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Male out-migration and women's work and empowerment in Agriculture: the case of Nepal
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This presentation was given by Vanya Slavchevska (CIAT), as part of the Annual Gender Scientific Conference hosted by the CGIAR Collaborative Platform for Gender Research. The event took place on 25-27 September 2018 in Addis Ababa, Ethiopia, hosted by the International Livestock Research Institute (ILRI) and co-organized with KIT Royal Tropical Institute. Read more: http://gender.cgiar.org/gender_events/annual-conference-2018/
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Male out-migration and women's work and empowerment in Agriculture: the case of Nepal
- Male Out-Migration and Women’s Work and Empowerment in Agriculture: the Case of Nepal Anuja Kar (World Bank Group), Vanya Slavchevska (CIAT), Susan Kaaria (FAO), Sanna Lisa Taivalmaa, Erdgin Mane (FAO), Riccardo Ciacci (FAO), Yurie Tanimichi Hoberg (World Bank Group), Robert Townsend (World Bank Group), and Victoria Stanley (World Bank Group) 25-28 September 2018
- Motivation • In many global regions, the female share of agricultural employment has been rising due to: • Male outmigration • Globalization of agri- food systems • Better statistics and awareness about rural women’s work • Other factors (climate change, conflict, disease, technologies, etc.) 0% 10% 20% 30% 40% 50% 60% 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013p 2014p 2015p 2016p 2017p 2018p Female Share of Agricultural Employment World Central and South-Eastern Europe (non-EU) and CIS East Asia South-East Asia and the Pacific South Asia Latin America and the Caribbean Middle East North Africa Sub-Saharan Africa
- Rural outmigration • Rural outmigration, whether to domestic or international destinations, is an important component of migration flows. • 266 million international migrants in 2017 (UN DESA, 2017) • 763 million internal migrants in 2005 (UN DESA, 2013) • Yet, it is not well accounted for in migration statistics and its drivers and consequences on rural areas are not adequately studied. • Migration originating from rural areas is predominantly male (Mueller et al. 2015) raising concerns about the consequences of migration on sending rural communities in terms: • Women’s work and empowerment; changes in traditional gender norms • Changes in household food security • Agricultural productivity and production, etc..
- Research objectives: Examine the linkages between male-dominated outmigration and women’s work and empowerment in agriculture in Nepal: In particular, the analysis aims to understand: 1. how outmigration influences women’s work in agriculture; 2. the consequences of male-dominated migration on gender roles and women’s empowerment.
- Conceptual Framework • Migration affects women’s work mainly through: • the loss of migrants’ labor, and • the remittance income -- the reservation wage hypothesis vs the investment hypothesis. • Migration may also alter intrahousehold power relations and individuals’ empowerment: • differential effects on the different domains of empowerment • the effect will likely be mediated by the receipt of remittances
- Methodology – base model • We model women’s labor allocation and empowerment as a function of whether the woman lives in a household with an international migrant (𝑀1ℎ) or internal migrant (𝑀2ℎ) , and her individual, household and community characteristics, 𝑋𝑖ℎ: 𝑌𝑖ℎ = 𝛼 + 𝛽1 𝑀1ℎ + 𝛽2 𝑀2ℎ + 𝛾𝑋𝑖ℎ + 𝜀𝑖 (1) where 𝑌𝑖ℎ is a set of different indicators for women’s work in agriculture and outside of agriculture and 𝜀𝑖 is the error term. • The same model is employed to study the impacts on women’s empowerment in agriculture, where the indicators of empowerment are based on the five domains of the A- WEAI and include: • i) an indicator for whether the respondent is adequately empowered in the decisions about agricultural production; • ii) whether she has adequate control and access to resources; • iii) whether she has control of income; • iv) whether she is overworked (based on a 24-hour time-use recall module); and • v) whether she is an active group in the community.
- Methodology – model with remittances • To differentiate the labor effect of migration and the income effect of remittances received, model 1 is re-estimated using the following model: 𝑌𝑖ℎ=𝛼+𝛽1 𝑀1𝑅1ℎ+𝛽2 𝑀1𝑅0ℎ+𝛽3 𝑀2ℎ+𝛾𝑋𝑖ℎ+𝜀𝑖 (2) with the following indicators: • (i) 𝑀1𝑅1ℎ - household has an international migrant who has sent any remittances in the last year; • (ii) 𝑀1𝑅0ℎ - household has an international migrant but has not received any remittances in the past year; and • (iii) 𝑀2ℎ - household has at least one internal migrant (and no international migrants), regardless of whether the internal migrant has sent remittances. • The base category includes women in households with no international or internal migrants and no remittances.
- Methodology - Instrumental variable approach • We use an instrumental variable (IV) approach to correct for the endogenous migration variable. • The first stage regression is: 𝑀ℎ = 𝑍ℎ + 𝑇ℎ + 𝛾𝑋𝑖ℎ + 𝜀𝑖ℎ • Where: 𝑍ℎ is the first instrument representing the family migration history (a dummy variable taking value 1 if the parents or the parents-in-law of the respondent have ever lived in another country), and • 𝑇ℎ is the second instrument representing the current migration network (the fraction of households with an international migrant in a given ward based on the listing. • Standard errors are robust (Huber-White).
- Data details Source: “Technical Report on Survey of Migration and Women’s Empowerment in Agriculture” prepared by Nepa School of Social Sciences and Humanities, September 2, 2017. • Primary survey data collected August-September 2017 • a sample of 1002 households • from 5 districts -- Achham, Rolpa, Nawalparasi, Makwanpur and Jhapa • representative at district-level • Detailed information on both migrants and non-migrant members in rural households; • modules on crop production, livestock rearing, social protection and employment of all household members. • The Abbreviated Women’s Empowerment in Agriculture Index (A-WEAI) questionnaire administered to ONE individual from each household. • The Food Insecurity Experience Scale (FIES).
- Country context – agriculture & migration • Agriculture is the main sector of employment for most men and women, but it has become much more important for women in Nepal • Agricultural work is the primary activity for almost 66% of working- age women (over 15 years) compared to 53% of working-age men. Female share of agricultural employment (%)
- Nepali Migration • International migration is an important HH livelihoods diversification strategy • Nepal has one of the highest shares of remittances in GDP – 29.2% (WDI) • International migration has become more important than internal migration – • around 15% of working-age population in our sample are current international migrants • Less than 3% of working-age individuals in our sample are classified as current internal migrants • Men dominate migration -- more than 93% of current migrants are men • Migrants tend to be: • younger than the average working-age population; and • better educated – only 9% of migrants, compared to 29% of the working-age population, have no education. • Destinations: • 35% of international migration to India • >60% to Malaysia and the Gulf countries • Internal migration – primarily to Kathmandu • Main reasons for migration: economic (looking for better jobs)
- Remittances • 45% of all households in our sample receive remittances • 87% of all households with a current international migrant receive remittances • The median amount of the remittances sent by all migrants over the past year was 160,000 Nepali rupees (or around 1,555 USD) • Almost 2/3 of remittance senders indicate how the remittances should be used
- Characteristics of women in sending communities, by migration status of the HH • Few noticeable differences in individual characteristics between women in international migrant HH and women in all other households • Yet, significant differences in HH demographic structures: • more young children (under 5 years) in migrant HHs; • more adult women and men in migrant HHs • Few clear difference in HH wealth: • Migrant households have better dwellings (proxied by the quality of roofs and floors) • Non-migrant and domestic migrant HH have better access to services (electricity and drinking water) • No significant differences landownership
- Labor market outcomes of women in sending communities, by HH migration status • Women (and men) in international migrant households are just as likely to be economically active as women (and men) in non-migrant households. • Nearly 90% of all adults participated in at least one employment activity in the year before the survey • A significant share on women in migrant households engaged in agriculture as self-employed rather than as contributing family workers
- Employed (any) Farm self- employed Farm contributing family workers Agricultural (wage) laborers Processing (agricultural products) Trading (agricultural products) Nonagricultural workers Professional (1) (2) (3) (4) (5) (6) (7) (8) A. Base model - no controls for remittances (N=1667) , OLS International migrant in household -0.00508 0.167*** -0.177*** 0.00199 -0.0332** 0.00309 -0.00604 0.00298 (0.0174) (0.0241) (0.0274) (0.0118) (0.0168) (0.00382) (0.0124) (0.00952) B. Controlling for migration and remittances (N=1618‡), OLS Household with an international migrant, with remittances 6.71e-05 0.214*** -0.218*** -0.00104 -0.0400** 0.00311 0.000198 0.00227 (0.0186) (0.0252) (0.0291) (0.0134) (0.0188) (0.00419) (0.0130) (0.0103) Household with an international migrant, no remittances -0.0419 0.0745* -0.135*** -0.0326 -0.00817 0.00372 0.00239 -0.00703 (0.0427) (0.0425) (0.0512) (0.0230) (0.0268) (0.00289) (0.0203) (0.00940) Internal migrant in household -0.0234 0.190*** -0.252*** -0.0320* -0.0403 0.000553 0.0248 -0.00813 (0.0382) (0.0499) (0.0589) (0.0190) (0.0337) (0.00174) (0.0278) (0.00774) Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The associations between migration and women’s work in Nepal, OLS
- Employed (any) Farm self- employed Farm contributing family workers Agricultural (wage) laborers Processing (agricultural products) Trading (agricultural products) Nonagricultural workers Professional (1) (2) (3) (4) (5) (6) (7) (8) B. Women (obs. 1,667) International migrant in household -0.136 0.253* -0.427*** 0.0596 0.108 0.0132 0.119 -0.0989 (0.0924) (0.135) (0.151) (0.0789) (0.0863) (0.0190) (0.0734) (0.0623) F-test 20.90 20.90 20.90 20.90 20.90 20.90 20.90 20.90 Sargan-Hansen (p value) 0.9147 0.368 0.0540 0.246 0.00303 0.251 0.904 0.257 Note: Robust standard errors in parentheses; 2SLS = two-stage least squares. *** p<0.01, ** p<0.05, * p<0.1 The impact of migration on women’s work in Nepal, 2SLS
- Land management and land ownership Male land manager(s) only Female land manager(s) only Joint land manager Male land owner(s) only Female land owner(s) only Joint land owner (1) (2) (3) (4) (5) (6) International migrant in HH -0.0550*** 0.220*** -0.165*** -0.0384 0.0944*** -0.0560 (0.0197) (0.0329) (0.0343) (0.0362) (0.0333) (0.0342) Observations 876 876 876 691 691 691 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
- # of activities in which individual participates # of AG activities in which individual participates Input in decisions in AT LEAST 2 domains Access info for at least 1 AG activity Solely or jointly owns AT LEAST two small assets Makes decisions about credit Access to a financial account (1) (2) (3) (4) (5) (6) (7) A. Base model - no controls for remittances, OLS International migrant in household -0.185* -0.093 0.001 -0.008 2.16e-05 -0.025 0.039 (0.097) (0.096) (0.018) (0.021) (0.005) (0.049) (0.042) Observations 726 726 699 698 726 726 726 B. Controlling for migration and remittances‡, OLS Household with an international migrant, with remittances -0.223** -0.104 -0.013 0.002 0.005 0.008 0.084* (0.107) (0.107) (0.015) (0.022) (0.006) (0.054) (0.047) Household with an international migrant, no remittances -0.547** -0.418* -0.021 -0.14** -0.022 -0.019 -0.063 (0.230) (0.218) (0.041) (0.07) (0.035) (0.087) (0.079) Internal migrant in household -0.271 -0.118 -0.048 -0.034 0.013 0.086 0.080 (0.179) (0.179) (0.046) (0.056) (0.011) (0.093) (0.010) Observations 706 706 680 679 706 706 706 Migration and women’s empowerment (based on A-WEAI modules), OLS
- Makes decisions about what to plant on ANY land Resp. solely or jointly owns land Decides about the use of AG income Decides about the use of non- AG income Member of at least 1 community group Minutes spent on work Respondent worked less than 10.5 hours in previous 24 hours (8) (9) (10) (11) (12) (13) (14) A. Base model - no controls for remittances, OLS International migrant in household 0.070** 0.064 0.014 -0.089*** 0.120*** -4.851 -0.032 (0.031) (0.043) (0.022) (0.034) (0.046) (12.59) (0.045) Observations 694 694 726 726 726 726 726 B. Controlling for migration and remittances‡, OLS Household with an international migrant, with remittances 0.084** 0.0679 0.031 -0.116*** 0.153*** 6.120 -0.066 (0.034) (0.0463) (0.025) (0.039) (0.052) (13.94) (0.050) Household with an international migrant, no remittances 0.074 -0.063 -0.083 -0.109* -0.072 -26.68 0.056 (0.074) (0.070) (0.060) (0.063) (0.084) (26.36) (0.091) Internal migrant in household 0.059 -0.094 0.016 -0.145** 0.086 19.62 -0.085 (0.049) (0.067) (0.050) (0.059) (0.081) (25.20) (0.091) Observations 675 675 706 706 706 706 706 Migration and women’s empowerment in Nepal, OLS
- Conclusions • This study adds to the scarce evidence on rural outmigration and its interlinkages with women’s work and empowerment in agriculture. • It finds that male outmigration from rural, primarily agricultural areas is not linked to a decrease in women’s employment, but it is associated with significant changes in women’s roles in agriculture. • Male-dominated outmigration is associated with improvements in some domains of women’s empowerment, but not all. • Some evidence of a reduction of income from agriculture, but no impacts on food security. • The effects are mediated by the receipt of remittances.
- Next steps: • Use climate data as instruments to isolate the causal effects of migration and remittances on the labor and empowerment outcomes of non-migrant women and men in sending communities • Explore the heterogeneity of impacts depending on the characteristics of migrants (e.g. destination, length of migration, etc.) and the characteristics of women who stay behind (e.g. age, etc.) • Assess the effects of migration on agricultural production and productivity • Ideally, collect qualitative data for a mixed methods study of the linkages between migration and women’s changing roles in agriculture
Editor's Notes
- Pic by Neil Palmer (CIAT) The presentation here is largely based on the working paper produced jointly by the World Bank Group and the Food and Agriculture Organization of the United Nations (FAO) and published at http://documents.worldbank.org/curated/en/653481530195848293/pdf/127755-REVISED-Male-Outmigration-and-Women-s-Work-and-Empowerment-in-Agriculture-The-Case-of-Nepal-and-Senegal.pdf
- Figure 1 provides aggregate estimates of the trends in the share of women in agricultural employment in the developing regions. Women’s share in agriculture is increasing in all developing regions, except East Asia and South-East Asia and the Pacific. Yet, note that in those regions women already form near or even more than half of the share of agricultural employment. The same holds for Sub-Saharan Africa where women have traditionally engaged strongly in agriculture. The average share of women in agriculture in the region is 47% and is well over 50% in many countries. While their employment rates in the sector have not changed significantly in the last few decades, their roles and responsibilities may be changing The change in women’s share in agricultural employment is steepest in North Africa and the Middle East. In the Middle East, the share of women in agricultural employment has almost doubled since 1990. In North Africa, it has increased from a quarter to more than 30% in the same period. Women’s share in agriculture employment is rising in South Asia and the Central and Eastern (non-EU) Europe and the Commonwealth of the Independent States (CIS). More remarkable than the regional averages are the trends of selected countries. For example, the share of women in agricultural employment in Bangladesh was already 50% in 1990 and it has risen to 66% since then. In Nepal, women’s share in agricultural employment continues to rise steadily -- from slightly more than half in 1990 it has reached 60% in recent years. Afghanistan and Pakistan also saw a significant expansion of the female share of agricultural employment – from slightly more than 15% in 1990 to 21% and 36%, respectively. In most Central Asian countries the share of women in agricultural employment has been traditionally high – above 40%. Yet, this share is continuing to rise. In Azerbaijan the female share rose from 51% to 58% in the last few decades. In Tajikistan women form more than 60% of the agricultural employment. Male outmigration and the growth of commercial farming are among the key factors driving women’s increasing employment in agriculture, with other factors, such as agro-technologies, conflict, and climate change, playing a contributing role, both directly and as factors in migration and rural development.
- In response to the absent migrant labor, women may have to increase their labor allocation to the family farm to keep agricultural production at the same level. (Alternatively, migrant households may change or reduce agricultural production.) Remittances have a separate effect on women’s labor supply – they may raise women’s reservation wages, resulting in reduced time in remunerated employment; or they may relax growth constraints for family farming, making family farming more attractive than other paid or unpaid activities. These hypotheses have been tested in various studies, though with little attention to the types of paid and unpaid work performed by women.
- The New Economics of Migration approach (Stark and Levhari, 1982; Lucas and Stark, 1985; Stark and Bloom, 1985; Katz and Stark, 1986) migration as a collective and not an individual decision >>> the unit of observation is a household, and the principal independent variable is a dummy variable for household with at least one migrant In Nepal, we only control for whether the household has at least one international migrant; the base category include both non-migrant household and households with domestic migrants only since the latter are very few (only about 55 households in the whole sample). In Senegal, however, we include controls for both international and internal migration since both types of migrations are important in the country. Furthermore, to understand whether the labor effect of migration or the income effect from the receipt of remittances is more important for women’s outcomes, in some models we also control for whether the household received any remittances in the last year, 𝑅 ℎ , as well as an interaction term between having a migrant in the household and having received remittances (M*R). Not all migrant households receive remittances and also some non-migrant household receive remittances, perhaps from more distant relatives. An indicator variable for remittance receipts is likely less subject to measurement or reporting errors as it is more likely that the respondent remembers whether someone in the household received remittances but may not remember the exact amount received over the whole year. Respondents may also have apprehensions about reporting the correct amount of remittances received. Vector X includes individual characteristics (age, age squared, marital status, education, ethnic and religious background), household demographic characteristics, household wealth and asset characteristics (quality of the construction materials of the dwelling, quality of sanitary facilities, source of drinking water, access to electricity, household ownership of land and land area owned and cultivated, and livestock ownership expressed in Tropical Livestock Units - TLU) and household non-wage income sources (a dummy for whether the household received any social assistance).
- In Nepal, we only control for whether the household has at least one international migrant; the base category include both non-migrant household and households with domestic migrants only since the latter are very few (only about 55 households in the whole sample). In Senegal, however, we include controls for both international and internal migration since both types of migrations are important in the country. Furthermore, to understand whether the labor effect of migration or the income effect from the receipt of remittances is more important for women’s outcomes, in some models we also control for whether the household received any remittances in the last year, 𝑅 ℎ , as well as an interaction term between having a migrant in the household and having received remittances (M*R). Not all migrant households receive remittances and also some non-migrant household receive remittances, perhaps from more distant relatives. An indicator variable for remittance receipts is likely less subject to measurement or reporting errors as it is more likely that the respondent remembers whether someone in the household received remittances but may not remember the exact amount received over the whole year. Respondents may also have apprehensions about reporting the correct amount of remittances received. Vector X includes individual characteristics (age, age squared, marital status, education, ethnic and religious background), household demographic characteristics, household wealth and asset characteristics (quality of the construction materials of the dwelling, quality of sanitary facilities, source of drinking water, access to electricity, household ownership of land and land area owned and cultivated, and livestock ownership expressed in Tropical Livestock Units - TLU) and household non-wage income sources (a dummy for whether the household received any social assistance).
- The key problem for studies on the impacts of migration is that migration is a selective process –migrants are likely significantly different from non-migrants in both observable and unobservable ways. The decision to migrate may be based on the same factors which also affect the employment and empowerment outcomes of interest – this is the classic omitted variable problem. Moreover, reverse causality may also be at play. Migration may change intra-household dynamics and women’s decision-making power, but if women and men value migration differently, women who are more empowered may exert a higher influence on the husband’s migration decision. Using longitudinal data from Mexico, Nobles and McKelvey (2015) show that an exogenous positive shock to women’s empowerment, proxied by decision-making over household resources, leads to a lower probability that the husband migrates. We employ an instrumental variable approach to solve the endogeneity problem. The ideal instruments must be correlated with the decision to migrate and uncorrelated with the error term; they should affect the outcome of interest only through their effect on migration. Therefore, drawing on the migration literature and taking into consideration the available data, we instrument the migration decision with the share of households with at least one migrant at the ward level in Nepal or the psu in Senegal. This is a proxy for current migration network at the place of origin. The extent of the current network should influence the decision to migrate by reducing costs. We also argue that it should not affect current outcome related to women’s labor allocation and empowerment decisions because migration networks take some time to develop and the current network is likely a result of many years of migration flows rather than a recent phenomenon. In addition, we use one more instrument: family migration history defined as an indicator for whether the parents or the parents-in-law of the household head have ever lived in another country. In Nepal, in 80% of the cases the respondent is either the head of the household or the spouse of the head of the household. In Senegal we have two potential endogenous regressors: both international and internal migration. The same IV as in Nepal are used, except that in Senegal we are further able to contract two migration network variables: one for internal and one for international migration since the listing which preceded the survey included this level of detail.
- a sample of 1002 households from 5 districts (Achham, Rolpa, Nawalparasi, Makwanpur and Jhapa), distributed across two ecological zones (the hills and the Terai) and the five former developmental regions representative at district-level
- In Senegal, (Chort, De Vreyer, & Zuber, 2017) find that women are more likely to migrate than men, but they often move much closer and tend to migrate from rural areas to other rural areas. Moreover, women’s migration is often driven by marriage or family reasons, while men are significantly more likely to migrate for work.
- Some of the remittances in Nepal are almost always used to purchase food. In addition, the remittances are used for clothing, education fees, payment of debts and health. A non-negligible share of households uses the remittances for household farming activities including for the purchase of land. Food is by far the most often stated use of remittances in Senegal as well. Similar to Nepal clothing, education fees, payment of debts and health comprise an important share of use of remittances. Unlike Nepal, farming activities are rarely listed as a use of remittances.
- Before looking at the linkages between male-dominated rural out-migration and women’s work and empowerment, we turn attention to the characteristics of the non-migrant women and their families in the sending communities in Nepal (Table 2 in the paper) and Senegal (Table 3 in the paper). We differentiate between three types of households, depending on the whether there is a migrant family member in the household – i) a household with an international migrant, ii) a household with an internal, but no international, migrant, and iii) a household with no current migrants. The Tables with the basic statistics of the female family member who currently live in Nepal and Senegal are too long to be included. Here we only highlight a few the of the more significant differences between women in migrant and non-migrant households in the two countries. The presence of more adults in migrant households may facilitate the decision to migrate.
- In Senegal, the main reason for not having worked in the past year is doing domestic work without pay, which means that these women are not part of the labor force as they are not actively looking for a job. Men offer different reasons for not having worked in the past year such as studying (in almost 40% of the cases) and actively looking for a job (in 25% of the cases). Unlike in Nepal, where there was no clear relationship between migration and the work status of the family members who stay behind, in Senegal the negative relationship between migration and the probability of having worked in the last year is glaring. In both countries, we collect information on all economic activities, not only on the primary activity. Almost all economically active women participate in farming as one of their activities. Engagement in other income generating activities including working as laborer in agriculture or outside of agriculture, working in processing or trading of agricultural products is rare in both Nepal and Senegal.
- ‡ For greater clarity, women in households that receive remittances but do not have an international migrant are excluded from the estimation in Panel B (these women constitute around 3 percent of the finale female sample). In Panel B the base category includes households with no internal or international migrants that do not receive remittances either. All models also include the following controls: age; age squared; marital status; educational attainment; whether the woman is high caste or low caste; whether she is Muslim; household demographic structure (the number of children under 5, children 5-10 years old, male and female children 11-14 years old, males and females 15-17 years old, number of adult men and adult women in the household); wealth variables (including material of walls, roof, and floor, the type of toilet, access to electricity, access to piped water, whether the drinking water source is on the household grounds, whether the household owns land and area of land owned, livestock ownership measured in Tropical Livestock Units (TLU)); and district dummies.
- This is at the household level and takes into account all women and men; reported by the respondent to the HH questionnaires. Some other impacts: reduction in income from agriculture; but no significant effect on food security
- This is for a subsample of women in HH; self-reported
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