DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2018 No. 140

Naba Raj Thapa

Sunil Adhikari

Pawan Kumar Budhathoki

June 2018

This document was produced for review by the United States Agency for 

International Development.

The Effect of Internal Migration on the Use of 

Reproductive and Maternal Health Services in Nepal

DHS Working Paper No. 140

The Effect of Internal Migration on the Use of Reproductive 

and Maternal Health Services in Nepal

Naba Raj Thapa1

Sunil Adhikari1

Pawan Kumar Budhathoki1

ICF

Rockville, Maryland, USA

June 2018

1 Department of Population Studies, Ratna Rajya Laxmi Campus, Tribhuvan University, Nepal

Corresponding author: Naba Raj Thapa, Department of Population Studies, Ratna Rajya Laxmi Campus, 

Tribhuvan University, Nepal. Email: nabarthapa@gmail.com
Acknowledgments: We would like to thank USAID and ICF for providing financial and technical support 

as well as offering ample opportunity for capacity building among researchers at various universities in

many countries. We express our sincere gratitude to Dr. Shireen Assaf and Dr. Wenjuan Wang for their 

encouragement, guidance, suggestions, and support in the course of completing this research paper. We are 

also grateful to our respected co-facilitators, Ehab Sakr and Henock G. Yebyo, for their support and 

guidance. Our special thanks go to our reviewer, Dr. Sarah Staveteig, for valuable comments and intensive 

review of the paper. We would like to thank Bryant Robey for editing this paper. We would like to 

acknowledge Tribhuvan University and Ratna Rajya Laxmi Campus for giving us permission to attend the 

training workshop.

Editor: Bryant Robey

Document Production: Natalie Shattuck

This study was carried out with support 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 DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about The DHS Program contact: 

DHS Program, ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 301-572-0950; fax: 

301-572-0999 or 301-407-6501; email: reports@dhsprogram.com; internet: www.dhsprogram.com.

Recommended citation:

Thapa, Naba Raj, Sunil Adhikari, and Pawan Kumar Budhathoki. 2018. The Effects of Internal Migration 

on the Use of Reproductive and Maternal Health Services in Nepal. DHS Working Paper No. 140. 

Rockville, Maryland, USA: ICF.
iii

CONTENTS

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

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

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

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

1.1 Research Question ............................................................................................................. 2

1.2 Objectives ........................................................................................................................... 2

1.3 Conceptual Framework....................................................................................................... 3

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

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

2.2 Variables ............................................................................................................................. 6

2.2.1 Dependent variables .............................................................................................. 6

2.2.2 Independent variables............................................................................................ 6

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

3 RESULTS ........................................................................................................................................ 9

3.1 Background Characteristics of Study Population................................................................ 9

3.2 Background Characteristics of Study Population by Duration of Stay.............................. 10

3.3 Background Characteristics of Study Population by Migration Streams .......................... 11

3.4 Descriptive Analysis of Modern Contraceptive Use, Antenatal Care Visits, and

Place of Delivery by Duration of Stay, Migration Streams, and Background 

Characteristics .................................................................................................................. 13

3.5 The Effect of Duration of Stay on Modern Contraceptive Use, Antenatal Care Visits, 

and Place of Delivery ........................................................................................................ 17

3.5.1 The effect of duration of stay on modern contraceptive use................................ 17

3.5.2 The effect of duration of stay on antenatal care visits ......................................... 19

3.5.3 The effect of duration of stay on place of delivery ............................................... 19

3.6 The effect of migration stream on modern contraceptive use, antenatal care 

visits, and place of delivery.................................................................................. 19

3.6.1 The effect of migration stream on modern contraceptive use ............................. 20

3.6.2 The effect of migration stream on antenatal care visits ....................................... 21

3.6.3 The effect of migration stream on place of delivery............................................. 22

4 DISCUSSION................................................................................................................................. 25

5 CONCLUSION............................................................................................................................... 29

REFERENCES............................................................................................................................................ 31

v

TABLES

Table 1 The sample population for each outcome of the study.......................................... 6

Table 2 Background characteristics of the study population .............................................. 9

Table 3 Duration of stay.................................................................................................... 11

Table 4 Migration streams ................................................................................................ 13

Table 5 Current use of modern contraception, antenatal care, and place of delivery ...... 14

Table 6 Duration of stay and use of modern contraception, antenatal care visits, 

and place of delivery ............................................................................................ 18

Table 7 Migration streams and use of modern contraception, antenatal care visits, 

and place of delivery ............................................................................................ 20

vii

FIGURES

Figure 1 Conceptual framework of the study ....................................................................... 3

Figure 2 Percentage distribution of women age 15-49 by duration of stay, 

Nepal DHS 2016 .................................................................................................. 10

Figure 3 Percentage distribution of women age 15-49 by migration streams, 

Nepal DHS 2016 .................................................................................................. 12

Figure 4 Percentage of eligible women age 15-49 using modern contraception, 

who had at least four antenatal care visits, and delivered their most recent 

birth in the 5 years preceding the survey in health facility, by duration of 

residence, Nepal DHS 2016 ................................................................................ 16

Figure 5 Percentage of eligible women age 15-49 using modern contraception, 

who had at least four antenatal care visits, and delivered their most recent 

birth in the five years preceding the survey in a health facility, by migration 

streams, Nepal DHS 2016 ................................................................................... 16

Figure 6 Adjusted odds ratio of modern contraceptive use and 95% confidence 

interval by migration streams ............................................................................... 21

Figure 7 Adjusted odds ratio of four or more antenatal care visits and 95% confidence 

interval by migration streams ............................................................................... 22

Figure 8 Adjusted odds ratio of delivering in health facility and 95% confidence 

interval by migration streams ............................................................................... 23

ix

ABSTRACT

The purpose of this study is to examine the effect of internal migration on the use of reproductive and 

maternal health services in Nepal, using data from the 2016 Nepal Demographic and Health Survey. The 

study population is married women age 15-49. The study used descriptive and logistic regression analysis,

with three outcome measures: current use of modern contraception, at least four antenatal care visits, and 

place of delivery. 

Overall, 44% of eligible women reported current use of modern contraception, 71% of women made at least 

four antenatal visits, and 58% of women delivered their most recent birth in the past 5 years in a health 

facility. Our findings show that, after adjusting for background characteristics, women who are recent 

migrants to the current district (arrived 0-4 years ago) have lower odds of using modern contraceptives, 

higher odds of attending at least four antenatal visits, and higher odds of delivering in a health facility. By

migration streams, modern contraceptive use is significantly higher among urban-to-urban migrants and 

urban non-migrant women. Urban-to-urban migrant women and rural-to-urban migrant women have 

significantly higher odds of attending at least four antenatal visits for the most recent birth compared with

rural-to-rural migrant women. Women who moved between urban areas, women who moved from an urban 

to a rural area, women who moved from a rural area to an urban area, and urban non-migrants are 

significantly more likely to deliver in a health facility compared with women who moved between rural 

areas. 

Several socioeconomic and demographic factors are also significant in their association with contraceptive 

use, antenatal visits, and place of delivery. These differences by internal migration status should be 

considered in reproductive and maternal health services interventions. 

KEY WORDS: Migration, migration streams, migrant, non-migrant, contraceptive use, antenatal care, 

duration of stay, place of delivery

1

1 INTRODUCTION

Nepal is a landlocked country, sandwiched between two giant nations—China in the north and India in the 

south. This is a country of multi-ethnic, multi-lingual, multi-religious, and geographical diversity. Nepal’s 

environmental, socioeconomic, demographic, cultural, and religious characteristics vary from one region 

to another. Migration is not new phenomenon in Nepal; it has existed since early days, when more people 

moved from western to eastern parts of the country (Subedi 1988). Since 1950, with the control of malaria 

in Terai region, people have been migrating from Mountain and Hill regions to Terai region (Gurung 1998). 

Nepal has been experiencing a rapid increase in the volume of internal migration over the last 30 years. The

volume of interdistrict lifetime migrants in Nepal increased from 9% in 1981 to 15% in 2011. Similarly, the

volume of inter-regional lifetime migrants increased from 7% in 1981 to 10% in 2011 (KC 2003, Suwal 

2014). Census data show that most internal migration in Nepal occurs from Hill to Terai regions, and from 

rural to urban areas (UNFPA Nepal 2017). 

Internal migration has been an integral part of socioeconomic transformation in a country. Managed internal

migration can bring benefits for countries as well as individuals. Poorly managed internal migration, 

however, can result in various difficulties, including reproductive and maternal health problems. Internal 

migration has both a direct and indirect impact on population dynamics. Changes in population structure 

and distribution are direct impacts of migration, whereas behavioral changes among migrants are an indirect

impact. When people migrate, they may interact with new social, cultural, and economic environments that 

could offer them different opportunities for health services, or could change their thinking and behavior 

related to using reproductive and maternal health services. 

Internal migration can be classified into four groups: rural-to-rural, rural-to-urban, urban-to-urban, and 

urban-to-rural. Of these, rural-to-rural migration and rural-to-urban migration are the most prominent 

migration streams in Nepal. Internal migration is the largest contributor to urban growth (Ministry of Urban

Development 2017); other contributors are an increase in the number of municipalities and expansion of 

urban areas. 

Types of migration and health are interconnected in different ways. Migrants are a more vulnerable group 

for reproductive and maternal health problems, which pose different challenges to meeting their medical 

needs (Evans 1987). The negative effects of migration are more prominent for women than men (Adanu 

and Johnson 2009), although the effect of migration (internal and international) on migrants’ health is 

complex, and variations exist between migrant groups. The effect of migration on health outcomes varies 

according to who migrates, when they migrate, where they migrate from, where they migrate to, and what 

health outcome is measured (McKay, Macintyre and Ellaway 2003). Nepal has witnessed a significant 

moment in its history, in which the country has transitioned into a new political system, from a kingship to 

federal system. During this transitional period, Nepal has been experiencing different socioeconomic and 

political obstacles as well as different challenges, including the destructive earthquake on 25 April 2015,

which may have had an effect on reproductive health among migrant women. Research on the use of 

reproductive and maternal health services has been given prime priority in Nepal. Very little research, 

however, has been conducted in relation to migration and use of reproductive and maternal health services 

in the context of Nepal. 
2

In the literature, three theoretical explanations have been used to explain the cause of differentials in use of 

reproductive and maternal health services between migrant and non-migrant women: selection, adaptation, 

and disruption. The selection theory explains that, outside of natural disasters, migrants are not randomly 

selected, but rather are selected on the basis of background characteristics such as age, education, and 

occupational status. Adaptation is the process of change in the migrants’ attitudes, values, culture, beliefs, 

and behaviors in line with their new environment. Adaptation in the new environment depends on the 

migrants’ sociocultural and economic background. Migration disruption refers to separation of spousal 

partners, family members, and relatives immediately after migration (Lindstrom 2003; Chattopadhyay, 

White and Debpuur 2006; Santelli, Abradio-Lanza and Melnikas 2009; Ochako et al. 2016). 

In Nepal a Demographic and Health Survey has been conducted every 5 years since 1996. Data from these 

five Nepal DHS show that the use of modern contraceptive methods has increased, from 26% in 1996 to 

43% in 2016. Over the past 20 years, the percentage of women who had at least four ANC visits during 

their pregnacy has also increased, from 14% in 2001 to 69% in 2011. Similarly, the percentage of women 

who delivered in a health facility has increased significantly, from 8% in 1996 to 57% in 2016. 

A study in Peru shows that rural migrant women face different problems in their health needs compared 

with non-migrant women. The study further shows that rural migrant women are less likely to use modern 

contraceptive methods and to receive appropriate ANC compared with urban non-migrant women. The 

study further pointed out that rural migrant women are more likely to have only a primary level of education, 

to have no health insurance, and to be in the lowest wealth category compared with urban migrants and 

urban non-migrants (Subaiya 2007). 

A study in Myanmar showed that female internal migrants had better reproductive health outcomes 

compared with non-migrants (Sudhinaraset, et al. 2016). Another study in Bangladesh showed that women 

who migrated to urban areas were significantly less likely than non-migrants to use reproductive health 

services related to pregnancy and ANC, or to use modern contraceptives (Islam and Gagnon 2016). Studies 

in Chinese contexts found that internal migrants used ANC significantly less than non-migrants (Shaokang, 

Zhenwei and Blas 2002). A hospital-based cross-sectional study on ANC use showed that rural-to-urban 

migrant women did not receive adequate antenatal care. Inadequate use of ANC is associated with low 

socioeconomic status and with demographic factors (Zhao et al. 2012). A study in Guatemala showed that 

current use of modern contraceptives was positively associated with women’s education. The study also 

found that urban non-migrants were more likely to use modern contraception compared with rural nonmigrants (Lindstrom and Hernandez 2006). 

1.1 Research Question

What are the net associations between the internal migration status of women and reproductive and maternal 

health outcomes? 

1.2 Objectives

 To analyze the use of reproductive and maternal health services by duration of residence, 

migration streams, and background characteristics of women age 15-49.
3

 To examine the effects of duration of stay on the use of reproductive and maternal health 

services.

 To examine the effects of migration streams on the use of reproductive and maternal health 

services.

1.3 Conceptual Framework

Women’s use of reproductive and maternal health services is determined by various factors. The conceptual 

framework of the study explains the interconnections between reproductive and maternal health services, 

duration of stay, migration streams, and background variables. Modern contraceptive use, ANC visits, and 

place of delivery are dependent variables, which are influenced by duration of residence and migration 

streams. Women’s age, education, caste/ethnicity, residence, occupation, and wealth status are 

socioeconomic and demographic factors that also affect the use of reproductive and maternal health 

services. Figure 1 depicts the hypothesized relationship between migration variables, background 

characteristics, and outcomes. 

Figure 1 Conceptual framework of the study

Migration variables

 Duration of stay

 Migration stream

Background characteristics

 Age of women

 Education

 Caste/ethnicity

 Residence

 Occupation

 Wealth quintile

Outcomes

Reproductive and maternal health services

 Use of modern contraception

 ANC visits (4+)

 Place of delivery

5

2 DATA AND METHODS

2.1 Data

This study used data from the 2016 Nepal Demographic and Health Survey. The NDHS is the fifth and 

most recent Demographic and Health Survey conducted in Nepal, and is a nationally representative

population-based survey. The survey was conducted under the aegis of the Ministry of Health, Government 

of Nepal, with the financial support of the United States Agency for International Development (USAID) 

and technical assistance from ICF through The DHS Program. The survey involved the use of two-stage

sampling in rural areas and three-stage sampling in urban areas. In rural areas, wards were selected as 

primary sampling units (PSUs), and households were selected from the sample PSUs. In urban areas, wards 

were selected as PSUs, one enumeration area (EA) was selected from each PSU, and households were 

selected from the sample EAs. In this survey, 11,473 households were selected for the sample, of which 

11,040 households were interviewed. Likewise, 13,089 women age 15-49 were identified for individual 

interviews, and 12,862 were successfully interviewed, yielding a 96% response rate.

The United Nations recommends that migration data should be collected from four routine direct questions 

on place of birth, place of last residence, duration of residence, and place of last residence at fixed prior 

date (United Nations 2008). Based on a single question, “How long have you been living in this place?” it 

is possible to identify whether a respondent is a migrant or not. Persons who have lived in the current place 

all their lives are considered to be non-migrants, while those who have moved to the area are considered as

migrants (United Nations 1970). With this theoretical consideration, reports on current place of residence 

and previous place of residence are taken into account in identifying migration variables for the study. 

The study population is women age 15-49. In order to ensure uniform comparisons, we excluded women 

who were temporary visitors to the surveyed household (n=410) and women who stated that they had moved 

to the area from abroad (n=685). The total weighted number of internal migrants and non-migrant women 

eligible for the study is 7,876 and 3,791 respectively. 

The sample population is different for modern contraceptive use than for ANC visits and place of delivery. 

The sample population for modern contraceptive use is restricted to the 8,937 (weighted) women who are 

currently married. The total weighted number of analytic sampled population for ANC visits and place of 

delivery is 3,300 women. Women whose most recent birth was longer ago than the year they moved to the 

district (n=283) are excluded from the analysis. The sample populations for each outcome are given in 

Table 1. 
6

Table 1 The sample population for each outcome of the study

Outcomes Description Exclusion criteria

Sample 

population

Modern contraceptive use Women who are currently married and are 

using any modern contraceptive methods 

Unmarried women; household visitors, and 

those who moved to the district from 

abroad

8,937

At least four ANC visits Women who attended at least four ANC 

visits for the most recent birth in the 5

years preceding the survey

Unmarried women; household visitors,

those who moved to the district from 

abroad; women who have not given birth in 

the past 5 years; women whose most 

recent birth was longer ago than the year 

they moved to the district. 

3,300

Delivered in heath facility Women who delivered in a health facility 

for the most recent birth in the 5 years 

preceding the survey

Unmarried women; household visitors and 

those who moved to the district from 

abroad; women who have not given birth in 

the past 5 years; women whose most 

recent birth was longer ago than the year 

they moved to the district.

3,300

2.2 Variables

2.2.1 Dependent variables

The study has three dependent variables. The first is current use of modern contraception. This variable is

coded as a binary outcome for whether using modern contraception, including male sterilization, female 

sterilization, injectables, intrauterine devices (IUD), pill, implants, male condoms, lactational amenorrhea, 

and emergency contraception. Traditional and folkloric method users were classified as nonusers.

The second dependent variable is ANC visits for the last pregnancy in the five years before the survey. The 

World Health Organization (WHO) recommends that women attend at least four ANC visits as a necessary 

part of maternal health care (World Health Organization 1994). The number of ANC visits is coded as ‘yes’ 

for women with at least four ANC visits before their most recent (4+ ANC visits), and coded as ‘no’ if there 

were fewer than four visits. 

The third dependent variable is place of delivery for the most recent birth in the last five years before the 

survey. It is categorized into the binary outcome ‘health facility’ and ‘home/elsewhere’. The place of 

delivery is coded as ‘yes’ for health facility and ‘no’ for home/elsewhere. 

2.2.2 Independent variables

The independent variables used in this study were selected based on the proposed conceptual framework. 

The migration-related variables are duration of stay and migration streams. The study also considered six 

background characteristics of the sampled women.

Migration variables: The 2016 NDHS asked the question: “How long have you been living continuously 

in the current place of residence?” Those women who answered “always” are treated as “non-migrants,” 

while those women who reported “number of years lived in current place of residence” are considered as 

“migrants” if they changed place of residence across district boundaries. Women who reported themselves 

to be “visitors” were excluded from the analysis. Women who reported previous place of residence as 

‘abroad’ are not included in the analysis. A further question was asked on previous place of residence before 
7

moved to current place of residence. This information was used to generate six categories of migration 

streams: urban-to-urban (U-U), urban-to-rural (U-R), rural-to-urban (R-U), rural-to-rural (R-R), urban nonmigrant, and rural non-migrant. A woman who reported previous place of residence as rural and current 

place of residence as urban is classified as a rural-to-urban migrant. Similarly, a woman who reported 

always lived in the current place is considered as non-migrant, either urban or rural. Duration of stay is 

classified on the basis of number of years lived in the current place. Duration of stay is classified in three 

categories: less than 5 years, 5-9 years, and more than 10 years. 

Background variables: The background variables included in this study are women’s age, education, 

caste/ethnicity, residence, occupation, and wealth quintile. Wealth quintile is a composite measure of 

household living standard. Data on household assets were collected in the NDHS. Household wealth index 

was constructed using household assets data, including ownership of a number of consumer items ranging 

from a television to a bicycle or car, and such housing characteristics as sources of drinking water, sanitation 

facilities, fuel used for cooking, room used for sleeping, types of materials used for flooring, and ownership 

of agricultural land (Ministry of Health, New ERA, and ICF 2017). Women’s age is categorized into four 

groups—15-19, 20-29, 30-39, and 40-49. In the NDHS, caste/ethnicity has 11 categories. However, for a 

better explanation, we grouped the variable into five categories where Brahman/Chhetri included Hill 

Brahanan, Hill Chhetri and Terai Brahman/Chhetri; Dalit included Hill Dalit and Terai Dalit; Janajati 

included Newar, Hill Janajati and Terai Janajati; and Muslim included Muslim and others. The variable 

occupation has eight categories. This variable is grouped in three categories—not working, agriculture, and

nonagriculture, where the nonagriculture group included professional/technical, clerical, sales/services, 

skilled manual, unskilled manual, and others. 

2.3 Statistical Analysis

This study employed three levels of statistical analysis. First, descriptive statistical techniques were used at 

the univariate level to analyze selected background characteristics of study population of women age 15-49. 

Second, to examine the association between outcome variables and selected background variables of 

women age 15-49, chi-square tests were conducted at the bivariate level. Results are considered significant 

at p<0.05. Third, logistic regression procedures were employed to examine the effects of duration of stay 

and migration streams on the three outcome variables related to reproductive and maternal health services: 

modern contraceptive use, at least four ANC visits, and health facility delivery. 

Adjusted logistic regression models were carried out to examine the association of duration of stay and 

migration streams with the reproductive and maternal health outcomes. This model also analyzes the effect 

of duration of stay and migration streams on the outcomes in the presence of the selected background 

characteristics—women’s age, education, caste/ethnicity, residence, occupation, and wealth quintile. The 

results are presented in odds ratios (OR). All the analyses were performed using STATA version 15.1. The 

complex sample design of the NDHS was taken into consideration. 

9

3 RESULTS

3.1 Background Characteristics of Study Population

Background characteristics of the study population of women age 15-49 are given in Table 2. The table 

shows that one-third of the women are age 20-29 and about one-quarter are age 30-39, while 21% are age 

15-19, and 20% are age 40 and above. Regarding education, 33% of women have no education, 26% have 

completed secondary level of education, and 25% have attained at least a School Leaving Certificate (SLC) 

level of education. Janajati (37%) and Brahman/Chhetri (33%) are the dominant caste/ethnicity of the study 

population. Nearly two-thirds of women (63%) are urban residents. In considering occupation status, 32% 

of women are not working and about 48% are working in agriculture. The study population is more or less 

evenly distributed across the wealth quintiles, with 18% of women in the lowest quintile and 22% in the 

highest. In all, a majority of the study population is under age 30, educated, of Janajati or Brahman/Chhetri 

caste/ethnicity, urban, and engaged in agriculture. 

Table 2 Background characteristics of the study population

Percent distribution of women age 15-49 by selected background 

characteristics, Nepal DHS 2016

Background 

characteristics Percent Total (N)

Age group

15-19 20.7 2413

20-29 33.4 3901

30-39 26.2 3062

40-49 19.6 2290

Education 

No education 32.8 3826

Primary 16.6 1938

Secondary 25.9 3026

SLC and above 24.7 2877

Ethnicity

Brahman/Chhetri 33.0 3845

Terai caste 13.3 1552

Dalit 12.4 1444

Janajati 37.0 4317

Muslim 4.4 509

Residence

Urban 63.3 7382

Rural 36.7 4285

Occupation

Not working 31.5 3677

Nonagriculture 20.3 2374

Agriculture 48.1 5615

Wealth quintile

Lowest 17.7 2063

Second 19.8 2312

Middle 19.9 2321

Fourth 21.0 2451

Highest 21.6 2519

Total 100.0 11667
10

3.2 Background Characteristics of Study Population by Duration of Stay

Table 3 presents the percentage distribution of women age 15-49 by socioeconomic and demographic 

characteristics according to duration of stay. The table and Figure 2 show that 67% of women have migrated 

at least once, while 33% are non-migrants. Thirty-five percent of women have been living at their current 

location for 10 years or more, 19% for less than 5 years, and 14% for 5-9 years. 

By age, as Table 3 shows, 64% of women age 15-19 are non-migrants, while 26% of women in this age 

group have lived in their present location less than 5 years. Similarly, 31% of women age 20-29 have lived 

in their present location less than 5 years. In contrast, a majority of women age 30-39 and age 40-49 have 

10+ years duration of stay. 

Among women with no education, 58% have at least 10 years duration of stay, as is the case for 38% of 

women who attained a primary level of education. Among women who completed secondary education, 

24% have less than 5 years duration of stay. Similarly, 29% of women who attained at least the SLC level 

have less than 5 years duration of stay. Among the caste/ethnic groups, 47% of women of the Tarai caste 

have 10+ years duration of stay, followed by Dalit, Muslim, Brahman/Chhetri, and Janajati. Over a third of 

both urban women (34%) and rural women (38%) have lived in the same place for at least 10 years. Among 

nonworking women, 28% have 10+ years duration of stay, as do 33% of women engaged in nonagricultural 

work, and 41% of women engaged in agriculture. A comparatively higher proportion of women in the 

lowest wealth quintile are non-migrants. A higher proportion of women in the second, middle, fourth, and 

highest wealth quintiles have been living in their current location 10+ years. 

Figure 2 Percentage distribution of women age 15-49 by duration of stay, Nepal DHS 2016

18.8

13.5

35.2

32.5

0-4 years

5-9 years

10+ years

Always (Non-migrant)
11

Table 3 Duration of stay

Percent distribution of women age 15-49 by selected background characteristics and duration of stay, Nepal DHS 

2016

Background 

characteristics

Duration of stay

0-4 years 5-9 years 10+ years

Always

(Non-migrants) p-value

Age group

15-19 25.9 3.9 5.7 64.4 <0.001

20-29 31.1 26.3 15.4 27.1

30-39 8.0 11.2 59.2 21.6

40-49 4.5 4.9 68.1 22.5

Education 

No education 8.0 9.3 57.9 24.8 <0.001

Primary 17.0 17.2 37.8 28.1

Secondary 23.9 14.3 20.9 40.8

SLC and above 28.9 15.7 18.5 37.0

Ethnicity

Brahman/Chhetri 20.5 15.4 35.1 29.0 <0.001

Terai caste 15.8 15.4 47.4 21.4

Dalit 19.0 12.4 38.1 30.5

Janajati 18.5 12.0 30.0 39.5

Muslim 16.2 9.2 36.0 38.7

Residence

Urban 20.6 14.3 33.9 31.2 <0.001

Rural 15.6 12.1 37.6 34.7

Occupation

Not working 25.5 15.7 27.8 30.9 <0.001

Nonagriculture 22.8 15.5 33.1 28.6

Agriculture 12.6 11.2 41.0 35.2

Wealth quintile

Lowest 12.2 9.0 32.7 46.2 <0.001

Second 15.3 11.7 36.8 36.2

Middle 16.8 14.1 38.4 30.7

Fourth 26.2 14.3 33.9 25.6

Highest 21.9 17.5 34.4 26.1

Total 18.8 13.5 35.2 32.5

N 2189 1576 4111 3791

3.3 Background Characteristics of Study Population by Migration 

Streams

Table 4 presents the percentage distribution of women according to migration streams by selected 

background characteristics. Overall, as the table and Figure 3 show, rural-to-urban and rural-to-rural are the 

dominant migration streams of women in Nepal. About one-third of women are rural-to-urban migrants, 

22% are rural-to-rural migrants, 10% are urban-to-urban migrants, and only 2% are urban-to-urban 

migrants. Twenty percent of women are urban non-migrants and 13% are rural non-migrants.
12

As Table 4 shows, among women age 15-19 the highest proportions are urban non-migrants and rural nonmigrants, while only 19% are rural-to-urban migrants. Among women age 20-29, 30-39, and 40-49, the 

highest proportions are rural-to-urban migrants, followed by rural-to-rural migrants. Rural-to-urban 

migrants tend to have higher percentages at each level of education compared with either urban-to-urban 

migrants or urban non-migrants. The proportion of women who completed primary, secondary, and SLC 

and above levels of education account for 36%, 31%, and 33% respectively among all rural-to-urban 

migrants. Women who attained secondary education and SLC and above education show the highest 

percentages of rural-to-urban migrants and urban non-migrants. 

Figure 3 Percentage distribution of women age 15-49 by migration streams, Nepal DHS 2016

The distribution of women by caste/ethnicity reveals that the highest proportions of women in each 

caste/ethnicity are rural-to-urban migrants, followed by rural-to-rural migrants. Among nonworking 

women, 34% are rural-to-urban migrants. Among women engaged in nonagricultural occupations, 36% are 

rural-to-urban migrants, followed by 22% urban non-migrants and 21% urban-to-urban migrants. Similarly, 

among women engaged in agricultural occupations, 33% are rural-to-urban migrants and 27% are rural-torural migrants. By wealth quintiles, among women in the highest wealth quintile the largest percentages are 

rural-to-urban migrants, urban-to-urban migrants, and urban non-migrants. In contrast, among women in 

the lowest wealth quintile the largest percentages are rural-to-rural migrants, followed by rural nonmigrants. 

10.0

33.5

19.7

21.8

2.2

12.7

Urban-to-urban

Rural-to-urban

Urban non-migrants

Rural-to-rural

Urban-to-rural

Rural non-migrants
13

Table 4 Migration streams

Percent distribution of eligible women age 15-49 by selected background characteristics and migration streams, Nepal DHS 2016

Migration streams

Characteristics

Current urban residents Current rural residents

U-U R-U p-value

Urban

non-migrants R-R U-R

Rural

non-migrants

Age group

15-19 6.3 18.6 37.9 9.6 1.1 26.5 <0.001

20-29 10.8 34.7 17.3 24.6 2.7 9.9

30-39 10.4 40.1 12.9 25.3 2.6 8.7

40-49 12.0 38.4 14.0 24.9 2.1 8.5

Education 

No education 4.9 34.7 13.0 33.9 1.7 11.8 <0.001

Primary 7.4 36.3 15.1 25.4 2.9 13.0

Secondary 10.3 31.0 24.1 15.7 2.2 16.8

SLC and above 18.2 32.7 27.3 9.6 2.5 9.6

Ethnicity

Brahman/Chhetri 13.4 38.4 17.7 17.3 1.8 11.4 <0.001

Terai caste 4.7 30.1 10.5 39.8 4.0 10.9

Dalit 6.6 35.0 21.1 25.8 2.1 9.5

Janajati 10.2 31.2 24.6 17.3 2.0 14.9

Muslim 9.0 23.1 19.1 26.9 2.4 19.5

Occupation

Not working 12.9 33.6 20.1 20.1 2.6 10.8 <0.001

Nonagriculture 20.9 35.5 22.0 12.2 2.8 6.5

Agriculture 3.5 32.7 18.5 26.9 1.7 16.7

Wealth quintile

Lowest 0.8 19.2 17.8 32.5 1.2 28.4 <0.001

Second 2.9 33.5 21.2 24.7 2.7 15.0

Middle 3.4 34.7 17.2 28.8 2.5 13.5

Fourth 13.7 36.6 17.5 21.2 2.9 8.2

Highest 26.5 41.2 24.6 4.4 1.8 1.6

Total 10.0 33.5 19.7 21.8 2.2 12.7

N 1166 3912 2304 2539 259 1487

3.4 Descriptive Analysis of Modern Contraceptive Use, Antenatal Care

Visits, and Place of Delivery by Duration of Stay, Migration Streams,

and Background Characteristics

Table 5 presents the associations of modern contraceptive use, ANC visits, and place of delivery with 

duration of stay, migration streams, and other background characteristics. Five of the eight variables—

duration of stay, women’s age, education, caste/ethnicity, and occupation—are significantly associated with 

modern contraceptive use. Migration streams, residence, and wealth quintile do not have statistically 

significant associations with modern contraceptive use. The table shows that use of modern contraception 

is positively associated with the duration of stay. The percentage use of modern contraception increases 

with increasing duration of stay (Figure 4). Twenty-five percent of women who moved within the past five 
14

years are using modern contraceptives compared with 55% of women who moved 10+ years ago. There are 

no significant associations between migration streams and the use of modern contraception. Urban nonmigrant women have the highest level of contraceptive use, followed by urban-to-urban migrant women 

(Figure 5). The use of modern contraception ranges from 41% among urban-to-rural migrants to 48% 

among urban non-migrants.

Women age 40-49 have the highest percentage of modern contraceptive use (57%), and women age 15-19 

have lowest percentage (16%). Regarding education, 53% of women with no education are currently using 

modern contraception, followed by women with a primary education (43%). The use of modern 

contraceptives has an inverse relationship with women’s level of education. This could be due to the high 

level of sterilization among less-educated women. Looking at caste/ethnicity, Janajati women have the 

highest proportion of modern contraceptive use (48%). In contrast, Muslim women (30%) and 

Brahman/Chhetri women (41%) have relatively low levels of modern contraceptive use. There are 

significant associations between women’s occupation and modern contraceptive use. Levels of modern 

contraceptive use are higher among working women than nonworking women. Women engaged in an 

agriculture occupation have a comparatively high level of modern contraceptive use (48%), while non--

working women have the lowest level (37%). There are no significant differences in use of modern 

contraception by wealth quintile or by place of residence. 

Eight variables showed a highly significant (p<0.001) association with at least four ANC visits in the chisquare test. Table 5 shows that the highest proportions of attending at least four ANC visits are among 

women who moved within the past 5 years, and lowest among women who moved 10+ years ago (Figure 

4). Overall, 71% of women made at least four ANC visits for the most recent birth. A high proportion of 

urban-to-urban migrant women (90%) made at least four ANC visits, while the lowest proportion was 

among rural non-migrant women (61%) (Figure 5).

Table 5 Current use of modern contraception, antenatal care, and place of delivery

Percentage of eligible women age 15-49 using modern contraception, who had at least four visits for antenatal care, and delivered 

their most recent birth in the five years preceding the survey in a health facility, according to duration of residence and migration 

stream categories and selected background characteristics, Nepal DHS 2016

Background 

characteristics

Modern contraceptive use 4+ ANC visits Health facility

% 95% CI p-value % 95% CI p-value % 95% CI p-value

Duration of stay

0-4 years 25.2 [22.3-28.3] < 0.001 82.1 [78.8-85.0] < 0.001 76.2 [72.7-79.4] < 0.001

5-9 years 37.1 [34.0-40.3] 72.7 [69.1-76.0] 58.8 [53.9-63.4]

10+ years 55.0 [52.7-57.3] 60.8 [56.5-64.9] 43.3 [38.9-47.8]

Non-migrants 46.4 [43.2-49.7] 68.7 [63.5-73.4] 51.6 [45.5-57.7]

Migration streams

Urban-to-urban 45.6 [41.7-49.6] 0.059 89.9 [83.4-94.1] < 0.001 87.5 [80.3-92.4] < 0.001

Urban-to-rural 40.7 [33.8-48.0] 71.9 [58.5-82.2] 66.5 [52.6-78.0]

Rural-to-urban 44.7 [42.1-47.3] 76.4 [72.8-79.6] 67.8 [63.1-72.1]

Rural-to-rural 40.9 [37.8-44.1] 62.4 [57.7-66.9] 42.7 [38.1-47.4]

Urban non-migrants 48.4 [43.9-52.9] 76.0 [70.4-80.9] 64.0 [55.5-71.6]

Rural non-migrants 43.7 [39.4-48.1] 61.0 [52.5-68.8] 38.6 [30.4-47.4]

Continued...
15

Table 5—Continued

Background 

characteristics

Modern contraceptive use 4+ ANC visits Health facility

% 95% CI p-value % 95% CI p-value % 95% CI p-value

Age 

15-19 15.7 [12.4-19.7] < 0.001 77.8 [71.4-83.1] < 0.001 69.7 [62.2-76.3] < 0.001

20-29 32.6 [30.3-34.9] 72.8 [70.0-75.4] 59.2 [55.6-62.8]

30-39 52.6 [49.9-55.4] 67.1 [62.5-71.4] 52.1 [47.0-57.0]

40-49 57.3 [54.3-60.1] 45.6 [35.5-56.0] 31.3 [21.6-43.0]

Education 

No education 53.4 [50.8-56.0] < 0.001 52.6 [48.0-57.2] < 0.001 35.1 [31.0-39.5] < 0.001

Primary 43.4 [40.3-46.5] 64.8 [60.2-69.2] 47.9 [42.9-52.9]

Secondary 34.8 [32.3-37.3] 79.6 [76.4-82.5] 68.9 [64.6-72.9]

SLC and above 35.3 [32.4-38.2] 92.8 [90.4-94.7] 84.6 [80.9-87.7]

Caste/ethnicity

Brahman/Chhetri 41.3 [39.1-43.5] < 0.001 81.0 [77.1-84.3] < 0.001 69.6 [64.5-74.2] < 0.001

Terai caste 45.1 [41.6-48.8] 61.4 [54.3-68.1] 43.9 [37.4-50.7]

Dalit 44.2 [40.4-48.1] 63.1 [57.1-68.7] 46.7 [40.6-52.9]

Janajati 47.6 [44.7-50.5] 72.6 [68.1-76.6] 60.4 [54.9-65.6]

Muslim 29.5 [22.8-37.2] 56.5 [41.9-70.1] 42.2 [30.3-55.1]

Residence

Urban 45.5 [43.6-47.5] 0.024 78.3 [75.3-81.0] < 0.001 69.9 [65.7-73.8] < 0.001

Rural 41.5 [38.6-44.4] 62.6 [58.2-66.8] 43.0 [38.4-47.7]

Occupation

Not working 36.5 [34.0-39.0] < 0.001 69.9 [65.4-74.0] 0.001 61.4 [57.0-65.5] < 0.001

Nonagriculture 46.3 [43.7-48.9] 81.3 [75.8-85.7] 75.2 [68.3-81.1]

Agriculture 47.5 [45.0-50.0] 69.0 [65.8-72.0] 49.7 [45.9-53.5]

Wealth quintile

Lowest 42.6 [39.2-45.9] 0.538 56.9 [51.8-61.8] < 0.001 35.0 [29.6-40.8] < 0.001

Second 45.8 [42.8-48.9] 67.7 [63.1-72.1] 46.8 [42.0-51.5]

Middle 44.8 [41.7-48.0] 70.3 [64.7-75.3] 58.6 [53.6-63.5]

Fourth 43.0 [40.0-46.2] 77.9 [73.5-81.8] 70.8 [65.9-75.3]

Highest 43.6 [40.7-46.6] 90.4 [85.9-93.6] 89.9 [85.0-93.4]

Total 44.0 [42.4-45.6] 71.1 [68.5-73.6] 57.6 [54.4-60.7]

The study found that younger women are more likely to make at least four ANC visits compared with older 

women. About three-quarters (78%) of women age 15-19 made at least four ANC visits for their most recent 

birth compared with less than half (46%) of women age 40-49. There are significant associations between 

women’s education and ANC visits. As education level increases, the percentage of women making at least 

four ANC visits increases. About half (53%) of women with no education made at least four ANC visits 

compared with 93% of women who attained SLC and above education. Regarding caste/ethnicity, 

Brahman/Chhetri women have the highest proportion making at least four ANC visits, followed by Janajati, 

Dalit, and Terai caste women. Muslim women have the lowest proportion, making at least four ANC visits 

(57%). A large proportion of women who live in urban areas (78%) and women engaged in a nonagricultural 

occupation (81%) reported at least four ANC visits. The proportion of women with at least four ANC visits 

ranges from 57% in the lowest wealth quintile to 90% in the highest wealth quintile.
16

Figure 4 Percentage of eligible women age 15-49 using modern contraception, who had at least four 

antenatal care visits, and delivered their most recent birth in the 5 years preceding the survey in 

health facility, by duration of residence, Nepal DHS 2016 

Bivariate analysis shows that eight variables are highly significant (<0.001) for the association with delivery 

in a health facility. Use of a health facility for the most recent birth is higher among women who moved 

within the past five years, and lower among women who moved 10+ years ago (Figure 4). By migration 

streams, 88% of urban-to-urban migrant women delivered in a health facility, over twice the level of rural 

non-migrants, at 39% (Figure 5). 

Figure 5 Percentage of eligible women age 15-49 using modern contraception, who had at least four 

antenatal care visits, and delivered their most recent birth in the five years preceding the survey 

in a health facility, by migration streams, Nepal DHS 2016

25.2

37.1

55.0

46.4

82.1

72.7

60.8

68.7

76.2

58.8

43.3

51.6

0-4 years 5-9 years 10+ years Non-migrants

Modern contraception 4+ ANC visits Health facility

45.6 40.7 44.7 40.9

48.4 43.7

89.9

71.9 76.4

62.4

76.0

61.0

87.5

66.5 67.8

42.7

64.0

38.6

Urban-to-urban Urban-to-rural Rural-to-urban Rural-to-rural Urban nonmigrantsRural nonmigrants

Modern Contraception 4+ ANC visits Health facility
17

Overall, 58% of women delivered in a health facility. Table 5 shows that younger women are more likely 

to deliver in a health facility than older women. Seventy percent of women age 15-19 delivered in a health 

facility compared with 31% of women age 40-49. There is a positive association between women’s 

education and health facility delivery. Women with primary education have the lowest percentage of health 

facility delivery (48%), whereas women with SLC and above education have the highest percentage (85%). 

Considering caste/ethnicity, Brahman/Chhetri women have the highest percentage of delivery in a health 

facility (70%), followed by Janajati (60%). Muslim women have the lowest percentage of health facility 

delivery (42%). Seventy percent of urban women delivered in a health facility compared with 43% of rural 

women. Women engaged in a nonagricultural occupation have the highest percentage of delivery in a health 

facility. By wealth quintile, health facility delivery ranges from 35% in the lowest wealth quintile to 90%

in the highest quintile. 

3.5 The Effect of Duration of Stay on Modern Contraceptive Use, 

Antenatal Care Visits, and Place of Delivery

The logistic regression that tests the effect of duration of stay on contraceptive use, ANC visits, and place 

of delivery is presented in the following sections. The outcome variables are binary, therefore binary logistic 

regression is used. Adjusted odds are obtained through binary logistic regression of duration of stay in

current place of residence, including other background variables with outcome variables (modern 

contraceptive use, at least four ANC visits, and health facility delivery). The background variables included 

in the model are women’s age, education attainment, caste/ethnicity, place of residence, occupation, and 

wealth quintile.

3.5.1 The effect of duration of stay on modern contraceptive use

Table 6 presents our examination of the effect of duration of stay on modern contraceptive use. It shows

that duration of stay has a significant effect on the use of modern contraception among internal migrant 

women. The results also show a significant association of age, education, caste/ethnicity (Muslim), 

residence, occupation, and wealth quintile on modern contraceptive use. After controlling the other 

variables, the analysis shows that women who moved within the past five years have significantly lower 

odds of modern contraceptive use (OR=0.59, p<0.001) compared with non-migrant women. Women who 

moved to their current place 10+ years ago have the highest odds of modern contraceptive use (OR=1.15). 

A demographic factor, women’s age, has significant effects on the likelihood of using modern

contraception. Compared with women age 15-19, women age 20-29, 30-39, and 40-49 have, respectively,

1.93 times, 3.22 times, and 3.44 times higher odds of using modern contraception. 

Women who attained a primary, secondary, or SLC and above level of education have statistically 

significant lower odds of modern contraceptive use (OR=0.80, p<0.05; OR=0.66, p<0.001; and OR=0.69, 

p<0.001 respectively). Modern contraceptive use varies by women’s caste/ethnicity. Brahman/Chhetri 

women are less likely (OR=0.89) to use modern contraception than Dalit women. The odds of modern 

contraceptive use are 53% lower among Muslim women (OR=0.47, p<0.001) compared with Dalit women. 

When compared with Dalit women, Janajati and Terai caste women are more likely than Brahman/Chhetri 

women to use modern contraception. Women who reside in an urban area have 1.22 times higher odds of 

using modern contraceptives than women who reside in a rural area. A significant association exists between 

occupation and modern contraceptive use. Women who engage in a nonagricultural occupation have 1.28 
18

times higher odds of using modern contraceptives compared with nonworking women. Similarly, odds of 

modern contraceptive use are significantly higher among women engaged in an agricultural occupation 

(OR=1.25, p<0.01). Odds of current use of modern contraceptives are 1.24 times higher (p<0.05) among 

women in the middle wealth quintile and 1.25 times higher (p<0.05) among women in the fourth wealth 

quintile compared with women in the lowest wealth quintile. 

Table 6 Duration of stay and use of modern contraception, antenatal care visits, and place of delivery

Odds ratio showing association between duration of stay and modern contraceptive use, ANC visits, and delivery in a health facility 

among women age 15-49, Nepal DHS 2016

Background 

characteristics

Modern contraceptive use 4+ ANC visits Health facility

Odds ratio 95% CI Odds ratio 95% CI Odds ratio 95% CI

Duration of stay

Non-migrants 1.00 1.00 1.00

0-4 years 0.59*** 0.49 - 0.72 1.34* 1.00 - 1.78 2.06*** 1.51 - 2.81

5-9 years 0.83 0.69 - 1.00 0.95 0.72 - 1.25 1.02 0.76 - 1.37

10+ years 1.15 0.98 - 1.35 0.85 0.66 - 1.09 0.86 0.64 - 1.15

Age

15-19 1.00 1.00 1.00

20-29 1.93*** 1.45 - 2.57 0.81 0.55 - 1.21 0.68 0.46 - 1.01

30-39 3.22*** 2.28 - 4.55 0.85 0.53 - 1.36 0.74 0.46 - 1.18

40-49 3.44*** 2.44 - 4.84 0.57 0.32 - 1.02 0.61 0.30 - 1.23

Education

No education 1.00 1.00 1.00

Primary 0.80** 0.68 - 0.94 1.45** 1.13 - 1.87 1.39* 1.08 - 1.80

Secondary 0.66*** 0.56 - 0.79 2.26*** 1.73 - 2.96 2.13*** 1.62 - 2.80

SLC and above 0.69*** 0.56 - 0.85 5.33*** 3.47 - 8.19 2.98*** 2.08 - 4.27

Caste/Ethnicity

Dalit 1.00 1.00 1.00

Brahman/Chhetri 0.89 0.73 - 1.08 1.51* 1.07 - 2.14 1.74** 1.19 - 2.55

Terai caste 1.03 0.81 - 1.31 0.73 0.47 - 1.12 0.52** 0.35 - 0.77

Janajati 1.13 0.93 - 1.36 1.05 0.76 - 1.46 1.16 0.82 - 1.62

Muslim 0.47*** 0.31 - 0.70 0.67 0.36 - 1.26 0.52* 0.29 - 0.92

Residence

Rural 1.00 1.00 1.00

Urban 1.22* 1.04 - 1.44 1.37* 1.06 - 1.77 1.86*** 1.43 - 2.43

Occupation

Not working 1.00 1.00 1.00

Nonagriculture 1.28** 1.09 - 1.51 1.11 0.79 - 1.56 1.12 0.80 - 1.57

Agriculture 1.25** 1.06 - 1.48 1.41** 1.09 - 1.82 1.03 0.82 - 1.30

Wealth quintile

Lowest 1.00 1.00 1.00

Second 1.20 1.00 - 1.44 1.48** 1.12 - 1.96 1.44* 1.06 - 1.95

Middle 1.24* 1.01 - 1.51 2.21*** 1.58 - 3.10 3.47*** 2.41 - 4.98

Fourth 1.25* 1.01 - 1.54 2.83*** 1.97 - 4.07 4.89*** 3.36 - 7.13

Highest 1.22 0.96 - 1.56 3.89*** 2.30 - 6.59 9.84*** 5.56 - 17.41

N 8937 3300 3300

* p<0.05, ** p<0.01, *** p<0.001 
19

3.5.2 The effect of duration of stay on antenatal care visits

Adjusted logistic regression analysis shows that duration of stay (0-4 years), education, caste/ethnicity 

(Brahman/Chhetri), residence, occupation (nonagriculture), and wealth quintile have a significant 

contribution on at least four ANC visits. As Table 6 shows, women who moved within the past five years 

have statistically significantly higher odds of making at least four ANC visits compared with non-migrant 

women (OR=1.34, p<0.05). Women who moved 5-9 years or 10+ years ago have lower odds of making at

least four ANC visits (OR=0.95 and OR=0.85) compared with non-migrant women. The odds of attending 

ANC are significantly higher among women with any level of education compared with women who have 

no education. Women who attained SLC or above level of education have 5.33 times higher odds of 

attending at least four ANC visits compared with women who have no education (p<0.001). The odds of 

attending ANC are significantly higher among Brahman/Chhetri women (OR=1.51, p<0.05) compared with 

Dalit women, women living in an urban area (OR=1.37, p<0.05) compared with rural women, and women 

engaged in an agricultural occupation (OR=1.41, p<0.01) compared with non-working women. Women in 

the highest wealth quintile have 3.89 times higher odds of attending at least four ANC visits compared with 

women in the lowest wealth quintile (p<0.001).

3.5.3 The effect of duration of stay on place of delivery

Table 6 also presents the results of the binary logistic regression conducted for place of delivery and 

duration of stay with other selected background variables. The results show a significant association 

between place of delivery and duration of stay (0-4 years), education, caste/ethnicity (Brahman/Chhetri, 

Terai caste, and Muslim), residence, and wealth quintile (middle, fourth, and highest). Women who moved 

within the past years have significantly higher odds of delivering in a health facility (OR=2.06, p<0.001)

compared with non-migrant women. Compared with uneducated women, the odds of facility delivery are 

significantly higher among women with SLC and above education (OR=2.98, p<0.001), secondary 

education (OR=2.13, p<0.001), and primary education (OR=1.39, p<0.05). The higher the level of 

education, the more likely women are to deliver in a health facility. By caste/ethnicity, the odds of facility 

delivery are higher among Brahman/Chhetri women (OR=1.74, p<0.01) and lower among women of the 

Terai caste (OR=0.52, p<0.01) and Muslim women (OR=0.52, p<0.05) compared with the reference group, 

Dalit. Considering place of residence, women living in urban areas are more likely to deliver in a health 

facility than women in rural areas. Women in the highest wealth quintile have 9.84 times higher odds of 

delivering in a health facility compared with women in the lowest wealth quintile. The likelihood of 

delivering in a health facility increases as the level of household wealth increases. 

3.6 The Effect of Migration Stream on Modern Contraceptive Use, 

Antenatal Care Visits, and Place of Delivery

The following sections present results of logistic regression that tests the effect of migration stream on 

modern contraceptive use, ANC visits, and place of delivery. Adjusted odds are obtained through binary 

logistic regression of migration streams including other selected background variables with the outcome 

variables: modern contraceptive use, at least four ANC visits, and place of delivery.
20

3.6.1 The effect of migration stream on modern contraceptive use

In Table 7 the results of adjusted logistic regression show that migration streams (urban-to-urban and urban 

non-migrants), age of women, education, caste/ethnicity (Muslim), occupation, and wealth quintile (second 

and middle) have significant associations with modern contraceptive use. The odds of modern contraceptive 

use are higher among urban-to-urban migrants (OR=1.37, p<0.05) and urban non-migrants (OR=1.29, 

p<0.05) compared with rural-to-rural migrants (Figure 6). Urban-to-rural migrants, rural-to-urban migrants,

and rural non-migrants appear more likely to use modern contraception compared with rural-to-rural 

migrants. However, odds of modern contraceptive use are not statistically significant for these migration 

streams. 

As expected, married women age 40-49 have substantially higher odds of using modern contraception

compared with women age 15-19. Women who attained SLC and above level of education have 

significantly lower odds of modern contraceptive use (OR=0.62, p<0.001) compared with uneducated 

women. The odds of modern contraceptive use are 37% lower for women who attained a secondary level 

of education, and 22% lower for women with a primary level of education compared with women who have 

no education. Regarding caste/ethnicity, the odds of modern contraceptive use are 52% lower for Muslim 

women compared with Dalit women. The odds of using modern contraceptives are 12% higher for Dalit 

women compared with Brahman/Chhetri women. Women engaged in agriculture or nonagricultural

occupations have significantly higher odds of modern contraceptive use compared with nonworking

women. Women in the second and middle wealth quintiles have significantly higher odds of using modern 

contraception (OR=1.20, p<0.05 and OR=1.24, p<0.05 respectively) compared with women in the lowest

wealth quintile. 

Table 7 Migration streams and use of modern contraception, antenatal care visits, and place of delivery

Odds ratio showing association between migration streams and modern contraceptive use, ANC visits, and delivery in a health facility 

among women age 15-49, Nepal DHS 2016

Background 

characteristics

Modern contraceptive use 4+ ANC visits Health facility

Odds ratio 95% CI Odds ratio 95% CI Odds ratio 95% CI

Migration streams

Rural-to-rural 1.00 1.00 1.00

Urban-to-urban 1.37* 1.06 - 1.77 1.89* 1.02 - 3.50 2.74** 1.46 - 5.11

Urban-to-rural 1.11 0.81 - 1.53 1.04 0.58 - 1.88 1.96* 1.12 - 3.42

Rural-to-urban 1.20 1.00 - 1.45 1.35* 1.02 - 1.78 1.92*** 1.43 - 2.58

Urban non-migrants 1.29* 1.02 - 1.64 1.40 0.97 - 2.01 1.60* 1.09 - 2.33

Rural non-migrants 1.12 0.94 - 1.34 1.03 0.74 - 1.42 0.94 0.66 - 1.33

Age

15-19 1.00 1.00 1.00

20-29 2.33*** 1.75 - 3.11 0.67* 0.47 - 0.97 0.47*** 0.32 - 0.69

30-39 4.77*** 3.40 - 6.69 0.64* 0.41 - 0.99 0.42*** 0.27 - 0.66

40-49 5.24*** 3.77 - 7.28 0.43** 0.24 - 0.75 0.35** 0.17 - 0.70

Education

No education 1.00 1.00 1.00

Primary 0.78** 0.67 - 0.91 1.47** 1.14 - 1.89 1.40* 1.07 - 1.82

Secondary 0.63*** 0.53 - 0.74 2.36*** 1.81 - 3.08 2.29*** 1.75 - 3.01

SLC and above 0.62*** 0.51 - 0.76 5.74*** 3.72 - 8.85 3.38*** 2.37 - 4.82

Continued...
21

Table 7—Continued

Background 

characteristics

Modern contraceptive use 4+ ANC visits Delivered in health facility

Odds ratio 95% CI Odds ratio 95% CI Odds ratio 95% CI

Caste/ethnicity

Dalit 1.00 1.00 1.00

Brahman/Chhetri 0.88 0.72 - 1.07 1.54* 1.09 - 2.17 1.78** 1.22 - 2.61

Terai caste 1.09 0.86 - 1.38 0.72 0.47 - 1.11 0.50*** 0.34 - 0.75

Janajati 1.09 0.91 - 1.32 1.08 0.78 - 1.49 1.21 0.86 - 1.70

Muslim 0.48*** 0.32 - 0.71 0.68 0.37 - 1.26 0.52* 0.30 - 0.92

Occupation

Not working 1.00 1.00 1.00

Nonagriculture 1.30** 1.11 - 1.53 1.06 0.76 - 1.49 1.04 0.75 - 1.45

Agriculture 1.32** 1.12 - 1.56 1.36* 1.06 - 1.76 0.97 0.77 - 1.21

Wealth quintile

Lowest 1.00 1.00 1.00

Second 1.20* 1.00 - 1.44 1.47** 1.11 - 1.94 1.41* 1.04 - 1.91

Middle 1.24* 1.01 - 1.52 2.19*** 1.57 - 3.07 3.35*** 2.32 - 4.82

Fourth 1.20 0.97 - 1.48 2.81*** 1.94 - 4.06 4.73*** 3.22 - 6.95

Highest 1.19 0.92 - 1.53 3.64*** 2.21 - 6.00 8.69*** 4.86 - 15.52

N 8937 3300 3300

* p<0.05, ** p<0.01, *** p<0.001 

Figure 6 Adjusted odds ratio of modern contraceptive use and 95% confidence interval by migration 

streams

Note: Based on Table 7

Rural-to-rural migration is reference.

3.6.2 The effect of migration stream on antenatal care visits

Concerning the effect of migration streams on ANC visits, Table 7 shows that urban-to-urban migrant 

women have statistically significant higher odds of ANC visits (OR=1.89, p<0.05), as do rural-to-urban 

migrant women (OR=1.35, p<0.05), compared with rural-to-rural migrant women. Considering other 

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Urban-to-urban

Urban-to-rural

Rural-to-urban

Urban non-migrants

Rural non-migrants

Odds ratios
22

background variables, there are statistically significant associations between women’s age, education, and 

wealth quintile and attending at least four ANC visits. Women age 40-49, 30-39, and 20-29 have statistically 

significant lower odds of ANC visits (OR=0.43, p<0.01; OR=0.64, p<0.05; and OR=0.67, p<0.05 

respectively) compared with young women age 15-19. The odds of receiving ANC are significantly higher

among women with SLC and above education (OR=5.74, p<0.001), secondary education (OR=2.36, 

p<0.001), and primary education (OR=1.47, p<0.01) compared with women with no education. 

Brahman/Chhetri women have 1.54 times higher odds of attending at least four ANC visits compared with 

Dalit women. Women engaged in agriculture have significantly higher odds of ANC visits compared with 

nonworking women (OR=1.36, p<0.05). There is also a statistically strong association between wealth 

quintile and ANC visits. Women in the highest wealth quintile are 3.64 times more likely to make at least 

four ANC visits compared with women in the lowest wealth quintile.

Figure 7 Adjusted odds ratio of four or more antenatal care visits and 95% confidence interval by 

migration streams

Note: Based on Table 7

Rural-to-rural migration is reference.

3.6.3 The effect of migration stream on place of delivery

Table 7 also shows that migration streams have a statistically significant association with place of delivery. 

Urban-to-urban migrants, rural-to-urban migrants, urban-to-rural migrants, and urban non-migrants have 

statistically significant higher odds of facility delivery (OR=2.74, p<0.01; OR=1.92, p<0.001; OR=1.96, 

p<0.05 and OR=1.60, p<0.05 respectively) compared with rural-to-rural migrant women. Rural nonmigrants have lower odds of health facility delivery (OR=0.94) compared with rural-to-rural migrant 

women (Figure 8). Women age 20-29, 30-39 and 40-49 have significantly lower odds (OR=0.47, p<0.001;

OR=0.42, p<0.001, and OR=0.35, p<0.01 respectively) compared with women age 15-19. More-educated 

women are more likely to deliver in a health facility compared with less-educated women. The odds of 

health facility delivery are significantly higher among women who attained a SLC and above level of 

education compared with women who have no education (OR=3.38, p<0.001). 

Brahman/Chhetri women are significantly more likely (OR=1.78, p<0.01) to deliver in a health facility than 

Dalit women. The odds of health facility delivery are significantly lower among Terai caste and Muslim 

women (OR=0.50, p<0.001 and OR=0.52, p<0.05) compared with Dalit women. There is a significant 

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Urban-to-urban

Urban-to-rural

Rural-to-urban

Urban non-migrants

Rural non-migrants

Odds ratios
23

association between wealth quintile and health facility delivery. Women in the highest wealth quintile have 

8.69 times higher odds of delivering in a health facility compared with women in the lowest wealth quintile. 

A statistically significant association does not exist between women’s occupational status and health facility 

delivery.

Figure 8 Adjusted odds ratio of delivering in health facility and 95% confidence interval by migration 

streams

Note: Based on Table 7

Rural to rural migration is reference.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Urban-to-urban

Urban-to-rural

Rural-to-urban

Urban non-migrants

Rural non-migrants

Odds ratios

25

4 DISCUSSION

The overall objective of the study is to investigate the effects of internal migration on the use of reproductive 

and maternal health outcomes in Nepal. The analysis has been drawn from the datasets of 2016 Nepal 

Demographic and Health Survey. Previous Nepal DHS surveys, except NDHS 2011, reported number of 

years lived in current place of residence, previous place of residence before moved, and current place of 

residence. Further analysis did not focus on the use of reproductive and maternal health services, as in the 

present study. However, two previous studies, Staveteig, et al. (2018) and Khanal (2013), focused on 

spousal migration and contraceptive use, which did not cover the effects of internal migration on the use of 

reproductive and maternal health services. Therefore, it is rational to analyze the effect of internal migration 

on the use of reproductive health and maternal health services in Nepal, and to focus policy attention on the 

effects of internal migration. 

The Nepal Health Sector Strategy 2016-2021 aims to expand equitable access to high-quality family 

planning services, increase the availability of modern family planning methods, and satisfy the demand for 

family planning (Ministry of Health 2017). National Population Policy 2015 also emphasizes the expansion 

of family planning services at the local level for population management (Ministry of Health and Population 

2015). 

The prevalence of contraceptive use has increased substantially over the past decade, from 29% in 1996 

(Macro International Inc. 2007) to 53% in 2016 (Ministry of Health, New ERA, and ICF 2017). Our study 

shows that the use of modern contraception varies with the socioeconomic, demographic, and migration 

status of women. It shows that the duration of stay is positively associated with modern contraceptive use.

Modern contraceptive use is significantly lower for all women who moved within the past five years. Long 

duration of stay is associated with more exposure to and contact with reproductive health services (Smith 

and Qian 2010). Therefore, the longer the duration of stay in the current place of residence, the more likely 

the use of modern contraception. Recent migrant women lack a social network and are not familiar with 

how to access reproductive health services in their new place of residence. Women who moved within the 

past five years are less likely to use modern contraception compared with non-migrant women. Many recent 

migrant women could be currently married and thus less likely to use contraception. This result is in line 

with previous studies in Guatemala (Lindstrom and Hernandez 2006) and Mozambique (Cau 2016). The 

adjusted results also indicate that women’s age, education, and occupation are factors associated with use 

of modern contraception.

The use of modern contraceptive methods is highest among urban non-migrants, followed by urban-tourban migrant women. This variation in results could be due to selectivity of migration. Bivariate analysis 

indicates that migration streams are not significantly associated with use of modern contraception. A study 

in Kenya reveals that rural-to-urban migrants and urban non-migrants are more likely to use modern 

contraceptive than rural non-migrants (Ochako et al. 2016). When all other factors are taken into 

consideration, urban non-migrants and urban-to-urban migrants are more likely to use modern 

contraception compared with rural-to-rural migrants. The odds of using modern contraception are higher 

among urban-to-urban migrant women, rural-to-urban migrant women, and urban non-migrant women 

compared with rural non-migrant women. The multivariate analysis indicated that women’s age and 
26

occupation status have a strong association with modern contraceptive use. This finding is consistent with 

previous studies (Mishra, Mishra, and Dixit 2014).

Women’s age has a positive association with modern contraceptive use. Younger women are less likely to 

use modern contraceptives, while older women are more likely. This finding is consistent with previous 

studies in Nepal (Mishra 2010), Kenya (Ochako et al. 2016), and Myanmar (Sudhinaraset et al. 2016). A 

study from rural Nepal shows that women age 45-49 are less likely to use modern contraception, and women 

age 25-34 are more likely to use modern contraception compared with women age 15-24 (Wang et al. 2013). 

Older women are more likely to be currently married, to have more children, and to desire no more children, 

which can be considered a possible explanation for these results.

Women’s education is the most important predictor of contraceptive use. A study conducted in Kenya 

showed that education has positive effects on the use of contraception (Omandi and Ayiemba 2003). This 

study found that the relationship between female education and use of modern contraception is not linear. 

Educated women may have a greater capacity of decision making regarding choice and use of contraceptive 

methods. They are more aware of the side effects of modern contraceptive use. Previous studies showed 

that more educated women were significantly less likely to use contraceptives than uneducated women 

(Karki and Agrawal 2008).

Caste/ethnicity background has been identified as one of the important factors affecting use of contraceptive 

methods (Bennett, Dahal, and Govindasamy 2008; Sharma, KC, and Ghimire 2011; Mishra 2010). Our 

study showed the ethnic variation in use of modern contraception. It is argued that the ethnic variation in

contraceptive use is due to religious faith, values, and cultural norms. The study showed that women’s place 

of residence has no significant association with use of modern contraceptives. Differences exist in the use

of modern contraception by women’s occupation. Women who are working, whether in agriculture or 

nonagricultural sectors, have higher use of modern contraception than women who are not working. This 

result is consistent with a previous study (Sharma, KC and Ghimire 2011). The possible reason could be 

that women who are working are more likely to be educated and to have more awareness of reproductive 

health. The association between modern contraceptive use and wealth quintile is not significant, and there 

are no significant differences in use of modern contraception between wealth quintiles. 

The government of Nepal has given prime attention to ensuring access to antenatal care for women of 

reproductive age (Ministry of Health and Population, New ERA, and ICF 2012). The bivariate results 

revealed that duration of stay, migration streams, and other background variables are significantly 

associated with ANC visits. The analysis shows that attending ANC visits decreases with an increase of 

duration of stay in current place of residence. Women who migrated within the past five years are more 

likely to make at least four ANC visits. This result supports that migrants are self-selected and that selection 

factors play an important role in the use of maternal health services (Amankwaa, Bavon and Nkansah 2003). 

Previous studies have shown that older women are less likely to attend four ANC visits than younger 

women, that there is a positive association between education and ANC visits, that a positive relationship 

exists between wealth quintile and ANC visits, and that women residing in urban areas are more advantaged 

in terms of ANC (Dahiru and Oche 2015, Joshi et al. 2014). The result of this study shows a similar trend. 

This study also finds that Brahman/Chhetri women are more likely to have at least four ANC visits. 

However, previous studies showed that women of Janajati and Newar ethnicity were more likely to attend 

at least four ANC visits (Pandey et al. 2013). Women in the low wealth quintile are less likely to have at 
27

least four ANC visits, which is consistent with a previous study (Joshi et al. 2014) but inconsistent with the 

finding of other studies in Nepal (Mehata et al. 2017). 

The results of multivariate analysis show that, compared with non-migrant women, women who migrated 

within the past five years have higher odds of making at least four ANC visits. For women who migrated 

more than five years ago, there are no statistically significant differences in making four ANC visits 

compared with non-migrant women. The possible explanation for this result is that migration is a selective 

process. Marriage is the single most important reason for female internal migration in Nepal (Suwal 2014). 

Therefore, women whose duration of residence is less than five years tend to be younger. They are aware 

of consequences of pregnancy and childbirth. The study found that education and wealth quintile have a 

significant association with making at least four ANC visits. Thus, duration of stay seems to be an important 

predictor of ANC visits after considering all factors that influence the likelihood of attending ANC. 

The logistic regression results show that urban-to-urban migrant women and rural-to-urban migrant women 

are more likely to make four ANC visits than rural-to-rural migrant women. Previous studies from Peru 

showed that rural migrant women were less likely to make ANC visits than urban non-migrant women 

(Subaiya 2007). The analysis further points out that women’s age, education, and wealth quintile are 

strongly associated with ANC visits, after considering all other factors. 

Maternal health is a prime concern in the National Health Policy in Nepal. The Government of Nepal has 

implemented various strategies to improve maternal and child health. Place of delivery determines the 

welfare of mother and babies. Increasing the percentage of births delivered in health facilities reduces 

maternal death due to complications of pregnancy and childbirth. Fifty-seven percent of women deliver in 

a health facility at the national level in Nepal (Ministry of Health, New ERA, and ICF 2017). The study 

reveals that migrant women are more likely to deliver in a health facility compared with non-migrant 

women. This finding is consistent with a study in Myanmar (Sudhinaraset et al. 2016). The choice of place 

to deliver is also influenced by duration of stay. Like ANC visits, women who moved to their current place 

of residence within the past five years are more likely to deliver in a health facility. This might be due to

selectivity of migrants. The choice of place of delivery varies with the differing demographic and 

socioeconomic characteristics of migrant women. The results of adjusted odds ratio analysis indicate that 

women’s education, caste/ethnicity, place of residence, and wealth quintile play a strong role in the odds of 

delivering in a health facility. This finding is consistent with previous studies in Kenya (Kitui, Lewis, and 

Davey 2013), Nigeria (Dahiru and Oche 2015), Ghana (Dickson, Adde, and Amu 2016), and Nepal (Mehata 

et al. 2017). However, in our study, women’s age and occupation are not found to be statistically significant 

in their association with health facility delivery. 

Results of adjusted logistic regression suggest that internal migration flow is an important factor that 

strongly affects delivery in a health facility. The results further show that some control variables—women’s

age, education, caste/ethnicity, and wealth quintile—are significant predictors of health facility delivery. 

The study demonstrates that women who are more likely to deliver in a health facility are urban-to-urban 

migrants or rural-to-rural migrants, have attained higher education, are of Brahman/Chhetri, and are 

wealthier. Women who are age 20-49 of Terai caste or are Muslim are significantly less likely to deliver in

a health facility. 
28

The effects of internal migration on the use of reproductive and maternal health services have been studied 

by comparison of six categories of internal migration flows. This study confirms that urban-to-urban 

migrant women and urban non-migrant women are more likely to use modern contraceptives compared 

with rural-to-rural migrant women. Urban-to-urban migrant women and rural-to-urban migrant women are 

more likely to receive four ANC visits compared with rural-to-rural migrants. Similarly, urban-to-urban, 

urban-to-rural, rural-to-urban, and urban non-migrant women are more likely to deliver in a health facility 

compared with rural-to-rural migrant women. The results of this study indicate that recent migrant women 

are more likely to attend four ANC visits and to deliver in a health facility. This could be because women 

in their first pregnancy might be more careful or anxious about problems of pregnancy and childbirth due 

to their inexperience of pregnancy and delivery.
29

5 CONCLUSION

The movement of people within and between countries is a complex phenomenon with different 

implications. It is difficult to generalize the impact of migration on the use of reproductive and maternal

health service due to limited availability of data.

The study indicates that duration of stay and migration streams are significantly but differentially associated 

with use of modern contraceptives, at least four ANC visits, and health facility delivery. Migration variables 

play a central role, which has direct and indirect consequences on socioeconomic and demographic 

characteristics of women that affect the use of reproductive and maternal health services. Migrant women 

who moved within the last 5 years are less likely to use modern contraceptives and are more likely to make 

at least four ANC visits, and deliver in a health facility.

This study also showed that women who migrate between urban areas and women who are urban nonmigrants are more likely to use modern contraception. In relation to ANC visits, women who migrate to an 

urban area are more likely to make at least four ANC visits. Women are more likely to deliver in a health 

facility if they migrated to an urban area or if they are urban non-migrants. In general, the primary 

conclusion of this study is that women who migrate to an urban area as well as urban non-migrants are 

more likely to use reproductive and maternal health services compared with rural non-migrants and women 

who migrate to a rural area. While we might imagine that urban residence would confer an advantage 

regardless of origin, it appears that the new environment does not significantly affect the outcomes of 

previous rural residence, suggesting either greater disadvantage within the urban environment or persistence 

of previous behavior regardless of residence. 

Among the different socioeconomic and demographic factors, the study identified women’s age, education, 

and occupational status as strongly associated with use of modern contraceptives. Women’s education and 

wealth quintile are strongly associated with ANC visits. Women’s education, caste/ethnicity, place of 

residence, and wealth quintile are associated with health facility delivery. Urban women, women age 20-

29 or 40-49, women engaged in agriculture or a non-agricultural occupation, and women in the middle or 

fourth wealth quintile are more likely to use modern contraceptives. Muslim women and women who have 

attained primary, secondary, or SLC and above level of education are less likely to use modern 

contraceptives.

Women who are urban-to-urban migrants or rural-to-urban migrants, women who are educated, 

Brahman/Chhetri women, women engaged in agriculture, and women in the highest wealth quintile are 

more likely to attend at least four ANC visits. The study also shows that women who are urban-to-urban, 

urban-to-rural, rural-to-urban migrants or urban non-migrants, have attained higher education, are

Brahman/Chhetri, and are in the highest wealth quintile are more likely to deliver in a health facility. Women 

who are in the Terai caste or Muslim, are rural non-migrants, and are engaged in agriculture are less likely 

to deliver in a health facility. This study has various implications for policymakers, program implementers,

and stakeholders that could help to improve women’s reproductive and maternal health services in Nepal. 

The differentials of use of reproductive and maternal health services by internal migration status should be 

considered when designing programs to improve women’s access to reproductive and maternal health 

services. 

31

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