Trends in Internal Migration and 

Women’s Empowerment in

Pakistan, 2012-2018

DHS Further Analysis Reports No. 130

March 2020

This publication was produced for review by the United States Agency for International Development. 

It was prepared by Courtney Allen, Aysha Sheraz, and Tesfayi Gebreselassie.

 

DHS Further Analysis Reports No. 130 

Trends in Internal Migration 

and Women’s Empowerment in Pakistan, 2012-2018 

Courtney K. Allen1,2 

Aysha Sheraz3 

Tesfayi Gebreselassie1 

ICF 

Rockville, Maryland, USA 

March 2020 

1

 ICF 

2

 The DHS Program 

3 National of Population Studies 

Corresponding author: Courtney Allen, The DHS Program, ICF, 530 Gaither Road, Suite 500, Rockville, 

MD 20850, USA; phone: 301-572-0994; email: courtney.allen@icf.com 
Acknowledgments: The authors would like to thank Lindsay Mallick for constant support in this study’s 

conceptualization, progress, and findings, as well as for reviewing the final draft. We are grateful for 

Shireen Assaf who developed the underlying Stata programs that were modified to conduct this analysis 

and who reviewed the methods and results sections of this report. We would also like to thank Fernanda 

Ewerling, along with her co-authors, for the development of the SWPER index and for additional guidance 

in using the SWPER index. Finally, we thank the National Institute of Population Studies of Pakistan not 

only for providing support for this further analysis, but also for collecting the data that made this analysis 

possible.

Editor: Diane Stoy 

Document Production: Natalie Shattuck 

This study presents findings from a further analysis undertaken as a follow-up to the 2017-18 Pakistan 

Demographic and Health Survey (2017-18 PDHS). This report is a publication of The DHS Program, which 

is designed to collect, analyze, and disseminate data on fertility, family planning, maternal and child health, 

nutrition, and HIV/AIDS. Funding was provided by the United States Agency for International 

Development (USAID) through the DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The 2017-18 PDHS was implemented by the National Institute of Population Studies (NIPS) from 

22 November 2017 to 30 April 2018, under the aegis of the Ministry of Health Services, Regulations and 

Coordination, Islamabad, Pakistan. ICF provided technical assistance through The DHS Program, which 

assists countries worldwide in the implementation of population and health surveys. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about The DHS Program can be obtained 

from ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850 USA; telephone: +1 301-407-6500, fax: 

+1 301-407-6501, email: info@DHSprogram.com, Internet: www.DHSprogram.com. 

Recommended citation: 

Allen, Courtney, Aysha Sheraz, and Tesfayi Gebreselassie. 2020. Trends in Internal Migration and Women’s 

Empowerment in Pakistan, 2012-2018. DHS Further Analysis Reports No. 130. Rockville, Maryland, USA: 

ICF.
iii 

CONTENTS 

TABLES ......................................................................................................................................... v 

FIGURES ..................................................................................................................................... vii 

ABSTRACT ................................................................................................................................... ix 

ACRONYMS AND ABBREVIATIONS .............................................................................................. xi 

1 INTRODUCTION ................................................................................................................. 1 

1.1 Women’s Empowerment in Pakistan .......................................................................... 1

1.2 Migration and Empowerment in Pakistan .................................................................... 1

1.3 Objectives ............................................................................................................... 2

2 DATA AND METHODS ........................................................................................................ 5 

2.1 Data........................................................................................................................ 5

2.2 Measures ................................................................................................................ 5

2.2.1 Empowerment .............................................................................................. 5 

2.2.2 Migration ..................................................................................................... 6 

2.2.3 Sociodemographic characteristics .................................................................. 7 

2.3 Analysis .................................................................................................................. 7

3 RESULTS ........................................................................................................................... 7 

3.1 Descriptive Results .................................................................................................. 9

3.2 Trends in Empowerment ......................................................................................... 12

3.2.1 Trends in the attitudes toward violence empowerment domain ....................... 13 

3.2.2 Trends in the social independence empowerment domain .............................. 14 

3.2.3 Trends in the decision-making empowerment domain .................................... 17 

3.3 Multivariate Analysis ............................................................................................... 19

3.3.1 Attitude toward violence .............................................................................. 19 

3.3.2 Social independence .................................................................................. 20 

3.3.3 Decision-making......................................................................................... 21 

4 DISCUSSION .................................................................................................................... 23 

REFERENCES .............................................................................................................................. 25 

APPENDICES ............................................................................................................................... 29 

v 

TABLES 

Table 1 Description of the Pakistan Demographic and Health Surveys (PDHS) 

included in the analysis ................................................................................ 5 

Table 2 DHS indicators used to construct SWPER empowerment domains ............. 6 

Table 3 Percentage of women in the study population by characteristics, 

Pakistan 2012-13 and 2017-18 .................................................................... 9 

Table 4 Percentage of married women who have moved or not by place of 

residence and reason and years since moved among migrant women, 

Pakistan 2012-13 and 2017-18 .................................................................. 10 

Table 5 Percentage of married women and empowerment indicators, Pakistan 

2012-13 and 2017-18 ................................................................................ 12 

Appendix Table 1 Trends in proportion of women with high empowerment in the 

SWPER attitude to violence empowerment domain, Pakistan 2012-13 

and 2017-18 ............................................................................................... 29 

Appendix Table 2 Trends in proportion of women with high empowerment in the 

SWPER social independence empowerment domain, Pakistan 2012-13 

and 2017-18 ............................................................................................... 30 

Appendix Table 3 Trends in proportion of women with high empowerment in the 

SWPER decision-making empowerment domain, Pakistan 2012-13 

and 2017-18 ............................................................................................... 31 

Appendix Table 4 Migration and SWPER attitude to violence empowerment domain. 

Adjusted relative risk ratios from multinomial logistic regression, 

Pakistan 2012-13 and 2017-18 .................................................................. 32 

Appendix Table 5 Migration and SWPER social independence empowerment domain. 

Adjusted relative risk ratios from multinomial logistic regression, 

Pakistan 2012-13 and 2017-18 .................................................................. 33 

Appendix Table 6 Migration and SWPER decision-making empowerment domain. 

Adjusted relative risk ratios from multinomial logistic regression, 

Pakistan 2012-13 and 2017-18 .................................................................. 34

vii 

FIGURES 

Figure 1 Trends in high empowerment, Pakistan 2012-13 and 2017-18 ........................... 13 

Figure 2 Trends in high empowerment in the attitude to violence empowerment 

domain by sociodemographic characteristics, Pakistan 2012-13 and 

2017-18 ............................................................................................................... 14 

Figure 3 Trends in high empowerment in the social independence domain by 

sociodemographic characteristic, Pakistan 2012-13 and 2017-18 ...................... 16 

Figure 4 Trends in high empowerment in the decision-making domain by 

sociodemographic characteristics, Pakistan 2012-13 and 2017-18 .................... 18 

Figure 5 Adjusted relative risk ratios of the multinomial regression examining the 

migration direction and attitude to violence empowerment domain, Pakistan 

2012-13 and 2017-18 .......................................................................................... 20 

Figure 6 Adjusted relative risk ratios of the multinomial regression examining the 

migration direction and the social independence empowerment domain, 

Pakistan 2012-13 and 2017-18 ........................................................................... 21 

Figure 7 Adjusted relative risk ratios of the multinomial regression examining the 

migration direction and the decision-making empowerment domain, 

Pakistan 2012-13 and 2017-18 ........................................................................... 22

ix 

ABSTRACT 

Background: For nearly three decades, gender equality and women’s health and well-being have been 

priorities on the international agenda. Empowered women have better health outcomes, such as the use of 

modern contraception and skilled birth attendance, and women who migrate can access opportunities that 

minimize inequality. Pakistan, the fifth most populous country in the world, is ranked low on the two 

indices that rank country progress in gender equality, the Gender Inequality Index and the Gender 

Development Index. This study describes and examines internal migration’s relationship with 

empowerment of women. 

Methods: This study analyzed data from currently married women who ever migrated in the Demographic 

and Health Surveys conducted in Pakistan in 2012-13 and 2017-18. We describe migration flows of 

currently married women who have ever moved internally within Pakistan, as well as trends of high 

empowerment in three empowerment domains: attitude to violence, social independence, and decision 

making, by background characteristics. After testing for difference of proportions, we illustrate trends over 

time in figures that display significant changes between surveys. We conduct multinomial logistic 

regression to compare the most empowered and moderately empowered with the least empowered women 

in terms of empowerment’s relationship with our key independent variable, migration, while controlling for 

age, working status, region, and wealth. 

Results: Some migration distinctions are significantly associated with high empowerment. Rural-urban 

migrants have decreased risk of high empowerment in the social independence domain, while urban-rural 

had an increased risk in the 2017-18 Pakistan Demographic and Health Survey. In the 2012-13 Pakistan 

Demographic and Health Survey, urban-urban migrant women had higher risk of high empowerment than 

their non-migrant counterparts in two empowerment domains: attitude to violence and decision making. 

Wealth and age were strong determinants of empowerment. 

Conclusions: Overall, we find that migrant women who originated in urban areas had positive associations 

with empowerment and that rural-urban movers were disadvantaged. Though women can access 

opportunities to better empower themselves through migration, migrant woman may also be disadvantaged 

and vulnerable. Policy makers should focus on creating pathways that increase the success of women’s 

migration strategies. 

KEY WORDS: Pakistan, empowerment, migration, gender 

xi 

ACRONYMS AND ABBREVIATIONS 

AJK Azad Jammu and Kashmir 

CI confidence interval 

DHS Demographic and Health Survey(s) 

FATA Federally Administered Tribal Areas 

GB Gilgit Baltistan 

GDI Gender Development Index 

GII Gender Inequality Index 

ICPD International Conference on Population and Development 

ICT Islamabad Capital Territory 

KPK Khyber Pakhtunkhwa 

NIPS National Institute of Population Studies 

PDHS Pakistan Demographic and Health Survey 

SDG Sustainable Development Goals 

SWPER Survey-based Women’s emPowERment Index 

UN United Nations 

UN United Nations Development Programme 

UNFPA United Nations Population Fund 

1 

1 INTRODUCTION 

1.1 Women’s Empowerment in Pakistan 

For nearly three decades, of gender equality and women’s health and well-being have been priorities on the 

international agenda. The 1994 International Conference on Population and Development emphasized the 

empowerment of women and their ability to control their own fertility (United Nations 2014). The 

Millennium Development Goals and more recently the Sustainable Development Goals (SDG) have 

established targets for attaining the empowerment of women and gender equality globally (United Nations 

2015, 2019). Empowered women have better health outcomes, such as use of modern contraception and 

skilled birth attendance (Do and Kurimoto 2012; Msuya, Adinan, and Mosha 2014; Pratley 2016; 

Shimamoto and Gipson 2015; Tadesse et al. 2013). 

Measuring the empowerment of women is imperative, though the process remains difficult. The subject of 

women’s autonomy appeared in the literature as early as the 1970s (Cornwall 2016). In the decades that 

followed, numerous definitions of empowerment and approaches to understanding its dimensions have 

emerged. Some measurements of gender equality, such as the Gender Inequality Index (GII) or the Gender 

Development Index (GDI) describe equality on a national level (United Nations Development Programme 

2019). Many studies have attempted to measure individual-level empowerment in low- and middle-income 

countries by using the Demographic and Health Surveys (DHS) data which have included an empowerment 

module since 1999 (Do and Kurimoto 2012; Ewerling et al. 2017; Msuya, Adinan, and Mosha 2014; Na et 

al. 2015; Shimamoto and Gipson 2015; Tadesse et al. 2013). The empowerment module asks women about 

their employment, control over cash earnings, ownership of assets, participation in decision making, and 

attitude toward wife beating (Kishor and Subaiya 2008). 

Because empowerment is multi-faceted, studies have often created measures that incorporate multiple 

aspects of empowerment. Previously used measures of empowerment included additive scores based on the 

number of decisions in which women participate (0 to 3 decisions), or the number of reasons a woman 

agrees would justify wife beating (0 to 6 reasons). In addition, it is widely accepted that empowerment is 

context specific (Agarwala and Lynch 2006; Ewerling et al. 2019 PREPRINT), and using single indicator 

proxies may have problematic implications for the relevance of policies (Agarwala and Lynch 2006). 

Factors that limit or promote empowerment in one context may have opposite or null effects elsewhere and 

internationally comparable measures of empowerment are even more scarce (Agarwala and Lynch 2006; 

Ewerling et al. 2019 PREPRINT; Ewerling et al. 2017; Gaye and JHA 2011). Some measures may include 

several dimensions of empowerment including spousal age gap, spousal education gap, and labor force 

engagement. The Survey-based Women’s Empowerment (SWPER) index has been a recent attempt to 

include all dimensions of empowerment captured by DHS data. The SWPER index measures empowerment 

in three dimensions: attitude to violence, decision making, and social independence (Ewerling et al. 2019 

PREPRINT; Ewerling et al. 2017). For international or regional comparability, weights have been created 

for each world region that adjusts for the relative empowerment in different regions of the world (Ewerling 

et al. 2019 PREPRINT). 

Pakistan, the fifth most populous country in the world (World Population Review 2020), is ranked low on 

the two gender indices that rank country progress in gender equality, the GII and GDI. Pakistan ranked 136 
2 

of 162 countries on the GII and ranked in the lowest group of countries on the GDI (United Nations 

Development Programme 2019). The country’s plan for future progress, the Pakistan Vision 2025, has 

specific goals for the promotion of women’s empowerment, which include “activities that promote women’s 

self-worth, right to determine their choices, access to opportunities and resources, right and power to control 

their lives – both within and outside the home – and ability to influence social change” (UNFPA 2020). 

Article 34 of the Constitution of Pakistan has also recognized the importance of women’s empowerment by 

stating that women should have full participation in all spheres of national life (National Assembly of 

Pakistan 2018). Further, women are protected legally against harassment in the workplace (Government of 

Pakistan 2014). Though women are acknowledged in legal and political conversations and print, continued 

lack of enforcement and environment that fails to encourage women’s prosperity ensure that women in 

Pakistan remain unempowered. 

1.2 Migration and Empowerment in Pakistan 

It is important to understand if some groups of women in Pakistan are more empowered than others. Among 

the different strata of women are those who have migrated internally. Though international migration has 

been found to have more negative effects for women than for men (Adanu and Johnson 2009), migration 

within a country can also increase women’s access to social, economic, or educational opportunities that 

were previously inaccessible in their original location (Eryar, Tekgüç, and Toktas 2018). Poor populations 

can access opportunities that minimize poverty through migration strategies (Hagen-Zanker, Postel, and 

Vidal 2017). Rural communities consider migration as a process through which they can be free from the 

vicious cycle of poverty (Islam and Azad 2008). One study in the Faisalabad district of Pakistan found that 

internal migration had a significant effect on reducing poverty in rural areas (Kousar et al. 2016). Other 

studies have demonstrated the association of rural-urban migration on fertility (Brockerhoff and Yang 

1994), child health outcomes (Brockerhoff 1994), health service utilization (Stephenson and Matthews 

2004), and mortality (Stephenson, Matthews, and McDonald 2003). Though women may move to secure 

better opportunities for themselves, oppositely, migrants may not be representative of the population and 

may be prone to positive selection based on wealth or education, which enable them to move (Ssengonzi, 

De Jong, and Shannon Stokes 2002). 

Despite the benefits of migration, studies have found that migrants remain among the most vulnerable 

members of society (Adanu and Johnson 2009). In Pakistan, most women move for marriage, while few 

move to pursue work or educational opportunities (Chen, Kosec, and Mueller 2019; National Institute of 

Population Studies (NIPS) and ICF International 2013; National Institute of Population Studies (NIPS) and 

ICF 2019). If women have no choice in migrating or do so to fulfill a need rather than personal 

advancement, moving may leave women ill-equipped to adapt and integrate into new environments. 

The issue of international migration, which has been on the agenda of the international community for 

several decades (United Nations 2014), is a primary focus on the SDG agenda, which not only includes 

several health and migration-related targets, but also encourages countries to disaggregate targets by 

migratory status (UN 2015). We know that the vulnerability of women is negatively affected by 

discriminatory migration policies. Policies that protect or enable internal migrants to integrate successfully 

are rare despite the fact that the number of internal migrants is four times the total number of emigrants in 

Pakistan (International Organization of Migration 2019). This report examines the association of internal 

migration and women’s empowerment, which can guide policies that promote and enable gender equality. 
3 

1.3 Objectives 

This report considers the following question: 

How does internal migration affect the empowerment of married women in Pakistan? 

Objectives of the study are to: 

1. Describe the migration and empowerment of married women in the 2012-13 and 2017-18 

Pakistan Demographic and Health Surveys (PDHS). 

2. Understand the association of migration and women’s empowerment, while controlling for 

important factors. 

5 

2 DATA AND METHODS 

2.1 Data 

This analysis uses data from the two most recent PDHS surveys in 2012-13 and 2017-18. Each survey 

followed a two-stage sampling designed to obtain data that are representative at the national and provincial 

level (NIPS/Pakistan and ICF 2019; NIPS/Pakistan and ICF International 2013). After excluding 

international migrants and restricting the sample to currently married women, our study included 12,728 

currently married women from the 2012-13 PDHS survey and 11,527 currently married women from the 

2017-18 PDHS survey (Table 1). 

Table 1 Description of the Pakistan Demographic and Health Surveys (PDHS) included in the analysis 

Year Date of fieldwork 

Implementing 

organization 

Number of 

households 

interviewed 

Number of 

currently married 

women age 15-49 

in interviewed 

HHs 

% of women age 

15-49 who are 

married in 

interviewed HHs 

Number of 

women 

interviewed 

included in 

study1

2012-13 October 2012 - 

March 2013 

National Institute 

of Population 

Studies 

12,943 13,757 62.4 12,728 

2017-18 November 2017 - 

April 2018 

National Institute 

of Population 

Studies 

14,540 12,304 63.5 11,527 

1

 Excludes international migrants and women with missing responses 

2.2 Measures 

2.2.1 Empowerment 

In this analysis, we examine three levels of empowerment (low, medium, and high) in three distinct domains 

of empowerment: attitude to violence, social independence, and decision making (Table 2). These measures 

are created using the SWPER index. The SWPER index is a measure of empowerment that has been 

validated for global comparisons with DHS data from 62 countries. This index uses 14 indicators to create 

three domains of empowerment (Table 2). This approach for measuring empowerment uses a principal 

component analysis to create scores for the three empowerment domains (Ewerling et al. 2017). Each 

domain is then weighted to reflect the empowerment norms of their geographic region (Ewerling et al. 2019 

PREPRINT). For this analysis, we use the weighting system for South Asia. Each domain has standard 

cutoffs to create a three-category classification of empowerment: low, medium and high empowerment. 

Since some of the questions used to construct the index are asked only of currently married women, our 

analysis is restricted to currently married women. Additionally, if women had missing responses for any of 

the key indicators, they were excluded. 
6 

Table 2 DHS indicators used to construct SWPER empowerment domains 

Domain Variable Unit or Response Code 

1. Attitude to violence Beating is justified if: 

1) wife goes out without telling husband 

2) wife neglects the children 

3) wife argues with husband 

4) wife refuses to have sex with 

husband 

5) wife burns the food 

Yes = -1 

DK = 0 

No = 1 

2. Social independence 1) Frequency of reading newspaper or 

magazine 

Not at all = 0 

< once a week = 1 

≥ once a week = 2 

2) Education difference: woman’s minus 

husband’s years of schooling Years 

3) Age difference: woman’s minus 

husband’s age years Years 

4) Age at first marriage Years 

5) Age of woman at first birth1

Years 

3. Decision making Who usually decides on: 

1) respondent’s health care 

2) large household purchases 

3) visits to family or relatives 

Husband/other alone = -1 

Respondent alone or joint with partner = 1 

1

 This variable was imputed for women who had not had a child using the hotdeck approach, as specified in the SWPER index 

method. 

2.2.2 Migration 

Migration direction 

Migration, the independent exposure of interest, is categorized into five distinctions according to the origin 

and direction of movement: urban-urban, rural-urban, rural-rural, urban-rural (origin-direction), and nonmigrant. During the household interview, the interviewer records information about each household 

member’s migration history: 

• Was (NAME) born in this village/city? 

• In which village/city was (NAME) born? 

• From where did (NAME) move to this village/city the last time? 

• In which year did (NAME) last move to this village/city? 

• What was the primary reason for (NAME) to move to this village/city? 

From these questions we can know where a household member moved from if they are not currently living 

in the village or city they were born. These questions also capture when and why they most recently moved. 

The city was recorded if a woman migrated from an urban location, while the district was recorded if a 

woman migrated from a rural location. Women who migrated from outside Pakistan were coded differently. 

Women who have migrated from within Pakistan are considered internal migrants. Women are considered 

lifetime migrants if they no longer live in the village or city where they were born. This study included both 

non-migrant women and internal, lifetime migrants, and excluded women who moved to Pakistan from 

outside of Pakistan. 
7 

Duration of continuous residence 

The duration of time since a respondent moved from her most recent place of residence to her current 

residence was recorded in years. This was categorized as less than 1 year, 1-5 years, 6-9 years, and 10 or 

more years. Women who did not know or whose responses were missing are grouped in a separate category. 

Reason for migration 

Understanding the reasons for a woman’s most recent migration, we grouped similar reasons into 

meaningful categories. Women who moved for better opportunities were grouped together, including 

women who moved for a job or school opportunity. Women who moved for marriage or due to a birth were 

grouped into one category. Some women cited moving with family or to join family. These women were 

grouped with women who cited moving in childhood, though there were few women in this category. There 

were also very few responses that cited moving because of violence, natural disaster, other reasons or 

offered “Don’t Know” responses, and these were grouped together. 

2.2.3 Sociodemographic characteristics 

Age 

Women’s current age is presented in 5-year age groups, from ages 15 to 49. A respondent’s age is based on 

her reported date of birth. The youngest age group in the study population, age 15-19, is the reference group. 

Work status 

A woman is coded as currently working if she reports having done any work in the previous 7 days, other 

than her own housework. Women who are not currently working are the reference category. 

Region 

The two PDHS collected data in at least five main regions. These include the four provinces of Punjab, 

Sindh, Khyber Pakhtunkhwa (KPK), and Balochistan, as well as the Islamabad Capital Territory (ICT). 

These five areas were included in this analysis, which used the most populous province, Punjab, as the 

reference category. Other areas, including the Azad Jammu and Kashmir (AJK), Federally Administrated 

Tribal Areas (FATA), and Gilgit Baltistan (GB), were also sampled in the 2017-18 PDHS. However, AJK 

and GB are weighted to be representative for their own area and should not be included in national 

estimates. Additionally, AJK and FATA were not included in the 2012-13 PDHS (NIPS/Pakistan and ICF 

2019; NIPS/Pakistan and ICF International 2013). To compare both data from both surveys, we exclude 

data from AJK, FATA, and GB. 

Wealth 

Wealth quintiles are constructed using information from the household interview about household 

characteristics and household assets. Women in the poorest category are used as the reference group. 

2.3 Analysis 

We present trends and multivariable analyses of the empowerments indicators and migration distinctions 

using data from the two most recent PDHS surveys. First, we examine migration distinctions and 
8 

empowerment indicators over time and across sociodemographic characteristics, including age, working 

status, region, and wealth. We test the difference in proportions of the migration and empowerment 

indicators between the two surveys and by sociodemographic subgroups. We define significance as a pvalue of less than 0.05. These trends are illustrated with solid lines for significant differences and dotted 

lines for the insignificant changes. 

Further we assess migration’s association with empowerment by performing bivariate analysis and 

multivariable multinomial logistic regression that controls for age, working status, region, and wealth. We 

examine the three empowerment domains in each survey in a separate model. Each domain has a threecategory outcome: low, medium, and high empowerment with low empowerment is the reference category. 

The results from these models are presented as relative risk ratios (RRRs).
9 

3 RESULTS 

3.1 Descriptive Results 

Table 3 presents the percentage of married women by age, working status, and region for the 2012-13 and 

2017-18 PDHS. The age distribution of currently married women in the two surveys is similar. In both 

surveys, about two in five women were age 15-29. The percentage of working women decreased from 26% 

in 2012-13 to 17% in 2017-18. Most women live in Punjab (58% and 54% in 2012-13 and 2017-18 PDHS 

respectively). In both surveys approximately 23% of women were living in the second most populous region 

of Sindhi, followed by KPK, Balochistan, and finally ICT Islamabad where fewer than 1% of women lived 

in either survey. Wealth is not shown below because the wealth quintiles divide the population into 

approximately five, equal wealth divisions. 

Table 3 Percentage of women in the study population by characteristics, Pakistan 2012-13 and 

2017-18 

 PDHS 2012-13 PDHS 2017-18 

% N % N 

Age 

15-19 4.6 584 4.9 568 

20-24 15.9 2022 15.7 1804 

25-29 20.6 2621 21.1 2434 

30-34 19.0 2414 19.7 2275 

35-39 16.5 2104 17.3 1993 

40-44 12.5 1596 11.2 1291 

45-49 10.9 1388 10.1 1162 

Work Status Not working 74.0 9410 83.3 9605 

Working 26.0 3305 16.7 1921 

Region 

Punjab 57.6 7333 54.1 6239 

Sindhi 23.3 2964 23.7 2729 

KPK 14.4 1828 16.0 1839 

Balochistan 4.3 543 5.4 619 

ICT Islamabad 0.5 60 0.9 102 

 

Total1 100% 12,728 100% 11,527 

1

 International migrants (women who migrated into Pakistan from outside of the 

country) are excluded 

Table 4 shows the reason for and the type of internal migration of married women in the two PDHS. 

According to the 2012-13 and 2017-18 PDHS, approximately one in five married women have moved 

within Pakistan at some point during their lifetime. In 2012-13, the migration streams of married women 

who are living in urban areas showed that 6% migrated from urban to urban areas, and another 6% moved 

from rural to urban areas. Among migrants who live in rural areas, 3% moved from rural to rural areas, 

while 5% moved from urban to rural areas. The percentage of migrants by duration of residence shows that 

over half of urban (57%) and rural (54%) migrants reported that they moved 10 or more years ago to their 

current place of residence. Similar patterns were observed in the 2017-18 PDHS. In the earlier survey, 

approximately 6% of rural migrants and 6% of urban migrants moved within the past year. In contrast, less 

than 1% of respondents in the most recent survey moved in the past year. 
10 

An important aspect of studying migration is identifying the reasons for moving from one location to 

another. In the 2012-13 PDHS, urban migrants cite moving for marriage (71%) as the most common reason 

for migration, followed by moving with family (26%). In the same survey, eight in 10 rural migrants (82%) 

cited moving for marriage as the most important reason for moving to their current residence and 16% cited 

moving with family. In the 2017-18 PDHS, two-third of the urban migrants and 80% of the rural migrants 

cited moving for marriage as a reason to migrate to their current residence. A slightly higher percentage of 

urban migrants (31%) cited moving with family as a reason to migrate compared to those surveyed in PDHS 

2012-13 (26%). 

Table 4 Percentage of married women who have moved or not by place of residence and reason 

and years since moved among migrant women, Pakistan 2012-13 and 2017-18 

 PDHS 2012-13 PDHS 2017-18 

% % 

Migrant status 

Ever migrated 19.3 20.2 

Migrant direction Non-migrant 80.7 79.8 

Moved urban to urban 5.7 4.2 

Moved rural to urban 6.0 7.1 

Moved rural to rural 2.8 3.5 

Moved urban to rural 4.8 5.4 

Total 100.0 100.0 

Total number of married women 12,728 11,527 

Urban migrants1 Rural migrants2 Urban migrants Rural migrants 

% % % % 

Years since moving 

<1 year 5.6 5.6 0.3 <0.1 

1-5 years 17.3 17.1 22.5 20.6 

5-9 years 16.8 20.6 22.1 24.7 

10+ years 57.4 54.3 54.2 54.2 

Don’t know/missing 2.9 2.3 0.9 0.5 

Reason for moving3 Better opportunities 2.6 1.2 2.9 1.7 

With family 26.0 16.0 31.0 18.1 

Marriage 70.5 82.2 65.6 79.2 

Escape violence, other/don’t know 0.9 0.7 0.5 0.9 

Total 100 100 100 100 

Total number of internal migrant 

women 1,346 1,117 1,109 1,220 

1 Urban migrants are women who have migrated and currently reside in a urban area 

2 Rural migrants are women who have migrated and currently reside in a rural area 

3

 Reasons for moving are grouped. Better economic opportunities include those who cited moving for a job, school, or better economic 

opportunity or infrastructure. Moving with family include women who moved to join family or who moved during childhood. Moving for 

marriage also includes a few respondents who cited moving for a birth. The last category includes women who moved to escape 

violence, natural disaster, other and “Don’t Know” responses. 

Table 5 shows the percentage of all women who did not agree with each of the five separate reasons that 

justify a husband beating his wife for the 2012-13 and 2017-18 PDHS. Results show that the majority of 

currently married women do not agree that any of the specified reasons justify wife beating. Notably, eight 

of 10 women agree that wife beating is not justified if the wife burns food. 
11 

The majority of women do not read. The frequency of reading newspapers or magazines among currently 

married women in Pakistan is generally low, with 76% in 2012-13 PDHS and 86% in the 2017-18 PDHS 

reporting that they did not read at all. Table 5 also shows the percentage of currently married women who 

read newspapers or magazines at least once a week declined from 20% in 2012-13 to 9% in 2017-18. 

In both surveys, nearly 8 in ten women in the study were age 16-29, while 16% were age 15 or younger at 

their first marriage. The age difference of couples in Table 5 shows that 14% were the same age as their 

husband, about 30% had an age difference of 1-4 years and 5-9 years, respectively, in both surveys. 

Education is associated with higher empowerment, but equality in education between the spouses is more 

important in decision making. Nearly 40% of couples had the same level of education in both surveys, 

while 14% in the 2012-13 PDHS and 18% of women in the most recent survey, had more education than 

their husbands. 

The distribution of currently married women by type of participation in decision making according to three 

types of decisions ̶ healthcare, large purchases, and visits to family—shows that approximately half of 

currently married women made decisions alone or jointly with their husband in both surveys. 
12 

Table 5 Percentage of married women and empowerment indicators, Pakistan 2012-13 and 

2017-18 

PDHS 2012-13 PDHS 2017-18 

% % 

Think beating is NOT justified if

wife goes out without telling husband 69.2 67.3 

wife neglects the children 67.7 70.6 

wife argues with husband 64.9 67.2 

wife refuses to have sex with husband 67.0 70.1 

wife burns the food 80.9 80.1 

Frequency of reading newspaper or magazine at all not at all 76.1 85.4 

less than once a week 19.9 9.4 

at least once a week 4.0 5.2 

Woman’s years of education 

no education 56.8 48.0 

1-6 years 16.9 18.1 

7-12 years 21.1 26.3 

>12 years 5.2 7.6 

Age of respondent at marriage 15 or younger 15.7 15.6 

16-19 46.3 40.3 

20-29 36.3 42.5 

30-39 1.5 1.5 

40 or older 0.1 0.0 

Age difference: woman’s minus husband’s age 

husband is older by 10 years or more 17.8 17.3 

husband is older by 5-9 years 31.3 32.0 

husband is older by 1-4 years 29.6 28.2 

woman and husband are the same age 13.8 14.1 

woman is older 7.5 8.4 

Education difference: woman’s minus husband’s years of 

schooling husband has more education by 5 years or more 33.1 29.6 

husband has more education by 1-4 years 14.6 15.0 

woman and husband have the same education 38.2 36.7 

woman has more education 14.1 18.7 

Respondent solely or jointly decides on

respondent’s health care 52.0 51.5 

large household purchases 47.1 44.9 

visits to family or relatives 50.0 49.3 

Total 100 100 

Number of married women age 15-49 12,728 11,527 

3.2 Trends in Empowerment 

Figure 1 shows the proportion of women who had high empowerment in each of the empowerment 

domains. High empowerment denotes the most empowered women. High empowerment has significantly 

increased in only one of three of the SWPER domains between the 2012-13 and 2017-18 PDHS. The social 

independence domain has significantly increased from 34% to 41%. The empowerment domain with the 

highest proportion of women in both surveys is the attitude to violence domain. Approximately 56% of 

women in 2012-13 PDHS and 58% of women in the 2017-18 PDHS had high empowerment in the attitude 

to violence domain. The social independence empowerment and decision-making domains were more 

similar in prevalence, with approximately 2 in 5 women having high empowerment in these domains in the 

most recent survey. 
13 

Figure 1 Trends in high empowerment, Pakistan 2012-13 and 2017-18 

3.2.1 Trends in the attitude to violence empowerment domain 

Overall, the attitude to violence domain did not significantly increase between the two PDHS (Figure 1). 

Figure 2 shows among migrant distinctions, urban-urban migrant women had the largest proportion of 

women with high empowerment in either survey (2012-13: 78%; 2017-18: 76%) (Figure 2). In both 

surveys, rural-urban migrant women had the second largest proportion with high empowerment, followed 

by urban-rural migrants. Non-migrants and rural-rural migrants had the smallest proportions of women with 

high empowerment. 

Women who were poorest saw a significant decrease in the proportion of women with high empowerment 

in the most recent survey, decreasing from 43% to 35% (see Appendix Table 1 for full details). While the 

poorest became less empowered, the richer women significantly increased in high empowerment attitude 

to violence from 63% to 69%. High empowerment was most common among women living in ICT 

Islamabad where 74% of women had high empowerment in the 2017-18 survey. Punjab follows closely 

with 65% of women with high empowerment, and KPK with lowest proportion of women empowered in 

either survey. 
14 

Figure 2 Trends in high empowerment in the attitude to violence empowerment domain by 

sociodemographic characteristics, Pakistan 2012-13 and 2017-18 

3.2.2 Trends in the social independence empowerment domain 

There is evidence that many subgroups saw a significant increase in the social independence empowerment 

domain between the two surveys (Figure 3). Among women who moved within urban areas, 65% had high 
15 

empowerment in the social independence domain in the most recent survey, the highest among any group. 

In both surveys women who were not currently working had higher social independence than women who 

were currently working, though employed women had a greater increase from 24% in 2012-13 to 36% in 

2017-18 (Appendix Table 2). 

Women in ICT Islamabad and Punjab had the highest proportions, while Balochistan and KPK had the 

lowest proportions of women with high social independence in either survey. There were significant 

increases in high social independence among women in Punjab, Sindh, and Balochistan. Women in all 

categories of wealth increased in high social independence, and proportions of social independence were 

higher with each increased category of wealth. There remains a large inequity between the richest women, 

where 71% of women have high empowerment, and the poorest women with 17% of women empowered 

in the most recent survey. Supporting details can be found in Appendix Table 2. 
16 

Figure 3 Trends in high empowerment in the social independence domain by sociodemographic 

characteristic, Pakistan 2012-13 and 2017-18 
17 

3.2.3 Trends in the decision-making empowerment domain 

Though the overall level of high decision-making empowerment did not change over time, subgroups of 

women experienced significant changes in empowerment (Figure 4, with supporting details in Appendix 

Table 3). Decision-making is highest among older women, while only women age 25-29 experienced an 

increase in high decision-making (2012-13: 29.1%; 2017-18: 33.2%). Women age 35-39 and 40-44 

experienced a significant decrease in proportion of women with high decision-making empowerment. 

Women with no education significantly increased by 4.4 percentage points, resulting in 40% of women with 

no education having high social empowerment in the 2017-18 PDHS. Women with the highest education 

had the highest proportion of high decision-making in both surveys. 

Women in Balochistan and KPK had the lowest proportion of women with high decision-making 

empowerment in either survey and both regions experienced significant decreases in the proportion of 

women with high empowerment. Women living in Sindh experienced the greatest increase high decisionmaking empowerment (2012-13: 35.2%; 2017-18: 46.4%) compared to any other subgroup. Women 

categorized in the poorer or middle wealth quintile experienced a significant decrease in high decisionmaking empowerment. Women in highest quintiles had the highest proportions of empowerment in either 

survey. 
18 

Figure 4 Trends in high empowerment in the decision-making domain by sociodemographic 

characteristics, Pakistan 2012-13 and 2017-18 
19 

3.3 Multivariate Analysis 

3.3.1 Attitude to violence 

In Figures 5-7, we present the results from the three multinomial logistic regression models that examined 

attitude to violence, social independence, and decision-making. The results from the model that assessed 

women’s attitude to violence were analyzed with results shown in in Figure 5. Women who moved from 

urban areas to urban areas in the 2012-13 PDHS have 2.3 times the risk of having attitude to violence that 

reflect high empowerment than low empowerment compared to women who have never migrated. 

However, in the more recent survey this effect is not apparent and there is some evidence that urban-urban 

migrant women have 1.7 times the risk of having medium empowerment attitude to violence than nonmigrant women. 

Age has little association overall with attitude to violence. Interestingly, women in age groups 30-34 and 

35-39 have an increased risk of high empowerment than low empowerment, compared to the youngest 

women in both surveys (Appendix Table 4). Though there is no evidence of a relationship between work 

status and empowerment in the older survey, the 2017-18 PDHS shows evidence that women who work 

have an 18% decreased risk of having high empowerment than low empowerment compared to women 

who have not worked recently. 

In both surveys, women living in KPK have decreased relative risk of high or medium empowerment than 

their Punjab counterparts. In fact, women living in KPK during the 2017-18 PDHS have a 73% decreased 

relative risk of having a high empowerment attitude to violence compared to women living in Punjab. 

Wealth is significantly associated with medium and high empowerment attitude to violence. The magnitude 

of the relationship between wealth and empowerment increased with more wealth and over time. In the 

2017-18 PDHS, the richest women had 13.9 times the risk of having high empowerment than low 

empowerment, compared to the poorest women. 
20 

Figure 5 Adjusted relative risk ratios of the multinomial regression examining the migration direction and 

attitude to violence empowerment domain, Pakistan 2012-13 and 2017-18 

3.3.2 Social independence 

The constructed empowerment domain that focuses on women’s social independence is fully described in 

Appendix Table 5. Women who have moved from rural to urban areas have a significantly decreased risk 

of having high social independence empowerment rather than low empowerment, as compared to nonmigrant women in the 2012-13 PDHS (RRR: 0.6) and the 2017-18 PDHS (RRR: 0.5) (Figure 6). 

Additionally, women who have moved from urban to rural areas have a 63% increased risk of high 

empowerment than low empowerment compared to non-migrant women. 

Age has strong associations overall with social independence. Though all women had a increased risk of 

empowerment compared to the youngest age group, women in their twenties has the highest risk of having 

high empowerment compared to women age 15-19 in both surveys (Appendix Table 5). In the most recent 

survey, this association was large. For example, women age 25-29 have 12.0 times the risk of high 

empowerment compared to the youngest women. 

Women who are working had a significant decrease in the risk of high empowerment compared to women 

who were not working in 2012-13 PDHS, though this was not significant in the most recent survey. Women 

who were living in Sindhi, KPK, and Balochistan had a decreased risk of having social independence 

empowerment than women who lived in Punjab in both surveys. However, women living in KPK have 

decreased relative risk of high or medium empowerment than their Punjab counterparts. In fact, women 

living in KPK during the 2017-18 PDHS have 73% decreased relative risk of having a high empowerment 
21 

attitude to violence compared to women living in Punjab. In the 2012-13 PDHS, there was evidence that 

women living in ICT Islamabad had a 42% increase in the risk of high empowerment than women living in 

Punjab, though there was no evidence of this relationship in the most recent survey. Wealth is significantly 

associated with high empowerment. There is strong evidence that the richest women had 17.2 times the risk 

of having high empowerment than the poorest women in the most recent survey. 

Figure 6 Adjusted relative risk ratios of the multinomial regression examining the migration direction and 

the social independence empowerment domain, Pakistan 2012-13 and 2017-18 

3.3.3 Decision-making 

Only one category of migrant has a significant relationship with medium or high empowerment. Urbanurban migrants had 62% of an increase in the risk of being highly empowered (RRR 1.6) than non-migrant 

women in the 2012-13 PDHS (Figure 7). This effect is not present in the most recent survey. 

However, all other covariates in both surveys are significantly associated with empowerment (Appendix 

Table 6). Older age groups have an increased risk for high empowerment than women age 15-19. In the 

2012-13 PDHS, women age 45-49 have 16.5 times the risk of having high empowerment. This dramatic 

result persists into the more recent survey, where the oldest age group of women had 11.6 times the risk for 

high empowerment in making decisions compared to the youngest age group of women. Women who are 

working had an increased risk of having high empowerment compared to women who were not recently 

employed in both surveys (2012-13 RRR: 1.7; 2017-18 RRR 2.1). 
22 

Women living in Sindhi, KPK, and Balochistan during the 2012-13 PDHS had decreased risk to have high 

empowerment compared to their Punjab counterparts. In the more recent survey, there was lower risk of 

having high empowerment among those women living in KPK and Balochistan as compared to women 

living in Punjab. Women living in Sindhi had a 65% increase in risk of having high empowerment to make 

decisions compared to women in Punjab, which was a reversal of effect from the earlier survey. Wealth is 

significantly associated with empowerment, and in both surveys, the richest women had higher risk of 

empowerment compared to the poorest women. 

Figure 7 Adjusted relative risk ratios of the multinomial regression examining the migration direction and 

the decision-making empowerment domain, Pakistan 2012-13 and 2017-18 
23 

4 DISCUSSION 

This study presented trends of empowerment among currently married women in Pakistan since 2012. We 

provided a snapshot of currently married women’s internal migration patterns and sought to understand the 

association between women’s migration and empowerment. Though the literature has found the 

empowerment of women to be vitally linked to positive maternal and child health outcomes (Do and 

Kurimoto 2012; Msuya, Adinan, and Mosha 2014; Pratley 2016; Shimamoto and Gipson 2015; Tadesse et 

al. 2013), it is critical to know if and how subgroups differ in empowerment in order for policy to target the 

least empowered women. 

Migration and empowerment in Pakistan have changed very little over time. In either survey, one in five 

currently married women are internal migrants and the majority of women have migrated for marriage. 

However, there is evidence that internal migration is decreasing as the proportion of women who moved 

within a year before the survey decreased from 5% in the 2012-13 PDHS to less than 1% in the most recent 

survey. We found no improvement in the proportion of women considered to have high empowerment in 

the attitude to violence and decision-making domains between the two most recent PDHS. However, the 

social independence domain increased significantly and in 2017-18, 41% of women were considered to 

have high empowerment in this domain. The highest proportion of high empowerment was in the attitude 

to violence domain compared to the other two domains in either survey. This domain is largely influenced 

by the fact that nearly 7 in 10 women in either survey do not believe that wife beating is justified by a wife’s 

actions. 

Women who moved from and to an urban environment in the 2012-13 PDHS were the only sub-group to 

have significantly higher risk of high empowerment in the attitude to violence and decision-making 

domains. We cannot differentiate if urban-urban migrants may be empowered to move or empowered 

because they moved, though other literature has found higher child survival among positively selected 

urban-urban migrants in Uganda, which suggested that urban-urban movers may be advantaged before 

moving. (Ssengonzi, De Jong, and Shannon Stokes 2002). These two domains reflect women’s spousal 

relationships – both her views on violence from a spouse and her engagement in decision making with or 

without her spouse. It is possible that these women moved from a less urban area to a more urban area, or 

to a wealthier urban area. Both would be an attempt to improve their situation reflected in their empowered 

attitude to violence and decision-making roles. Interestingly, this effect dissolves in the most recent survey, 

with urban-urban migrant women having no higher risk of empowerment than any other category. 

In the 2017-18 PDHS, urban-rural migrant women have a 63% increased risk of high empowerment than 

women who have never moved. Women who originate in urban areas may be wealthier or more educated 

than their rural counterparts and therefore positively selected for migration. This domain of empowerment 

is largely influenced by the spousal age gap, spousal education gap, age at first marriage and birth, and 

exposure to media. Considering that most women move for marriage, it is possible that urban women are 

moving to rural areas where there is less likely to be an education gap or age gap between them and their 

spouses. 

This work has several limitations. First, empowerment is difficult to capture. Since our approach uses a 

newer measurement, the SWPER index, there is a dearth of literature which would allow us to compare our 
24 

estimates of high empowerment in these unique domains. Secondly, we define migrant status based on any 

move regardless of time that has passed. Because migration’s effect is diluted with time, we may have 

included women whose most recent migration was long enough ago that they have fully assimilated and 

are no different than non-migrants. Third, we do not adjust for important migration details, such as time 

since last migration and reason for migration. Since these details are not available for non-migrants, we 

only included covariates that were available for all categories of migrants, including non-migrants. 

This study adds to the literature that seeks to understand how migrant women may differ in empowerment. 

Other studies have found positive associations between rural-urban migration and health outcomes, labor 

force engagement, and health care utilization (Bello-Bravo 2015; Brockerhoff 1994; Brockerhoff and Yang 

1994; Stephenson and Matthews 2004; Stephenson, Matthews, and McDonald 2003). However, there is a 

distinct paucity of research on the migration and empowerment in Pakistan, even though internal migration 

is widespread and gender inequality remains high among international rankings. Additionally, this study 

utilizes the DHS data to understand the migration context of these issues. While studies on migration often 

use other data sources, DHS data are the most current source of migration data since the Pakistan census 

conducted in 2017 did not collect information about migration status (NIPS/Pakistan and ICF 2019). 

Overall, we find that migrant women who originated in urban areas had positive associations with 

empowerment and that rural-urban movers were disadvantaged. Though women can access opportunities 

to better empower themselves through migration, migrant woman may also be disadvantaged and 

vulnerable. Policy makers should focus on creating pathways that make women’s migration strategies more 

successful. 
25 

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APPENDICES 

Appendix Table 1 Trends in proportion of women with high empowerment in the SWPER 

attitude to violence empowerment domain, Pakistan 2012-13 and 2017-18 

 2012-13 2017-18 

Variable % [95% C.I.] p1 % [95% C.I.] p1

Difference2 

 2017-18 – 2012-13 

Total 55.7 [53.4,58.0] 57.6 [54.5,60.7] 1.9 

Age ** *** 

15-19 42.7 [37.3,48.3] 42.2 [37.0,47.6] 0-.6 

20-24 53.9 [50.0,57.8] 55.6 [51.3,59.9] 1.8 

25-29 56.2 [52.7,59.7] 58.2 [53.9,62.5] 1.9 

30-34 57.4 [54.1,60.7] 59.1 [55.0,63.2] 1.8 

35-39 56.8 [53.4,60.2] 60.1 [56.1,63.9] 3.2 

40-44 57.3 [53.6,60.9] 59.3 [54.5,63.8] 2.0 

45-49 56.3 [52.4,60.2] 58.1 [53.6,62.6] 1.7 

Education *** *** 

No education 44.9 [42.1,47.7] 43.0 [39.6,46.4] 0-1.9 

Primary 57.9 [54.2,61.5] 59.1 [54.7,63.4] 1.1 

Secondary 73.1 [69.9,76.1] 72.9 [68.9,76.6] 0-.2 

Higher 84.4 [81.1,87.2] 83.2 [79.9,86.0] 0-1.0 

Currently working ** 

No 56.4 [54.0,58.7] 58.8 [55.6,61.9] 2.4 

Yes 54.0 [50.4,57.5] 51.8 [46.9,56.6] 0-2.1 

Region *** *** 

Punjab 63.5 [60.5,66.4] 65.1 [60.5,69.5] 1.6 

Sindh 57.3 [52.6,61.9] 59.2 [53.5,64.6] 1.8 

KPK 25.4 [21.0,30.2] 34.0 [26.5,42.4] 8.7 

Balochistan 41.5 [34.7,48.7] 43.1 [33.5,53.2] 1.3 

Ict Islamabad 77.5 [72.3,81.9] 74.1 [67.4,79.9] 0-3.5 

Wealth Quintile *** *** 

Poorest 43.1 [36.9,49.7] 35.1 [30.7,39.8] 0-8.0* 

Poorer 41.4 [37.4,45.4] 41.9 [37.6,46.3] 0.6 

Middle 50.0 [46.3,53.7] 55.1 [50.8,59.4] 5.1 

Richer 62.7 [59.1,66.1] 69.3 [65.4,72.9] 6.6* 

Richest 79.4 [76.6,81.8] 81.1 [78.0,83.9] 1.7 

Notes: *p<0.05, **p<0.01, ***p<0.001 1

 p-value significance of the covariate in each survey 2

 Difference between the two surveys with the p-value of the difference. 
30 

Appendix Table 2 Trends in proportion of women with high empowerment in the SWPER 

social independence empowerment domain, Pakistan 2012-13 and 2017-18 

 2012-13 2017-18 

Variable % [95% C.I.] p1 % [95% C.I.] p1

Difference2 

 2017-18 – 2012-13 

Total 34.1 [32.0,36.4] 41.4 [38.8,44.0] 7.3*** 

Age *** *** 

15-19 11.4 [8.4,15.3] 8.2 [5.6,11.8] 0-3.2 

20-24 32.0 [28.5,35.6] 34.2 [30.3,38.3] 2.3 

25-29 41.6 [38.3,45.1] 44.7 [41.0,48.4] 2.9 

30-34 40.5 [37.1,44.1] 49.3 [45.5,53.2] 9.0*** 

35-39 33.9 [30.6,37.3] 48.2 [44.8,51.6] 14.4*** 

40-44 28.5 [25.5,31.8] 38.1 [34.1,42.3] 9.6*** 

45-49 28.4 [25.0,32.1] 38.3 [34.2,42.7] 9.9*** 

Education *** *** 

No education 13.0 [11.6,14.6] 16.6 [15.0,18.3] 3.6** 

Primary 33.0 [30.0,36.3] 34.6 [31.8,37.5] 1.6 

Secondary 70.5 [67.6,73.3] 67.8 [65.2,70.3] 0-2.7 

Higher 94.2 [92.3,95.6] 95.2 [93.6,96.4] 1.1 

Currently working *** ** 

No 37.6 [35.6,39.6] 42.4 [39.8,45.1] 4.9** 

Yes 24.4 [20.9,28.2] 36.3 [32.3,40.4] 11.9*** 

Region *** *** 

Punjab 38.8 [35.2,42.5] 48.6 [44.6,52.6] 9.9*** 

Sindh 31.1 [27.9,34.6] 37.4 [33.3,41.8] 6.4* 

KPK 23.9 [21.4,26.7] 26.6 [21.6,32.2] 2.6 

Balochistan 18.3 [15.1,22.0] 26.6 [23.3,30.1] 8.2** 

Ict Islamabad 67.0 [60.7,72.8] 61.4 [56.6,65.9] 0-5.7 

Wealth Quintile *** *** 

Poorest 12.4 [10.0,15.3] 17.4 [15.2,19.8] 5.0** 

Poorer 18.9 [16.5,21.6] 24.2 [21.9,26.7] 5.3** 

Middle 28.7 [26.4,31.3] 34.4 [31.4,37.5] 5.6** 

Richer 43.5 [40.5,46.4] 53.7 [49.8,57.6] 10.3*** 

Richest 64.4 [61.5,67.1] 71.0 [67.9,73.9] 6.8** 

Notes: *p<0.05, **p<0.01, ***p<0.001 1

 p-value significance of the covariate in each survey 2

 Difference between the two surveys with the p-value of the difference. 
31 

Appendix Table 3 Trends in proportion of women with high empowerment in the SWPER 

decision-making empowerment domain, Pakistan 2012-13 and 2017-18 

 2012-13 2017-18 

Variable % [95% C.I.] p1

 % [95% C.I.] p1 Difference2 

 2017-18 – 2012-13 

Total 38.3 [36.5,40.1] 36.5 [34.4,38.6] 0-1.8 

Age *** *** 

15-19 9.6 [6.6,13.8] 12.5 [9.3,16.5] 2.8 

20-24 21.3 [18.4,24.5] 21.9 [18.7,25.6] 0.7 

25-29 29.1 [26.4,32.0] 33.2 [30.4,36.2] 4.1* 

30-34 38.9 [35.7,42.1] 38.2 [35.0,41.5] 0-.5 

35-39 48.3 [44.7,52.0] 41.5 [38.0,45.0] 0-6.9** 

40-44 56.5 [52.8,60.1] 48.0 [44.0,52.0] 0-8.6** 

45-49 55.4 [51.8,59.0] 52.9 [48.7,57.1] 0-2.5 

Education * *** 

No education 37.1 [34.9,39.3] 32.6 [30.1,35.2] 0-4.5** 

Primary 38.1 [35.0,41.3] 36.4 [32.6,40.4] 0-1.8 

Secondary 38.7 [35.5,42.0] 37.5 [33.9,41.2] 0-1.3 

Higher 45.4 [40.4,50.4] 48.7 [43.9,53.5] 3.5 

Currently working *** *** 

No 35.5 [33.7,37.3] 34.2 [32.1,36.4] 0-1.3 

Yes 46.4 [42.8,50.0] 47.9 [44.0,51.8] 1.5 

Region *** *** 

Punjab 44.2 [41.5,46.9] 39.7 [36.5,43.1] 0-4.4* 

Sindh 35.2 [31.8,38.8] 46.4 [43.0,49.8] 11.1*** 

KPK 25.6 [22.3,29.3] 19.0 [15.6,23.0] 0-6.6* 

Balochistan 17.7 [13.2,23.4] 10.0 [6.2,15.7] 0-7.8* 

Ict Islamabad 45.4 [41.2,49.7] 46.9 [42.0,51.9] 1.5 

Wealth Quintile *** *** 

Poorest 29.2 [26.0,32.6] 34.5 [30.3,38.9] 5.3 

Poorer 38.1 [34.9,41.4] 30.8 [27.3,34.5] 0-7.3** 

Middle 39.3 [36.1,42.5] 33.6 [30.1,37.2] 0-5.7* 

Richer 40.2 [37.4,42.9] 38.5 [35.4,41.7] 0-1.7 

Richest 44.2 [40.5,47.9] 44.0 [39.5,48.5] 0-.2 

Notes: *p<0.05, **p<0.01, ***p<0.001 1

 p-value significance of the covariate in each survey 2

 Difference between the two surveys with the p-value of the difference. 
32 

Appendix Table 4 Migration and SWPER attitude to violence empowerment domain. Adjusted 

relative risk ratios from multinomial logistic regression, Pakistan 2012-13 

and 2017-18 

 PDHS 2012-13 PDHS 2017-18 

 med vs low high vs low med vs low high vs low 

 RRR [95% CI] RRR [95% CI] RRR [95% CI] RRR [95% CI] 

Migration

Non-migrant (ref) 

Moved urban to urban 2.21** [1.35 - 3.61] 2.29*** [1.60 - 3.27] 1.70* [1.07 - 2.71] 1.43 [0.88 - 2.32] 

Moved rural to urban 1.18 [0.85 - 1.63] 0.99 [0.66 - 1.49] 1.06 [0.74 - 1.53] 1.12 [0.80 - 1.56] 

Moved rural to rural 1.52* [1.05 - 2.19] 0.92 [0.68 - 1.24] 1.24 [0.89 - 1.73] 1.00 [0.72 - 1.39] 

Moved urban to rural 1.41 [0.83 - 2.40] 1.32 [0.92 - 1.91] 0.84 [0.48 - 1.47] 1.40 [0.92 - 2.12] 

Age 15-19 (ref) 20-24 0.85 [0.52 - 1.38] 1.34* [1.00 - 1.80] 0.83 [0.58 - 1.20] 1.23 [0.93 - 1.63] 

25-29 0.99 [0.60 - 1.62] 1.41* [1.06 - 1.88] 0.87 [0.62 - 1.22] 1.33 [0.97 - 1.82] 

30-34 1.04 [0.61 - 1.77] 1.50** [1.12 - 2.01] 1.12 [0.76 - 1.65] 1.45* [1.04 - 2.03] 

35-39 0.93 [0.61 - 1.44] 1.46** [1.10 - 1.95] 1.00 [0.69 - 1.45] 1.55** [1.13 - 2.13] 

40-44 0.94 [0.59 - 1.49] 1.39* [1.04 - 1.86] 0.97 [0.68 - 1.39] 1.40 [0.99 - 1.99] 

45-49 0.67 [0.42 - 1.08] 1.20 [0.89 - 1.62] 0.95 [0.65 - 1.39] 1.30 [0.92 - 1.83] 

Currently working

No (ref) 

Yes 1.18 [0.95 - 1.46] 0.99 [0.82 - 1.20] 0.99 [0.77 - 1.27] 0.82* [0.68 - 0.99] 

Region Punjab (ref) Sindhi 0.97 [0.73 - 1.28] 0.73* [0.54 - 0.99] 1.46* [1.09 - 1.96] 1.21 [0.89 - 1.63] 

KPK 0.54*** [0.41 - 0.73] 0.19*** [0.14 - 0.26] 0.67** [0.49 - 0.91] 0.27*** [0.18 - 0.40] 

Balochistan 1.78** [1.18 - 2.68] 0.64* [0.42 - 0.98] 1.38 [0.94 - 2.05] 0.72 [0.43 - 1.21] 

ICT Islamabad 1.15 [0.77 - 1.72] 1.09 [0.76 - 1.57] 1.15 [0.72 - 1.84] 0.93 [0.58 - 1.49] 

Wealth

Poorest (ref) 

Poor 0.99 [0.75 - 1.30] 0.95 [0.69 - 1.32] 1.61** [1.21 - 2.14] 1.70*** [1.34 - 2.16] 

Middle 1.38* [1.04 - 1.83] 1.36 [1.00 - 1.87] 2.34*** [1.71 - 3.19] 3.14*** [2.40 - 4.13] 

Rich 1.81*** [1.36 - 2.41] 2.24*** [1.61 - 3.11] 2.78*** [2.01 - 3.84] 5.63*** [4.14 - 7.65] 

Richest 3.83*** [2.72 - 5.40] 7.14*** [5.04 - 10.11] 4.71*** [3.26 - 6.81] 13.93*** [9.96 - 19.49] 

Notes: *p<0.05, **p<0.01, ***p<0.001

 
33 

Appendix Table 5 Migration and SWPER social independence empowerment domain. 

Adjusted relative risk ratios from multinomial logistic regression, Pakistan 

2012-13 and 2017-18 

 PDHS 2012-13 PDHS 2017-18 

 med vs low high vs low med vs low high vs low 

 RRR [95% CI] RRR [95% CI] RRR [95% CI] RRR [95% CI] 

Migration

Non-migrant (ref) 

Moved urban to urban 0.90 [0.65 - 1.25] 1.02 [0.77 - 1.35] 0.87 [0.60 - 1.28] 1.09 [0.75 - 1.59] 

Moved rural to urban 0.87 [0.69 - 1.09] 0.64** [0.47 - 0.86] 0.94 [0.72 - 1.21] 0.49*** [0.34 - 0.71] 

Moved rural to rural 0.79 [0.62 - 1.01] 0.83 [0.64 - 1.09] 0.79 [0.59 - 1.05] 0.83 [0.63 - 1.09] 

Moved urban to rural 1.10 [0.77 - 1.57] 1.31 [0.86 - 1.99] 1.32 [0.88 - 1.99] 1.62** [1.12 - 2.35] 

Age 15-19 (ref) 20-24 1.46** [1.11 - 1.93] 3.46*** [2.34 - 5.13] 1.93*** [1.44 - 2.60] 8.00*** [4.86 - 13.19] 

25-29 1.60*** [1.22 - 2.09] 5.16*** [3.54 - 7.54] 1.71*** [1.32 - 2.21] 11.90*** [7.30 - 19.39] 

30-34 1.40* [1.05 - 1.86] 4.52*** [3.00 - 6.79] 1.62*** [1.22 - 2.16] 13.39*** [8.04 - 22.30] 

35-39 1.33* [1.01 - 1.75] 3.21*** [2.13 - 4.83] 1.39* [1.03 - 1.88] 12.46*** [7.53 - 20.62] 

40-44 1.52** [1.14 - 2.03] 2.28*** [1.50 - 3.45] 1.27 [0.94 - 1.73] 6.70*** [4.02 - 11.17] 

45-49 1.11 [0.83 - 1.50] 1.95** [1.27 - 2.99] 1.32 [0.94 - 1.86] 7.01*** [4.02 - 12.22] 

Currently working

No (ref) 

Yes 0.77*** [0.67 - 0.88] 0.67*** [0.55 - 0.80] 0.79* [0.66 - 0.95] 0.84 [0.69 - 1.01] 

Region Punjab (ref) Sindhi 0.94 [0.74 - 1.18] 0.70** [0.54 - 0.89] 0.70*** [0.57 - 0.85] 0.66*** [0.52 - 0.84] 

KPK 0.71*** [0.58 - 0.86] 0.54*** [0.43 - 0.68] 0.61*** [0.50 - 0.74] 0.35*** [0.26 - 0.46] 

Balochistan 0.79 [0.61 - 1.02] 0.46*** [0.33 - 0.64] 0.61*** [0.49 - 0.76] 0.49*** [0.36 - 0.67] 

ICT Islamabad 0.92 [0.70 - 1.20] 1.44* [1.04 - 1.99] 0.88 [0.65 - 1.19] 0.93 [0.69 - 1.25] 

Wealth

Poorest (ref) 

Poor 1.04 [0.81 - 1.35] 1.56* [1.11 - 2.18] 1.00 [0.83 - 1.22] 1.60*** [1.28 - 2.00] 

Middle 1.09 [0.82 - 1.44] 2.65*** [1.81 - 3.90] 1.17 [0.92 - 1.48] 2.75*** [2.11 - 3.59] 

Rich 1.37* [1.02 - 1.82] 5.34*** [3.65 - 7.83] 1.34* [1.05 - 1.71] 6.49*** [4.89 - 8.60] 

Richest 1.78*** [1.31 - 2.43] 15.92*** [10.77 - 23.54] 1.95*** [1.51 - 2.54] 17.37*** [12.91 - 23.36] 

Notes: *p<0.05, **p<0.01, ***p<0.001

 
34 

Appendix Table 6 Migration and SWPER decision-making empowerment domain. Adjusted 

relative risk ratios from multinomial logistic regression, Pakistan 2012-13 

and 2017-18 

 PDHS 2012-13 PDHS 2017-18 

 med vs low high vs low med vs low high vs low 

 RRR [95% CI] RRR [95% CI] RRR [95% CI] RRR [95% CI] 

Migration

Non-migrant (ref) 

Moved urban to urban 1.11 [0.81 - 1.53] 1.62*** [1.23 - 2.14] 0.85 [0.60 - 1.20] 0.97 [0.71 - 1.31] 

Moved rural to urban 0.95 [0.73 - 1.23] 0.92 [0.69 - 1.22] 0.94 [0.69 - 1.27] 1.10 [0.82 - 1.47] 

Moved rural to rural 1.00 [0.72 - 1.39] 0.87 [0.62 - 1.23] 0.84 [0.63 - 1.11] 0.82 [0.59 - 1.13] 

Moved urban to rural 1.19 [0.77 - 1.82] 1.05 [0.68 - 1.61] 1.02 [0.67 - 1.55] 1.16 [0.81 - 1.66] 

Age 15-19 (ref) 20-24 0.96 [0.70 - 1.31] 2.44*** [1.58 - 3.78] 1.62** [1.16 - 2.24] 1.92** [1.27 - 2.89] 

25-29 1.39* [1.03 - 1.88] 4.04*** [2.55 - 6.41] 2.01*** [1.44 - 2.80] 3.74*** [2.62 - 5.33] 

30-34 1.68** [1.22 - 2.32] 6.75*** [4.38 - 10.41] 2.75*** [2.00 - 3.78] 5.20*** [3.60 - 7.52] 

35-39 1.87*** [1.36 - 2.58] 10.46*** [6.72 - 16.29] 2.95*** [2.12 - 4.13] 6.26*** [4.37 - 8.95] 

40-44 2.22*** [1.55 - 3.17] 15.80*** [9.96 - 25.06] 3.82*** [2.75 - 5.33] 9.53*** [6.38 - 14.24] 

45-49 2.51*** [1.79 - 3.52] 16.53*** [10.16 - 26.89] 4.06*** [2.84 - 5.82] 11.62*** [7.84 - 17.22] 

Currently working

No (ref) 

Yes 1.32** [1.09 - 1.61] 1.66*** [1.41 - 1.96] 1.89*** [1.51 - 2.37] 2.07*** [1.69 - 2.54] 

Region Punjab (ref) Sindhi 0.79 [0.61 - 1.03] 0.64*** [0.49 - 0.82] 1.39** [1.10 - 1.76] 1.65*** [1.29 - 2.11] 

KPK 0.43*** [0.34 - 0.55] 0.34*** [0.27 - 0.44] 0.39*** [0.30 - 0.49] 0.29*** [0.21 - 0.39] 

Balochistan 0.24*** [0.16 - 0.36] 0.20*** [0.14 - 0.30] 0.57** [0.40 - 0.82] 0.15*** [0.08 - 0.27] 

ICT Islamabad 1.41* [1.08 - 1.85] 1.03 [0.77 - 1.38] 1.79*** [1.36 - 2.35] 1.59** [1.14 - 2.21] 

Wealth

Poorest (ref) 

Poor 1.37* [1.06 - 1.78] 1.67*** [1.33 - 2.10] 1.35* [1.05 - 1.73] 1.16 [0.87 - 1.56] 

Middle 1.44** [1.14 - 1.81] 1.70*** [1.33 - 2.17] 1.69*** [1.27 - 2.25] 1.37* [1.01 - 1.86] 

Rich 1.40** [1.09 - 1.81] 1.64*** [1.28 - 2.11] 1.70*** [1.27 - 2.27] 1.56** [1.17 - 2.09] 

Richest 1.97*** [1.52 - 2.56] 2.07*** [1.58 - 2.72] 1.78*** [1.31 - 2.43] 1.84*** [1.33 - 2.56] 

Notes: *p<0.05, **p<0.01, ***p<0.001