)</td><td>-0.030<br>(0.024)</td><td>-0.012<br>(0.013)</td></tr><tr><td>Man has more education</td><td>-0.026<br>(0.018)</td><td>-0.037**<br>(0.015)</td><td>-0.038<br>(0.025)</td><td>-0.011<br>(0.016)</td><td>0.118<br>(0.465)</td><td>-0.331***<br>(0.055)</td><td>-0.004<br>(0.072)</td><td>0.008<br>(0.045)</td><td>-0.013<br>(0.036)</td><td>-0.004<br>(0.021)</td></tr><tr><td>Woman is older than man</td><td>-0.020*<br>(0.011)</td><td>-0.026*<br>(0.013)</td><td>-0.019<br>(0.018)</td><td>-0.008<br>(0.010)</td><td>-0.358*<br>(0.192)</td><td>0.150**<br>(0.067)</td><td>0.059<br>(0.042)</td><td>-0.048**<br>(0.023)</td><td>-0.064***<br>(0.019)</td><td>-0.027*<br>(0.015)</td></tr><tr><td>Man is older than woman by 2/4 years</td><td>0.020*<br>(0.010)</td><td>0.007<br>(0.011)</td><td>0.044***<br>(0.012)</td><td>0.010<br>(0.011)</td><td>0.060<br>(0.325)</td><td>0.011<br>(0.035)</td><td>-0.009<br>(0.042)</td><td>-0.037*<br>(0.019)</td><td>-0.006<br>(0.012)</td><td>0.007<br>(0.012)</td></tr><tr><td>Man is older than woman by more than 5 years</td><td>-0.004<br>(0.009)</td><td>-0.012<br>(0.010)</td><td>0.010<br>(0.015)</td><td>-0.005<br>(0.007)</td><td>-0.162<br>(0.171)</td><td>0.050<br>(0.033)</td><td>-0.073<br>(0.046)</td><td>0.008<br>(0.019)</td><td>0.011<br>(0.017)</td><td>-0.004<br>(0.013)</td></tr><tr><td>Husband's ethnicity is Kurdish</td><td>-0.033<br>(0.020)</td><td>-0.029*<br>(0.016)</td><td>-0.054*<br>(0.029)</td><td>-0.018<br>(0.015)</td><td>0.459<br>(0.479)</td><td>-0.072<br>(0.056)</td><td>0.128<br>(0.084)</td><td>0.229***<br>(0.043)</td><td>-0.008<br>(0.024)</td><td>-0.033*<br>(0.017)</td></tr></tbody></table>
<table><caption>TABLE 6 (Continued)</caption><thead><tr><th>Variables</th><th>Overall</th><th>Economic</th><th>Emotional</th><th>Physical</th><th>Sexual</th><th>Arranged marriage</th><th>Consanguine marriage</th><th>Husband paid a bride price</th><th>Controlling behavior</th><th>Socially unacceptable behavior</th></tr></thead><tbody><tr><td>Husband's ethnicity is others</td><td>-0.064***<br>(0.016)</td><td>-0.040**<br>(0.018)</td><td>-0.099***<br>(0.026)</td><td>-0.044***<br>(0.013)</td><td>-1.074**<br>(0.425)</td><td>0.008<br>(0.076)</td><td>0.142<br>(0.102)</td><td>0.398***<br>(0.087)</td><td>-0.066**<br>(0.028)</td><td>-0.057***<br>(0.014)</td></tr><tr><td>Wife's birth family does not support her in case of need</td><td>0.016<br>(0.014)</td><td>0.010<br>(0.013)</td><td>-0.001<br>(0.023)</td><td>0.024*<br>(0.013)</td><td>0.466<br>(0.324)</td><td>-0.107***<br>(0.037)</td><td>0.028<br>(0.052)</td><td>0.110***<br>(0.037)</td><td>0.021<br>(0.025)</td><td>-0.004<br>(0.019)</td></tr><tr><td>Wife is not sure of family's supports in case of need</td><td>0.005<br>(0.025)</td><td>-0.008<br>(0.024)</td><td>-0.007<br>(0.033)</td><td>0.018<br>(0.028)</td><td>1.222<br>(0.922)</td><td>0.061<br>(0.114)</td><td>-0.076<br>(0.068)</td><td>0.089<br>(0.068)</td><td>0.066<br>(0.050)</td><td>0.029<br>(0.032)</td></tr><tr><td>Wealth index</td><td>0.009<br>(0.030)</td><td>0.000<br>(0.027)</td><td>0.028<br>(0.044)</td><td>0.004<br>(0.022)</td><td>-0.704<br>(0.715)</td><td>0.093<br>(0.081)</td><td>-0.452***<br>(0.094)</td><td>-0.035<br>(0.081)</td><td>-0.062<br>(0.049)</td><td>0.045<br>(0.031)</td></tr><tr><td>Difference in ethnicity</td><td>0.009<br>(0.023)</td><td>0.016<br>(0.022)</td><td>0.009<br>(0.036)</td><td>0.000<br>(0.018)</td><td>0.351<br>(0.415)</td><td>-0.107*<br>(0.065)</td><td>-0.064<br>(0.072)</td><td>-0.118***<br>(0.040)</td><td>0.027<br>(0.037)</td><td>0.031<br>(0.024)</td></tr><tr><td>Employed women</td><td>0.012<br>(0.010)</td><td>0.001<br>(0.009)</td><td>0.024<br>(0.018)</td><td>0.007<br>(0.008)</td><td>-0.111<br>(0.247)</td><td>0.028<br>(0.033)</td><td>-0.018<br>(0.045)</td><td>-0.010<br>(0.022)</td><td>-0.007<br>(0.017)</td><td>0.049***<br>(0.012)</td></tr><tr><td>Unemployed husband</td><td>-0.001<br>(0.014)</td><td>0.022<br>(0.021)</td><td>-0.010<br>(0.020)</td><td>-0.005<br>(0.012)</td><td>-0.304<br>(0.244)</td><td>0.004<br>(0.076)</td><td>-0.037<br>(0.058)</td><td>-0.019<br>(0.050)</td><td>0.014<br>(0.028)</td><td>0.077***<br>(0.028)</td></tr><tr><td>Asset ownership index of wife</td><td>-0.036**<br>(0.018)</td><td>-0.002<br>(0.016)</td><td>-0.062**<br>(0.031)</td><td>-0.024*<br>(0.014)</td><td>-0.385<br>(0.403)</td><td>-0.159**<br>(0.080)</td><td>-0.093<br>(0.122)</td><td>-0.059<br>(0.044)</td><td>-0.032<br>(0.040)</td><td>0.051<br>(0.038)</td></tr><tr><td>Family lives far</td><td>-0.003<br>(0.010)</td><td>-0.005<br>(0.007)</td><td>0.010<br>(0.014)</td><td>-0.005<br>(0.009)</td><td>0.100<br>(0.259)</td><td>0.029<br>(0.056)</td><td>0.018<br>(0.031)</td><td>0.044**<br>(0.018)</td><td>-0.008<br>(0.015)</td><td>0.005<br>(0.009)</td></tr><tr><td>Rural</td><td>-0.011<br>(0.009)</td><td>-0.014<br>(0.009)</td><td>-0.022**<br>(0.011)</td><td>-0.002<br>(0.009)</td><td>0.226<br>(0.229)</td><td>0.054*<br>(0.031)</td><td>0.007<br>(0.043)</td><td>0.077*<br>(0.040)</td><td>0.014<br>(0.015)</td><td>-0.027**<br>(0.011)</td></tr><tr><td>Observations</td><td>1120</td><td>1140</td><td>1142</td><td>1138</td><td>1149</td><td>1150</td><td>1148</td><td>1149</td><td>1140</td><td>1147</td></tr></tbody></table>

Note: This table reports the coefficients of the explanatory variables estimated with the two-stage least squares model reported in the third row of Table 5. Robust standard errors adjusted for clustering at the 26 regions of residence are reported in parentheses. All models control for fixed effects of 26 regions of residence and include the instrumented schooling variable.

\*\*\* *p* < .01, \* *p* < .05, and \* *p* < 1.
than women without any assets: Again, this probably reflects the different distribution of relative power in households where the wife has ownership claims in her own right. Lastly, wealthy families are less likely to live in consanguineous marriages.

The support that a woman can receive from her family can play an important role for the incidence of domestic violence. A woman who does not think her birth family would support her in an emergency experiences more physical violence, although this effect is not very precisely estimated; similarly, positive effects, although insignificant, are also observed for economic, sexual, and overall domestic abuse. The husband of such a woman is also more likely to pay a bride price, and they are less likely to be in an arranged marriage.

## 6.4 | Sensitivity tests: Alternative specifications

Our model includes a number of variables that could be impacted by the reform: employment status of the husband and wife, difference in ethnicity between the spouses, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, and differences between the spouses in education and age. Because education might impact these variables, it is vital to test the robustness of the first-stage and second-stage results by removing them from the estimations. The results of both the first-stage and second-stage regressions are robust to this—see Tables A2 and A3 in Appendix S2.<fnref n="11" />

We also check the robustness of our results to using alternative functional forms of birth cohort trends: We use the higher order polynomial of centralized year of birth, the distance to the pivotal cohort 1986, its square, and higher order polynomials of the distance to the pivotal cohort (Clark & Roayer, 2013). The results are robust to these specifications, as reported in Table A4 in Appendix S2.

The school starting age is determined by the birth year of the individual in Turkey. The problem is that for individuals in the same cohort, standard errors may be related; not taking this possibility into account will invalidate the inference from these estimates. The standard solution to address this concern is to cluster the standard errors at the appropriate level, for example, by clustering standard errors at the year of birth, as Lee and Card (2008) suggest (see also Erten & Keskin, 2018; Fort et al., 2016; McCrary & Royer, 2011). However, clustering standard errors at the year of birth level yields a maximum of 10 clusters in the largest prediction window (i.e., 5 clusters for the treatment group and 5 clusters for the control group). Because the region of residence variable is present, standard errors can also be clustered by year of birth and region of residence, assuming that the error terms for birth cohorts in a region may be relevant (see Agüero & Bharadwaj, 2014; Brunello et al., 2013; Grépin & Bharadwaj, 2015; Pischke & Von Wachter, 2008). As a robustness check, we therefore cluster standard errors at the region of residence and birth year level separately. As shown in Tables A5 and A6 in Appendix S2, the results are robust for these alternative clustering units.

As explained above, we drop the males born in 1986 from the analysis, as they could be either affected or unaffected by the CER. Nevertheless, our findings are robust to including the 1986 cohort in either the treatment or control groups (Tables A7 and A8 in Appendix S2).

The domestic violence indexes used in the analysis were constructed additively. An alternative approach is to use PCA—see Table A9 in Appendix S2. Our results are robust to using this alternative construction of the indexes (note that magnitudes of the coefficients are different because our baseline indexes are normalized to range between 0 and 1, which is not the case with the PCA indexes).

Finally, the males in the treatment group might be less inclined to be violent than those born earlier because of other societal trends other than education: For instance, it could be a result of the pre-existing trend towards lower acceptance of domestic violence, either because of gradual cultural and social change or because of deliberate

<footnote n="11">¹¹While the ethnicity of the husband and wife is predetermined, education can affect the willingness of the two spouses to marry a person of a specific ethnicity. In Table A14 in Appendix S2, we show the results of regressing variables that could potentially be influenced by education on our instrument (exposure to the CER). Most of these effects are insignificant (and some are counter-intuitive, such as the CER increasing the likelihood of living in a rural area), suggesting that our benchmark results should not be confounded by potential effects of education on the control variables.</footnote>
government policies. The data used in this paper constitute a cross section, so that we are unable to separate the impact of such time-related changes from the effects of the education reform. To address the possibility of age-related trends, we run four falsification tests. Recall that our baseline analysis comprises individuals aged 23–33 (birth years 1981–1991): 23–27 in the treatment group and 29–33 in the control group. The falsification tests are carried out with males aged 29–36 (those aged 29–32 belonging to the treatment group), 30–37 (30–33 in the treatment group), 31–38 (31–34 in the treatment group), and 32–39 (32–35 in the treatment group). All of these individuals are too old to have been impacted by the education reform. Thus, if the educational attainment of the younger cohort is significantly higher than that of the older untreated group, it could that the results in Table 5 are driven by improvements in education over time. However, we see little evidence of a time trend in education prior the reform: three falsification tests are statistically insignificant; the last one, for males aged 32–39, is significant but the coefficient estimate is much smaller than the one reported in column (2) or Table 5 (see Tables A10 and A11 in Appendix S2). This provides extra evidence for our identification assumption.

## 6.5 | Sensitivity tests: Restricted sample analyses

The 2SLS estimates show the impact of education on compliers (local average treatment effect, or LATE) only. As shown before, the education reform has had no impact on graduating from SHS or university. If the reform does not have any spillover effects on completion of higher stages of education, removing SHS and university graduates from the sample does not alter the composition of compliers. However, some members of the control group hold SHS and university degrees, and, to some extent, their observed control variables should be dissimilar from the others in the control group. Because of this, excluding them from the sample might have a significant impact on the estimated coefficients (e.g., Aydemir & Kırdar, 2017). To explore this possibility, we impose different restrictions on the highest educational attainment in the sample.<fnref n="12" /> The results are reported in Tables A12 and A13 in Appendix S2. First, we remove the university graduates: Our findings are not substantively affected by this. Next, we exclude also SHS graduates. Now, the subsample includes only compliers, many of whom would complete only 5 years of schooling in the absence of the reform but were required to complete 8 years of compulsory schooling after the CER was implemented. Again, the first-stage estimates and the education effects are very similar to baseline estimates in Tables 4 and 5.

As a further robustness check, we employ a narrower window of birth years around the treatment year: We consider individuals aged 24–32 (rather than 23–33). The individuals aged 24–27 form the treatment group, whereas the older individuals form the control group. By doing this, the two groups became more similar in terms of age; the downside is a much smaller sample size. As shown in Tables A12 and A13 in Appendix S2, after narrowing the window around the treatment, the 2SLS estimates and their standard errors get larger for some outcome measures. This is to be as expected given the smaller sample size. The estimates thus become statistically less significant but otherwise are very similar to those reported as our baseline estimates.

## 6.6 | Joint effect of male and female education

As discussed above, many marriages display a preference for assortative mating, so that the education levels of the two spouses are correlated. This implies that an individual (whether male or female) who was affected by the CER and therefore attained a higher level of education is likely to marry someone with a higher level of education too (whether the spouse was subject to the CER or not). In contrast, an otherwise similar individual who was not

<footnote n="12">¹²The downside of restricting the sample, however, is that reducing the sample size may reinforce any bias remaining in the estimates. We are grateful to an anonymous referee for alerting us to this implication.</footnote>
<table><caption>TABLE 7 The effect of CER on couple's educational attainment: First stage.</caption><thead><tr><th></th><th>Average education (years) (1)</th><th>Average JHS completion (0/0.5/1) (2)</th><th>Years of education Males (3)</th><th>Years of education Females (4)</th><th>JHS completion Males (5)</th><th>JHS completion Females (6)</th></tr></thead><tbody><tr><td>IV males</td><td>0.3 (0.373)</td><td>0.087 (0.040)</td><td>0.358 (0.398)</td><td>0.142 (0.402)</td><td>0.1 (0.049)</td><td>0.093 (0.055)</td></tr><tr><td>IV females</td><td>-0.397 (0.336)</td><td>-0.1 (0.035)</td><td>-0.344 (0.335)</td><td>-0.45 (0.383)</td><td>-0.083 (0.043)</td><td>-0.118 (0.046)</td></tr><tr><td>F statistics</td><td>3.58</td><td>27.24</td><td>4.53</td><td>2.45</td><td>12.28</td><td>18.37</td></tr><tr><td>Observations</td><td>890</td><td>890</td><td>890</td><td>890</td><td>890</td><td>890</td></tr></tbody></table>

Note: Robust standard errors clustered at the region of residence by-birth year are reported in parentheses. All regressions control for linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence. The dependent variables are average of husband's and wife's years of education; average of husband's and wife's JHS completion, measured as 0/1 dummy variables so that the average can take values of 0, 0.5, or 1 (column 2); husband's years of education (column 3) and wife's years of education (column 4)—these are then used in the same second-stage model; and husband's JHS completion (column 5) and wife's JHS completion (column 6)—these are then used in the same second-stage model.

Abbreviations: CER, compulsory education reform; IV, instrumental variable; JHS, junior high school.

**$p < .01$, **$p < .05$, and *$p < .1$.

impacted by the CER may choose a less educated spouse. Thus, the incidence of domestic violence within a couple could depend on the couple's joint educational attainment rather than on husband's or wife's education.<fnref n="13" />

To explore this possibility, we consider the education of both spouses, as well as their average level of education. As before, we use years of education (of each spouse) and JHS completion as alternative measures of education. In both cases, the instruments for education are the husband's and wife's exposure to the CER: Thus, we use two instrumental variables for two endogenous variables (husband's and wife's education) or for their combined (average) educational outcome as a single endogenous variable. Table 7 collects the first-stage results. As before, the regressions with years of education are somewhat weak: The *F* statistic is low, and the instruments are insignificant. When we use JHS completion, the results become stronger, in line with the previously reported findings. In column (2), the education variable combines the husband's and wife's dummies for JHS completion: The average therefore can take the value of 0 (neither husband nor wife graduated from JHS), 0.5 (only one of them completed JHS), or 1 (both are JHS graduates). Finally, in columns (5) and (6), each spouse's JHS completion is instrumented separately, using both spouses' CER exposure as instruments. Both instruments are significant but have the opposite signs, which is probably because, given assortative mating, the two spouses' CER exposure are correlated. The high *F* statistic suggests that the instruments are strongly relevant when education is measured with JHS completion. Therefore, in the second stage, we use only education measured as JHS completion, either as the average value for the two spouses (corresponding to the first stage reported in column 2) or for each spouse separately (columns 5 and 6).

The second-stage results obtained with average JHS completion are presented in Table 8. The pattern is very similar to that obtained before: Education lowers the incidence of domestic violence (with the exception of economic and sexual violence), as well as the frequency of arranged marriage. On the other hand, when using individual (husband's and wife's) JHS completions, the effect of male education is again negative but the second-stage estimates are not significant (these results can be found in Table A15 in Appendix S2). Finally, we do not estimate the second

<footnote n="13">¹³We are grateful to an anonymous referee for suggesting the existence of this channel.</footnote>
<table><caption>TABLE 8 The effect of couple's average education (JHS completion) on domestic violence against women (2SLS).</caption><thead><tr><th></th><th>Overall<br>(1)</th><th>Economic<br>(2)</th><th>Emotional<br>(3)</th><th>Physical<br>(4)</th><th>Sexual<br>(5)</th></tr></thead><tbody><tr><td>Average JHS<br>completion</td><td>-0.190**<br>(0.076)</td><td>-0.078<br>(0.063)</td><td>-0.278**<br>(0.115)</td><td>-0.154**<br>(0.066)</td><td>0.875<br>(1.566)</td></tr><tr><td>Observations</td><td>890</td><td>903</td><td>904</td><td>901</td><td>908</td></tr><tr><td></td><th>Arranged<br>marriage<br>(6)</th><th>Consanguine<br>marriage<br>(7)</th><th>Bride price<br>paid<br>(8)</th><th>Controlling<br>behavior<br>(9)</th><th>Socially<br>unacceptable<br>behavior<br>(10)</th></tr><tr><td>Average JHS<br>completion</td><td>-0.715***<br>(0.197)</td><td>0.226<br>(0.222)</td><td>-0.066<br>(0.108)</td><td>0.047<br>(0.112)</td><td>-0.128<br>(0.084)</td></tr><tr><td>Observations</td><td>909</td><td>908</td><td>908</td><td>901</td><td>906</td></tr></tbody></table>

Note: Robust standard errors clustered at the region of residence by-birth year are reported in parentheses. All regressions control for linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence. Average education is measured as average value of husband's and wife's JHS completions and thus can take values of 0, 0.5, or 1. The second-stage results are based on first-stage results reported in column (2) of Table 7.
Abbreviations: 2SLS, two-stage least squares; JHS, junior high school.
***p < .01, **p < .05, and *p < .1.

stage with average years of education as the low F statistics suggest that the instrument is not sufficiently strong in this case. In summary, the results obtained when considering both male and female education together (as average JHS completion) are consistent with, and similar to, our earlier findings.

## 6.7 | Separating the effects of male and female education

Assortative mating implies that the two spouses not only have similar educational attainments but also tend to be of similar ages. Therefore, many men in the treatment group (who were affected by the CER) have wives who were also affected by the reform (because they are of similar age or younger). Similarly, many men in the control groups have wives who, like them, were unaffected by the CER. Akyol and Kirdar (2021) argue that this causes the exclusion restriction to be violated: The CER can affect the incidence of domestic violence through its impact on either spouse whereas 2SLS estimation assumes that the CER only affects the husband's education. Hence, the effect documented by Erten and Keskin (2018) for women could occur because of increased education of women, or because of increased education of their husbands, or indeed because both husband and wife obtained a higher level of education.

To ensure that we measure only the effect of male education, we take advantage of the fact that most Turkish men (as men elsewhere) tend to be slightly older than their wives. That means that the vast majority of the men who were affected by the CER are married to women who were also affected. Similarly, many of the men in the control group, who were not affected by the CER, nevertheless have younger wives who were affected by the reform. Therefore, as our final robustness check, we only consider men whose wives were affected by the CER. Specifically, this means that the men in the control group (born in 1981-1985) and those in the treatment group (born in 1987-1991) all have wives born in 1987-1991. In this way, the only difference between treated and untreated men is the men's exposure to the reform whereas their wives' CER exposure is held constant.

One implication of restricting the sample in this way is that the men in the control group may have a bigger age gap vis-à-vis their wives than the men in the control group. This, in turn, means that being in the treatment group is
<table><caption>TABLE 9 The effect of CER on education (first-stage results): Only men whose wives were exposed to the CER.</caption><thead><tr><th></th><th>HGC<br>(1)</th><th>JHS<br>(2)</th><th>SHS<br>(3)</th><th>UEDC<br>(4)</th></tr></thead><tbody><tr><th>Instrument</th><td>0.27<br>(0.227)</td><td>0.119***<br>(0.029)</td><td>-0.008<br>(0.057)</td><td>-0.008<br>(0.037)</td></tr><tr><th>F statistics</th><td>1.41</td><td>16.54</td><td>0.02</td><td>0.05</td></tr><tr><th>Observations</th><td>734</td><td>734</td><td>734</td><td>734</td></tr></tbody></table>

Note: Robust standard errors clustered at the region of residence by-birth year are reported in parentheses. All regressions control for linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence. The instrument is the husband's exposure to the CER.

Abbreviations: CER, compulsory education reform; HGC, highest grade completed; JHS, junior high school; SHS, senior high school; UEDC, university degree.

***p < .01, **p < .05, and *p < .1.

<table><caption>TABLE 10 The effect of husband's education on domestic violence (2SLS, second-stage results): Only men whose wives were exposed to the CER.</caption><thead><tr><th></th><th>Overall<br>(1)</th><th>Economic<br>(2)</th><th>Emotional<br>(3)</th><th>Physical<br>(4)</th><th>Sexual<br>(5)</th></tr></thead><tbody><tr><th>Male education</th><td>-0.396***<br>(0.144)</td><td>-0.271**<br>(0.133)</td><td>-0.551**<br>(0.223)</td><td>-0.289**<br>(0.113)</td><td>-4.34<br>(2.842)</td></tr><tr><th>Observations</th><td>712</td><td>724</td><td>728</td><td>728</td><td>733</td></tr></tbody><thead><tr><th></th><th>Arranged<br>marriage<br>(6)</th><th>Consanguine<br>marriage<br>(7)</th><th>Bride price<br>paid<br>(8)</th><th>Controlling<br>behavior<br>(9)</th><th>Socially<br>unacceptable<br>behavior<br>(10)</th></tr></thead><tbody><tr><th>Male education</th><td>-0.689<br>(0.433)</td><td>0.798<br>(0.547)</td><td>0.019<br>(0.210)</td><td>-0.333*<br>(0.196)</td><td>-0.297**<br>(0.139)</td></tr><tr><th>Observations</th><td>734</td><td>732</td><td>734</td><td>728</td><td>732</td></tr></tbody></table>

Note: Robust standard errors clustered at the region of residence by-birth year are reported in parentheses. All regressions control for linear and quadratic terms of centered year of birth, employment status of man and woman, difference in ethnicity between husband and wife, ethnicity of husband, asset ownership index, wealth index, rural residence, husband's and wife's maternal physical abuse history, differences in education and age, and fixed effects of 26 regions of residence. Education is measured with JHS completion, and the first-stage results are reported in column (2) of Table 9.

Abbreviations: 2SLS, two-stage least squares; CER, compulsory education reform; JHS, junior high school.

***p < .01, **p < .05, and *p < .1.

correlated with the age gap. However, the age gap (the husband being older than the wife) does not have a significant impact on the incidence of domestic violence, as we show in Table 6. Moreover, the regressions in this robustness check again control for the age gap.

Our approach is thus akin to difference in difference: We consider the effect of the reform on men's education while keeping the status of their wives the same for both groups of men. We believe our paper is the first to explicitly address the effects of exposure to the reform by the two spouses in this way.

The results of this exercise are presented in Tables 9 and 10 for the first and second stages, respectively. They are qualitatively very similar to the benchmark estimates in Tables 4 and 5. In the first stage, exposure to the reform results
in significantly increased probability of completing the JHS; the magnitude of the effect is approximately two thirds of that observed in the benchmark estimates. In the second stage, we again observe that education is associated with significantly lower incidence of overall, economic, emotional, and physical violence, as well as lower probability of socially unacceptable behavior. Moreover, the effects are even stronger than in the benchmark estimates. The only deviation from the results in Table 4 is the absence of a significant impact of education on arranged marriage. Hence, separating the effects of male from female education by considering only men whose wives were exposed to the CER confirms our previous finding of a strong negative effect of male education on the incidence of domestic violence and socially unacceptable behavior. This result, furthermore, implies that the effects that we measure can be causally attributed to the men's education rather than being driven by increased education of their wives.

7 | CONCLUSIONS

This paper constitutes, to the best of our knowledge, the first conclusive empirical investigation of the relationship between men's education and violent and abusive behavior against women, estimated in a way that is robust to the likely endogeneity of education (an earlier study by Erten & Keskin, 2018, yielded inconclusive findings about the role of male education as a factor of domestic violence). To this effect, we take advantage of a natural experiment, a CER in Turkey in 1997, which increased the legally mandated length of schooling from 5 to 8 years. The results of our analysis suggest that increasing male education reduces the incidence of domestic violence for most types of abusive behavior: physical, emotional, and economic. Higher education also reduces the frequency of arranged marriages and makes men less prone to engage in socially unacceptable behavior such as drinking, gambling, and drug abuse.

An issue overlooked in the previous literature is that, because of assortative mating (preference to find spouses with similar characteristics), the educational outcomes of husbands and wives are correlated. That also means that the CER is likely to affect the education of both spouses, which violates the exclusion restrictions when considering only the education of one of them. We show that our findings are robust to accounting for the education of the two spouses together. Importantly, we also separate the effect of male education from the effect of education of their wives. By considering only men whose wives were affected by the reform, we show that the observed favorable effects of education can be attributed to the men's education.

Education has important private and social returns, which have been well documented in previous literature. Our analysis suggests a range of additional benefits. Domestic abuse against women unfortunately remains widespread in developed and developing countries alike. Improving educational outcomes of men should not only have positive implications for them, but there are also important spillover effects in terms of improved women's well-being. Furthermore, these spillovers are not only contemporaneous: Our results show support for the cycle of violence hypothesis whereby history of maternal domestic abuse in the past, on either husband's or wife's side, increases the incidence of domestic violence at present. Therefore, reducing violence against women today can lead to improvements for the current generation of women, and also for future generations of both women, and men.

ACKNOWLEDGMENTS

The authors benefited from many helpful comments and suggestions received from Ruxanda Berlinschi, Ralitza Dimova, Ansgar Wohlschlegel, and seminar/conference participants at Brunel, Portsmouth, KU Leuven, and the Slovak Economics Association Meeting in Košice.

DATA AVAILABILITY STATEMENT

The National Research on Domestic Violence against Women surveys, the data used in this paper, are maintained by the Turkish Statistical Institute (TURKSTAT). The data are proprietary and the authors are not allowed to share them with third parties. Researchers wishing to obtain the data should contact TURKSTAT.
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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Özer, M., Fidrmuc, J., & Eryurt, M. A. (2023). Education and domestic violence: Evidence from a natural experiment in Turkey. *Kyklos*, 76(3), 436–460. https://doi.org/10.1111/kykl.12334