metadata: authors: 0: author: Bussolo,Maurizio
1: author: Ezebuihe,Jessy Amarachi
2: author: Munoz Boudet,Ana Maria
3: author: Poupakis,Stavros
4: author: Rahman,Tasmia
5: author: Sarma,Nayantara
count: South Asia
docna: 0: docna: Social Norms and Gender Equality : A
            Descriptive Analysis for South Asia
docty: Policy Research Working Paper
abstracts: cdata!: Despite decades of economic growth,
            gender inequality in South Asia remains remarkably high.
            Although not the only one, social norms are a crucial driver
            of various gender outcomes, including differential economic
            participation. Using repeated cross-sectional data from
            nationally representative surveys, this paper explores the
            long term trends of gender outcomes and social norms
            (proxied by attitudes towards gender roles) in South Asia.
            The results corroborate the evidence that there has been
            almost no progress in gender equality in South Asia over the
            past half-century. There has been little progress on female
            labor force participation, marriage age, agency, intimate
            partner violence, and preference for sons, with education
            being the only exception. The lack of progress is apparent
            among all socioeconomic groups, including women who live in
            urban areas, are educated, and have higher incomes. Gender
            attitudes also remain unchanged, and in some cases, have
            become more conservative and have a negative relationship
            with gender outcomes. Better measurements of social norms
            and better understanding of how their constraining role can
            be loosened may be critical for achieving gender equality in
            the region.
display_title: Social Norms and Gender Equality : A
            Descriptive Analysis for South Asia
listing_relative_url: /research/2022/08/33882785/social-norms-gender-equality-descriptive-analysis-south-asia
topicv3: Gender m1327621 615,Social Norms and Poverty m1503846 3751,Female Labor Market m1504222 2950,Womens Empowerment m1504369 3664
publishtoextweb_dt: 2022-08-16T00:00:00Z
dois: 10.1596/1813-9450-10142
author_keys: 0: author_key: 669228
1: author_key: 1996254
2: author_key: 950872
3: author_key: 1499387
4: author_key: 1499763
5: author_key: 1812473
model_extractions: Policy Research Working Paper 10142 Social Norms and Gender Equality A Descriptive Analysis for South Asia _Maurizio Bussolo Jessy Amarachi Ezebuihe Ana Maria Munoz Boudet Stavros Poupakis Tasmia Rahman Nayantara Sarma_ South Asia Region & Poverty and Equity Global Practice August 2022
Policy Research Working Paper 10142

# **Abstract**

Despite decades of economic growth, gender inequality in South Asia remains remarkably high. Although not the only one, social norms are a crucial driver of various gender outcomes, including differential economic participation. Using repeated cross-sectional data from nationally representative surveys, this paper explores the long term trends of gender outcomes and social norms (proxied by attitudes towards gender roles) in South Asia. The results corroborate the evidence that there has been almost no progress in gender equality in South Asia over the past half-century. There has been little progress on female labor force participation, marriage age, agency, intimate partner violence, and preference for sons, with education being the only exception. The lack of progress is apparent among all socioeconomic groups, including women who live in urban areas, are educated, and have higher incomes. Gender attitudes also remain unchanged, and in some cases, have become more conservative and have a negative relationship with gender outcomes. Better measurements of social norms and better understanding of how their constraining role can be loosened may be critical for achieving gender equality in the region.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, jezebuihe@worldbank.org, amunozboudet@worldbank.org, s.poupakis@gmail.com, trahman4@worldbank.org, and nsarma@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Social Norms and Gender Equality:**

# **A Descriptive Analysis for South Asia**

Maurizio Bussolo, Jessy Amarachi Ezebuihe, Ana Maria Munoz Boudet, Stavros Poupakis, Tasmia Rahman, Nayantara Sarma

**Keywords** : social norms, women’s empowerment, female labor force participation, South Asia. **JEL codes** : J16, J21, O15.
# **1. Introduction**

Gender inequalities across a variety of dimensions of well-being, education, economic participation, health, access to assets, services, and voice have been highlighted as key features of inequality and poverty in the South Asia region (Klasen and Lahoti 2021, Vijaya, Lahoti, and Swaminathan, 2014; Alkire and Santos 2014). Although progress has been achieved in several of these dimensions, gender inequalities persist and remain unchanged in some areas. A major area of concern is the low female labor force participation (FLFP) in the region. Notwithstanding some within-region differences, FLFP rates in South Asia remain below all other world regions, apart from the Middle East and North Africa, by a large margin (at 23.6 and 19.8 percent, respectively, for 2019)<sup>1</sup> and have remained broadly unchanged since 2001 (Najeeb et al., 2020).

The theoretical and empirical literature consistently recognizes social norms as a key driver of various gender outcomes (Galvan & Garcia-Penalosa, 2018; Heise, 1998; Munoz Boudet et al., 2013; The Social Norms Learning Collaborative, 2021; Weber et al., 2019; The World Bank, 2012; Verick, 2014). A recent review by Jayachandran (2020) illustrates how norms act as a barrier to women’s employment and the impact of various policy measures when strong norms are present. By placing restrictions on women’s mobility, control over finances, roles within the household and attitudes around gender-based violence, cultural norms make it harder for women to participate and thrive in the labor market. Other studies have confirmed this to be true in the specific context of South Asia (Amir et al., 2018; Bernhardt et al., 2018; Heintz, Kabeer, and Mahmud, 2017; Klasen, 2019; Bridges, Lawson, & Begum, 2011).

The role of norms may explain why, in the past two decades, FLFP has continued to be low in the region despite significant gains in GDP growth, women’s educational attainment, and total fertility rate<sup>2</sup> - factors typically associated with improvements in women’s economic participation (Mammen and Paxson, 2000; Goldin, 1994; Olivetti, and Petrongolo, (2016); Luci _et al._ , 2012; Hosney, 2016; Zaheer and Qaiser, 2016; Verick, 2014). Indeed, trends observed in South Asia also do not conform to the traditionally hypothesized U-shaped relationship between economic development and FLFP (Boserup, 1970; Durand, 1975; Goldin, 1995). Examination of data from two time periods – 1995 and 2020 – shows that FLFP in most South Asian countries tends to fall substantially below global employment rates in the U-shaped curve (see Figure _1_ ), at the same level of GDP per capita compared to the rest of the world.

Social norms often influence behavior in combination with or through interactions between households, markets, and formal and informal institutions (The World Bank, 2012). To understand the drivers of gender inequality, it is therefore important to look at changes over time in various macro and micro level

> 1 For women 15+. Source World Development Indicators, using the International Labour Organization, ILOSTAT database. Data retrieved on June 15, 2021. The main outlier in the region is Nepal with rates over 75%. 2 See Annex Figure A1 for graphs on observed trends over time in FLFP, gross domestic product (GDP) growth rate (data from WDI), female educational achievement (data from DHS), and the national total fertility rate (TFR) (data from WDI) across South Asian countries.

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indicators that influence gender outcomes and their relationship with the evolving norms and attitudes. At the macro level, this includes markets and the types of opportunities they generate (Lopez Acevedo and Robertson, 2016; Mani et al., 2020), large infrastructure investments (Samad and Zhang, 2016), and formal institutions, such as laws (Gonzales et al. 2015). At the micro level, the literature points to several supply-side factors, including lifecycle events such as educational attainment, age of marriage, and age of first birth (Parsons et al., 2015). Another important gender outcome is women’s agency within the household (Kabeer, 1999; Afridi, et al., 2018; Klasen and Pieters, 2015; Rahman and Islam, 2013). Agency can manifest in decisions about economic participation through women’s role in decision-making within the household, realization of desired fertility, timing of first birth after marriage, and intimate partner violence (IPV) experience (Jayachandran, 2020; Kabeer, 1999; The World Bank, 2012).

Despite the wide recognition of social norms as an important determinant of gender outcomes, the evolution of these norms has not been adequately examined or measured. While norms around various dimensions of gender have been explored in isolation in specific geographical contexts,<sup>3</sup> the literature is still scant, relying generally on available proxy measures of norms, especially at the regional level. This is mainly due to the lack of available data on norms and normative shifts. Consequently, adequate insights are lacking on how gender norms - even when measured through proxies - have evolved and interacted with these gender outcomes over time.

This paper attempts to look at this relationship across the South Asia region, using available proxies for social norms measurement. We try to understand the persistence of gender inequality in the region by exploring trends in gender outcomes and related attitude variables. Using large-scale, cross-country data over multiple time periods, we provide a long-term, regional view of these trends. Using a cohort analysis approach, we unpack what has changed and what has remained stagnant. This allows us to assess gaps in measurement to inform future regional data-collection efforts and gender-related policies.

Our analysis documents four stylized facts on gender attitudes and gender outcomes in South Asia. _First_ , South Asian women born across half a century have experienced little progress, or even regression, in terms of a series of norms-sensitive outcomes, such as labor force participation, marriage age, agency, IPV, and even the preference for the offspring’s gender; the only exception has been education. _Second_ , economic development had almost no impact on progress. This comes in contrast to what would be expected after decades of strong economic growth in the region. As countries develop, these gender disparities should decrease, but this is not the case. In fact, the stagnation of gender outcomes is quite widespread and applies to women living in urban areas, who are educated, and have higher incomes. _Third_ , gender attitudes – often conservative – have not changed or have become even more conservative for men and women born across that same period. These gender attitudes involve views on a woman’s right to a job, higher education, contributing to household income, and childbearing. This extends even 3 See for example Pande et al. (2019), De Hoop et al (2020), Dhar et al. (2018), Buchmann et al (2021) among others.

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to acceptance of violence, with a slightly increasing trend in justification of IPV. While attitudes are incomplete and noisy proxies for social norms, this stable trend in conservative attitudes is indicative of the persistence of regressive gender norms in the region. _Fourth_ , a negative relationship exists between these conservative gender attitudes and their respective gender outcomes. Although this is not a causal relationship, their interdependence is important to note, given the difficulty in explaining the lack of progress in gender outcomes in the region.

The remainder of the paper is organized as follows. Section 2 discusses the related literature on social norms and women’s outcomes focusing on South Asia. Section 3 describes the secondary data sources used to explore these trends in the region, while Section 4 outlines the methods used to analyze them. In Section 5, we present the four stylized facts and discuss their implications. Finally, Section 6 concludes.

# **2. Literature Review**

Women’s economic participation may be influenced by several factors operating at different levels -from the macro to the individual. While there are both demand and supply-side factors that may predict and determine women’s labor force participation and employment outcomes, we focus primarily on barriers on the supply-side, especially those that interact strongly with social norms to influence FLFP.<sup>4</sup> Many studies argue that gender norms are behind the low levels of women’s economic participation (Heintz, Kabeer, and Mahmud, 2017; Bridges, Lawson, and Begum, 2011; Luci, Jütting, and Morrisson, 2012, Blau et al., 2013; Fernandez 2013, Fernandez and Fogli, 2009, Giuliano, 2017).<sup>5</sup> In some South Asian countries, economic growth has reshaped long-standing norms, as in Bangladesh, where women’s employment and mobility in cities have risen due to the growth of the garment sector (Heath and Mobarak, 2015). Nevertheless, many norms remain unchanged or have regressed along with this progress; for example, child marriage remains a widespread practice in Bangladesh despite legal frameworks against it (Male and Wodon, 2017). Advancement in some areas and lack of progress in others illustrate the complex links between norms, gender disparities, and economic development.

While studies in the region are relatively small-scale and country-specific, the literature does converge on social norms being one of the key drivers of low FLFP in the region. In India, Deshpande and Kabeer (2021) find that detrimental social norms that reinforce expectations of women being primarily responsible for household chores lower their probability of employment. Analysis of national household survey data in Bangladesh reveals a strong correlation between a conservative outlook (as indicated by wearing a _burqa_

> 4 For a discussion on demand-side factors see for example, for the case of India, Deshpande and Singh (2021), Klasen and Pieters (2015), Chatterjee et al. (2015), Mehrotra and Parida (2017).

> 5 The reverse is also true, female labor force participation and employment has a transformative potential on gender norms (Seguino 2007).

4
or veil) and a lower probability of female employment (Ahmed & Sen, 2018). Another study in Madhya Pradesh finds that men’s perceptions about community norms against FLFP are predictive of their spouse’s employment status, suggesting that husbands, and their concern for their own reputation, may act as a conduit for social norms to affect women’s labor force participation (Bernhardt et al., 2018). Men’s own support for FLFP has also been found to be more predictive of women’s labor force participation in their household in Pakistan than women’s own opinions (Amir et al., 2018).

Social norms also influence FLFP via other micro level indicators that are associated with women’s employment. Education is frequently cited as one of the strongest determinants of FLFP (Heath and Jayachandran, 2017). However, cultural and social norms can affect both decisions regarding girls’ education and its ability to influence employment outcomes of women. For example, in Nepal, Vogel and Korinek (2012) find that households are more willing to invest in boys’ than girls’ education based on norms about their role in society. Another study in Pakistan (Sawada and Lokshin, 2009) finds strong supply-side constraints when it comes to school due to low demand for primary schooling for girls in the villages studied, which seems to come from views about their roles in the future. In Sri Lanka, Malhotra and DeGraff (1997), as well as Gunatilaka (2013), found that the labor market behavior of educated young women is shaped by family expectations about their role as daughters in the household, cultural differences in the acceptability of young women working, marital status, and more.

Women’s employment outcomes are also closely linked to key lifecycle events such as the age of marriage and the age of first birth, which are frequently and systematically reported as barriers to employment in the literature. Early marriage -a practice that strongly depends on collective arrangements and preferences derived from social norms (Greene and Stiefvater, 2019), can limit women’s access to education (Delprato et al., 2015, Field and Ambrus, 2008), limiting future employment opportunities, and lead to early childbearing, which further restricts their ability to participate in employment (Parsons et al., 2015). Similarly, spousal age gaps that normally accompany early marriage, education gaps, and others have been associated with different outcomes for women.<sup>6</sup> Across the world, entrenched gender norms around women’s role within the household create expectations for women to be responsible for household chores and child-rearing, often irrespective of their employment status (Bittman et al., 2003; Sayer, 2005). In South Asia, where early marriage, arranged marriage, within-kin marriage, large spousal age gaps, and other practices are still prevalent, women face additional normative pressures once married. In some cases, women are expected to have children soon after getting married to prove their value (Scott et al., 2021), for example, by birthing sons (Mitra 2014, Javed and Mughal 2019). Adding to marriage and childbearing, the added expectations around domestic responsibilities women face, and the low availability of childcare options, women in the South Asia region often face insurmountable barriers in pursuing employment due to conflicting demands on their time after marriage (Kantor, 2009). For example, a cohort analysis of barriers to FLFP in Pakistan finds that household and childcare

> 6 For a discussion of marriage markets in developing countries see Anukriti and Das Gupta (2017).

5
responsibilities are frequently attributed as a reason for women’s absence from the labor force. This trend has remained mostly unchanged in the 15-year period since 1999 (Amir et al., 2018).

Women’s ability and voice in decision-making within the household, and the social norms, such as expected gender roles, that regulate it, have implications beyond childbearing. These include women’s mobility (Jejeebhoy, and Sathar, 2001, Kantor 2002) when norms bound women to limited access to means of transport, or limited ability to leave their homes; their ability to build their capabilities and access economic opportunities (Afridi, et al., 2018; Rahman and Islam 2013; Kabeer, 1999), and their experience of IPV when participating in the labor market or earning an income (Krishnan et al. 2010; Weitzman 2014; Raj et al. 2018).

# **3. Data**

To explore the role and evolution of social norms in women’s outcomes in South Asia, we searched for sources of data on both gender outcomes and their related attitude indicators that were available for more than a single period. While most countries in South Asia have several waves of comprehensive household surveys, the combination of outcome and attitude variables is not common in any single survey. Surveys were mapped based on these criteria, and several potential secondary data sources were identified.<sup>7</sup> In our analysis, we use nationally representative data from the Demographic and Health Survey (DHS) and the World Value Survey (WVS) surveys. Among the secondary data sources considered, DHS provided the most comprehensive combination of gender-relevant outcome variables across all countries and time periods. However, one limitation of the DHS sample is that it is restricted to women of childbearing age (and in some countries, it covers only women who have married or live in a consensual union). WVS, though not available for all countries in the region, was selected due to the availability of gender attitude variables that could serve as a proxy for relevant norms (see Table A1 in the Annex for more details).

Following the survey identification, we proceeded to review the indicators in the identified surveys that were related to norms and FLFP. This was done in an iterative process, similar to the one Malhotra, Shuler and Boender (2002) follow for identifying empowerment indicators. Building on the literature on agency and empowerment, and publications on social and gender norms, we identified a set of indicators and variables repeatedly appearing in the literature as norms-related indicators.<sup>8</sup> We then looked at how

> 7  This included the Demographic and Health Survey (DHS), World Values Survey (WVS), Multiple Indicator Cluster Surveys (MICS), Gallup Poll, Gender and Adolescence: Global Evidence (GAGE) (see Table A1 in the Annex for more detail on these data sources). Upon careful review, the latter three were dropped due to limited data availability (GAGE) and duplication of variables already available in the DHS and WVS surveys (MICS and Gallup) .

> 8 For the review we assume social and gender norms to be conditional preference for following behavioral rules based on expectations of other’s approval and/or sanctions for conformity or transgression of that rule (Bicchieri, 2006); that are embedded in formal and informal institutions, as well as individuals’ minds, and specifically define

6
these specific variables from these specific surveys had been used in the literature (i.e., whether as an outcome or as explanatory variables, or as indicative of social norms). We were able to detect that for a given topic, data were used as indicative of a presence of social norms when the indicators referred to were closer to beliefs or opinions (for example, acceptance of wife-beating, intensity of preference for sons), and as an outcome when reporting occurrence or frequency of a behavior (for example, incidence of IPV, fertility). We found many indicators being used both to refer to dimensions of women’s empowerment or agency and to social norms. We then proceeded to sort indicators from the closest proxy of a possible social norm (i.e., beliefs and normative explanations for behaviors), to the ones that reported individual-level outcomes that appear to be the most independent from a possible gender norm. We then identified which indicators were available across countries and survey rounds. The final classification of indicators labels them as either ‘gender norm-influenced outcomes’ or ‘gender-normative attitudes’. Most of the first set of variables come from the DHS data, while the latter is mainly from the WVS data.

Since our primary gender outcome of interest is employment status, that variable is included, followed by education (years of education), which impacts gender outcomes across multiple domains. Next, we look at key hypothesized determinants of labor force participation around marriage and children (age of marriage and age at first birth). The next set of variables relate to intrahousehold power and negotiationagency (role in household decision-making), IPV (incidence and acceptance), age and education gap with spouse, difference between desired fertility and realized fertility, time between marriage and age at first birth, and son preference. The literature has identified these variables as strongly influenced by social norms. Clearly, some variables have a more direct link with social norms, such as the age of marriage and IPV, while others, such as educational achievements, are determined by multiple factors, and their links with social norms may be less direct. Lastly, we look at a list of subjective attitudes around gender that are expected to have a strong correlation with underlying gender norms: gender attitudes towards education, economic empowerment, IPV, and fertility. While attitudinal data remains an imperfect and likely incomplete proxy for underlying social norms, cross-country data on influential social norms are not available. Table 1 summarizes these indicators across the different domains.

# **4. Methods**

_Pseudo-panel from repeated cross-sections_ Using repeated cross-sections from the Demographic and Health Surveys and the World Value Surveys, we create pseudo panels, where individuals from specific birth cohorts are followed as they age. Using

> what is acceptable and appropriate as an action for women and men in a given group or society (Cislaghi and Heise, 2020).

7
these pseudo panels allows us to compare relevant outcomes for generations born more than 50 years apart.

Consider, for example, Table 2, which presents data on women’s employment rate for the pseudo panel created using eight cross-section surveys for Bangladesh. The first cross-section is from the DHS of 1993, and then seven more surveys provide data every three or four years, covering the period up to 2017.<sup>9</sup> Each cell of Table 2 contains two pieces of information: a) the size of a group, i.e., how many people were born in a certain period and observed at a certain age range, and b) the share of women employed in that group. Reading from top to bottom along the columns, the table shows how the employment rate for a specific cohort changes as that cohort ages, thus providing an estimate of the age effect for each cohort. Reading from left to right along the rows, the table shows the employment rates for different cohorts observed at the same age, thus revealing the cohort effect. The bottom row of the table is the weighted average of the ‘lifetime’ employment rate for all the cohorts of Bangladeshi women that we observe in the pseudo-panel. And the rightmost column illustrates the weighted average employment rate at each age across all cohorts.

There are two important issues to be noted. The first is that the size ‘N’ for some cells in the table is relatively small. This means that the employment shares (or any other relevant outcome variable) for these cells are estimated on a restricted number of observations, and they will be less precise (Verbeek and Nijman, 1992). The second issue is that the full lifecycle – or at least the period from age 15 to 49 – is observed only for some cohorts, those born between the 1960s and 1980s, in the middle of the table. This means that the lifetime employment rate (the bottom row of the table) is biased at the extreme ends of the table. It overrepresents older people at the left end (for the 1940s cohorts) and overrepresents younger people at the right end (for the 1990s cohorts). This is because the earliest surveys for the DHS are in the 1990s, and so we can observe people who were born in the 1940s only when they are about 50 years old, but we have no information about them when they were younger because we do not have surveys fielded before the 1990s. Conversely, since the most recent DHS surveys were collecting data around 2015, we can observe cohorts who were born in the 1990s only when they are about 25 years old or younger, but not when they become older. A similar bias affects the weighted average for the rightmost column of the table. The employment rates for the young ages are overrepresenting the cohorts born more recently, while those for the older ages overrepresent cohorts born in earlier years.

# _Two types of variables: With and without age profiles_

The first issue means that the estimates for the oldest and youngest cohorts (and ages) should be taken with a grain of salt. In fact, when summarizing the long-term evolution of women’s economic engagement

> 9 See table A2 in the annex for details about the availability of DHS and WVS for the countries covered in this study.

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and other outcomes, we drop some of the earliest and latest cohorts, restricting our analysis to the cohorts born between 1950 and 1990 (see Table 3 footnote for details).

The second issue is dealt with differently for different types of outcomes. Outcomes such as the age at first marriage, or the years of education, do not change after a certain age. In fact, all variables relating to a specific event that happens only once, or at least that happens before a certain age, do not have an ‘age profile’ and therefore the mentioned bias does not affect the time trend observed across cohorts, even if the cohorts are observed at different ages. However, employment, agency and IPV do have age profiles and thus, changes across cohorts need to be assessed when cohorts are of the same age.<sup>10</sup> Moreover, the level of a variable in these data is also determined by the year (or period) effect. Variables such as employment may change because in a specific _year_ the economy experiences a shock that affects everyone, no matter their age or the cohort they belong to. A more precise way to estimate the long-term trend across cohorts is by applying an econometric decomposition.

# _Econometric methods for Age-Period-Cohort decomposition_

This decomposition of employment rate (or any outcome for that matter) into age, year (or period) and cohort (year of birth) effects can be formulated using a set of dummy variables as follows where A is a dummy variable that takes value 1 for those aged _a,_ and 0 otherwise; is a dummy variable that takes value 1 for those interviewed at year/period _p,_ and 0 otherwise; and is a ee<sup>aa</sup> PPeeP PPdd<sup>pp</sup> dummy variable that takes value 1 for those born in _c_ , and 0 otherwise. This equation suffers from two YYPPYY<sup>cc</sup> identification problems: each set of dummies sum to one, and there is an exact linear relationship between age, period and cohort. While the first problem can easily be addressed by dropping the first dummy variable from each set, the second problem remains, making identification of all these effects impossible. Thus, there is no one solution to this problem, rather several different approaches to address this so-called age-period-cohort identification problem.

The popularity of this type of analysis, along with the unsolvable problem of identification gave rise to a large literature in many disciplines, such as statistics, epidemiology, and economics, using a wide range of different approaches. All of them making different identifying assumptions, therefore each having its limitations and popularity in different disciplines. In this analysis, we use five different approaches, which we name as follows: Prime Working Age (PWA), period restriction (HHDP), Intrinsic Estimator (IE), Maximum Entropy (ME), and Hierarchical Age-Period-Cohort (HAPC) (Browning et al., 2012; Deaton and Paxson, 1994; Fu, 2000; Hanoch and Honig, 1985; Nientker and Alessie, 2019; Yang et al., 2004; Yang and

> 10 See Annex 6 for an illustration of this issue for the two types of outcomes.

9
Land, 2006).<sup>11</sup> We apply all these five models in the data from Bangladesh and Nepal, for which we have an adequate number of cross-sectional surveys to conduct such analysis.

Up to this point, the discussion focused on outcome variables. But this paper examines also social norms or, rather, attitudes variables towards gender roles and women’s position in society. In the case of norms variables, it may be more difficult to establish whether they have or not a life cycle. The sociology literature and some economic literature view that what happens in the formative years tends to leave permanent marks (Giuliano and Spilimbergo, 2014). Others found that life experiences, which are different across cohorts, are stronger predictors of gender roles attitudes than the age of individuals (Lynott and McCandless 2000). If these are valid conclusions, it means that attitudes will not change as the cohorts age.

# _Heterogeneity analysis_

While the described methods so far relate to the ‘average’ woman in the countries of the region, there is potentially quite a bit of dispersion around this average, and to capture this heterogeneity we analyze separately different groups of women. The groups are formed according to the level of education, wealth quintiles, and rural versus urban location. This heterogeneity analysis is based on the following regression specification: The focus is on the marginal effects of education, location, and wealth. More precisely, for example in the case of wealth ‘W’ the marginal effect for the outcome variable ‘Age at first marriage’ is defined as: Likewise, one can estimate the marginal effects of the other two independent variables. This analysis allows to identify whether the trends across cohorts are different for women who are more educated, live in urban areas, or are richer.

> 11 See Annex 7 for a detailed discussion on each of these models.

10
The results of applying these methods are described in the next section which organizes them around the four stylized facts mentioned at the outset. In some cases, the results emphasize the long- term changes across cohorts for all variables as if none of them had age effects, as this allows to observe the longest time span. In other cases, the results present more precise cohort effects using simple age and cohort decompositions. For Bangladesh and Nepal, which have a sufficient number of repeated cross-sections, the results of more sophisticated econometric decompositions are discussed.

# **5. Results**

**Stylized fact 1: Apart from education, norms-sensitive outcomes – such as economic participation, marriage age, agency, IPV – for South Asian women born across half a century remained stagnant and, in some cases, regressed.**

The main result from analyzing a large set of norms-sensitive outcomes for women is that their improvement has been mostly elusive. We assessed outcomes in various domains and across cohorts spanning half a century, as we start our analysis for generations of women born in the 1940s. The lack of progress on some indicators during such a long time, and especially during the more recent decades of strong economic growth, is notable. The result of our analysis is summarized in Table 3 for all six South Asian countries considered in the study.<sup>12</sup> The norms-sensitive variables, whose general trends are described in the table, can be grouped, as noted in Table 1, in three broad categories: a) employment and education, b) marriage and children, and c) intrahousehold power/negotiation.

Table 3 summarizes results that are shown in more detail in Figure 2 to Figure 8 in the following pages. These figures show the time evolution of the outcome variables – or, more precisely, how these variables have changed for the _average_ women across cohorts – while Table 3 shows the decadal average change in these variables comparing cohorts born in 1950 to cohorts born 40 years later, in 1990. To provide a reference point, Table 3 also reports the value for the different variables calculated for the full population, not just a specific cohort, from the most recent survey. An example is helpful here. Consider women’s employment in Bangladesh, in the top two rows of Table 3. The percentage of women working in Bangladesh in 2017/18 was 48 percent, as shown in the first line of the table. The average employment rate for women born in the 1950s was about 22.8 percent and this increased to 29.3 percent for women born in the 1990s. The cumulative change between these two cohorts is 6.5 percentage points, and the average decadal change is thus 1.625 which in the table is rounded to 2 percentage points. In short,

> 12 The 6 countries correspond to a subset of the 8 countries included in the World Bank-defined South Asia region: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka.

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employment for women in Bangladesh increased on average just 2 percentage points during each of the decades that separate the generations of women born in the 1950s from those born in the 1990s. Decadal changes for the other variables are calculated in the same way.

Table 3 offers a bird’s-eye view of the recent 40 plus years evolution of norm-sensitive outcomes for women in the South Asia region. To facilitate the interpretation of the evolution, consider the color of the decadal changes rather than their sign. Red means that there has been a worsening trend, such as the reductions of employment rates for women in Afghanistan, India, Maldives, and Nepal in the first row of the table (this independent of starting or ending levels, which for Nepal are high). Dark orange signals that there has been neither progress nor deterioration but, given the low levels of these variables in the region, dark orange has a negative connotation and should be interpreted as stagnation. Finally, green indicates progress and, as mentioned above, this is recorded only in the case of education, with very few exceptions. Thus, overall, the bird’s-eye ‘color’ view emphasizes lack of progress and, in some cases, even deterioration.

In more detail, and starting from the first group of variables, employment and education, Bangladesh and Pakistan are the only cases where there are decadal improvements of women’s employment while, for all the other countries, it shrunk. Changes in education are expressed in number of additional months of education accumulated during each decade. This is the only variable registering improvements, ranging between 6 months and 3.3 years each decade.

Consider next the second group of variables, marriage and children. For marriage age and age at first birth, changes are measured in number of months to be added, or in some cases subtracted, for each decade to the average age at which women marry the first time and to the average age women gave birth to their first child. Son preference is the percentage points to be subtracted for each decade from the percent of women who prefer more sons. Almost no improvement is recorded for these variables, and in some cases – age at marriage and at first birth in Afghanistan – the situation worsened.

For the last group, intrahousehold power/negotiation, changes are expressed as percentage points (for agency and experience or justification of IPV), months (for the spousal difference in age or education) or number (for fertility). And again, apart from spousal difference in age and education in Bangladesh, there has been no improvement in this group of outcomes.

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Figure 2, Figure 3, and Figure 4 depict the detailed trends across all cohorts for all 12 norm-sensitive outcomes, and the next subsections provide a discussion of these trends.

# **Employment and education**

Low female labor force participation is a crucial concern for the South Asia region both in terms of its implications for gender equality and for overall economic growth. In 2019, women’s labor force participation in the region stood at 25 percent while for men it was 80.5 percent (WDI). Against a background of strong economic growth and, as discussed further below, increased levels of education, economic participation has not changed significantly in the region among women born across over 50 years. Figure 2 (left panel) shows that women born in recent years are _not_ participating in the labor market significantly more than women of older generations. In fact, for Nepal, India, and Pakistan, there has been some reduction in the share of working women among cohorts born more recently. For a cohort born in the early 1970s, the share of working women was 36%, 77% and 28% in India, Nepal and Pakistan, respectively. For cohorts born in the 1990s, these shares shrink significantly by 13, 23, and 10 percentage points in these three countries. Bangladesh appears to be a slight exception as women born in recent years witness an increase in employment compared to the oldest generations, but even for the most recent cohort this share, at 23.5%, remains at a quite low level. As documented in Figure _2_ , this persistently low share of employment places South Asia off the empirical regularity of the “U” curve linking female employment and level of development.

Albeit Nepal sits as an exception for the region with high levels of FLFP, it appears to conform with the U shape downward slope of increased GDP and possible reduction of FLFP due to processes such as agricultural substitution, availability of old age pensions and increased education levels and trajectories for young people associated with higher levels of economic development.<sup>13</sup> As discussed in the methods section, the comparison of employment shares across these cohorts does not provide an accurate picture of the long-term trend as mainly older women are represented in the 1940s cohorts, while mainly younger ones are found in the 1990s cohorts. Comparing different cohorts at the same age eliminates this age composition bias. This more accurate comparison is graphically shown in Figure 5. For the case of Nepal, Figure 5 shows the same data used in Figure 2 but organized differently. In Figure 5, the dots represent the shares of working women calculated for each cohort in each of the

> 13 See Besamusca et al (2015) for a cross-country comparison and discussion.

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years in which a DHS survey is available. The lines connect the dots that belong to the same cohorts as they age. For easier interpretation, we group cohorts from different decades by color.

The typical inverted ‘U’ life cycle profile for employment, with the middle part of the life cycle with the highest participation, is clearly shown in Figure 5. Zooming in on the cohort of women who were born in 1970-74 (middle dark blue line in the figure), it appears that about 70 percent of these women were working at age 20-24, then this percentage increases to 80 percent when they are 25–35-year-old, and finally it declines to below 70 percent when they reach 45 years.<sup>14</sup> Figure 5 also highlights the cohort effect, namely that lines of different cohorts, when compared at the same age, display different levels of participation in the labor market. The darker shaded lines plotting labor participation for more recent cohorts lie mostly below the lighter shaded lines of their predecessors, confirming the declining trend in participation found in Figure 2 (left panel) even when the age composition is controlled for. The vertical distances between the lines of Figure 5 can be explicitly plotted as the cohort effect, and this is done in Figure 6 for Nepal and other countries in the region.<sup>15</sup> In this figure, the lines connect different cohorts observed at the same age.

In the case of Nepal, the left panel of Figure 6 shows cohorts born more recently have lower participation in the labor market at all ages. In the cases of India and Pakistan, the recent decreasing participation is confirmed by the cohort plots shown in the right panel. The exception of Bangladesh with an increase in participation for the recent cohorts is confirmed.<sup>16</sup> Going beyond this graphical examination, we applied the formal econometric Age-Period-Cohort (APC) decompositions discussed in the methods section for two countries where we have a sufficient number of evenly repeated cross section data sets. The results from the models in the APC analysis for Bangladesh and Nepal complement the conclusions from the graphical examination of these effects on female employment. Tables A5 and A6 in the Annex present the parameter estimates and their standard errors

> 14 This inverted “U” shape for the age effect, with a peak around 30-40 years of age and lower participation at the other ages, is typical for countries in South Asia as well as in other regions (see Figure A4 in the Annex).

> 15 Afghanistan is not included because it has only one round of survey in 2015.

> 16 Interestingly, the sharp rise in employment happens at the same time for the two cohorts born most recently, i.e. it seems mainly driven by data from the latest two survey rounds. Thus, what in the figure is labeled as a cohort effect, it may in fact also be a ‘year effect’. As Deaton and Paxson (1994) clearly explain, one has to make some strong assumptions to identify the age, cohort and year effects in these panel data. And clearly the graphical decomposition in Figure 7 and Figure 8 are not fully identifying these three components. For a similar analysis see also Yang, Schulhofer-Wohl and Land (2008); Fosse and Winship (2019); Tunalı, Kırdar and Dayıoğlu (2019).

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for all the five APC models. For ease of reading and comparison across models, we also present the coefficients from these models in Figure 7.

Both countries exhibit the typical life cycle profile for employment. However, we observe a flattening of the employment rate, rather than a decline in the later age groups, but this is probably because of the range of age groups we have in the data. Indeed, the older group is 46-50 for Nepal, and 47-50 for Bangladesh.<sup>17</sup> Nevertheless, we can see that the employment rate for women stops increasing around their late 30s/early 40s, with some evidence of decline, albeit rather slowly. This can be seen by the magnitudes of the dummy variables for age, but also from the positive sign in the linear term and negative sign in the quadratic term.

Looking at the period and cohort effects, we can highlight several conclusions about their patterns. First, period seems to drive employment rate more than cohort. We can see this by the larger variation in the period coefficients compared to the ones of cohort. Moreover, the estimated variances for period are much larger than the estimated variances for cohort in the HAPC model, for both countries. This shows that the evolution of female employment rate is affected more by business cycle effects or wider policy changes which affect everyone equally, rather than by cohort-specific attributes. In Nepal, we can see an overall decline in employment rate. This is driven mostly by time, as can be seen in the decline of period effects since 2001, whereas the cohort effect shows either flat effect or slight decline for cohorts born more recently. In contrast, the period effects in Bangladesh have a cyclical pattern. However, this can be due to the time range in the data, as for Bangladesh we have a larger span of years (30 years) than for Nepal (20 years). In terms of cohort effects, while all models show an initial increase in employment rate with each subsequent cohort, this seems to stabilize for the IE and ME. This is coupled with a similar nonincreasing rate for age observed for these two models. This might be driven by the fact that Bangladesh has a much larger variation explained by period, rather than cohort, as pointed out in the estimates of the variances for the random effects in HAPC.

In brief, even when controlling for the age composition and using formal econometric methods to separate age, period, and cohort effects as precisely as possible, the long term _stagnant_ or _decreasing_ trend for women’s employment shown in Table 3 remains a valid summary result for the region.

> 17 This is in line with the prime working age assumption we made for the PWA, where we imposed these groups' effect to be equal with their previous ones.

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This employment trend is concerning when compared with the progress recorded in women’s education. The average years of education has increased significantly over time for most South Asian countries (Figure 2 right panel). For women born before 1965, average educational attainment was low, at less than 4 years for all six countries. The trend begins to rise after 1965, reflecting greater access and investment in education in the region (and across the world, see Barro and Lee, 2013 and Ahmed et al, 2020). Women born after 1990 achieve more than twice the years of education of women born before 1965 in all countries. The highest growth in years of education is seen in the Maldives and the lowest in Afghanistan, where women born after 1990 attain less than 2 additional years of education compared to those born in 1965. Although an upward trend is evident in all countries, the average years of education of women born after the 1980s stagnates in Pakistan. Comparing the years of education of men and women (Figure _4_ middle left panel), education inequality is closing in the region, except for Afghanistan. Women are catching up with men in the number of years they spend in school. However, even if women of more recent cohorts have a higher number of years of education than those born in the 1970s or 1980s, they still seem to be below having completed the full cycle of secondary education, and this gap is particularly severe for Pakistan, Nepal and Bangladesh. This is linked to another set of important decisions in the lives of women: marriage and childbearing which is discussed next.

# **Marriage and children**

The fact that the ages at which women first marry or have their first child remain overall unchanged for more than 50 years covered in this study is remarkable. Women born in 1950 in Bangladesh were getting married at about 13 years old, and those born in the 1990s were marrying at 16. Bangladeshi women’s marriage age increases by less than 9 _months_ per decade.

In the cases of Pakistan and India the marriage age started a little higher than in Bangladesh, but its pace of change has also been glacially slow: slightly more than 1 month per decade in Pakistan and about 5 months in India. Even less progress is recorded for the age at first birth shown in the top right panel of Figure 3: 8.6 months per decade for Bangladesh, -14 months for Pakistan, a decrease in this country, and 7 months for India. Two other countries in the region are at the extremes of these stagnant trends: Afghanistan for which both age at first marriage and at first birth go down, and Maldives where some improvement is recorded.

In South Asia, women give birth to their first child a few years after getting married. Going through childbirth at a young age has both economic, psychological, and health implication for women. Early 16
childbirth may truncate the continuation of a woman’s education and hence limit her labor market opportunities. The gap (in months) between marriage and first born and its evolution for women born in different years is shown in Figure 3, bottom left panel. This graph combines the trends in the two top panels of the figure. The gap is decreasing as women in younger cohorts are marrying slightly later but giving birth at the same age or slightly earlier than women in older cohorts.

Parental son preference is prevalent in South Asia and has been widely documented for India.<sup>18</sup> The DHS ask women about how many children they would choose to have in their whole life if they could go back to the time before they had children. A following question asked for the ideal number of sons, daughters, and ideal number of either sex. Son preference (as a reported choice and not the outcome) is estimated as the share of women who reported a higher number for the ideal number of sons than ideal number of daughters. The long-term trend of son preference across different birth years is shown in the bottom right panel of Figure 3. This graph paints a picture where son preference is decreasing in Bangladesh, India, and Nepal. While over 30 to 60 percent of women in these countries born in the late 1940s to early 1950s prefer to have more sons, less than 20 percent born in mid-1990s have this preference. For the Maldives and Pakistan, the trend is stagnant.

# **Intrahousehold power/negotiation**

Gender norms are intrinsically connected with roles and expectations for women and men that are played out inside the household and particularly in spousal power relations. These relations (and norms) are connected to issues such as women’s agency (Donald et al. 2020; Bussolo et al. 2021), which in turn responds to changes such as spousal gaps in education and age, differences between partners in terms of their desired and realized fertility, and experience and justification of IPV. As noted by Bernhardt et al. (2021), when men and women bear different costs of violating norms, intra-household bargaining will mediate the role that norms play in governing female labor supply.

Agency, according to Kabeer (1999), is the “ability to define one’s goals and act on them”. One of the ways in which researchers have measured agency is by considering the process of decision making inside households. The DHS collects data on self-reported participation in this process. In more detail, women

> 18 See Pande and Astone 2007 and Mitra, 2014 for India, Hafeez and Quintana-Domeque 2018, for Pakistan, Asadulla et al. 2021 for Bangladesh, Hattlebakk 2017 for Nepal.

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(and men) are asked whether they have a say in decisions about different issues, from large household expenses to personal health care, and mobility.<sup>19</sup> The top left graph in Figure 4 plots, for each birth cohort, the share of women who have a say in at least one of the three household decisions. The remaining group is thus that of women who have no say at all. Apart from Maldives, women born in recent years have less agency than women born in early years. This is especially the case for women in Afghanistan, Nepal, and Pakistan. To unpack the trends of this aggregate indicator, the separate trends of the three specific decision are shown in the Annex (Figure A2). In general, we observe near-identical trends of the three separate decisions to that of the aggregate agency shown in Figure 4.

Agency, like employment, has a life-cycle profile, as shown in Figure 8 for Nepal.<sup>20</sup> Not surprisingly women’s bargaining might increase due to having children, increased wealth, and other age-related factors (Mahmud et al. 2012; After and Chindarkar 2020). It is thus necessary to compare cohorts at the same age to be able to gauge more precisely the cohort effect. Apart from Bangladesh (Figure A5 in the Annex), there is no clear evidence that women of younger generations have higher agency than women of older generations at the same age. While in other countries such as Nepal, there is an initial rise for each cohort which then flattens out as the cohort ages, in Bangladesh (Figure A5 in the Annex ) the case is different. Women born in recent cohorts in Bangladesh have more agency, at a particular age, than women born in the preceding years. The gap is however almost negligible.

Agency, and more generally women’s empowerment, is influenced by age and education differentials among spouses.<sup>21</sup> The two middle panels of Figure 4 show these differentials, which are calculated by subtracting the years of schooling and age of a woman from the years of schooling and age of their spouse. In most countries, a positive gender gap in education remains between men and women across time but for more recent cohorts in Bangladesh and Maldives, the gap is negative. The reversing of the gap in Bangladesh is encouraging and may be a consequence of targeted policies for girls’ education. The

> 19 Questions included here are those referring to who has a say on (i) large household purchases, (ii) woman’s health care, and (iii) women’s visits to relatives and friends. Possible answers are: (a) respondent alone; (b) respondent and husband/partner jointly; (c) husband/partner alone; (d) someone else; and (e) other. Having a say is defined as choosing answers (a) or (b).

> 20 The age profiles of agency in Bangladesh and India show that women older than 30 years have higher agency within their households on average than women less than 30 (Figure A4 in the Annex).

> 21 See for example Carmichael, 2011 and Mogford, 2011, Nyyssola 2007 for different implications of the age and education gaps among spouses.

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difference in years of schooling between husbands and wives in India and Nepal is reducing as well, from around 1 to 3 years for women born before 1980, to less than 1 year for the most recent birth-cohorts. Despite the drastic reduction in the schooling gender-gap, couples in Bangladesh throughout the past five decades have the highest, albeit decreasing, age gaps. Spousal differences in age are important as they have a bearing on women’s agency within the marriage and are often indicative of marriage quality (Carmichael, 2011). In the other South Asian countries, spousal age gaps have been mostly stable since the 1970s, going from 6 to 5 years in India and holding steady at 5 and 4 years in Pakistan and Nepal, respectively.

Figure 4 (top right) also shows the long-term trends in fertility as the difference between self-reported desired and realized fertility.<sup>22</sup> Apart from India and Afghanistan, the rest of the countries have witnessed a declining difference meaning that women born in later years are more likely to give birth to their ideal number of children. Whether this decline is a result of women using their agency in the household to bargain for their desired number of children or the availability of contraceptives is not investigated in this study.

The last outcome considered in this group is experiencing IPV. Social norms can play a role in the acceptability of violence, and when and why is it justified (Weber et al., 2019; ALIGN 2021). Regarding the incidence of IPV, a set of questions were asked to the women whether they have ever experienced any form of physical and sexual violence by their current or previous partners.<sup>23</sup> The bottom graph in Figure 22 We construct our outcome variable as the difference between actual and ideal number of children for women 40 years and above. The DHS asks women questions on the ideal number of children that she would have liked to have in her whole life, irrespective of the number she already has. Another question asks the total number of children actually born who are living. We arrive at the plotted fertility variable by subtracting the ideal number a children a women reported at age 40 from the actual number of living children. We focus on 40 years because, at this age, women are less likely to continue childbearing (United Nations Demographic Yearbook 2019, available at: https://unstats.un.org/unsd/demographic-social/products/dyb/dyb_2019).

23 We coded the IPV experience variable as 1 if a woman has experienced any of these two forms of violence and 0 for no violence. The options included are whether the respondent has ever been either slapped, punched with fist or hit by something harmful; strangled or burnt; threatened with knife/gun or other weapon; had arm twisted or hair pulled; physically forced into unwanted sex; forced into other unwanted sexual acts; physically forced to perform sexual acts respondent did not want to. There is no response for the sexual violence experience for Bangladesh. These data were collected in limited surveys: one survey round for Afghanistan (2015), Bangladesh (2007), Maldives (2016-17), and Pakistan (2017-18) and two survey rounds in India (2005-06 and 2015-16) and Nepal (2011-2016).

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4 shows a stagnant trend for IPV experience across all countries. Bangladesh has the highest share of women who experience physical or sexual violence by their husband/partner.<sup>24</sup>

# **Stylized fact 2: Urbanization, education, and higher incomes do not seem to be correlated with better outcomes for women.**

The results discussed up to this point refer to the average woman in each birth cohort. However, it may be the case that there is dispersion around the average results and that the trends have been different for some groups of women. A recurring theme in debates about gender disparities is that these disparities should decrease, if not disappear, as countries develop. Higher levels of income, education and urbanization usually support the closing of the gaps between women and men. The fact that these gaps remain may indicate that social norms are acting as a barrier to gender equality over and above the usual barriers faced by poorer and less educated, rural women. In fact, things can be more complex as the direction of influence is not unequivocally positive. Education and place of residence can ease the pressures of social expectations or norms to marry young. Education may operate by changing a woman’s goals and opportunities away from early marriage. Place of residence may change the nature of social norms that women are exposed to, but it is not clear that in urban areas they encounter norms that are less conservative than those in rural areas. When families migrate from rural to urban areas, as is common in South Asia, the influence of their relevant reference group may diminish if urban areas are more heterogeneous or dispersed. However, this might also work in the opposite direction as cities are considered more “dangerous” for women and hence their “purity” more needing of protection and control. Communities also tend to concentrate in specific neighborhoods and socially segregate themselves even in urban areas, which can further strengthen the influence of social norms. Using the regression approach described in the methods section and by focusing on more affluent, better educated, and urban women within each cohort, this section addresses the questions of whether the trends are different and the gender gap is closing for these groups of women in South Asia.

This heterogeneity analysis for labor market participation shows that women in the top 20 percent of the wealth distribution have enjoyed stronger progress when compared with their poorer counterparts. However, more educated women, or women living in urban areas do not seem to have followed a different path than less educated or rural women. So, this confirms that the stagnant trends and decadal

> 24 Although IPV prevalence is not static and likely to change over the life cycle, we do not show the age by cohort graphs because the variable has too few data points in the DHS data set to claim any meaningful result.

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changes discussed above for the _average_ woman indeed affected _most_ women and that urbanization and education have not contributed much to boost employment, while rising incomes seem to provide some upward lift. The same conclusion – namely some positive influence of wealth and lesser to no impact of education and place of residence – can be drawn for the heterogeneity analysis of the other normsensitive outcomes. The remaining of this sub-section provides additional details.

Figure 9 shows the marginal effect of wealth across cohorts for women’s engagement in the labor market. Within each cohort and holding constant other determinants, the most affluent group of women, which is represented by women in the top quintile of the wealth distribution, has a lower probability than women in the bottom quintile (the reference, or omitted group) of being employed. For Bangladesh, India and Pakistan, the relative difference between richer and poorer women has been decreasing from the early to the recent cohorts. This means that women of the richer group have progressed faster, i.e. they have experienced larger increases in employment rates, than women of the reference group, quintile 1. In fact, quintile 5 has progressed faster than any other quintiles.<sup>25</sup> This is shown, in the figure, by the steeper slope of the marginal effect for quintile 5 vis-à-vis quintile 2. The case of Nepal is slightly different. As shown by Figure _2_ , the employment rates are really high in this country averaging more than 80 percent in earlier cohorts. This means that with rising incomes, employment of women of more recent cohorts has decreased; however, the marginal effect graph shows that the employment for the richer group has not ‘decreased’ faster than that of quintile 2, as the two lines in the graph are almost parallel.

Consider next the groups of more educated and urban women in the following Figure 10. As for the case of wealth, the marginal effects show the impact of belonging to one category – having more education or living in an urban area – vis-à-vis the reference group, while holding other factors constant. Starting with education, it does not seem that having secondary or higher education confers a substantial advantage in the probability of being employed; at best better educated women’s employment probabilities are about 2 percentage points higher than lower educated ones. Then comparing the trend across cohorts, we note that, for the cases of Bangladesh, India, and Pakistan, the premium of education is decreasing, i.e., the differential in probability of being employed between educated and not educated women is smaller in cohorts born more recently. This is probably linked to the expansion of education. Conversely, in Nepal,

> 25 For clarity the graphs show only quintile 5 (the richest group) and quintile 2 (the second poorest group). Quintiles 3 and 4 are, as expected, for this and all other variables, in between quintiles 5 and 2. The levels for quintile 1, the reference group are normalized to zero for all cohort, so the graph does not show the levels at which the various quintiles are, but just their differences with respect to quintile 1.

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more educated women in recent cohorts are doing a little better. The slope of the marginal effect across cohorts for the urban dummy tends to be quite low and for the case of Bangladesh negative, indicating that, at best, there is no differential progress for urban women with respect to rural ones and, in the case of Bangladesh, urban women have fared slightly worse than rural ones.

In terms of the other norms-sensitive outcomes, the trends for more affluent women have been better in all countries for education, marriage age (with India being an exception), first birth age, son preference, spousal difference in age, and for IPV experience (only in India for this last variable). For some of these outcomes, a better trend for richer women means that the marginal effect amounts to a few percentage points. For example, in the case of marriage age for Pakistan, while the difference in the average marriage age of women in all wealth quintiles born from 1940 to 1965 was not statistically different, this difference became significant, in favor of the richer quintiles, for women born afterwards and reached about 2 percentage point difference for the richest and the poorest quintiles.

Better educated women enjoyed progress only in the case of a) the agency outcome variable, in India and Pakistan and marginally in Nepal, b) differentials in spouses’ education, and c) experience of IPV, apart the case of Bangladesh. For all other outcomes, educated women and not educated women did not experience different trends. Similarly, more recent cohorts of women in urban areas did not experience stronger progress than women in rural areas in any variables with the exception of spousal differences in education for India and in age in Nepal, and experience of IPV in Nepal.<sup>26</sup>

# **Stylized fact 3: Attitudes towards gender roles in South Asia have remained conservative for men and women born across half a century.**

In parallel to the outcome variables, we examine the long-term evolution of attitudes towards gender roles and find no progress in the four attitudes studied. Using various waves of the World Values Surveys, we construct pseudo-panels for three countries in the region, Bangladesh, India and Pakistan, and follow the evolution of attitudes from cohorts born in the late 1940s to cohorts born in the late 1990s. Like Table 3, Table 4 offers a bird’s-eye view of the decadal changes of key beliefs, while Figure 11 shows the detailed trends.

The share of people agreeing with conservative attitudes – such as the priority for becoming breadwinners should be given to men, or women’s preeminent goal is maternity – are remarkably high in the most

> 26 Detailed graphs and comments for all these norms-sensitive outcomes are available in Annex Figures A7 to A26.

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recent surveys. Between 70 to more than 90 percent of the population agree or strongly agree that men have precedence over women in obtaining jobs when these are scarce and, at best, less than 30 percent agree or strongly agree that both spouses should contribute to the household income. At the same time, 85 to almost 100 percent view maternity as key to a woman’s fulfillment. The most progressive position is that towards education where the population is about 50-50 divided between agreeing and disagreeing that university education is more important for boys than for girls. More importantly, the table highlights that these large majorities holding conservative views have been either stable for decades or even increased in some cases. Referring again to the same color codes of Table 3 – where red means worsening, dark orange stagnation and green improvements – Table 4 has mainly red numbers, and no green number.

# **Right to a job**

Discrimination by gender in employment contributes to low female labor force participation ( <mark>Özbilgin et al., 2012</mark> ). The attitudes in a society over who gets a job may influence women’s supply of labor. The top left panel of Figure 11 shows that in Bangladesh, India, and Pakistan, the majority of respondents agree that when jobs are scarce, men have more rights to a job than women. In all three countries, more than half of the respondents agree with this attitude. Examining the trend across cohorts, this conservative attitude has increased among more recent cohorts. For instance, in Pakistan, the share of respondents who believe that men have more right to jobs averaged about 70 percent for cohorts born until 1970. This share increased to over 80 percent for cohorts born from the late 1980s. Similar increase can be seen in Bangladesh and, though less obvious, in India.

# **University education**

While the years of education has increased for South Asian women, the conservative attitude towards girls’ education is not reducing as one would expect (Figure 11, top right panel). However, the attitude about university education being more important for boys than for girls, although conservative, is relatively less so among respondents, compared to the other attitudes examined in this paper. About 30 to 60 percent of respondents in the three countries believe that university education is more important for boys than for girls. Comparing the trend across cohorts, the share had reduced slightly for the younger cohorts in Bangladesh, remained constant across cohorts for India, and has a U-shape for Pakistan.

# **Contribution to household income**

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When women contribute to household income, they are also more likely to contribute to household decision making. Figure 11 (middle left panel) shows that less than half of respondents in Bangladesh, India, and Pakistan agree than women and their spouses should contribute to household income. Across cohorts, this share has reduced in all three countries. This regression is more obvious in Bangladesh and Pakistan where the share of respondents born in the 1940s and 1950s who agree that both partners should contribute to household income is twice the share of respondents born in the 1970s and 1980s.

# **Having children is key for women’s fulfillment**

Figure 11 (middle right panel) shows that more than four-fifths of respondents in all countries agree that a woman has to have children to be fulfilled. This share, which is over 90 percent in Bangladesh and India, has remained stagnant across generations of respondents. In India, this share fell by about 10 percentage points for respondents born in 1980 compared to those born in 1941.

# **Justifying intimate partner violence**

Figure 11 (bottom panel) shows an upward trend for some countries for justification of IPV.<sup>27</sup> In general, women in Afghanistan are more likely to justify, about 80 percent in all cohorts justify beating. Pakistan and Bangladesh women born after the 1960s are more likely to justify beating than women from earlier generations. IPV justification is not static and likely to change over the life cycle. We show the age profiles for IPV justification of different cohorts in Figure A6 in the Annex. In general, these age profiles for Bangladesh, India, Nepal, and Pakistan do not highlight any clear cohort effect. So, it is difficult to conclude that women of recent cohorts are less likely to justify beating than earlier cohorts at a specific age. Overall, the increasing trend in IPV justification, along with no progress in IPV experience, are worrisome.

# **Stylized fact 4: A negative relationship links gender outcomes and attitudes: the larger the share of people holding conservative attitudes, the worse the gender outcomes.**

Gender norms have received increased attention in recent years by researchers and policy makers, to a large extent, motivated by the search for determinants of the stubbornly low rates of female labor force

> 27 We coded the IPV justification variable as 1 if a woman responds ‘yes’ to at least one of the following circumstances/questions and 0 if she responds ‘no’ to all of them: In what circumstances is a husband hitting his wife justified? a) when she goes out without telling the husband, b) neglects the children, c) argues with him, d) refuses to have sex with him, and e) when she burns the food. Data were collected in one survey round in Afghanistan, two rounds in India, Maldives, and Pakistan and in four rounds in Bangladesh and Nepal.

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participation or the lack of progress in closing other gender gaps. While we cannot capture the underlying norms themselves nor establish causality, the following graphs plot the correlation between normssensitive outcomes and corresponding attitudes and provide some initial evidence of the strength of the linkages between these two sets of variables.

For all the countries with WVS data (Bangladesh, India and Pakistan), Figure 12 depicts a clear negative association between women’s employment rate and the attitude toward precedence of men in obtaining scarce jobs. It is interesting to note that while India and Pakistan have similar slopes, the association in Bangladesh is weaker. This means that even if the share of people supporting the right of men to jobs increases (as it is visible for the more recent cohorts), the share of women working does not decrease by a large amount. Further, despite Bangladesh and Pakistan experiencing large overlaps on the horizontal dimension, such that 70-80 percent of respondents in both countries believe that men have more right to jobs, Bangladesh is placed higher on the vertical axis in terms of the share of women working.

The linkage of educational outcome with the related attitudinal variable is shown in Figure 13. This figure plots the correlation between the share of women having secondary or higher education against the share of respondents who agree that “university is more important for boys than girls” in Bangladesh, India, and Pakistan. We group observations into 5-year birth cohorts with darker shades indicating more recent birth cohorts. In the case of education, the results are mixed. A strong negative association is found only for Bangladesh. In addition, the change in attitudes and education levels is consistent with the passing of time as more recent (darker) cohorts display more progressive views and higher levels of education. The correlation, however, is not present or has the ‘wrong’ sign in the cases of India and Pakistan. In India, recent (darker) cohorts have higher levels of education even if attitudes towards gender roles with respect to tertiary education do not change. In Pakistan, even if attitudes become less progressive, the share of women receiving higher education increases. The persistence of regressive gender attitudes has, seemingly, not been a binding constraint for women’s education outcomes. However, the consequences of static and regressive attitudes may show up in other proximate outcomes to education, like employment.

Financial empowerment can increase the agency of women in the household. Women who contribute financially to household income are more likely to participate in household decision making. The share of women who agree that both spouses should contribute to household income is generally low in all three countries – less than 40 percent, and this share is decreasing with more recent cohorts. As shown in Figure 25
14, the correlation between the attitude towards shared economic responsibilities and agency seems positive for the cases of India and Pakistan, while no significant association is found for Bangladesh.

The onset of motherhood has been strongly linked to withdrawal of women from the labor force and selection into casual work or more flexible jobs which allow time for childcare. Deep-seated beliefs about women as mothers are evident in Figure 11 (middle right panel), where overwhelmingly, above 80 percent of respondents agree that women need to have children to be fulfilled. This holds across countries and cohorts, ranging from the 1940s -1980s. Correspondingly, the average age of women at their first birth has remained mostly stable, at around 17-18 years in Bangladesh and above 20 years in Pakistan. Figure 15 highlights this by showing how concentrated within a small area are the observations for these two countries. In the case of India, the slight increase of age at first birth is mirrored by a corresponding decline in the share of respondents who agree that women need to have children to be fulfilled.

IPV has generated a great deal of concern among policy makers and is of interest to researchers ( <mark>Chibber and Krishnan, 2011</mark> ). In finding solutions to IPV, understanding attitudes towards this violence, its justification especially, is important. Researchers have found a strong positive relationship between IPV experience and justification in several countries in Africa and Asia (Abramsky et al., 2011). Their results are not different from what we find in Afghanistan, India, Maldives, and Pakistan in Figure 16. In these countries, the linear relationship between IPV experience and IPV justification is positive —the higher the share of women that justify beating the higher the shares that are likely to experience beating (and verse versa). However, the slopes of the relationship are almost flat. On the contrary there is a negative relationship between the two variables for Bangladesh and Nepal. An explanation for the opposing relationships may not be available but the clustering of the different cohorts leads us to the conclusion that domestic violence, its justification and incidence, is sticky and warrants effective interventions.

# **6. Conclusion**

This paper explores the evolution of gender-normative attitudes and gender norm-influenced outcomes in South Asia. Using repeated cross-sectional data from nationally representative surveys in the region, we examine the levels, trends, and relationship of these outcomes for women born across half a century. Our results highlight the presence of wide gender disparities and of conservative gender attitudes, which are a proxy for norms. In terms of trends, all gender outcomes relating to employment, marriage and children, and intrahousehold power/negotiation have remained stable or even regressed, with the exception of education. These trends were observed for all women, irrespective of location, education, 26
and income. A similarly stable, or even negative, trend was observed for gender attitudes relating to these categories.

This persistence of gender disparities and conservative attitudes is particularly remarkable when contrasted with the sustained economic development observed in the last two or more decades in most South Asian countries. Gender equality in the region does not seem to be an automatic byproduct of economic development, indicating that restrictive gender norms are constraining progress in gender outcomes. Stylized fact 4 highlights exactly this. Attitudes are related to gender outcomes, and conservative gender attitudes are linked to worse gender outcomes. However, gender norms are still poorly understood and measured, generally, with inadequate proxies. Attitudes may not always correspond with the norms prevalent in society, and do not adequately indicate the strength of a norm (which can potentially override individual attitudes in influencing behavior). To properly address these normative barriers, future research should be focused to: (i) measure gender norms more systematically, (ii) identify causal links between norms and outcomes, and (iii) inform norm sensitive policies and interventions.

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32
# **Tables and Figures**

Table 1: Norm-related variables from DHS surveys by domain (top panel) and WVS attitudes (bottom panel)

|**Gender norm-influenced**<br>|**outcomes**<br>||
|---|---|---|
|**Do**|**main**|**Measure**|
|Economic<br>empowerment<br>Human Capital|Economic empowerment<br>Human Capital|Labor force participation<br>Years of education*|
|Marriage and children|Human capital|Age at marriage|
|||Age at first birth|
|Intrahousehold|Decision|Say in HH decisions – mobility,|
|bargaining and agency|making/autonomy|householdpurchase and health^|
||Intrahouseholdpower|Educationgapwith spouse|
|||Agegapwith spouse|
||Control over own|Marriage to first birth interval|
||body/aspirations re<br>children|Son preference (difference in ideal<br>number of boyandgirl children)|
|||Difference between desired fertility and<br>realized fertility **<br>IPV experience ^^|
|**Gender norm-influenced**<br>|**attitudes**<br>||
|**Do**|**main**|**Measure**|
|Human capital||University is more important for a boy<br>than agirl *|
|Voice and agency||Both the husband and wife should<br>contribute to household income ^|
|||Justification for IPV ^^|
|Fertility||A woman has to have children to be<br>fulfilled******|
|Economic empowerment||When jobs are scarce, men should have<br>more right to ajob than women**#**|

The symbols *, ^, **, ^^, and # are used to link gender outcomes variable of the top panel of the table with gender norm-influenced attitudes of the bottom panel. For instance, “*” links years of education to the attitude towards university education for boys vis-à-vis girls.

33
Table 2: Women employment rates in Bangladesh by cohort and age group

||||||||||Cohorts<br>|<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Age|**Statistics**|_1940-_|_1945-_|_1950-_|_1955-_|_1960-_|_1965-_|_1970-_|_1975-_|_1980-_|_1985-_|_1990-_|_1995-_|_2000-_|**_Wgt_**|
|group||_44_|_49_|_54_|_59_|_64_|_69_|_74_|_79_|_84_|_89_|_94_|_99_|_04_|**_Ave_**|
|**15-19**|Share(%)|||||||11%|15%|16%|13%|8%|17%|22%|15%|
||N|||||||116|1772|1819|2098|1837|3243|683||
|**20-24**|Share(%)||||||12%|20%|23%|19%|18%|25%|35%||23%|
||N||||||150|2633|2589|3218|3378|5510|2411|||
|**25-29**|Share(%)|||||19%|24%|29%|27%|22%|31%|47%|||30%|
||N|||||116|2810|2844|3020|3193|5853|2490||||
|**30-34**|Share(%)||||30%|28%|34%|30%|26%|36%|54%||||35%|
||N||||107|2052|2289|2673|2591|5221|2470|||||
|**35-39**|Share(%)|||25%|28%|34%|34%|23%|37%|57%|||||36%|
||N|||93|1684|1800|2253|2304|4313|2138||||||
|**40-44**|Share(%)||9%|26%|34%|30%|23%|36%|58%||||||34%|
||N||57|1234|1490|1874|1886|3924|1682|||||||
|**45-49**|Share(%)|7%|19%|25%|25%|22%|37%|56%|||||||32%|
||N|75|949|1336|1867|1820|3486|1662||||||||
|Weighte|d average|7%|19%|25%|29%|28%|31%|31%|31%|30%|29%|27%|25%|||

Source: DHS surveys for 1993, 1996/97, 1999/20, 2004,2007, 2011, 2014, and 2017. Note: For each age group and birth cohort, i.e. for each ‘cell’, this table shows the number of (unweighted) observations (N) and the share (as percentages) of women who are employed in Bangladesh. Reading the table along the column provides an approximation to the age effect, i.e., what happens to the employment rate of women from a specific birth cohort as they age. Reading along the rows, the table provides an approximation of the cohort effect, i.e., the differences in the employment rate among cohorts observed at the same age. The last row, “weighted average”, represents the employment rate for a specific cohort average along its whole life cycle. The weights from the different surveys as well as the size of the cell are used in the calculation.

34
Table 3: Summary of findings: some improvements but trends have been mostly stagnant or regressive

|**Variable**|**Afghanistan**|**Bangladesh**|**India**|**Maldives**|**Nepal**|**Pakistan**|
|---|---|---|---|---|---|---|
|Employment (% working)|12%|48%|24%|42%|57%|17%|
|Decadal change (percentage points)|-2|2|-4|-1|-7|0.3|
|Education (yrs. of education)|1.2|5.6|6.9|9.5|5.2|4.4|
|Decadal change (months)|5|15|15|40|17|11|
|Marriage age (yrs.)|17.9|16|18.3|20.4|17.7|19.4|
|Decadal change (months)|-6|9|5|15|7|1|
|Age at first birth (yrs.)|19.3|18|20.3|22.1|19.8|21.3|
|Decadal change (months)|-7|2|2|8|-1|-4|
|Son preference (% preferring more boys as ideal)|42%|10%|19%|10%|20%|29%|
|Decadal change (percentage points)|-2|-7|-4|0.7|-12|-2|
|Agency (% having say in 1 or more decisions)|64%|88%|84%|97%|73%|61%|
|Decadal change (percentage points)|-6|-1|-2|-1|-10|-13|
|Spousal difference in age (yrs.)|5.3|8|4.7|3.6|4.2|4.6|
|Decadal change (months)|-6|-11|-3|-6|-4|-0.2|
|Spousal difference in education (yrs.)|2.4|0|1.5|-0.8|2.2|2.1|
|Decadal change (months)|0.1|-8|-5|-5|-0.4|0.2|
|Fertility (actual minus ideal # of children)|-0.1|0.8|0.5|-0.2|1|0.4|
|Decadal change (number)|0.3|-0.6|-0.1|-0.1|-0.1|-0.1|
|IPV experience (% reporting)|7%|13%*|6%|2%|6%|4%|
|Decadal change(percentagepoints)|-0.02|-0.04|-0.1|-1|-3|0.2|

Source: DHS rounds (see, for details, table A2 in the annex). Notes: for each outcome variable, the table reports two rows of numbers. The first row represents the most recent level of the variable, and the second row shows the decadal changes. The levels, in the first rows, are calculated as averages across the full DHS population (i.e. 15 to 50 years old ever married women), and for the most recent survey year; for Afghanistan (AFG) this is the year 2015, for Bangladesh (BGD) is 2017/18, for India (IND) is 2015/16, for Maldives (MDV) is 2016/17, for Nepal (NPL) is 2016/17, and for Pakistan (PAK) is 2017/2018. The * in the cell of IPV for Bangladesh indicates that this value is for 2007, which is the only year for which IPV experience was collected. The decadal changes, in the second row of each variable, are calculated as the difference between two numbers – the average for a recent cohort (mostly women born in 1990) minus the average calculated for an early cohort (mostly women born in 1950) – divided by 4 (the number of decades between 1950 and 1990). Depending on the variable, the decadal change is expressed in percentage points or fraction (months) of a year. The font color of the numbers for the decadal changes, rather than their sign, guides the interpretation of the change. Red means that a specific outcome has worsened in these decades, dark orange means stagnation, and green signals an improvement. There are some exceptions to the birth years used for the calculations of the decadal change. For Afghanistan, for the variable ‘age at first marriage’ the cohorts considered are those of women born in 1970 versus 1990, for fertility outcome, the cohorts are those of 1965 and 1975, for IPV experience, they are 1970 and 1990; and for the remaining variables the cohorts are those of 1965 and 1995. For Maldives, the 1960 and 1990 cohorts are used to calculate the decadal difference in agency (these are also the cohorts used for agency for Bangladesh, India, and Nepal) and IPV justification; the 1960 and 1970 cohorts are used for the decadal change in fertility outcome, the 1970 and 1990 ones for IPV experience; for the remaining variables, the 1960 and 1990 cohorts are used. For Bangladesh, India, Nepal, and Pakistan, 1950 and 1970 are used to calculate the decadal change in fertility outcome, and 1970 and 1990 for IPV justification. For Pakistan, 1970 and 1990 are used to calculate the decadal change in IPV justification.

35
Table 4: Summary table of cohort trend for attitudes variables (from World Values Surveys)

|**Variable(percent of men and women who agree)**|**Afghanistan**<br>**Bangladesh**|**India**|**Maldives**|**Nepal**<br>**Pakistan**|
|---|---|---|---|---|
|When jobs are scarce, men have more right to a job (%)|83%|69%||92%|
|Decadal change (percentage points)|5|4||0.5|
|University edu. more important for boy than for girl (%)|45%|38%||61%|
|Decadal change (percentage points)|1|-0.2||-2|
|Both partners/spouses should contribute to hh income (%)|12%|16%||28%|
|Decadal change (percentage points)|-7|-4||-15|
|A woman has to have children to be fulfilled (%)|98%|85%||98%|
|Decadal change (percentage points)|2|-2||2|
|IPV justification (% justifying)|80%<br>20%|8%|22%|29%<br>41%|
|Decadal change(percentagepoints)|-2<br>8|-2|-5|2<br>9|

Source: World Value Survey rounds (see table A2 in the annex). Notes: for each question proxying an attitude or belief, the table reports two rows of numbers. The first row represents the most recent level of the variable, and the second row shows the decadal changes. The levels, in the first rows, are calculated as averages across men and women respondents aged 15 to 50 years, and for the most recent survey year; for Bangladesh is 2018 for “jobs for men” and “university education for boys”, and 2002 for “contributions to household income” and “women and children”; for India is 2012 for the first and second variable, and 2001 for the third and fourth variable; and for Pakistan is 2018 for the first and second variables and 2001 for the third and fourth variables. The decadal changes, in the second rows, are calculated as the difference between two numbers – the average for a recent cohort ( _mostly_ people born in 1990) minus the average calculated for an early cohort ( _mostly_ people born in 1950) – divided by 4 (the number of decades between 1950 and 1990). The exceptions are as follows: for Bangladesh and India 1950 and 1980 year of births are used to calculate the decadal change for “contributions to household income” and for “women and children”. For Pakistan, 1950 and 1980 years of birth are used to calculate the decadal change for “contributions to household income” while 1955 and 1975 years of birth are used in the case of “women and children”. The color of the numbers for the decadal changes, rather than their sign, guides the interpretation of the change. Red means that a specific outcome has worsened in these decades, brown means stagnation, and green signals an improvement.

36
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<!-- Start of picture text --> Women aged 25 years and above<br>22<br>@&5D 2120 4 n IN OR4 ) .~s7stARAN NELANS,<br>E N 4 TM do AN NRO<br>ge 2 / “sy N IN Lear rr7<br>isB 18- / VENv NTSC\/ PN NAT TRYaYIN ITSiat arenea >~ti|<br>=@ 17 ripe. are Wooteter- ZNWe as<br>2oS 16 ‘preotWeogeNSl rtePRE Ene tne seas rages uote .<br>§ / oN oc? _--<br>@ 15 yeoet<br>S eer<br>145 pore<br>sy<br>13 Mevsseeett<br>T T T T T<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Afghanistan —— Bangladesh —-— India<br>—-— Maldives —— Nepal —-— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> Women aged 25 years and above<br>A<br>£2*2 | bt NNr ani\h Ee py MENT,NN ~~ Se,. yn~ rs\e-N-Sor saeN~s, XY<br>=z Lh \V NN a A eae NL<br>2 20 ! oy \ “N PTAA Daten PRESB Nees some ~<br>ew2 >ee er eePorgyAa) ae TOA ANVee_<br>-@ ig ee win, | UNH<br>©gc 417 NKMON enAwen <- see ye -e<br>o O 16<br>=<br>15><br>14-<br>13<br>T T 7 T T<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—-— Afghanistan —— Bangladesh —— India<br>—-— Maldives —— Nepal —-— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> @ 60<br>P= a<br>5 NY<br>é rl? \<br>= as SMW VN<br>oc 1 WY VISNL<br>¢ m tv yee<br>= 1\ A pee PO.<br>= 36 nly Ue 1 NARA<br>=z Aven ITA Ah CAL MRS<br>22 YA TREATw“ ah eavy "AL oF WNOSSg xo 35 A<br>=2 24 MSN OY DN RSA<br>gs . ‘ VrveySs5‘ --o0SSS=SAS~ES=.<br>5oS—5o 124 ~<br>= 0<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>—-— Afghanistan —— Bangladesh —-— India<br>—-— Maldives —— Nepal —-— Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>8 804<br>g<br>i}<br>5<br>5 60 ,<br>c ny<br><8 SATS\" =AVOPR AON eM m\4 peor<br>= 40 eee 4 Nei sorihliean gaitonen dy _<br>=5 AN/ \OQLoAYrsNOY= \ are \NNNN>Sy ext~ ate~ ~~ ae<br>a2D 41 VeVASSAL\/arereeINDYSooNAS tennTONS TaT ONm-~L>eyesaONs<Sl“Me.Sore - vov o/ ‘<br>0)<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>—-— Afghanistan —— Bangladesh —-— India<br>—-— Maldives —— Nepal —-— Pakistan<br><!-- End of picture text -->
<!-- Start of picture text --> 3 3<br>$so />~nL4 !r-+ L1<br>°LZ 24 NJa ae one \a<br>S2=<5©o 1- /PN\ NVNsyYY / nl,Ki(1I \ycY\ en\\VA\\ 1/V KSSAA,ONpalaNoN>iope<br>5 prety ML_---- Ke, SSN ~- Sa<br>— | YO Np ett some ey<br>Zz 1 \ ow wy<br>=g 0. ! rNPRS ASNNerAween WwOs<br>32 / ONT \1, 7%RY<br>3 7<br>2G4 “<br>1940 1945 1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>—- Afghanistan —— Bangladesh —-— India<br>—-— Maldives —— Nepal —— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> 100 ~aon+ra= = ae<br>90>) HAAN<br>=® ONSPen teewnSeseetTSs<br>=S&ogZB 80-70- \ Nehey!\Ve1os1PNG/N-WoeY Aaa~ eo TAyNLtayNSoRLOAvt meITNTNRG~. /\\NAWNVana A~canNNNissen-\U/NoATNE7 a ANs~SS<br>= 604 VASA,<br>8 \ WRN<br>o 50, a anes<br>a \ 1orw<br>40-| n ! S“ A Y<br>v 4<br>30 \\<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>—- Afghanistan —— Bangladesh —— India<br>—— Maldives —— Nepal —-— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> 4<br>i<br>3 pedNon AbVNYMAOA GETON eee es VN<br>nSBS~ al XviVINnC7xCNANNAVetASEayy‘4 ems PF SIArrYARISOY OFOPSae acrnd KYLANIN LC \ L<br>en=rOL I ~ ekVeMN\/ Saletan WNVey i ss *Nea\ wTNos<br>8SecSs256 1 a \\ ~.N \BN ~\ \Y X xNeeNavA\L \<br>€5=§6a ° Vin\iW /ArNYPN A Aan N— Sy f~>.ae<br>- 1 yoo WN VN aes v]<br>i \ \f<br>2<br>i i i T T T T T T T<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>—- Afghanistan —— Bangladesh —-— India<br>—-— Maldives —-— Nepal —-— Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> 12 N<br>VIN i,<br>> \ SAYSowes\<br>=x=®= 40 VoLVT. NUAALAL. rareoe SHy<br>Pa>s 1i ~ Sse . ’ ~<br>rd£85 ,] plowali \ , A Neue ~~ aahd<br>=gS<3 6 0 ~ KAUN\NONNAPA\WAMOAIph\LOLdySayLowyING\ it ete1 i\ i; aie<br>g WN eal ASA AY Nor SFY,<br>4 \YWy \yootns7 YRNNa ANGSea ARO!NN~~<br>\ 7 / \as7 NI<br>T T 1 T T T T T<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>—- Afghanistan —— Bangladesh —-— India<br>—-— Maldives —-— Nepal —-— Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> 35<br>> 305<br>a<br>@ 254<br>i=<br>2=g 20-<br>£goO2s 15 1 NTNN vr[If y INIa “ aNA ey Ne<br>= Vi y Via ONY Vf \ J WN<br>& 10- an eS to er eer<br>FY5 OMVr yaMIEN aLTSOMNCPRE OPEN2 a LD ,<br>54 / Ya SA Le NG Whew.<br>° VAUL MAT NXA NUKEVil7 YX\ NY -7aN=v"WoeUNnN 7<br>. : . : : - . :<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Yearof birth<br>—-— Afghanistan —— Bangladesh -—-— India<br>—— Maldives —-— Nepal —-— Pakistan<br><!-- End of picture text -->
<!-- Start of picture text --> Age profile for LFP - Nepal<br>1970 aL)1368 1sSe_io_<br>—<br>£ 4 9875 a=|. ieee aT 1965 1 360s<br>ce _ 10 ; ee<br>Ld 198) 1 13975id ——___ 196<br>3S o0-Ee Gages, 19001988 1385<br>4 _<br>a + {aed —— i<br>1 Sri.<br>ax<br>u zt Fig bs Bd a id<br>Age<br>* 1990-04 145 1980-84 — 150889<br>104 eso —Ci- nM —— BM<br>3-fa Eo 190- —— T<br>= 2004<br><!-- End of picture text -->

<!-- Start of picture text --> 25 years 30 years<br>100<br>90<br>80<br>Zz 7<br>Q<br>romSo 60<br>5 50<br>5<br>€ 35 years 40 years<br>$100<br>‘S 90<br>2<br>g *<br>So 70<br>ao 60<br>50 Sanne<br>1950 1955 1960 1965 1970nEann Rn1975 1980EEE1985 1990 1995 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Nepal<br><!-- End of picture text --> <!-- Start of picture text --> 25 years 30 years<br>60<br>DoO 50<br>Ss 40 ZY<br>‘32 3<br>G 2<br>co 10<br>5<br>= 35 40<br>6 60 years years<br>2<br>3%<br>é 40<br>so<br>20<br>10 OO<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Bangladesh —— India —— Maldives<br>—— Pakistan<br><!-- End of picture text -->
<!-- Start of picture text --> Bangladesh<br>Age Period ‘Gohort<br>+22<br>i<br>“ —-—“ if“4 Pe_il—<br>/ —_<br>- a —— ..— * - fiF/ii - ~+.— ~._<br>ji te<br>° ° ' 4 i ° =<br>~ ~ \/e a<br>18 Pad a4 42 wo 1561 1966 mor 215 isd 1943 1867 1565 1973 1581 1588<br>2 ao Ee ] 6 1565 mo Feb) m1 1345 1583 1961 1569 1ST? 1583 1963<br>—e— HAPC =—e— CGLIM = —e*— IE —e— HAPC =—e— OGLIM =—e*— IE —e— HAPC =—#®— CGLIM = —#*— IE<br>o— OF o— ME o— oP eo ME e— oP eo ME<br><!-- End of picture text --> <!-- Start of picture text --> Nepal<br>Age Period ‘Gohort<br>“ ~ “4<br>— — —<br>F , ; —<br>Ss . Ss eS SSE<br>=Y=Y_—e =Y<br>~w a<br>Pan) a 4 be) 1586 2006 me 16 1586 1566 1876 1586 1566<br>Ba BS Ci] 2001 mi 1561 1561 1971 1581 1581<br>—e— HAPC =—e— CGLIM = —e*— IE —e— HAPC =—e— OGLIM =—e*— IE —e— HAPC =—#®— CGLIM = —#*— IE<br>o— OF o— ME o— oP eo ME e— oP eo ME<br><!-- End of picture text -->
<!-- Start of picture text --> Age profile for Agency- Nepal<br>1005:<br>o= i|. ‘naS25 188 ~ 1999 ——i<br>”2 80-3 1985SSG 196: 1360. eer 1955_<br>g i 990 1970 — 1950<br>q i i56 1960 — 1955<br>es 19907 1338<br>= i B95<br>S i A388<br>oO 40 4-3 975<br>D 1923<br>a. :<br>20 1996<br>T<br>15 20 25 30 35 40 45<br>Age<br>— 1950-54 1955-59 1960-64 — 1965-69<br>1970-74 1975-79 — 1980-84 — 1985-89<br>1990-94 — 1995-99 @ 2000-04<br><!-- End of picture text -->
<!-- Start of picture text --> g Bangladesh:Employment<br>=<br>r=5SL<br>or<br>§<br>g<br>a<br>s<br>—°<br>5<br>Byn<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>*—— Quintile2 '—— Quintile 3<br>-—_ Quintile4 "——— Quintile 5<br>withGenerated vear-ofby bithregressing and state work fixed status effects. on wealth,Standard urban, higher errors Clustered education by state. and interactions<br><!-- End of picture text --> <!-- Start of picture text --> Ps India:Employment<br>ge<br>~=<1g<br>a<br>5°<br>%<br>a<br>G<br>go<br>5<br>oYn<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 '—— Quintile 3<br>-—— Quinte 4 '——— Quintile 5<br>withGeneratedyear-of-birth by regressing and state fixed work status effects. on Standard wealth, urban, errorshigher clustered education by state. and interactions<br><!-- End of picture text --> <!-- Start of picture text --> gO22 Pakistan:EmploymentPloy<br>a<br>°<br>2<br>ao<br>g'<br>3<br>ke]<br>o<br>a<br>a<br>5<br>2<br>e?<br>w<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintile2 '—— Quintile 3<br>-—— Quintile4 '——— Quintile 5<br>withGeneratedyear-of-birth by.regressingand state workfixed statuseffects. on wealth,Standard urban, highererrors clustered educationby state. and interactions<br><!-- End of picture text --> <!-- Start of picture text --> 2® Nepal:Employment<br>Bn<br>=<<br>8<br>3°<br>©<br>3<br>2<br>oN<br>er<br>a<br>es<br>8<br>e¢<br>TM<br>4940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 +—— Quintile 3<br>-—— Quintile4 '———— Quintile 5<br>withGeneratedyear-of-birth by regressingand state workfixed statuseffects. on wealth,Standardurban, highererrors clustered educationby state. and interactions<br><!-- End of picture text -->
<!-- Start of picture text --> . Bangladesh: Employment<br>3<br>s<br>x<br>5=<br>26 Urban<br>5<br>s<br>37<br>a P<br>£ Higher education<br>or<br>6<br>2<br>[s)<br>£<br>ey<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Employment<br>N<br>oI<br>% Higher education<br>zo<br>5<br>c<br>g<br>2<br>3°<br>= Urban<br>oo5o<br>a<br>aa]<br>=<br>ue<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India: Employment<br>g<br>§<br>n<br>x<br>2on ; ;<br>io]c Higher education<br>2<br>2<br>a :<br>& Urban<br>cy<br>ao<br>2<br>o<br>£<br>toy<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Employment<br>8<br>“ nN<br>:<br>x}<br>5" . .<br>s Higher education<br>oO<br>3<br>So<br>Q<br>£ Urban<br>4<br>[s)<br>©<br>Mey<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> When jobs are scarce men have more right to a job<br>100<br>80 anger<br>& 6 oan RET js<br>2= ae ween ener<br>Poeeisea-<br>5 40<br>29<br>a<br>20-<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>-- Bangladesh -— India -—— Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> University education is more important for a boy<br>100<br>80<br>& 60 ie Serer eee oe<br>2 wee yee “<br>3 i a fe<br>5 40 Predict “ erm<br>2a inka NauCtigt | telahsoleaan<br>a<br>20-<br>T<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>-- Bangladesh -— India -—— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> 400 Both partners should contribute to household income;<br>80<br>f=))i& 60<br><£Q$ \a<br>2 re<br>5io wo \ \ . Ps.<br>a \, on a“<br>20 ete > Sk ae a erssl<br>Ue Ss. ee tsa<br>0 :<br>T<br>4940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>-- Bangladesh —-— India -— Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> Awoman has to have children for fulfillment<br>100 _ a--- 557oe Sir reseese===__- _<br>80 rosfesnng<br>aD©° 60<br>€Z<br>4<br>a3o 4<br>20<br>0<br>a<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of birth<br>—— Bangladesh —— India —— Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>=£ w \ \e%\ pas ~ a Not Neve wees<br>a<br>83 6<br>bd Wy VALS ae. fae<br>S pn Nef aK Mycpee Dara -<br>§ > Pom VIM Nn eB APT NN nr f<br>~ DA LPDRESe re oN<br>tA N-+-<br>°<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Yearof birth<br>—— Afghanistan —— Bangladesh —-— India<br>—— Maldives —-— Nepal —-— Pakistan<br><!-- End of picture text -->
<!-- Start of picture text --> Employment and attitude towards right to work<br>50<br>== 40- 1850-1854198858959 1945-1940<br>Ey 1940-1 1985-1960<br>S= 75-1979<br>5 30- Tyan. <187F-19789965-1060= =<br>E= imeses 1855-188-1085979-197Fo50ze.1g79,_toa, 1880-1994<br>=°2 1985-198050-1954 toes nag.Ter HEEazsMEBO-1984<br>al<br>s 365-1 pes1059<br>@ 205 1900-7004 1845-1040 seeeiBee<br>1950-1954<br>1905-1900<br>10-<br>f T T T T T 1<br>40 50 60 70 80 90 100<br>Share who agree that men have more right to a job (9%)<br>— BGD — IND — PAK<br>Bangladesh<br>0 India Pakistan<br>. 0 80<br>2 15-100<br>2 . 5<br>-°° tee0s064 599 en0-1004 £22<br>§ 60 £33 ra<br>q so :<br>§ 1988-1980 H55 60 es Hb g33 6<br>2 3 “e é<br>g66 ‘960-1904 A 255<br>§88 40 Hy5454 ors070 zg4g4<br>5é316761078 5H ——_. é$$ tee 1900-1004 bea]saresare oa,<br>isensors 5 g<br>a) 9 3 » 30 smo<br>if5£6o feié°2 3z8z88 sal<br>o0 0<br>0 40 50 6 70 E) rT) 50 C) 10 e) r) 50 60 70<br>Share of of respondents agreeing that that university is more important important for boys (%) ‘Share of respondents respondents agreeing that that university is more important fo important fo fo boys (9) (9) Share of respondents respondents agreeing that university is more more important for boys (9) (9)<br><!-- End of picture text --> <!-- Start of picture text --> Bangladesh<br>0<br>.<br>2 15-100<br>2<br>-°° tee0s064<br>§ 60<br>q<br>§ 1988-1980<br>2<br>g66 ‘960-1904<br>§88 40<br>16761078<br>sensors<br>a) 9<br>o<br>0 40 50 6 70<br>Share of of respondents agreeing that that university is more important important for boys (%)<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan<br>80<br>5<br>£22<br>ra<br>:<br>g33 6<br>é<br>255<br>zg4g4<br>é$$ tee 1900-1004 bea]saresare oa,<br>g<br>30 smo<br>sal<br>3z8z88<br>0<br>e) r) 50 60 70<br>Share of respondents respondents agreeing that university is more more important for boys (9) (9)<br><!-- End of picture text --> <!-- Start of picture text --> India<br>0<br>.<br>599 en0-1004<br>£33<br>so<br>H55 60 es Hb<br>3 “e<br>A<br>Hy5454 ors070<br>H ——_.<br>5<br>3 »<br>°22<br>0<br>E) rT) 50 C) 10<br>‘Share of respondents respondents agreeing that that university is more important fo important fo fo boys (9) (9)<br><!-- End of picture text -->
<!-- Start of picture text --> Agency and household income contribution<br>100<br>=<br>= 90<br>a)<br>S 1975-1979 1970-1974 _ sR ERRE,, 1955-1959<br>a 1oggSaeee* 1975-1979<br>° oes Os0-1984 1955-1958 oo<br>£ 970-1974<br>= 1980-1964<br>A7a<br>S<br>3<br>o60<br>oO<br>=<br>7)<br>50<br>0 10 20 30 40<br>Share agreeing that both spouses contribute to income (%)<br>— BGD — IND — PAK<br><!-- End of picture text --> <!-- Start of picture text --> First birth age and having children<br>22<br>21 z eee:ghee!<br>6 20 L 1984 1ASAgtaa9a79<br>=<br>B 1 451-1944<br>we 19-4<br>ic)<br>S 18 aah<br>< 1970-1974 1868-181<br>a5-1949 1955-1959<br>17) 1950-1954<br>16- t 1<br>75 30 85 90 95 100<br>Share who agree that child fulfils a woman (3)<br>— BGD — IND — PAK<br><!-- End of picture text -->
<!-- Start of picture text --> IPV experience and justification<br>—> 15<br>=<br>a | icckieeOL<br>a -1984<br>Ba=7 1965-19695-196" 4999-1994<br>2<br>a 1985-1989<br>S 1 La79 _. , _ 1980-1984<br>o 70-1979965-1969 EAB S69 ASTD AGTENSTS<br>5 158ho$08fo94 of 4995-1999<br>E 5 18 19 50 94 1985-4989<br>3S Holsoat e4g9 -|ere 8604<br>1965-1969<br>2 7 .46pksgpg- LSS 1999<br>co 1990-1994 1995-1999<br>9<br>rsSs|<br>10 20 30 40 50 60 70 80 90<br>Share of women justifying beating (%%)<br>— Aghanistan — Bangladesh — India<br>— Maldives — Nepal — Pakistan<br><!-- End of picture text -->
<!-- Start of picture text --> Year trends for employment<br>1<br>c)<br>§<br>= 8<br>S= 7<br>GB 6<br>8s<br>2<br>FaSs 4<br>-°<br>& 5<br>Es<br>‘4 °<br>O-- - - - - 5 -<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Survey years<br>-® Afghanistan. -@®- Bangladesh -® India.<br>-* Maldives —* Nepal -* Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> GDP per capita (constant prices)<br>400<br>350<br>x, 300<br>5 250<br>Vv<br>£& 200<br>cg 150<br>g<br>100 a<br>50<br>0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Year<br>—— Afghanistan; ——Bangladesh —India.<br>— Maldives —Nepal — Pakistan<br><!-- End of picture text --> <!-- Start of picture text --> Total fertility rate (per woman)<br>8<br>7<br>c2 6<br>ne)<br>Fa<br>05<br>“4<br>§3<br>2<br>2<br>1<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Year<br>—— AFG — BoD — IND<br>——MDV —NPL —— PAK<br><!-- End of picture text --> <!-- Start of picture text --> Year trends for years of education<br>10<br>9<br>J<br>Ps<br>Oo 74<br>2<br>3 6<br>2<br>g 3<br>= 2<br>14 bs<br>0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Survey years<br>—® Afghanistan -®- Bangladesh -@ India<br>—® Maldives —® Nepal —® Pakistan<br><!-- End of picture text -->
# **2. Data Sources**

Five data sources containing information on gender outcomes and attitudes have been examined:

- Demographic and Health Survey (DHS)

- World Values Survey (WVS)

- Multiple Indicator Cluster Surveys (MICS).

- Gallup Poll

- Gender and Adolescence: Global Evidence (GAGE)

Table A1 below summarizes the availability of data from these surveys across countries in the region and through different time periods.

Table A1: Data Availability by Survey

||Years available (by su|rvey)|||||
|---|---|---|---|---|---|---|
|Country|DHS<br>(Cross-sectional)|MICS<br>(Cross-sectional)|WVS<br>(Cross-sectional)|GAGE<br>(Cross-sectional)|Gallup<br>(Panel)||
|Afghanistan|2010<sup>+</sup>,2015(2)|2011(1)|(0)|None|||
|Bangladesh|1993-94, 1996-97,<br>1999-00,<br>2001<sup>*</sup>,<br>2004, 2007, 2011,<br>2014,2017(9)|2006, 2012-2013,<br>2019 (3)|1994-1998, 1999-<br>2004,<br>2017-2019<br>(3)|Baseline<br>survey<br>(2017/2018)<br>Only conducted in<br>Dhaka(1)|Every<br>between<br>2020 (15)|year<br>2005-|
|India|1992-93, 1998-99,<br>2005-06, 2015-16<br>(4)|(0)|1989-1993, 1994-<br>1998, 1999-2004,<br>2005-2009, 2010-<br>2014(5)|(0)|Every<br>between<br>2020 (15)|year<br>2005-|
|Maldives|2009,2016-17(2)|(0)|(0)|(0)|2017 (1)||
|Nepal|1987<sup>**</sup>, 1996, 2001,<br>2006, 2011, 2016<br>(6)|2010, 2014, 2019<br>(3)|(0)|Baseline survey (1)|Every<br>between<br>2018(13)|year<br>2005-|
|Pakistan|1990-91, 2006-07,<br>2012-13, 2017-18<br>(4)|2010, 2011, 2014,<br>2016-2017, 2017-<br>2018(5)|1994-1998, 1999-<br>2004, 2010-2014,<br>2017-2019|(0)|Every<br>between<br>2018(13)|year<br>2005-|
|Sri Lanka|1987<sup>**</sup>, 2006, 2016<br>(3)|(0)|(0)|(0)|Every<br>between<br>2015 and<br>2019(13)|year<br>2005-<br>2017-|
|Bhutan|(0)|2010 (1)|(0)|(0)|2013, 201<br>(3)|4, 2015|

Among these data sources, DHS provided the most comprehensive combination of gender-relevant outcome variables across all coutnries and time periods. WVS, though not available for all countries in the region, was selected due to availability of gender attitide variables that could serve as proxies for relevant norms. A brief description of each is included below.

> + Special DHS data type and not used.

> * Special DHS data type.

> ** Not used in the analysis.

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- _The Demographic and Health Surveys (DHS)_ : these surveys cover seven out of the eight countries in the region; they are nationally representative and provide data on women and their children’s health and other outcomes. The data collected in different countries is comparable because the surveys implement near-identical questionnaires across countries. Three core questionnaires are used to collect data by the DHS: a household questionnaire, a women’s questionnaire, and a men’s questionnaire. In this study, we mostly focus on the women’s questionnaire.

- _World Value Survey (WVS)_ : WVS is a cross-national opinion survey that monitors cultural values, attitudes, and beliefs using nationally representative surveys. It uses a rigorous high-quality research design to collect data using a standardized questionnaire in almost 100 countries. The WVS measures, among others, support for gender equality, attitudes toward work and family, and subjective wellbeing, and uses agree-disagree Likert scales to measure socio-cultural and political attitudes from respondents.

For the DHS we focus on six South Asian countries —Afghanistan, Bangladesh, India, Maldives, Nepal and Pakistan. For WVS, we focus on three South Asian countries —Bangladesh, India and Pakistan—for which data are available. The DHS and WVS data have been collected in several waves roughly over a period of 20 years, from 1990 to 2018. The coverage however, varies by country (see Table A2). For instance, while DHS data is available for Bangladesh in eight waves, there is only one wave of data, 2015, available for Afghanistan. The DHS collects data from women aged 15-49 years. The age range in WVS is wider, however, so we restrict our sample to 15-49 for comparability with DHS. The length and breadth of coverage of the DHS and WVS data across countries in South Asia allow us to examine changes in key gender-related outcomes and social attitudes. We trace women born over half a century, from 1940 to 2000.

Table A2: Data availability for DHS and WVS surveys:

||**1990**|**1991**|**1992**<br>**1993**|**1994**<br>**1995**|**1996**<br>**1997**|**1998**<br>**1999**<br>**2000**<br>**2001**<br>**2002**<br>**DHS**|**2003**<br>**2004**<br>**2005**<br>**2006**||**2007**<br>**2008**<br>**2009**<br>**2010**<br>**2011**<br>**2012**|**2013**<br>**2014**|**2015**<br>**2016**|**2017**<br>**2018**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**AFG**|||||||||||x||
|**BGD**||||x|x|x|x||x<br>x|x||x|
|**IND**|||x|||x|x||||x||
|**MDV**|||||||||x|||x|
|**NPL**|||||x|x|x||x|||x|
|**PAK**|x|||||||x||x||x|
|||||||**WVS**|||||||
|**BGD**|||||x|x||||||x|
|**IND**|x|||x||x|x||x||||
|**PAK**|||||x|x|||x|||x|

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# **3. Variable Description**

Table A3: Variable description

|**Variables**|**Definition**|**Variable name (original)**|
|---|---|---|
||**DHS Variables**||
|_Employment_|1 if having worked in the past 7 days, including<br>women who did not work in the past 7 days but<br>who are regularly employed and were absent from<br>work for leave, illness, vacation, or any other such<br>reason. 0 otherwise|V714|
|_Education_|Education in singleyears|V133|
|_Marriage age_|Age at first cohabitation (for women aged above 24<br>years)|V511|
|_Age at first birth_|Age of the respondent at first birth (for women<br>aged above 24 years)|V212|
|_Son preference_|1 if respondents’ ideal number of sons > ideal<br>number of daughters, 0 otherwise. (Ideal number<br>of each gender that the respondent would have<br>liked to have in her whole life irrespective of the<br>number she alreadyhas).|V627(ideal number of sons)<br>V628 (ideal number of<br>daughters)|
|_Agency_|1 if a woman has a say in at least one of the three<br>decisions within the household|(i) V743b (large household<br>purchases), (ii) V743a<br>(woman’s health care), and<br>(iii) V743d (visit to relative<br>and friends).|
|_Spousal difference in_<br>_age _|Husband/partner’s age minus woman’s age (both<br>ages are reported bythe wife)|V730 (Husband’s/partner’s<br>age)|
|_Spousal difference in_<br>_education_|Most recent husband or partner's education in<br>single years minus woman’s education in single<br>years(bothyears reported bythe wife)|V715 (recent husband or<br>partner's education in single<br>years)|
|_Fertility outcome_|Total number of living children minus ideal number<br>of children. This variable is calculated only for<br>women above 40 years|V218 (Total number of living<br>children)<br>V613 (Ideal number of<br>children)|
|_IPV justification_|1 if a woman responds ‘yes’ to at least one of the<br>five circumstances that justifies wife beating and 0<br>if she responds ‘no’ to all circumstances or missing<br>for all circumstances|(i) V744a (She goes out<br>without tellinghim)<br>(ii) V744b (She neglects the<br>children)<br>(iii) V744c She argues with<br>him<br>(iv) V744d (She refuses to<br>have sex with him)<br>(v) V744e (She burns the<br>food)|

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|_IPV experience_<br>**World Value Survey Vari**|Ever experienced any form of physical or sexual<br>violence by past or current partner. Coded 1 if a<br>woman has ever experienced any violence and 0<br>for no violence and missing for women selected<br>and interviewed for the domestic violence module.<br>**ables**|D105a-k (has ever been<br>slapped; punched with fist or<br>hit by something harmful;<br>strangled or burnt;<br>threatened with knife/gun or<br>other weapon; CS physical<br>violence by partner; had arm<br>twisted or hair pulled;<br>physically forced into<br>unwanted sex; forced into<br>other unwanted sexual acts;<br>physically forced to perform<br>sexual acts respondent<br>didn't want to.|
|---|---|---|
|_Men have more right to a_<br>_job_|<br>1 if a woman or man agrees that when jobs are<br>scarce, a man has more right to a job than a woman,<br>and 0 if theydisagree or “neither” agree or disagree.|C001_01|
|_University is more_<br>_important for a boy_|1 if a woman or man agrees or strongly agrees that<br>university education is more important for a boy than<br>for agirl,and 0 if theydisagree or stronglydisagree.|D060|
|_A woman has to have_<br>_children to be fulfilled_|1 if a woman or man thinks that for a woman to be<br>fulfilled, it is necessary that she has children, and 0 if<br>theythink that it is not necessary.|D019|
|_Both spouses should_<br>_contribute to household_<br>_income_<br>Source: DHS and WVS|1 if a woman or man agrees or strongly agrees that<br>both spouses/partners should contribute to<br>household income, and 0 if they disagree or strongly<br>disagree.|D058a|

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# **4. Cohort and age statistics**

Table A4: Share of men and women who believe that a woman has to have children to be fulfilled in India

|Age<br>|Statist<br>|||||Coh<br>|ort<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|group|ics|1940-<br>44|1945-<br>49|1950-<br>54|1955-<br>59|1960-<br>64|1965-<br>69|1970-<br>74|1975-<br>79|1980-<br>84|1985-<br>89|_Wgt_<br>_Ave_|
|**15-19**|_Share_|||||||84%|84%|82%||84%|
||_N_|||||||177|63|11|||
|**20-24**|_Share_||||||87%|83%|81%|78%||84%|
||_N_||||||332|289|201|60|||
|**25-29**|_Share_|||||89%|80%|80%|91%|||84%|
||_N_|||||265|294|245|162||||
|**30-34**|_Share_||||87%|85%|85%|86%||||86%|
||_N_||||215|299|276|118|||||
|**35-39**|_Share_|||89%|86%|89%|85%|||||88%|
||_N_|||276|287|225|142||||||
|**40-44**|_Share_||91%|88%|87%|82%||||||87%|
||_N_||143|197|249|111|||||||
|**45-49**|_Share_|93%|93%|84%|91%|||||||90%|
||_N_|90|176|162|93||||||||
|_Weight_<br>_average _|_ed_<br>|93%|92%|88%|87%|87%|84%|83%|85%|79%|||

Source: WVS for India. Note: The number, N, of each cell counts the number of women in each cell who responded to the question, excluding counting missing responses. This variable has high share of missing values. For Bangladesh, there is about 28 percent missing values, for India, 46 percent missing values and for Pakistan, 72 percent missing values. A closer look at the data shows that this question was not asked in more recent survey rounds. For Bangladesh, the question was not asked in 2018, for India, the question was not asked in 2006, and for Pakistan, it was not asked in 2012 and 2018.

54
<!-- Start of picture text --> 100<br>2<br>23 90 A<br>a 80 ev<br>ofOe 70 | 4A #8: gxok“Surei «c\ es os So vf fi MM‘<br>ag Le. _ ron Sey AN<br>£3 0 poe Ay yy wy vA woos.<br>vo “Ve frr- ny ~ = »<br>goaw \ vivl sea! ay Any hyVier Ny a ee \<br>oe 50 \ H | Py APY | NA TRS New<br>55go -~ i | ~ VOSAiN Ss<br>e3 4o- v teas<br>sgo erny WANs<br>D 30 \ Ye<br>5 ‘I a<br>5 2 Vo Seyh<br>9 \y :<br>T T T 1<br>1950 195519801985 197019751980 19851990 19952000<br>Year of birth<br>== Afghanistan -— Bangladesh —-=— India<br>—=— Maldives —— Nepal —— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> 100<br>a0 oth<br>r5 a Nex ls rad ‘\al<br>Eg8 a A\seavepoosgyonte. ‘<br>ge<br>85 70 “VAN hgh i rage aed wy<br>‘0.0 v MTT NK ay ee uf “N<br>Bo “el “| oe ne<br>ag a0 /*pene,v nV, SALwyAer asa<br>ae \y f- royal rye tah<br>£9%= 50- yarnty; \lyi yySAaR“A SN‘<br>oc | wos J4ee<br>£= 4g c~nj x velMANA<br>an yy yA<br>30 TNT<br>\<br>20<br>T T T 1<br>4950 1955 «1950 «1965 «1970 «1975 «= 1980 «1985 «= 1990» 1995 ~=—2000<br>Year of birth<br>== Afghanistan == Bangladesh == India<br>—-— Maldives —— Nepal —— Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> 100 ~ . i: . .<br>\ sar i as ae |<br>\ \NU¢Z ¥ ~s7e \? \ l<br>co. % / \<br>#&Se=osoaoc=v 8070 NytNe y=veereStL-sADRSy \oe tt WDee 5 37, y7<br>27 \ 7 KAN, SKN i Ne Sey se.<br>SZee 60 Mesesifeoeudle\e 7 bi weakaN ASOE “Ne faa NLL<br>So Ys Men SUH y ~~<br>ooa: 50 ~ YAW\-) \y7y -<br>= o 7 Yr \ 4\<br>ve yo vo<br>fe 40 an \<br>£o \ L—p<br>no \ \\<br>30 Ninh we<br>\l Y 4<br>20 \ *\<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Yearof birth<br>—-— Afghanistan —— Bangladesh —— India<br>—— Maldives —— Nepal —— Pakistan<br><!-- End of picture text -->
# **6. Age profile graphs**

Figure A3 illustrates the age profiles of three birth cohorts for different types of variables. The panel on the left shows a stylized example for ‘years of education’, a variable with no age profile. After age ‘z’, the years of education achieved by a person remains constant throughout the life cycle. Clearly there are differences in years of education for persons belonging to different cohorts. However, because there is no age effect, the measurement of the cohort effect is unaffected if one compares cohorts at different ages. As the cohorts are parallel lines after age ‘z’, measuring the vertical distance between cohorts ‘1’, ‘2’ and ‘3’ is the same at ages ‘a’, ‘b’ or ‘c’. This is not the case for a variable such as labor force participation which has an inverted U age profile. For this type of variable, measuring the cohort effect correctly requires comparing cohorts at the same age. The right panel clearly shows that the cohort effect is (incorrectly) much larger if cohorts 1 and 2 are compared when cohort 1 is at age ‘a’ and cohort 2 is at age ‘b’, rather than when they are both at age ‘c’.

Figure A3: Age profiles for different types of variables <!-- Start of picture text --> cohort effects<br>C<br>cohort 3<br>B<br>cohort 3 cohort 2<br>cohort 2<br>cohort 1<br>cohort 2 A<br>z a b c a b c<br>Age Age<br>Education<br>Labor Force Participation<br><!-- End of picture text --> 56
<!-- Start of picture text --> Age profile for Employment - India<br>100<br>g 80<br>5<br>E 60 _<br>5 ; o_oza :ai ;Ay<br>ola a —— i is<br>D 2 8itt —t—1996 Lg<br>15 20 25 30 35 40 45<br>Age<br>@ 1940-44 1945-49 —— 1950-54 —— 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>—*> 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Employment - Bangladesh<br>100<br>.)s 80<br>5 1975<br>E 60 1985 1980, 1970<br>é 1990 , 4965<br>»<br>® en ae a<br>D£2000204i ne638 Airo ——_ 3 —<——fore——thes1945 19403<br>e<br>520 5 30 35 40 45<br>Age<br>@ 1940-44 1945-49 —— 1950-54 —— 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>> 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Employment - Pakistan<br>100<br>=>4 80<br><7<br>£60<br>°<br>=<br>So 40<br>=2a<br>1986 1988 et | 672 —<br>2000<br>e<br>0<br>tp<br>15 20 25 30ops35 40 45<br>Age<br>@ 1940-44 1945-49 1950-54 — 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>—® 2000-04<br><!-- End of picture text -->
<!-- Start of picture text --> Age profile for Agency- Bangladesh<br>100 :<br>i 40 1985 1980 1975 1970<br>=® iH 1995 1 7070568 18981965 1)<br>©2 804:HHi 1088 aa1950<br>£ 3<br>=z of<br>£ i<br>5 404)<br>20-3a a<br>15 20 25 30 35 40 45<br>Age<br>—— 1950-54 1955-59 1960-64 —— 1965-69<br>1970-74 1975-79 —— 1980-84 — 1985-89<br>—— 1990-94 —— 1995-99 © 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Agency- India<br>100 ;<br>i<br>= H 1888a8 eyea : — —_"er)<br>B©o 80-i{98H‘ 48324980 —Hs 0 —<br>£<br>% 2900 / 1<br>s 4&5<br>5 40. |<br>20/3ee<br>15 20 25 30 35 40 45<br>Age<br>—— 1950-54 1955-59 1960-64 —— 1965-69<br>1970-74 1975-79 —— 1980-84 — 1985-89<br>—— 1990-94 —— 1995-99 @ 2000-04<br><!-- End of picture text -->

<!-- Start of picture text --> ot i Age profile for ipv justification - Bangladesh<br>go= |<br>5 a4}<br>3 i<br>B2 31 joao-4985-——_191 9 850 1980 197580 18 ~ 4398 1960Pr<br>Bx> apo; 995> TM~1990 4985 980 975 1970<br>s " i985 1 1955<br>G : 1 1974 1970 1965<br>1 }<br>i<br>15a 20 25 30 35 40 45<br>Age<br>1950-54 1955-59 — 1960-64 1965-69<br>1970-74 — 1975-79 — 1980-84 1985-89<br>— 1990-94 — 199599 © 2000-04<br><!-- End of picture text -->

<!-- Start of picture text --> Age profile for ipv justification - India<br>ot<br>Zo= hs 1885 1986 1978 1858 1988_i7955<br>2 :<br>eft<br>3BaiDNH \<br>c=ce i \<br>5 | \<br>H 1 4655 jase 4886 a5 1878 1898<br>- _<br>aii15—_—_4-120tH 25 30 35 40 45<br>Age<br>1950-54 1955-59 — 1960-64 1965-69<br>1970-74 — 1975-79 — 1980-84 1985-89<br>— 1990-94 — 1995-99 © 2000-04<br><!-- End of picture text -->

<!-- Start of picture text --> Age profile for ipv justification - Nepal<br>61<br>3 41 i<br>S 2p0<br>Sie© 31 4a89 10003881990 186 9sd a 1970<br>B54 4995 19) = Q 4085 __—<br>2. 1980 < 1988 S 1908 — 1853 1085<br>& " an 1985 4980 6 4970 ~ 4965 960<br>eS<br>15 20 25 30 35 40 45<br>Age<br>1950-54 1955-59 — 1960-64 1965-69<br>1970-74 — 1975-79 — 1980-84 1985-89<br>— 1990-94 — 1995-99 @ 2000-04<br><!-- End of picture text -->
# **7. Age-period-cohort analysis**

The literature has offered different methods to solve the identification issue of age-period-cohort (APC) decompositions. The PWA model is one of these specific solutions used in analyses of female labor force participation (Nientker and Alessie, 2019). This model assumes a constant effect of age for women in their 40s, thus imposing the needed further restriction on two dummy variables. The equating of coefficients of two adjacent age groups falls within the wider family of constrained APC models, where any two coefficients of two adjacent dummies (that being from the age, period, or cohort set) are set to be equal. The theoretical intuition and the preliminary evidence in our data for both countries suggest that this is a worth-considering assumption. While the restriction is non-testable in the full APC model, the fit of simple age-only regression model, and age-period and age-cohort models, all revealed similar coefficients for the dummies in these age groups (42-46 and 47-50 for Bangladesh; 41-45 and 46-50 for Nepal). The HHDP model, first proposed by Hanoch and Honig (1985) and Deaton and Paxson (1994) applies a restriction on the period effects. In particular, this is done by applying a detrending, such that the period effect is orthogonal to a trend and sums to zero. This approach relies on the assumption of unanticipated business cycle shocks and usually requires several survey years to sufficiently separate trend and cycle.

While the previous two models impose specific restrictions, the approaches of IE and ME suggest two different generalized solutions to the APC identification problem. The IE approach, proposed by Fu (2000) and Yang et al. (2004), makes use of least squares by applying a Moore-Penrose generalized inverse to deal with the collinearity in the design matrix and estimated via a principal component regression algorithm. The estimator provides a general solution without imposing specific restrictions on any of the dummy variables, but which depends on several choices made in the optimization algorithm. Similarly, the approach of ME proposed by Browning et al. (2012) suggests the use of maximum-entropy principle to address the problem. This is a general framework that represents the uncertainty around the parameters as a probability distribution and using an entropy measure to select the probabilities that represent the information in the data the best. This approach falls within the wider family of solutions using maximum-entropy estimation and is more appropriate when the set of possible solutions is bounded.

Finally, the HAPC, first proposed for APC models by Yang and Land (2006), is a solution that uses the principles of standard multilevel analysis, that is using a mixed (fixed and random) effects model and relies on non-linearity. Age and age-squared enter as fixed effects, whereas period and cohort enter as crossclassified random effects. These mixed effects models are popular within economics (as random effects models or random coefficients models in panel data analysis) and education research (e.g., students cluster in classrooms, which further cluster in schools). In this application, the random effects are crossed, in the sense that they are not nested (e.g., students clustered in their primary schools and in their subsequent secondary schools). These models rely on the distributional assumptions made on the error 59
terms, being zero-mean normally distributed, thus interpreting period and cohort effects as deviations or shocks.

Tables A5 and A6 present the parameter estimates and their standard errors for all the five APC models. While for PWA, HHDP, IE, ME these include the estimates for the age, period, and cohort dummies, for HAPC we have the coefficients for age and age-squared, along with the estimates of the variances for the random effects of period and cohort.

Table A5: Age-period-cohort effects using five different models for Bangladesh

|||**(1)**|**(**|**2)**|**(**|**3)**|**(**|**4)**||**(5)**|
|---|---|---|---|---|---|---|---|---|---|---|
||P|WA|H|HDP|I|E|M|E|H|APC|
||Coef.|(SE)|Coef.|(SE)|Coef.|(SE)|Coef.|(SE)|Coef.|<br>(SE)|
|**age**|||||||||0.0303|<br>(0.0013)|
|**age-squared**|||||||||-0.0004|(0.0000)|
|**age 22**|0.046|(0.022)|0.032|(0.023)|0.029|(0.010)|0.025|(0.015)|||
|**age 26**|0.099|(0.035)|0.087|(0.024)|0.065|(0.010)|0.058|(0.016)|||
|**age 30**|0.155|(0.049)|0.147|(0.025)|0.104|(0.010)|0.093|(0.017)|||
|**age 34**|0.205|(0.064)|0.202|(0.027)|0.137|(0.010)|0.123|(0.018)|||
|**age 38**|0.206|(0.079)|0.208|(0.029)|0.12|(0.010)|0.103|(0.020)|||
|**age 42**|0.228|(0.094)|0.236|(0.030)|0.126|(0.010)|0.105|(0.021)|||
|**age 46**|0.225|(0.116)|0.240|(0.032)|0.106|(0.010)|0.082|(0.023)|||
|**age 50**|0.225|(0.116)|0.233|(0.032)|0.088|(0.010)|0.061|(0.022)|||
|**period 1995**|0.192|(0.020)|0.139|(0.017)|0.209|(0.009)|0.212|(0.014)|||
|**period 1999**|0.020|(0.033)|-0.040|(0.016)|0.054|(0.009)|0.061|(0.014)|||
|**period 2003**|0.001|(0.047)|-0.064|(0.015)|0.053|(0.009)|0.063|(0.015)|||
|**period 2007**|0.080|(0.062)|0.010|(0.014)|0.148|(0.009)|0.162|(0.016)|||
|**period 2011**|-0.121|(0.077)|-0.196|(0.015)|-0.036|(0.009)|-0.019|(0.017)|||
|**period 2015**|0.075|(0.092)|-0.002|(0.016)|0.178|(0.009)|0.198|(0.018)|||
|**period 2019**|0.218|(0.108)|0.153|(0.017)|0.338|(0.010)|0.361|(0.020)|||
|**cohort 1945**|0.041|(0.038)|-0.057|(0.052)|0.024|(0.019)|0.020|(0.036)|||
|**cohort 1949**|0.106|(0.042)|0.020|(0.049)|0.072|(0.017)|0.065|(0.032)|||
|**cohort 1953**|0.155|(0.051)|0.078|(0.046)|0.104|(0.015)|0.094|(0.029)|||
|**cohort 1957**|0.188|(0.063)|0.119|(0.044)|0.12|(0.014)|0.106|(0.026)|||
|**cohort 1961**|0.206|(0.076)|0.143|(0.042)|0.121|(0.013)|0.103|(0.024)|||
|**cohort 1965**|0.228|(0.090)|0.170|(0.041)|0.126|(0.012)|0.105|(0.021)|||
|**cohort 1969**|0.245|(0.104)|0.190|(0.039)|0.125|(0.011)|0.101|(0.019)|||
|**cohort 1973**|0.259|(0.118)|0.206|(0.039)|0.123|(0.011)|0.095|(0.017)|||
|**cohort 1977**|0.260|(0.134)|0.217|(0.039)|0.106|(0.012)|0.075|(0.016)|||
|**cohort 1981**|0.271|(0.149)|0.232|(0.040)|0.1|(0.012)|0.066|(0.015)|||
|**cohort 1985**|0.272|(0.164)|0.237|(0.041)|0.084|(0.013)|0.046|(0.015)|||
|**cohort 1989**|0.273|(0.179)|0.240|(0.042)|0.068|(0.014)|0.027|(0.015)|||
|**cohort 1993**|0.276|(0.194)|0.244|(0.045)|0.054|(0.016)|0.010|(0.017)|||
|**Var(period)**|||||||||0.0108|<br>(0.0054)|
|**Var(cohort) **|||||||||0.0030|(0.0017)|

60
Table A6: Age-period-cohort effects using five different models for Nepal

||**(**|**1)**|**(2**|**)**|**(**|**3)**|**(**|**4)**||**(5)**|
|---|---|---|---|---|---|---|---|---|---|---|
||PW|A|HH|DP|I|E|M|E|H|APC|
||Coef.|(SE)|Coef.|(SE)|Coef.|(SE)|Coef.|(SE)|Coef.|<br>(SE)|
|**age**|||||||||0.0239|<br>(0.0017)|
|**age-squared**|||||||||-0.0003|(0.0000)|
|**age 25**|0.047|(0.011)|0.057|(0.026)|0.04|(0.005)|0.046|(0.008)|||
|**age 30**|0.107|(0.018)|0.108|(0.027)|0.093|(0.005)|0.104|(0.008)|||
|**age 35**|0.155|(0.025)|0.148|(0.030)|0.134|(0.005)|0.151|(0.009)|||
|**age 40**|0.172|(0.032)|0.155|(0.032)|0.144|(0.005)|0.168|(0.009)|||
|**age 45**|0.173|(0.043)|0.146|(0.035)|0.138|(0.005)|0.167|(0.010)|||
|**age 50**|0.173|(0.043)|0.146|(0.034)|0.131|(0.005)|0.166|(0.009)|||
|**period 2001**|0.059|(0.009)|0.113|(0.015)|0.066|(0.004)|0.060|(0.006)|||
|**period 2006**|-0.027|(0.016)|0.038|(0.013)|-0.013|(0.004)|-0.025|(0.006)|||
|**period 2011**|-0.139|(0.023)|-0.066|(0.013)|-0.118|(0.004)|-0.136|(0.007)|||
|**period 2016**|-0.154|(0.031)|-0.085|(0.015)|-0.125|(0.004)|-0.149|(0.007)|||
|**cohort 1951**|0.033|(0.015)|0.077|(0.046)|0.025|(0.007)|0.031|(0.014)|||
|**cohort 1956**|0.041|(0.018)|0.071|(0.043)|0.027|(0.007)|0.039|(0.012)|||
|**cohort 1961**|0.043|(0.023)|0.060|(0.040)|0.022|(0.006)|0.040|(0.011)|||
|**cohort 1966**|0.049|(0.029)|0.057|(0.037)|0.02|(0.005)|0.045|(0.009)|||
|**cohort 1971**|0.053|(0.036)|0.054|(0.038)|0.017|(0.006)|0.047|(0.008)|||
|**cohort 1976**|0.028|(0.044)|0.024|(0.036)|-0.014|(0.005)|0.022|(0.007)|||
|**cohort 1981**|0.025|(0.051)|0.004|(0.037)|-0.025|(0.006)|0.017|(0.007)|||
|**cohort 1986**|0.002|(0.059)|-0.025|(0.040)|-0.055|(0.006)|-0.006|(0.007)|||
|**cohort 1991**|0.003|(0.067)|-0.028|(0.043)|-0.062|(0.007)|-0.007|(0.008)|||
|**cohort 1996**|-0.048|(0.075)|0.000|(0.000)|-0.12|(0.012)|-0.059|(0.010)|||
|**Var(period)**|||||||||0.0071|<br>(0.0045)|
|**Var(cohort) **|||||||||0.0004|(0.0002)|

61
# **8. Heterogeneity in trends of norm-related outcomes**

This sections discusses in detail the marginal effects of wealth, location, and education (the independent variables) on outcome variables (the dependent variables, see for more details the equations and explanations of page 10 in the main text). We estimate the marginal effects by regressing the outcome variables on wealth, location (rural versus urban), higher education (secondary and tertiary education versus no or primary education), their interactions with year of birth, and state fixed effects. Standard errors are clustered by state.

For variables that have a life cycle, i.e., variables that change as an individual ages, for example employment, we control for age in the regression. We run the heterogeneity analysis for Bangladesh, India, Nepal, and Pakistan. Data in Afghanistan (and Maldives) were collected only in one (and two) surveys, and this does not permit to trace cohorts as they age.

As the independent variables are categorical, the marginal effects are represented as the impact of moving from the reference category to one of the other categories. In the case of wealth, the reference is the poorest quintile; for the urban group, the reference is rural; for education, the higher education group is compared against the lower or no education group. Thus for example, if moving from the poorest quintile (the reference category for the wealth variable) to a richer quintile does not produce any effects, then the plotted marginal effect will be at the zero level on the vertical axis, meaning that there is no statistically significant difference between one category with respect to the reference category.

For each of the outcomes variables the marginal effect figures include four panels, one for each country. Each panel provides information about the level of the marginal effect (the position of the line) and whether this is different for different cohorts (the slope of the line). More in detail, moving from left to right allows to compare marginal effects between cohorts born in the past and more recent cohorts.

In extreme synthesis, a common finding across different outcomes is that wealth seems to have some marginal effect, but that urban location and education do not tend to be associated with better outcomes, as pointed out in stylized fact 2. However, there is quite a bit of heterogeneity across outcomes and countries. The details are discussed in the remainder of this section.

62
<!-- Start of picture text -->
- Bangladesh:Education<br>Se<br>53<br>0<br>5<br>2<br>me}5.3<br>7<br>co<br>)<br>o<br>ns]<br>eT<br>Wl<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 +—— Quintile 3<br>+——  Quintile4 +——— Quintile 5<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text -->
- India:Education<br>Se<br>%<br>om<br>rs<br>Sy<br>223)<br>a<br>=<br>co<br>°<br>5]<br>B..<br>Ti<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 t-—— Quintile 3<br>-—— Quintile4 H—— Quintile 5<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> 5. Nepal:EducationP<br>Ss<br>Be<br>re)c<br>ER<br>a.o= ee ae oe ee<br>3.<br>=<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 +——— Quintile 3<br>—— Quintile4 +——— Quintile 5<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text -->
- Pakistan:Education. .<br>Oo<br>3<br>2°°<br>5<br>no]oE<br>8<br>2o<br>Ww 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>——+ Quintile 2 t—— Quintile 3<br>— Quintile 4 +———— Quintile 5<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> = Bangladesh: Education<br>2<br>3<br>S<br>Pa3°5re)<br>3<br>Ss2<br>iUrban<br>&<br>doc5<br>oO<br>8<br>it]<br>re)<br>r<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> a5 India: Education<br>S<br>3<br>o<br>r a<br>x)<br>ge<br>5<br>&c<br>ie}<br>3 Urban<br>Eo<br>oO<br>=£<br>6<br>22)hae)<br>2 2<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Education<br>5<br>g<br>a o ]<br>2°<br>o=<br>xe)<br>5<br>s Urban<br>8<br>So<br>o<br>3<br>5<br>2<br>Cwm<br>2 2<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> : i<br>5 Nepal: Education<br>a”<br>Ss<br>3<br>-S<br>=8.0<br>S<br>6 Urban<br>3<br>3<br>Bo<br>oc<br>B<br>5<br>2<br>o<br>2"<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> &@ India:Marriagege ageag<br>0 +<br>-~)<br>Ew<br>5<br>c<br>2ae<br>a<br>5<br>gy<br>W 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 +——— Quintile 3<br>+—— Quintile4 +——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> .<br>[i]% Bangladesh:Marriage age<br>ot<br>a<br>6<br>Ew<br>6<br>55°<br>o<br>=<br>Byn<br>i 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 +—— Quintile 3<br>'— Quintie4 +——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 7 Pakistan:Marriage age<br>gs<br>7<br>eC9<br>go<br>ao<br>G<br>8:<br>by<br>g!<br>wi Fe ee eee oO eee i aiay, (el 1980 1885 1880. 1885<br>+—S Quinte 2 +—— Quintile 3<br>+—— Quintile 4 +——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> g Nepal:Marriage age<br>ot<br>Le]<br>&<br>N<br>§Lo<br>5<br>a<br>=<br>c<br>m<br>3 t<br>Po4940 1945 1950 1955 1960 Year1965 of1970birth 1975 1980 1985 1990 1995<br>+— Quintile2 +—— Quintile 3<br>+— Quintile 4 ——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> Q India: Marriage age<br>?<br>a<br>Bw<br>& t<br>2<br>2<br>~ . .<br>an Higher education<br>2<br>&<br>5<br>4<br>Q Urban<br>iT}<br>SO | |<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> nN Bangladesh: Marriage age<br>e<br>e<br>&<br>FI<br>& 2<br>®<br>5<br>3<br>ra<br>on Higher education<br>Q<br>£<br>5<br>o<br>o£94 Urban<br>wu ee<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> N Nepal: Marriage age<br>o®<br>fei]<br>v<br>EW<br>ES<br>6<br>3 Higher9 education<br>a<br>5Q<br>&Urban<br>By<br>3<br>i<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> nN Pakistan: Marriage age<br>©<br>v<br>—<br>gg<br>3 Higher education<br>Bal<br>g<br>a<br>£<br>:<br>4 N Urban<br>ioO<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> soeS India:Firsta birth. age<br>3<br>-<br>& N<br>—<br>=<br>ge<br>Ba<br>oOo<br>=y<br>2<br>ae<br>ui 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintile2 —— Quintile 3<br>—— Quintile4 '——— Quintile 5<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> egev Bangladesh:First, birth. age<br>=<br>2a<br>Bn<br>eS<br>‘6<br>Bo<br>3<br>-sS<br>z +<br>3°a<br>i 4940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintile2 '—— Quintile 3<br>—— Quintile4 "——— Quintile 5<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> Pe)v Nepal-First‘Fi birthi age<br>st<br>5<br>2<br>Bn<br>P=<br>8<br>go<br>8<br>5v<br>a‘<br>5<br>:<br>6<br>#<br>g°<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-— Quintile2 +—— Quintile 3<br>— Quintile4 +——— Quintile 5<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 2 Pakistan:Firsti i birthi age<br>at<br>€<br>5<br>@<br>gE “<br>%<br>go<br>3<br>3<br>5%<br>&<br>2<br>§<br>a<br>ge<br>© W 1949 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>H— Quintile2 + Quintile 3<br>-—— Quintile 4 +——— Quintile5<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>yeat-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> x) India: First birth age<br>«a<br>=<br>a<br>or<br>haa<br>5c<br>2 . .<br>By Higher education<br>fyaiQ Urban<br>5<br>3a<br>o<br>P=Woy<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> oo<br>Bangladesh: First birth age<br>) o<br>e<br>s<br>5<br>Ber<br>ba5<br>ro]c<br>ki<br>o N Higher education<br>a<br>o<br>eo<br>°<br>a<br>o<br>iio n<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: First birth age<br>& o<br>o<br>ra<br>=<br>a}<br>tr<br>=<br>os3<br>fe]acHigher education<br>5N<br>a<br>QUrban<br>Eyc3 a<br>ia]“<br>S(a)<br>Wey<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: First birth age<br>& o<br>o<br>ra<br>=E<br>a]<br>at<br>&<br>—3<br>7g& Higher. education.<br>oBN<br>a<br>d<br>Eocio] oo Urban<br>i]a<br>ia<br>Wey<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing first birth age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> o& India:Son2° preference<br>o O ~<br>a<br>co<br>3.<br>5°<br>2<br>By<br>50<br>§<br>{i 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+——+ Quintile2 +—— Quintile 3<br>-— Quintile4 +——— Quintile 5<br>Generated by regressing son preference on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 8o Bangladesh:Son preference<br>i<br>a<br>5<br>a<br>3<br>BN<br>a'<br>5<br>5<br>ii 61940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 —— Quintile 3<br>+—— Quintile4 '——— Quintile 5<br>Generated by regressing son preference on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal:Son preference<br>OL<br>cae<br>a<br>co<br>8<br>6<br>&*<br>g<br>oN<br>a!<br>G<br>§<br>ti 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 '—— Quintile 3<br>-—— Quintile4 +——— Quintile5<br>Generated by regressing son preference on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 8 Pakistan:Son preference<br>oT<br>go<br>a<br>co<br>8<br>6<br>50<br>2<br>Ba<br>a<br>G<br>§<br>i 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>*— Quintile2 +—— Quintile 3<br>+—— Quintile4 +——— Quintile 5<br>Generated by regressing son preference on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> g<br>2<br>8<br>go Urban<br>9 _———<br>Cc . 5<br>g_ Higher education<br>gy<br>o<br>a<br>&i N<br>43)o<br>in<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing son preference on wealth, urban, higher education and<br>interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>o<br>8<br>fo Urban<br>7)<br>fo} SS SS__—_—_E_<br>c<br>6 ee<br>cer Higher education<br>ne]©<br>a<br>25 N<br>2(s)<br>ie<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing son preference on wealth, urban, higher education and<br>interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Son preference<br>gt<br>Cc<br>v<br>G]<br>5 Urban<br>a<br>fo]co<br>%<br>c a<br>g<br>Bo<br>ko]o<br>af<br>g<br>o<br>&% Higher education<br>io)c<br>i”9<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing son preference on wealth, urban, higher education and<br>interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Son preference<br>gt<br>Cc<br>v<br>o<br>0 Urban<br>a<br>fo)co _—_—————————<br>|c<br>ie)<br>oF Higher education<br>ko]©<br>5<br>g<br>o<br>EN<br>c6<br>i”9<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing son preference on wealth, urban, higher education and<br>interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> oe Bangladesh: Agency<br>v<br>o<br>Bo<br>5°<br>5<br>=~<br>3<br>a<br>as<br>50<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile 2 -—— Quintile 3<br>—— Quintile 4 +——— Quintile 5<br>Generated by regressing agency on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> oo india: Agency<br>o<br>is)<br>a<br>3°<br>5<br>So<br>3<br>a<br>5%<br>50 So a<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-— Quintile2 +—— Quintile 3<br>-—— Quinte 4 +—— Quintile 5<br>Generated by regressing agency on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Agency<br>ae<br>Cc<br>o<br>a® oe<br>5°<br>5<br>oe<br>3<br>a<br>g%<br>So<br>g<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 +—— Quintile3<br>-— Quintile4 +——— Quintile 5<br>Generated by regressing agency on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Agency<br>ro)<br>a<br>o<br>fo)<br>o<br>5°<br>§<br>oe<br>3<br>a<br>5<br>50<br>P(3)<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 +——— Quintile3<br>-— Quinte 4 +——— Quintile 5<br>Generated by regressing agency on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> o India: Agenc’gency<br>B<br>.°)]<br>Say<br>o<br>c<br>°<br>=<br>a:OL<br>®<br>=¢ ee<br>¢<br>Higher education<br>5 ° Urban<br>S<br>e<br>nf<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Bangladesh: Agency<br>oO<br>o<br>a<br>aN<br>°<br>Cc<br>2<br>3e-<br>5<br>£o<br>_<br>; ——EE<br>So<br>7 Higher education<br>8<br>5<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text -->
-<br>Pakistan: Agency<br>o<br>a<br>aN<br>3 Higher education<br>5<br>s<br>oe2<br>5<br>g<br>¢5<br>g<br>3<br>i<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> ro) Nepal: Agency<br>&<br>o<br>e<br>5%<br>g<br>s3=Higher education-<br>oe<br>o| |<br>So Urban<br>2<br>3<br>ii<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
# vi. <u>Spousal Difference in Age and Education</u>

Figure A17: The marginal effect of wealth on spousal difference in age This graph considers a triple difference: women versus men, rich and poor quintiles, and early versus recent cohorts. The first difference is the age gap between husbands and wives measured as the age of the husband minus the age of the wife (this is the left-hand side variable in the equation of page 10 of the main text). The second is the difference between this age gap across quintiles for each cohort (this is the actual marginal effect of wealth on the spousal age gap). For a specific cohort, a negative marginal effect in the graphs below thus means that the difference in age between husbands and wives is smaller for richer quintiles vis-à-vis the poorest quintile. Consider, for example the richest quintile in the 1950 cohort in Bangladesh. The spouse age gap for this quintile is almost 3 years smaller than the spouse age gap for the poorest quintile. For the poorest quintile, this spouse age gap was about 14 years, while for the richest quintile this differential was 10 years<sup>28</sup> . Now, we can consider the third difference: the (i) age difference between spouses of (ii) a rich quintile versus the poorest quintile (iii) across time. This third difference is ‘visible’ by considering the slope of the lines. Again, considering Bangladesh as an example, and moving along the yellow (richest quintile) line from left to right, the differential is becoming smaller. From the initial 4 years it becomes 0 years for the 1975 birth cohort, i.e., there is no more difference between richer and poorer cohorts<sup>29</sup> . So, we can say that richer cohorts (and women in these cohorts) are not progressing – in terms of reducing the age differential between spouses – more rapidly than the poorest cohort. In fact, these richer cohorts seem to be losing the initial ‘advantage’ of having a smaller age differential. And these losses seem to continue for the most recent cohorts. Also, the pace at which the initial advantage is reduced (the steepness of the lines) is largest for the richest quintiles. And this is true across all countries, even if, in Pakistan and India, everything seems quite compressed around the zero level (no effects at all).

> 28 Note that the marginal effect of 3 years is not the same as 14 years minus 10 years. This is because the marginal effect is estimated in a regression setting where other controls are held constant, while the 14 and 10 years are unconditional means.

> 29 Indeed, the unconditional mean for the spouses age gap for the poorest quintile in the 1975 cohort was equal to 10.8 years and that for the richest quintile in the same cohort was equal to 9.1.

73
<!-- Start of picture text --> Bangladesh:Spousal age difference<br>osn<br>25<br>St<br>cB<br>Sfo<br>po<br>tec<br>iN<br>52<br>£3<br>584<br>25<br>ee<br>us<br>9<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 t—— Quintile 3<br>—— Quintile4 +——— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal:Spousal age difference<br>os<br>#=<br>ot<br>naySo<br>ec<br>oo%<br>£a<br>cw<br>oY<br>ge<br>us,<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>*— Quintile2 +——— Quintile 3<br>— Quinte 4 +——— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India:Spousal age difference<br>N<br>gs<br>at<br>oF<br>§u0<br>oF<br>Ge<br>23%<br>aS!<br>£3<br>S8y<br>25<br>38<br>eS<br>WS wo<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Quintile2 t— Quintile 3<br>—— Quintile4 H—— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan:Spousal age difference<br>on”<br>83<br>3<br>oeo<br>tec<br>a3<br>£a<br>cw<br>oot<br>ge<br>ws ©<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintile2 +—— Quintile 3<br>—— Quintile4 +——— Quintile 5<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> N Bangladesh: Spousal age difference<br>om83<br>ott<br>c<br>£3<br>2 a Urban<br>ec °o<br>aoa)<br>§2<br>£3 -<br>g 2 Higher education<br>og<br>is<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India: Spousal age difference<br>N<br>3S<br>st<br>OFT<br>&3 Urban<br>g8<br>nom)<br>o55<br>go<br>£235 Higher education<br>Lor<br>8 =<br>is<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Spousal age difference<br>N<br>on<br>oz.77<br>gc<br>ego _<br>s—TME<br>5 g Higher education<br>goryi]og<br>is<br>4<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Spousal age difference<br>N<br>os<br>5xSe<br>lc<br>eBo<br>52 |<br>§ g Higher education<br>2o4iyoe<br>hip<br>4<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> G6 India:Spousal education difference<br>sa<br>ss34<br>3t,,<br>62<br>co<br>Si<br>Be°<br>oo<br>Bg<br>sot<br>oo<br>co<br>£8<br>55%<br>of |¢-—@—@—44+ _+ + + + + #5<br>= °<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile2 +——— Quintile3<br>+—— Quintile4 +——— Quintile5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> c Bangladesh:Spousal education difference<br>go<br>ss[s}<br>SL<br>2g<br>54<br>58<br>5 eo<br>35<br>20<br>o2<br>G5<br>oy<br>=o<br>5 oN<br>a2<br>£Ww O°<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 +——— Quintile3<br>+—— Quintile4 '——— Quintile5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> c Pakistan:Spousal education difference<br>g 2<br>ie<br>ol<br>Loe<br>of<br>mae)<br>Qo<br>=eoo<br>O68<br>ee<br>gf<br>£8<br>cn68<br>ge<br>wo<br>Ww<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile2 +——— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> c Nepal:Spousal education difference<br>gc<br>is<br>- x<br>boo<br>62<br>ame)<br>go<br>=coo<br>8<br>oe<br>54<br>£8<br>c5Non<br>ae<br>g Po<br>Ww<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile2 +—— Quintile 3<br>-— Quinte4 '——— Quintile 5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> Bangladesh: Spousal education difference<br>N<br>5<br>8_o Urban<br>35ad239eoI SS_————<br>g8 Higher education<br>oY<br>ee<br>aoe<br>¢o>) z<br>=o<br>Ce<br>a°9<br>o<br>o<br>i<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Spousal education difference<br>*<br>Cc<br>a)<br>: 3<br>oon<br>33"<br>ao ;<br>ee Higher education<br>~ oy<br>23SSSve<br>5<br>g°%<br>it<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India: Spousal education difference<br>N<br>5<br>i<br>aagoaSe0 Urban<br>59!<br>38<br>0<br>a5<br>QSa z Higher education<br>=5<br>ce<br>2°9<br>is}<br>o<br>i<br>9<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Spousal education difference<br>N<br>6x “ca<br>ev<br>3°<br>3a<br>a”<br>og<br>i-ie ot<br>se |<br>nee Higher education<br>ti<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
# viii. <u>Fertility outcome</u>

Figure A21: The marginal effect of wealth on fertility outcome Fertility outcome is expressed as actual minus ideal number of children; hence, a positive number means that a woman has more children than she desires, and this may suggest lack of agency or control over her fertility choices. As for the two previous sets of variables, the figures below represent the marginal effects as a triple difference: (i) actual minus desired number of children (fertility gap), (ii) the fertility gap of rich quintile vis-à-vis the poorest quintile (iii) fertility gap between the rich and poor quintiles across time. Consider the case of Nepal. For the 1955-59 cohort, the differential between actual and desired children was 1.21 for women in the bottom quintile and 1.18 for women in the top quintile. This means that the difference between the rich and poor women’s differentials is close to zero (as shown in the graph), or that the gap between actual and desired number of children is the same for both groups of women. For more recent cohorts, for example the one of 1975-79, the differential is 1.41 for the poor women and 0.52 for the rich ones. The difference between these two differentials thus becomes -0.89 (as shown in the graph). This means that while the gap between actual and desired number of children is closing for the richer women, this gap is widening for poorer women. This is reflected in the negative (downward) slope of the yellow line that indicates that women in the top quintile have made some progress in their fertility outcomes. However, note that this improvement is only recorded for the case of Nepal. Even if Bangladesh graph looks similar, the confidence intervals are much larger, and the marginal effect is not statistically different from zero. A similar lack of impact is seen for the cases of India and Pakistan.

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<!-- Start of picture text --> v5 . :a<br>Soi Bangladesh:Fertility outcome<br>°<br>=2<br>Ew<br>ie)<br>-<br>Zu<br>ks )<br>heoO<br>fon0<br>5<br>2 x<br>3!<br>i 1940 1945 1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>+— Quintile2 +—— Quintile3<br>+—— Quintile4 +——— Quintile 5<br>Generated by regressing fertility outcome on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> gaoe India:Fertility. a outcome<br>2 N<br>£<br>Ew<br>.<br>e<br>S<br>oe<br>ue]®<br>inate!a Py<br>c<br>2<br>on<br>hi 1940 1945 1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>+——+ Quintile2 +——— Quintile3<br>+—— Quintile4 +——— Quintile 5<br>Generated by regressing fertility outcome on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> v . a<br>E Pakisatn:Fertility outcome<br>Bu<br>as |<br>.<br>2<br>ge-<br>c<br>=&.,<br>e<br>a<br>BD<br>:<br>a2<br>oy;o<br>«1940:«1945« 195019551960 1985 1970 1975 1980<br>Year of birth<br>——i Quintile 2 +——— Quintile 3<br>—— Quinte 4 +——— Quintile 5<br>Generated by regressing fertility outcome on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> ® a:<br>E Nepal:Fertility outcome<br>ow<br>50<br>3<br>2pas)<br>3<br>ge5<br>3<br>4<br>-<br>©<br>cSe<br>oeo*,<br>5 1940 1945 1950 1955 1980 1965 «1970 «1975<br>Year of birth<br>+—— Quintile2 +—— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing fertility outcome on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> EN Bangladesh: Fertility outcome<br>6<br>gFe<br>z.<br>c<br>55<br>5 Urban<br>go<br>a<br>; .<br>Fi<br>ee Higher education<br>=!<br>5<br>g<br>nh)ao<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> on India: Fertility outcome<br>E<br>53<br>2ifo)<br>55<br>5 Urban<br>5 O—_—_—_—_—_—_—_—_—_—_—_____SS|<br>a Higher educati<br>5 ‘gner eeucasion<br>=2<br>5<br>a<br>u<br>is<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> i) Nepal: Fertility outcome<br>g<br>:<br>5<br>6<br>ks}3 =]a<br>ev ere<br>é"5Higher education<br>ec<br>5<br>4<br>&<br>i)<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Fertility outcome<br>4<br>:<br>5 Urban<br>5<br>Goa]<br>vo<br>“an5 Higher education<br>i=<br>ie)<br>4<br>a<br>is<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> v . .<br>DrQ India:IPV experienceP<br>a<br>a<br>é<br>><br>2<br>=<br>°<br>53<br>ve<br>a<br>c N<br>20<br>© 1950 1958 1960 1965 1970 1975 1980 1985 ©1990 1995<br>Year of birth<br>-— Quintile2 +—— Quintile 3<br>+— Quintile4 +——— Quintile 5<br>Generated by regressing ipv experience on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> .<br>2 Bangladesh:IPV experience<br>oe<br>oc<br>&<br>vu<br>2s<br>a<br>5<br>g=<br>3<br>=<br>2~<br>2 en<br>5 1960 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile 2 t—— Quintile 3<br>-—— Quintile 4 -——— Quintile 5<br>Generated by regressing ipv experience on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> » . . .<br>an: Pakistan:IPV experience<br>oe<br>a.<br>is)<br>Be<br>be<br>a!<br>|<br>ia]<br>La<br>7<br>i 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 + Quintile 3<br>— Quintile 4 + +——— Quintile 5<br>Generated by regressing ipv experience on wealth, urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> .<br>an8 Nepal:IPV experience<br>O.<br>2°<br>‘s<br>5°<br>By<br>a<br>=<br>om<br>at<br>g<br>i1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 +—— Quintile 3<br>—— Quintile 4 +——— Quintile 5<br>Generated by regressing ipy experience on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> .<br>g Bangladesh: IPV experience<br>c<br>ia)<br>5<br>a<br>x<br>vo<br>2<br>= Urban<br>ie}<br>§<br>8 °<br>5 Higher education<br>tr<br>5<br>4a<br>i<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipy experience on urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> India: IPV experience<br>an<br>c o Oo<br>t<br>a<br>Fy<br>am<br>&<br>He<br>ie]<br>S Urban<br>Bog an<br>ra Higher education<br>o<br>§<br>pa<br>tiea<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipv experience on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>8 Nepal: IPV experience<br>x<br>OL<br>><br>Qa<br>9°<br>5 Urban<br>2<br>a<br>8 - Higher education<br>=";<br>5<br>4<br>(a)<br>us<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipv experience on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> on Pakistan: IPV experience<br>a<br>x<br>ve<br>><br>2<br>5<br>5 Urban<br>3<br>a Higher education<br>Q<br>=.<br>5<br>fa<br>i]<br>ii(a<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing ipy experience on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> S e India:IPV }justification<br>=.<br>i<br>=z><br>Be<br>i)<br>g<br>°<br>5<br>Sa<br>§<br>fn<br>PS W 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-— Quintile2 +——— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> § Bangladesh:IPV justificationee<br>on<br>5<br>28<br>Bo<br>Peal<br>c<br>5<br>5<br>.<br>E<br>De<br>ii 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintie2 t—— Quintile3<br>+—— Quintile 4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 5 Nepal:|PV justification<br>8<br>Oe<br>z-<br>><br>2<br>So<br>i<j<br>5<br>&<br>no<br>or<br>a<br>ey5<br>c<br>()<br>i 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 +——— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> 5 Pakistan:|PV justification<br>gx<br>ra<br>=<br>><br>Bo<br>6<br>im<br>S.<br>gr<br>ne]<br>e<br>a<br>5%<br>&<br>on<a<br>ii 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2_ +——— Quintile 3<br>+— Quintile4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
<!-- Start of picture text --> Bangladesh: IPV justification<br>en<br>g<br>a<br>6<br>§ Urban<br>Bo PS<br>E<br>a —TS,/- HS<br>& :<br>Er Higher education<br>2!<br>is)<br>4<br>i)o<br>i<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> India: IPV justification<br>EN<br>2°=<br>%<br>8 Urban<br>Bo<br>3 ——— are<br>5 Higher education<br>G<br>ee<br>cc!<br>9<br>4<br>i](a<br>Ti<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> =n Pakistan: IPV justification<br>oa<br>2<br>a<br>>3<br>a<br>5 Urban<br>3§<br>go a ese<br>2<br>CR“<br>g<br>=" Higher education<br>e<br>is)<br>g<br>3)<br>o<br>is<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: IPV justificationeg<br>en<br>g<br>‘a5<br>Ae<br>ana<br>&ef Urban<br>€<br>2<br>Bo<br>5<br>Blo ———~—“‘“<br>5 Higher education<br>Er<br>c<br>5<br>a<br>a]<br>O<br>iri<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing work status on ipv justification, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->