Gendered Mobility: Women Migrants and Work in Urban India

SPECIAL ARTICLEEconomic & Political Weekly EPW july 11, 2009 vol xliv no 28115Gendered Mobility: Women Migrants and Work in Urban IndiaArpita Banerjee, Saraswati RajuSaraswati Raju(saras@mail.jnu.ac.in) is with the Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University. Arpita Banerjee (bannerjee_arpita@yahoo.co.in) is pursuing her doctoral studies at the Centre for the Study of Regional Development.This article focuses on the changing work profile of migrant women and the avenues available to them. The central question posed is whether women’s post-urban continuation in the workforce as well as fresh work status destabilises any of the established stereotypical gendered codes woven around familial and domestic responsibilities and if caste, class and accessibility to human resources (education in particular) intersect with such codes. Migration, a physical and social transaction, is also an instrument of cultural diffusion and social integration even though most of the earlier studies on migration are centred on its economical aspect. Often framed by men’s experiences, such research ignores women’s role therein. 1 IntroductionCensus data show that there has been a progressive increase in fe-male migration with a slight dip in the 1990s when overall migration also declined. The average annual growth rate of the migrant popu-lation over the last 30 years (1971-2001) was 2.12% – female migra-tion showed a growth rate of 2.24% as compared to 1.85% for males during the same period. Rural females were most mobile although urban females have picked up over the decades (Figure 1, p 116). Although marriage continues to be the predominant reason for the overwhelming presence of women amongst the migrants, the increase is also because of the gender-specific pattern of labour movement (Sassen-Koob 1984; Shanti 1991; Ghosh 2002). Of late, the emergence of nuclear families, increasing participation of edu-cated women in activities outside homes and the changing pattern of consumption have resulted in demand for women-centred serv-ices such as domestic help, childcare giver and full-time home-based caretakers, etc (Majumdar 1980; Martin 2004; Pillai 2007). This article focuses on the changing work profile of migrant women and the avenues available to them. Drawing from our academic and ideological position that it is the relational domain within which women’s work needs to be placed, we look at migrant men and their work pattern even as women qua women remain our prime concern.We argue that women’s migration is not as parasitic as it is of-ten thought to be. The central question posed is whether women’s post-urban continuation in the workforce as well as fresh work status destabilises any of the established stereotypical gendered codes woven around familial and domestic responsibilities and if caste, class and access to human resource (education in particu-lar) intersect with such codes. Our contention is that overall, as constituent players in the ongoing social processes that allow one to negotiate expanded economic as well as social spaces, migrat-ing women seem to be contesting, even if marginally, some of the traditional social and caste constructs in making the moves. 2 Data Source and MethodologyFor the present study, the unit level data of the National Sample Survey (NSS), 55th round (1999-2000) have been used, which is also the latest data available on migration. The study is confined

SPECIAL ARTICLEEconomic & Political Weekly EPW july 11, 2009 vol xliv no 28117is not to suggest a complete exclusion of illiterate women from the workforce, but as compared to 65% of illiterate migrant women as a whole, 76% of graduate and postgraduate migrant women continued to be in the workforce. Women with higher secondary education were the least affected group in terms of having to discontinue working in urban locations – 79% contin-ued working while the rest opted out. The vulnerabilities of the labour market for migrant women thus seem to be particularly skewed in favour of those who were located at the extreme ends of the educational hierarchy (Schultz 1982; Kingdon and Unni 1997; Singh and Agrawal 1998; Jong 2000).1Household responsibilities such as childcare and care for the aged are known to keep women away from formal employment. Although a crude measure, we looked at household dependencies on women workers by taking into account the number of child population (0-6 years) and old age population (60 and above years) as a proxy for each category of women, i e, (a) continuing work prior to and after migration, and (b) working prior to but discontinued after migration (Table 3). As expected, those who discontinued work in the post-migration period had a heavier de-pendency burden than those who continued working.2 In con-trast, men who left work after migration were mainly in the age group of 50-59 years. This could be due to the difficulties faced by older people in finding work once they move, coupled with barri-ers posed by ill health. Alternatively, as the data seem to suggest, the relatively better off could afford to leave their jobs whereas those at the lower socio-economic ladder were compelled to con-tinue working – for example, slightly more than half of the men who left work after migration were in regular salaried jobs as comparedto one-fourth in self-employment and one-fifth in casual labour.Itmay be recalled that 48% men and 91% women were non-workers at the time of migration. However, out of these, about 69% men and 16.8% women entered employment after mi-grating to urban areas.3.2 Post-Migration:ContinuingWorkers and Fresh Entrants The following section explicitly focuses on those whose status continued to be that of workers prior to and after migration as well as those who entered the urban labour market for the first time. These workers are divided into three categories: self-employed, regular salaried and casual labour. Although the diversities within these categories make it difficult to assign any hierarchal order to the type of work, casual work can be considered as the most erratic sort of employment (due to the uncertainties involved) which the migrants are forced to under-take for survival. These workers have lower bargaining power and no social securities to cover them. If so, it is inevitable that the women casual workers would have even lower bargaining power with poorer working conditions and no prospects of up-ward mobility as compared to their male counterparts. This category is followed by self-employment in household enter-prises as paid or unpaid labour. Here, the risk associated with the nature of employment is entirely borne by the self-employed person. It is often argued, particularly in official and masculine discourses that self-employed women, mainly working in household enterprises are protected from the many travails of the outside world and are therefore safer (Mazumdar 2004). However, scholars have routinely pointed out the work insecu-rities these women face (Srivastava and Sasikumar 2003; Srivastava 2005). Thus, regular salaried jobs remain the best options available, with assured wages and various forms of so-cial security cover. Not surprisingly, the work statuses are intrinsically linked with education as illiteracy is much higher among the casual labourers (men 37.7%, women 82.5%) as compared to the self-employed (men 21.5%, women 59.1%) and regular salaried persons (men 6.9%, women 17.9%).The nature of work changes because of differential opportu-nities in rural versus urban environs. The emergence of self-employment as a major avenue for women in general and for the first-time workers in particular needs to be framed in the larger societal environ which still sees women’s primary loca-tion within domesticity, more so if they are married women – a point we have discussed later. That said, a prior work experi-ence does seem to help women in expanding their chances in the labour market as those who were working at the time of migration were almost equally distributed across casual work, self-employment and salaried jobs after their move to urban Table 3: Continuing and Non-Continuing Women Workers by Work Status, Educational Levels and Household BurdenWork Status Working Prior to Discontinued Work and After Migration After MigrationSelf-employed 28.943.8 (i) Own account worker 10.8 10.9 (ii) Employer 0.7 0.8 (iii) Unpaid household helper 17.4 32.1Regular salaried 31.7 15.8Casual labourers 39.4 40.3Educational qualification Illiterates 52.0 50.4 Up to secondary 20.7 28.8 Higher secondary and above 20.9 11.2 All others 6.4 9.6 100.0 100.0Household Burden per woman (Based on dependent population age 0-6 and 60 and above) 8.20 10.43Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.Table 4: Urban Migrants and Their Pre-and Post-Work Status by Sex Men Women Working Prior to and After Migration Working Prior to and After MigrationWorkforce Prior to After Percentage Fresh Prior to After Percentage FreshParticipation MigrationMigrationIncrease or Entrants Migration Migration Increaseor Entrants Decrease DecreaseSelf-employed 35.9 29.3 (-) 18.5 32.1 28.9 33.3 (+) 15.24 52.4Regular salaried 40.5 56.4 (+) 39.06 57.7 31.7 35.1 (+) 10.65 28.3Casual labourers 23.6 14.3 (-) 39.02 10.2 39.4 31.6 (-) 19.76 19.3Total 100.0 100.0 100 100 100 100Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.Table 5: Changes in Work Status of Urban Migrants in Pre-and Post-Migration Period Before AfterMigrationMigration Men Women Self-RegularCasualTotalSelf-RegularCasualTotal EmployedSalariedLabourerEmployedSalariedLabourer Self-employed 57.934.1 8.0100 81.94.613.5100Regular salaried 7.6 91.2 1.2 100 4.9 93.9 1.2 100Casual labourer 22.9 30.4 46.7 100 20.5 10.1 69.4 100Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.

SPECIAL ARTICLEjuly 11, 2009 vol xliv no 28 EPW Economic & Political Weekly118locations. In contrast, self-employment seemed to be the only “choice” that the first time women entrants had (Table 4, p 117).3.3 Shift in Pre-and Post-Migration: Continuing Work Status It can be seen (Table 5, p 117) that more than 50% of the migrant workers, both men and women continued to retain their pre-migration work status, be it self-employed or regular salaried – with the most vulnerable section being that of casual labour-ers. Nearly half of the men in regular salaried jobs moved on account of transfer of job/contracts whereas another three-fourth moved for better employment. As far as regular salaried women were concerned, about 40% had moved for reasons related to employment particularly on account of transfer of jobs and services followed by marriage. One can thus see that women were no longer primarily associ-ational migrants and their mobility was triggered by reasons other than marriage. Whether they were autonomous migrants is a vexed question because even if women migrate alone, the decision to migrate may well be a part of family strategy and therefore may not be truly autonomous (Chant 1996; Schenk–Sandbergen cited in Rao 2006). So far the discussion was confined to workers with un- changed work status post-migration. In the subsequent section, we discuss the post-migration shifts that did happen across work categories.One critical shift could have been from regular salary to casual labour, such a shift is not only negligible, but seems to decline in urban locations for both men and women, but more so for men (Srivastava and Bhattacharyya 2003). This is accom-paniedbyincrease in regular salaried jobs for some who were previously working as casual labour. This particular observation requires further study of workers who break through the casual workers’ status. Ideally, we should have looked at educational levels, but be-cause of the inadequate sample size at such disaggregation, we could group educational attainments in two broad categories only: illiterate and literate (Table 6). Accordingly, the shift from casual work to regular salaried work was possible for those who were literate albeit usually to low-level/low-ranking jobs in manufacturing (44%), trade (17%) and transport (9%). Only 6% and 8% of self-employed and casual labourers who moved to regular salaried jobs after migration were in public administration. These workers had a somewhat higher level of literacy – 71% and were educated up to the second-ary level or below (about 27% with primary and middle level each) than those who continued as casual labour. A very high proportion of women workers (77%) were illiterate and most of them – as high as 63% – were domestic servants (Raghuram 2001; Pillai 2007). Thus, with education, a certain “upward” mobility could be seen.3.4 Continuing Workers and Fresh Entrants: A ComparisonAn industrial classification of workers in pre-and post-migration period reveals that prior to migration most of the self-employed migrants were engaged in primary activities such as agri-culture. The very nature of urban areas restricts such activities and it is not surprising that after migration most of these men and women shift to manufacturing. Within manufacturing certain industries seem to have attracted them, i e, 56% of men and 67% of women were in food, textile, transport and communication industries. The fresh entrants to the workforce as self-employed were concentrated in manufacturing (one-third of men and women were in food processing), trade and transport. In case of regular salaried jobs, not much shift in pre-and post-migration status has been observed except in the case of men whose proportion has increased in manufacturing after migration. Public administration was an Table 6: Urban Migrants by Educational Standard and Change in OccupationBefore Migration Men Women AfterMigration Self-RegularCasual Self-Regular Casual Employed SalariedLabourers Employed Salaried LabourersSelf-employedIlliterate 18.4 10.6 35.6 55.9 49.4 66.9 Literate 81.6 89.4 64.4 44.1 50.6 30.3 Total 100 100 100 100 100 100Regular salaried Illiterate 7.9 3.1 14.3 20.1 8.6 69.7 Literate 92.1 96.9 85.7 79.9 91.4 15.1 100 100 100 10 100 100Casual labourers Illiterate 41.4 20.1 39.3 76.1 77 84.9 Literate 58.6 79.9 60.7 23.9 23.1 15.1 Total 100 100 100 100 100 100Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000. Table 7: Industrial Classification ofOccupation of Urban Migrant Workers MenWomen Continuing Work Continuing Work Work Status Prior to After Fresh Prior to After Fresh MigrationMigrationEntrantsMigration MigrationEntrantsSelf-employedAgricultural and alliedactivities 48.9 6.0 3.6 35.9 21 26Manufacturing 12.220.517.531.134.730.1Construction 3.4 7.2 5.5 0.0 0.8 0.0Trade, hotel andrestaurants 24.7 42.2 45.8 17.2 27.8 30.3Transport and communication 3.6 11.7 11.0 1.3 1.1 0.7Education 0.7 2.3 3.1 1.1 1.8 5.1 Totala 93.589.886.486.687.292.1Regular salariedManufacturing 18.027.7 35.511.812.7 17.4Trade, hotel and restaurants 17.5 12.2 17.9 8.6 2.8 3.6Transport and communication 11.5 11.6 10.4 3.7 3.0 3.6Public administration 27.3 23.4 13.6 17 16.6 9.2Education 6.25.46.432.631.834.5Health 2.92.81.91312.78.3Private households with employed persons 0.5 1.7 1.6 3.2 9.9 16.4Total 84.084.987.290.089.493Casual labourersAgricultural and alliedactivities 49.9 7.4 6.4 79.5 43.6 37.7Manufacturing 9.4 20.2 25.31.8 8.3 12.9Construction 19.942.136.99.426.422.6Trade, hotel and restaurants 13.0 13.5 17.2 4.3 3.5 6.4Transport and communication 4.9 9.4 7.7 1.3 0.0 0.5Total 97.192.793.596.381.880.2a Total comprises only those industries which employ maximum number of migrants.Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.

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SPECIAL ARTICLEjuly 11, 2009 vol xliv no 28 EPW Economic & Political Weekly120others – the social composition of the migrant population almost corresponds with the overall social composition of the popula-tion (Table 10, p 119). It is often argued that the entry of “higher” caste women into the labour market is curtailed by their caste status because very often the behavioural codes for them are more restrictive in terms of approval/reticence towards their presence in public spheres (Srinivas 1977; Sundaram and Vanneman 2008). One can also argue that these women are relatively not as com-pelled to work as women coming from poorer families because of the caste/class overlap, even if imperfectly matched (Das and Desai 2003). The data suggest that women belonging to OBCs not only had the highest proportion of work force participation; fewer of them withdrew from work after migrating to urban areas (Table 11). Also, it is mostly the “others”– higher caste women who discontinued work after migrating to urban areas – an observation noted inearlier studies as well (Singh 1976; Das and Desai 2003). However, a caveat is in order. We had observed earlier that highly educated women were least likely to withdraw from the labour market as a result of their migration to urban locations, which seems to be at odds with the observation here as the “other” higher caste women seem to be opting out of the work-force. What it shows is that contrary to the usual assumption of overlap between (high) caste and high education, the category of “highly educated women” contains educated women from castes other than the high castes alone (Table 12). In contrast, no such pattern was observed for the migrant men.However, caste affiliation had a bearing on the work women would take up. Table 13 shows that the lower caste women were mainly confined to the lower rung of the job hierarchy while the “others” were mainly employed as regular salaried workers. A closer look, however, indicates an overall, albeit slight, in-creaseintheproportion of regular salaried workers among the lower caste women including the STs, an outcome of bet-ter access to educational opportunities for these groups in ur-ban locations (Raju 2008). In case of migrant men, other caste andST men were at the higher rung of the job scale whereas men from the Sc andOBCs were in low paid jobs. Post-migra-tion witnessed a major increase of migrants in regular sala-ried jobs within each strata of the society suggesting some sort of breakthrough of traditional caste barriers in the more anonymous and liberal urban environment although they continue to be concentrated in lower paid jobs. This was spe-cially the case with women migrants. 3.7 Poverty and MigrationAn association between poverty and women’s participation in paid work (Chen 1995; Mammen and Paxson 2000; Unni and Rani 2004) overshadowing other concerns such as childcare burden has also been talked about in literature (Sundaram and Tendulkar 2004) and yet the relationship between poverty and women’s participation in the labour market remains some-what ambivalent. In the absence of direct measurement of poverty and migra-tion, monthly per capita consumption expenditure (MPCE) classes have been used. The lowest and the highest two consump-tion classes, i e, Rs 0-300 and Rs 300-350 and Rs 1,500-1,925 and Rs 1,925 and above have been clubbed together to estimate the “poorest of the poor” and the “richest of the rich” migrants. Rural and urban locations denote the source regions from where men and women had migrated (Table 14).This somewhat limited analysis seems to suggest that as compared to those in the highest consumption classes, the poorer people were more likely to migrate to urban areas (Ko-thari 2002; Omelaniuk 2005; Rao 2006). Whereas the differ-ence between rural and urban men was not so significant in the poorest consumption classes, at the highest end of con-sumption classes, those who migrated belonged to urban backgrounds; almost a similar situation can be noted for women belonging to this income cohort. It is implied that urbantourban Table 11: Migrant Workers and Non-Workers by Social Group MenWomenSocial Group Working Prior and Discontinued Work Working Prior and Discontinued Work After Migration After Migration After Migration After MigrationScheduled tribe 4.1 0.54 8.2 7.2Scheduled caste 13.2 12.4 20.2 20.7Other backward caste 30.5 38.5 38.8 34.1Others 52.248.532.838.0Total 100.0100.0100.0100.0Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000. Table 12: Women Migrants by Social Group across Educational StandardSocial Group Illiterates Primary Middle Secondary Higher Secondary and Above All Others TotalST 9.9 6.9 5.8 8.9 6.1 4.9 8.2SC 30.314.9 9.9 7.9 6.8 13.520.2OBC 44.447.742.233.3 18.2 54.938.8Others 15.530.442.149.9 68.8 26.7 32.8Total 100.0 100.0 100.0100.0 100.0 100.0100.0Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.Table 14: Urban Migrants by Place of Last Residence and the MPCE ClassesMonthly Per Capita Men WomenConsumption Classes Rural Urban Rural UrbanRs 0-350 28.6 26.0 41.7 30.0Rs 1,500 and above 4.9 15.4 4.2 17.7The rest 66.5 58.6 54.1 52.3 Total 100.0 100.0 100.0 100.0Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000. Table 13: Urban Migrant Workers by Social Group Men Women ScheduledScheduledOther OthersScheduledScheduled Other Others Tribe CasteBackward Tribe CasteBackward Class Class Before migrationSelf-employed 21.629.637.239.033.323.632.328.0Regular salaried 51.5 29.8 33.9 45.0 20.5 15.0 21.1 57.3Casual labourers 26.9 40.6 28.9 15.9 46.3 61.4 46.6 14.7Total 100.0100.0100.0100.0100.0100.0100.0100.0After migrationSelf-employed 15.426.632.929.831.728.340.629.8Regular salaried 64.8 45.3 49.0 61.4 24.5 20.7 25.6 58.1Casual labourers 19.8 28.1 18.1 8.8 43.8 51.0 33.8 12.1Total 100.0 100.0 100.0100.0100.0 100.0 100.0100.0Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.

SPECIAL ARTICLEEconomic & Political Weekly EPW july 11, 2009 vol xliv no 28121migration in the highest income groups was not necessarily in search of better work opportunities and largely involved lat-eral movement, i e, transfers across the income categories. This proposition is strengthened when seen in combination with the work status change of salaried men prior to and after migration (Table 15). Regular salaried jobs had emerged as the main category of work for men after migrating to cities, but this observation has tobe seen in terms of the kind of jobs they had. The main avenues open for poor rural men in regular salaried jobs were manufac-turing (34%), trade and restaurants (13%) and transport (10%). Some of them were also employed in public administration (18.3%). The regular salaried rural men drawn from the upper cohort of the income group were mostly in public administration (28%), manufacturing (19%), and transport and in finance (about 10% each).Since migrant men at the polar ends of consumption catego-ries seemed to have a different trajectory in the urban labour market, a multinomial logistic regression is done based on the work status and the consumption classes.4 A similar analysis could not be done for women because of the inadequate sample size. The odds ratio shows that as compared to the richer sections of the migrants, poorer migrant men are more likely to work as cas-ual labourers, i e, the likelihood of poor migrants, both rural and urban to be in casual work is 4.9 and 5.9 times higher than the richer migrants respectively. Likewise, the richer migrants, inde-pendent of their rural or urban backgrounds, are more likely to be in regular salaried jobs. For example, the possibilities of poorer rural and urban migrants to be in regular salaried jobs are almost half of that of richer migrants (Table 16).The nexus between poverty, restricted access to education and opportunities available in urban labour market is thus implicit. 3.8 Emerging Interdependencies and Their ImplicationsWe began the discussion by saying that the nature of work done by the migrants, especially women, is determined by not only their status as migrants, but also by a host of factors such as edu-cation and marital status and membership in particular social and religious groups. A multinomial logistic regression shows how each of these factors plays out in influencing labour market outcomes for migrants.It can be seen that the multinomial regression results endorse what we have already pointed out. The relationship between lit-eracy/education and better employment avenues is clear, as the probability of literates to work as casual labour is much lower and educated workers are more likely to be in regular salaried jobs. This holds true for both the sexes. However, the likelihood of edu-cated women being in regular salaried jobs is much higher as com-pared to men (Duraiswamy 2002). Marital status does not constrain men as much as it does mar-ried women and yet an intriguing relationship emerges – currently married and widowed/divorced/separated men were less likely to be in casual work (48% and 52%, respectively) than the unmar-ried man. Quite possibly, the widowed/divorced/separated group would have had a longer trajectory of work history and may therefore no longer be in casual work whereas the currently married men had to move beyond casual work in order to get married – propositions that require study outside the domain of this data set. In case of women migrants in casual labour, the results are not at all significant. However, the likelihood of widowed and divorced/separated and married women to be in Table 15: Migrants by Place of Residence, Work Status and MPCE Classes MenWomen RuralUrbanRuralUrban0-350 Before After Before After Before After Before After MigrationMigrationMigrationMigrationMigration Migration Migration MigrationSelf-employed 39.3 30.7 25.627.828.0 32.627.930.7Regular salaried 22.1 47.0 61.0 63.1 11.3 17.0 50.1 49.2Casual labourers 38.6 22.3 13.4 9.1 60.7 50.4 22 20.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100 1001,500 and aboveSelf-employed 39.6 26.1 19.421.06.4* 4.1*6.3*11.4*Regular salaried 51.5 69.8 79.0 78.6 75.9 94.0 93.2 88.6Casual labourers 8.9 4.1 1.6 0.4 17.7* 1.9* 0.5* 0*Total 100.0 100.0 100.0100.0 100.0 100.0 100.0100.0* Sample size is not adequate . Source: Computed from the Unit Level Data of NSS, 55th Round 1999-2000.Table 17: Migrants and Associated Characteristics: Multinomial Logistic Regression Factors Odds Ratio Men Odds Ratio WomenCasual labourers Educational standard Literates0.475**0.374** Illiterates(ref)- -Maritalstatus Currentlymarried0.512*1.498 Widowedand divorced/separated0.474**0.878 Never married (ref) --Socialgroup Scheduled tribe 3.468 2.619** Scheduled caste 3.258 3.565** Other backward caste 1.737 1.274 Others(ref)- -Regular salaried Educational standard Literates3.053**8.292** Illiterates(ref)– – Maritalstatus Currentlymarried0.9540.402* Widowedand divorced/separated0.9600.316** Never married (ref) – – Socialgroup Scheduledtribe2.541**0.924 Scheduledcaste1.185*0.777 Other backward caste 0.782** 0.389** Others(ref)--**Significance level is less than equal to 0.001.* Significance level is less than equal to 0.05.Table 16: Migrants by Place of Residence, MPCE Classes and Work Status: Multinomial Logistic RegressionDependent Variable Factors Odds Ratio Rural Men Urban MenCasual labourers 0-350 4.903**6.932** 1,500 and above --Regular salaried 0-350 0.466** 0.496** 1,500 and above – –**Significance level is less than equal to 0.001.Source: Computed from the Unit Level Data, 55th Round 1999-2000.

SPECIAL ARTICLEEconomic & Political Weekly EPW july 11, 2009 vol xliv no 28123Notes 1 This may not be true of women in general. As Das and Desai (2003) point out, it can be status-linked withdrawal from public sphere of formal work or desire to confine themselves to only white-collar jobs. They found lack of appropriate jobs to be the main reason for educated women’s absence from the workforce. However, migrants are in a some-what different league. 2 As per the NSS report for 2004-05, a sizeable pro-portion of urban women (around 27%) were will-ing to accept work if it is available within the household (NSSO, 2004-05). 3 However, as pointed out by Kelkar and Nathan, in some cases, particularly in more competitive areas of the industry such as multinational com-panies or large Indian firms where poaching is common, employers seem to prefer married women with children, as they are not too mobile and would be willing to stay in “a boring job” for domestic reasons. Women were viewed as “effi-cient in the work and do not leave the company as soon as they get better opportunities” (2005:13).4 Multinomial logistic regressions are done in cases where the dependent variable is not in a binary form, i e, the dependent variable is not restricted to two categories. In the present analysis the de-pendent variable is migrant workforce, catego-rised into self-employed, regular salaried and casual labour. Self-employment is taken as the reference category and regular salaried work or casual labour is placed as a function of independ-ent variables such as education, marital status, social group, etc. ReferencesAgrawal, A (2006): “Women, Work and Migration in Asia” in Anuja Agrawal (ed.),Migrant Women and Work (New Delhi: Sage Publications). 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