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Middle-class Women’s Labour Migration in Post-liberalised Cities in India

Tina Dutta (tina.dutta@marwadieducation.edu.in) teaches at the Marwadi University, Rajkot. Annapurna Shaw (ashaw@iimcal.ac.in) teaches at the Indian Institute of Management Calcutta.

Despite the growing visibility of middle-class women in diverse service sector jobs in Indian cities post 1991, scant research has been directed to study the linkage between their migration dynamics and post-liberalisation changes in the country. This article investigates the patterns and trends of urban migration of middle-class women through the period of pre- and post-liberalisation (1983 to 2007–08); and the socio-economic correlates of their contemporary migration using the data from the National Sample Surveys. Contrary to the dominant stereotypes around women’s “unproductive” migration, the middle-class women’s employment- and education-linked migration turns out to surpass their marriage and family associated movements. The multivariable regression analysis shows that labour migration of educated middle-class women becomes more probable for single, Scheduled Tribe women, aged 21–59 years, having a certificate/diploma, and work experience as a regular/salaried employee at the origin, and coming from rural areas of another state.

Indian women’s migration mostly bears an image of being social, non-economic and unskilled. Poor women migrating long distances as agricultural labourers (Sundari 2005), wage earners (Rao and Rana 1997; Mukherjee 2001), domestic maids (Neetha 2004; Jha 2005; Kaur 2006) and nurses (Nair 2011) have been much cited in the literature. Middle-class women, who might not have financial issues to leave their home and might migrate to cities for better education/career prospects, hardly feature in Indian migration literature. Following the economic reforms in 1991 and service sector boom in India, the presence of middle-class women in medium to high skill jobs in Indian megacities has been documented (Basi 2009). However, very little research has focused on how their migration has responded to these post-liberalisation economic changes. This article is an attempt to fill this glaring gap in the migration literature.

Following Giddens’ structuration theoretical framework,1 women’s contemporary migration is amidst the major structural changes in economic, technological, social and cultural institutions in the country in the last two decades. These are broadly identified as economic liberalisation and globalisation. There is a proliferation of technical as well as “soft skill” jobs in modern service sectors, such as information technology (IT), IT enabled services (ITeS), business process outsourcing (BPOs), hotel, airlines, media, modelling, advertising, etc. At the same time there is privatisation and expansion of traditional service sectors and revolutions in IT. The middle-class society is witnessing changes in cultural institutions of education and employment of women, more than ever.

Economic development and technological progress have led to increased demand for educated women. At the same time, the changes in cultural ideologies such as individualism and consumerism, and changes in the notion of “respectable women” (Ganguly-Scrase 2003) have nurtured a potential pool of middle-class women ready to tap into the new economic opportunities. The nature of jobs also encouraged women to acquire higher education and professional degrees/diplomas. Employment and educational opportunities primarily emerged in urban areas, especially in big metropolitan cities (Shaw 2012). Thus, the rise in employment- and education-related migration to urban centres is expected among educated middle-class women in modern India.

However, the deep-rooted patriarchy in Indian society and its manifestations through undermining of women’s work, mobility and freedom is also at work. Hence, a decision for movement to cities for taking up jobs or education has to involve women’s agency to structural forces as perceived by them. Thus it is conceptualised that the interplay of both structure and agency has set off a growing stream of middle-class women’s migration towards cities and metro-cities in post-liberalised India. Notwithstanding its significance and far-reaching implications for the economy and society, this phenomenon has remained somewhat invisible in the recent migration literature. The present article is a step towards bringing this new trend of migration to the fore of scholarly discourses by empirically establishing its existence.

The analytical part of this article has three sections. In the first section, the quantification of the middle-class population for urban India based on the monthly per capita expenditure (MPCE) data as obtained from the National Sample Surveys (NSS) 1983, 1993, 1999–2000, and 2007–08. Second, analysis of the trends and patterns of migration of middle-class women to urban India is done during the period 1983 to 2007–08. Third, a multivariable analysis of the socio-economic and demographic factors influencing the labour migration of the middle-class women to Indian cities is carried out.

One of the most noteworthy findings obtained is that the total share of job and education-linked migration of educated middle-class women to large cities not only equalises but surpasses the share of their marriage and family associated movements in the contemporary period. This remarkable rise in their economically motivated migration indeed questions the stereotypes against women’s so-called “unproductive” and “social” nature of migration. It also comes out that a middle-class woman’s human capital in the form of her previous work experience more than her acquired education, and her singlehood status, that is, her apparent freedom from familial responsibilities increases her likelihood of labour migration to a greater extent.

Conceptualisation of Middle-class Women’s Migration

Both optimistic and pessimistic world views exist on how trade liberalisation and economic reforms in the 1990s affected women as a whole. Dejardin (2008) explains that the “optimist” viewpoint emphasises the widened opportunities of paid jobs for women in export manufacturing, agriculture and business services and their increased autonomy and bargaining power at home as a result of economic self-reliance. On the other hand, the “less optimist” viewpoint criticises the increased job insecurity, women’s polarisation at low-end jobs and gender inequality manifested in women’s segregation in so-called “feminine type” jobs, which are unskilled, flexible and low-paid. Liberalisation and globalisation also fuelled ardent discussion on the “feminisation of labour” all over the world. In its literal meaning, this meant a rise in women’s labour force participation in export-based manufacturing industries in many developing countries, mostly in the South East and East Asian region after trade expanded and became more globalised. This process, however, carries a global negative connotation for its role in accentuating exploitation of women’s cheap labour and their disadvantaged position in the economy. In the case of India, the macro databases show a declining trend of women’s urban workforce participation rate in the 1990s suggesting little evidence about the process of “feminisation” of employment in the country (Ghosh 2002). However, micro evidences of feminisation of the workforce in export-based manufacturing industries have been documented (Rao and Husain 1991; Pore 1991; Banerjee 1991).

While the manufacturing sector does hold significance in the country’s economy, the service sector, especially the new non-traditional service sector, has been crucial for India’s economy in the last two decades. The much-hyped IT sector has been termed as “the new great white hope of the Indian middle class” (EPW Editorial 2001: 1271). The Indian call centre industry is stated to employ 40%–70% women in its total workforce (Basi 2009). Basi (2009) calls it a phenomenon of “pinking” of the Indian call centre profession, wherein “pinking” of a profession, following Howe (1977), means the emergence of a particular feminine type of job needing the “natural” skills of a woman. These natural skills can be anything from being calm, tolerant and caring to their natural attractiveness to the opposite sex. Even non-IT sectors such as the airlines industry in hiring air hostesses (Nair 2004), and the hotel industry in recruiting receptionists and room attendants exploit these “natural” skills of women. These emergent non-traditional service sectors largely target modern middle- class women, a group of educated, dynamic, career-aspirant women, who have culturally imbibed the idea of becoming economically independent.

After liberalisation and economic reforms opened up opportunities in the knowledge sector in India, women have been observed to enter many high-skilled service sector jobs in science, engineering, IT, and research and development as well. Mohapatra and Srivastava (2011) note that a considerable number of women have entered the profession of consultancy, an erstwhile male-dominated career domain, in post-liberalised India. Liberalisation and globalisation have also revamped the traditional service sector, such as education and health, where educated women were prevalent as teachers and nurses. The growing privatisation of these sectors has led to the expansion of educational institutions, training centres, hospitals and diversification of related jobs. So, the opportunities for educated women have increased in traditional service sectors also. The post-liberalisation emergence of new modern service sector jobs, as well as diversification of traditional service sector jobs has added a new dimension to women’s work altogether. A demand base is created for women’s labour and this is largely supplied by the educated middle-class women.

Cultural Shift in India’s Middle-Class

Just as the structural changes in India’s macroeconomy boosted the demand side of the labour for middle-class women, a simultaneous cultural shift in Indian middle-class families seems to contribute to the supply side of it. For example, Ganguly-Scrase (2003) remarks that the notion of respectable women has changed from being confined to the household in the colonial and postcolonial era to being professional, working and well educated in the modern era. As she elaborates,

For men, it is a recognition of improvement and a sense of pride that “my” wife goes out to work or “my” daughter is doing well in her studies and the hope that she will be well placed in a good job. These are subtly linked to ideologies of consumerism. For women, it is renewed confidence and a sense that paid work brings autonomy. One of the crucial markers of emerging class identity among this fraction of the middle class is the desire for the public visibility of women and their relative freedom to pursue careers. (Ganguly-Scrase 2003: 554)

Such attitudinal changes have facilitated middle-class women to access resources and to reap the emergent economic opportunities. Also, the ideologies of “individualism” and “consumerism” have made Indian middle-class women more aspiring.

Thus, the structural changes in macroeconomy, social and cultural institutions in the country have affected the demand–supply dynamics of women labour considerably in the last two decades. These structural developments are conceptualised to set the background for middle-class women’s migration in the post-liberalised India. As the majority of new economy jobs have emerged and proliferated in the cities, especially in the metro cities (Shaw 2012), this migration flow is hypothesised to be high for urban areas and steeper for large urban centres. This article attempts to uncover some of these trends and patterns of migration of middle-class women in post-liberalised India.

Data and Methods

Four rounds of NSS data on migration, conducted in 1983 (38th round), 1993 (49th round), 1999–2000 (55th round) and 2007–08 (64th round) have been utilised for the analyses. The NSS, conducted by the National Sample Survey Office (NSSO), Ministry of Statistics and Programme Implementation, is an all India based sample survey carried out regularly (at five year intervals) to capture various socio-economic demographic characteristics of population in India. The NSSs have asked for migration particulars and other socio-economic and demographic characteristics of a total of 6,23,494 persons in the 38th round, 5,96,712 persons in the 49th round, 5,96,686 persons in the 55th round, and 5,72,254 persons in the 64th round. The individuals are identified as migrants if their places of enumeration differ from their places of last residence and they reside in the destination for a minimum of six months. The data and the measures are presented at the state level and not at further lower administrative levels.

Quantifying the Middle Class

While the empirical quantification of the poor has become quite standardised in developing countries, it is not so for the middle class. Researchers have used different operational definitions to quantify the size of the middle class. Ravallion (2010) estimates the middle-class population using a broader and a narrower definition. The broader definition typically includes people above the median poverty line of most developing countries that is above $2 per capita per day and below the United States (US) poverty line that is $13 per capita per day at 2005 purchasing power parity (PPP). By this definition, India has around 263.7 million people as its middle class, which is 24.1% of the total population of the country in 2005 (Ravallion 2010). The narrower definition raises the lower bound of income to $9 per capita per day at 2005 PPP in order to include only those people who are not poor by the definition of any developing country. Banerjee and Duflo (2008) define the middle class as households having daily per capita expenditure (DPCE) valued at PPP between $2 and $10.

Using the NSS MPCE data, Sengupta et al (2008) take middle class as the population with MPCEs lying between twice the poverty line and four times the poverty line, which comes to around $3.75 to $7.5 DPCE for urban India at 2004–05 PPP. They calculated the Indian middle class as 19.3% of the total population in 2004–05. Sridharan (2004) estimates the volume and growth of the middle class in India by invoking income and income-cum-occupation criteria using the Market Information Survey of Households (MISH) data for the period of 1990–91 to 1998–99. He defines household income below ₹ 35,000 per annum (at 1998–99 prices) as the poor and categorises all households above that threshold in different layers of middle class. The specific ranges are: lower middle class (LM) lying between ₹ 35,000 and ₹ 70,000, middle middle class (M) between ₹ 70,000 and ₹ 1,05,000, upper middle class (UM) between ₹ 1,05,000 and ₹ 1,40,000, and high income group (H), which is also termed as the “elite middle class,” having income above ₹ 1,40,000 per annum. The income slabs remain the same for both rural and urban households. Meyer and Birdshall (2012) take the range of $10 to $50 daily per capita income (DPCI) as the middle class and based on NSS 2010 data, they estimate India’s urban middle class as 11.79%. On stretching the lower limit to $8 DPCI, the percentage of middle-class population becomes 12.2% in urban areas.

The definition of middle-class varies considerably depending on the objective and context of the studies. The present article takes a wider range of income to define urban middle class in India. This is done in order to capture the wide heterogeneity of occupations of modern middle-class women in contemporary Indian cities. Due to unavailability of income data in the NSS, consumption expenditure data have been used as a proxy for income. The lower and the upper limit for urban middle class have been set at $2 DPCE and $13 DPCE at 2007–08 PPP. The economic criterion and the choice of the lower ($2) and the upper limits ($13) are solely based on the available literature. The set lower/upper bounds lie within the lowest and the highest boundaries of the middle class given in the literature. Moreover, this definition serves the purpose of this study as it encompasses a wider and heterogeneous group of middle class in the country, from vulnerable middle class to the affluent middle class.

Boundaries of Middle-class Based on the NSS Data

Women having DPCE from $2 to $13 (both inclusive) at 2007–08 PPP are considered as middle-class women for the study. In the same line, the lower class women are defined as having less than $2 DPCE at 2007–08 PPP.

The MPCE lower bound and upper bound have been obtained for the NSS 2007–08 data using the below mentioned formula:

DPCE in US$ at 2007–08 PPP

MPCE/30 MPCE/30

=ConversionRate(CR)for 2007–08 = 15.7265

The PPP conversion rate for 2007–08 (average of 2007 and 2008) has been utilised from International Monetary Fund’s (IMF) World Economic Outlook (WEO) database of April 2012.2 The average conversion rate turns out to be ₹ 15.7265 for $1 for 2007–08.

For lower bound, DPCE $2 and upper bound, DPCE $13 at 2007–08 PPP, the MPCE lower bound and upper bound will be:

MPCElow = 2 * CR2007–08* 30 and MPCEHigh = 13 * CR2007–08* 30

The range of the middle class in terms of MPCE limits in INR and percentage share of population in lower, middle and higher classes for all four NSS rounds are presented in Table 1.

As apparent from Table 1, the middle class that constitutes more than half of the total urban population in 2007–08, represents a dominant class in Indian cities. With its rising volume, the migration dynamics of this class holds importance for the city’s growth and planning. It will be insightful to see how the confluence of this class, gender, economic and cultural institutions gets manifested in the migration outcome of middle-class women in contemporary India. The subsequent sections will attempt to highlight the same by uncovering subtle trends, patterns and determinants of urban migration of middle-class women.

Data Preparation for Analysis of Migration Trend

For analysing the migration trend to cities, the sample of middle-class women aged 15–59 years who migrated to cities in last one year preceding the survey has been considered. The migration stream under study is urban-bound that includes both rural–urban and urban–urban migration. Since the primary focus of the article is to highlight how middle-class women’s urban migration has changed over years, the segregation of rural/urban origin has not been done. Such segregation would also reduce the sample size.

The educational status of a woman is considered to be an important mediator of structural forces. The demand for educated labour has increased in white-collar jobs in recent times. Moreover, educational attainment can be regarded as a crude proxy of a woman’s agency. Hence, it is conceived that a woman’s education is likely to arbitrate the fruits of liberalisation and globalisation with respect to migration outcome. For this, the sample of middle-class women is divided according to below 10th standard educated, 10th standard plus educated and 12th standard plus educated and then the migration trends have been analysed. The education categories have been kept broad as many semi-skilled new economy jobs require just basic education of 12th standard. It is also done to have adequate sample size.

Empirical Model Specification for Regression Analysis

The article also investigates the incentives and costs that drive middle-class women migrants to the cities for jobs. Using the unit level data from the NSS 2007–08, the marginal effects of various socio-economic and demographic factors on the propensity of labour migration of middle-class women have been analysed using binary logit regression. The response variable is defined as success (=1) if the woman of interest migrated for job related reasons and failure (=0) if she migrated for other reasons. A range of relevant socio-economic and demographic covariates of migration, as suggested by existing literature, has been incorporated in the model. The NSS 2007–08 captures a wide range of reasons for migration4 for individual migrants. The first six reasons, namely in search of employment, in search of better employment, business, to take up employment/better employment, transfer of service/contract, and proximity to place of work have been identified as “job related reasons.” Rest of the reasons have been clubbed as “others.” Only the sample of migrants from particular background (such as aged 15–59 years, females, middle class background, urban destination, etc) has been considered for the regression analyses and the non-migrants have been excluded. It is because some of the pre-migration variables, such as the “last usual place of residence” and the “last activity status,” which seem to be important determinants of job migration for women, are available for the migrants only.

So, if the non-migrants are also included in the sample as a third category of the response variable in a multinomial logit regression, the model will need to drop those pre-migration variables. It has been seen that dropping these two variables reduces the model’s explanatory power massively. Hence, the binary logit model in the present study attempts to understand that among all middle-class women migrants, who migrated to Indian cities in last 0–2 years, which factors increase or decrease in their propensity of job-related migration over other types of migration. The time period is taken as 0–2 years in order to have a sample of current migrants as well as a relatively bigger sample size for regression analysis.

The regression models are specified as below:

Model-1: Binary logit regression for middle-class women, aged 15–59 years, who have migrated to urban areas within two years preceding the survey of 2007–08:

P(job migration) log = b0+b1agei+b2marsti+b3 educationi
P(other migration)
+b4lastUPRi+b5activityi+b6castei …(1)

Model-2: Binary logit regression for educated middle-class women (10th standard plus), aged 15–59 years, migrated to urban areas within two years preceding the survey of 2007–08:

P(job migration)log = b0+b1agei+b2marsti+b3 educationNEWi
P(other migration)+b4lastUPRi+b5activityi+b6castei …(2)

where : odds of job migration to other types of migration. Other variables are described in Table 2.

Results

Migration Trends (1983 to 2007–08): Figure 1 (p 60) represents the reasons/drivers of urban migration for middle-class women with different educational backgrounds in the last 25 years (1983 to 2007–08). An unprecedented and sizeable decline is noted in the shares of marriage and associational migration for 10th and 12th standard plus educated middle-class women migrating to urban areas during 0–1 year preceding the survey in 2007–08 (Figure 1(c), p 60). While for these women, marriage and family linked movements accounted for 75%–79% of total migration in the years 1983, 1993 and 1999–2000, it dropped to 54%–57% during 2007–08. This decline is complemented by a remarkable increase in the joint share of study- and job-related migration (from Figures 1(a) and 1(b)), of which study-related migration has increased eight times more than the 1983 level. The job- and study-related migrants together constitute almost 40% and 35% of the total current migrants in urban areas among middle class 12th standard plus and 10th educated women respectively. This gives a clear indication that the migration of educated middle-class women is now witnessing an encouraging change, from socially-driven migration to economically-driven migration. A peak in the job migration during the early 1980s is likely due to the significant growth in the overall economy during that period (Ghate et al 2010). As the economic boost gets over, a decline in the jobs and job-linked migration is evident. Also, a sharp decline in the job linked migration of educated middle-class women during 1999–2000 is noted. The overall fall in women’s urban labour force participation during this period could be one of the plausible reasons for the same. Important to note is the resurgence in labour migration of educated middle-class women at the later phase of liberalisation, that is, in 2007–08. This is plausible given the time lag required for an economy to realise the fruits of macroeconomic reforms.

As suggested by Figure 1(b) (p 60), study/education-related migration has always been higher for the 10th and 12th standard plus educated middle-class women as compared to the undereducated (below 10th standard) ones. After completion of 10th or 12th standard, middle-class families are likely to send their daughters to cities for further education in the case of absence of better educational opportunities in the origin. What is more encouraging is that in 2007–08, the share of this education-linked migration soared high to constitute more than one-fourth of the total migrations of this subgroup. Such a steep rise can be explained in both demand and supply terms. On the one hand, the post-liberalised mega cities have emerged as destinations that offer diverse avenues for higher education, professional training, and part-time jobs; on the other hand, modern middle-class families valuing their daughters’ higher education and future employment also contributed to this increasing education-linked migration to cities. Such welcoming change in the migration dynamics of middle-class women has however, hardly received due recognition till date.

Interesting to note that while employment-related migration for the educated women had been lower than their undereducated counterparts in the years 1993 and 1999–2000, it grew sharply during the later phase of liberalisation that is in 2007–08. This sharp rise in job-related migration of educated middle-class women and a complementing dip in the same for below 10th standard educated women testify to the recent urban job market having been favourable to the educated pool of women labour.

Migration to Large Urban Centres, 2007–08: The rise in job and education linked migration of educated middle-class women to Indian cities coincides with the later phase of the post-liberalised era, when the service sector economy witnessed a boom. As the majority of the emergent service sector jobs have been concentrated in big metro cities, it is expected that the economically-driven migration of middle-class women to these urban hubs will be steeper than that to the rest of urban India. As the NSS does not give migration records by cities or even states of India, the NSS state-regions5 containing cities with five million plus population have been identified and the migration pattern of middle-class women to those regions has been analysed. Table 3 presents the list of the cities with five million plus population and their corresponding NSS regions. The city of Pune with three million plus population has also been considered because of its proximity to Mumbai, the largest urban centre of India.

Figure 2 (p 61) presents the migration pattern of middle-class women by broad educational groups to large urban centres in India in 2007–08. As anticipated, the migration of educated middle-class women has been more economic in nature than their undereducated counterparts. For women with 12th standard plus education, job- and study-related reasons constitute 45% of their total urban migration in 2007–08. The share of marriage and family associated migration stands at 43%. Although the stereotypes around women’s passive and unproductive nature of migration still prevail (Banerjee and Raju 2009), the empirical evidences unequivocally show that for educated middle-class women, marriage and associational migration no longer remain dominant. For 12th standard and above educated women, the proportion of job-related migration to the large urban centres (LUCs) (23.25%, Figure 2) comes out to be substantially higher than that to urban India as a whole (13.52%, Figure 1) in 2007–08. The sheer volume of employment-induced migration establishes the point that the job market in large urban centres, largely characterised by a flourishing service sector, has been favourable for educated middle-class women. It is also to be noted that the above figures are for the NSS state regions containing large urban centres and not for the LUCs per se. This implies that the share of job-related migration of educated middle-class women could be even higher if their migration to the cities, such as Mumbai, Delhi, Kolkata, Bengaluru, Hyderabad and Pune could be estimated.

The job linked migration to LUCs among the middle class below 10th standard educated women is almost half (12.92%) as compared to that of the 12th standard plus educated (23.25%) in 2007–08. However, the labour migration of below 10th standard educated women to large urban hubs is more than twice than that of their migration to urban India as a whole (5.53%, Figure 1). This implies that though the labour market opportunities in big urban centres have become more favourable to educated middle-class women, the undereducated women still find more work options over there than in urban India as a whole. Many of these women could be working in menial and domestic work in big metro cities, post migration.

The LUCs do not seem to offer any added advantage than that of the entire urban India as far as education linked migration is concerned. The LUCs attract only 22% of middle class 12th standard plus educated women migrants as against 26% attracted by the latter in 2007–08 (from Figure 2 to Figure 1). However, this category of education encompasses all possible types of education after the 12th standard and hence does not reflect the situation of professional, technical, and other specialised courses in which the LUCs might be in demand. Nevertheless, the rise in education linked migration of educated middle-class women to cities and metro cities is a welcoming change as that would imply enhancement of their human capital as well as future rise in their labour force participation.

Regression Results

Further, the regression analysis identifies the determinants of urban migration of middle-class women. Both the models, depicting the likelihood of job migration for middle-class migrant women (Model-1), and educated middle-class migrant women (Model-2) fit fairly well as suggested by the Hosmer-Lemeshow (H–L) goodness of fit test (Table 4, p 62). The H–L test statistic is calculated as , where, O and E are the observed and the predicted values of the dependent variable, here, incidence of job migration (0 or 1), in each group respectively. The null hypothesis assumes that there is no significant difference between the observed and the predicted values and a statistically insignificant chi-square statistic signifies a good fit as the null hypothesis of goodness of fit does not get rejected. From Table 4, it is observed that for all three models, the chi-square statistic comes out to be insignificant at 1% level of significance, suggesting a good fit of the models.

The impacts of most of the explanatory variables in both the models come out to be in the expected direction, though the magnitudes vary quite substantially between the models. Table 4 shows that the highest variation in migrants’ job migration in both the models is explained by women’s prior employment in regular/salaried jobs followed by their single marital status. With reference to the women migrants not in labour force before migration, the odds of job migration is more than 52 and 45 times higher for a woman migrant previously employed in regular/salaried jobs among middle-class and educated middle-class women migrants respectively. This validates our assumption that women’s work experience in regular/salaried jobs increases their bargaining power in urban job market because of their acquired marketable skills.

Both the models show a high positive effect of women’s single marital status on their propensity of job migration. Single middle-class women with any education as well as with 10th plus education are almost 15 times more likely to migrate for jobs as compared to their married counterparts. It is not surprising given the fact that marriage comes with added responsibilities of household, family and childcare for women. Single women are freer to move and live independently and hence are more likely to migrate for jobs than married women. This also implies that prevalent gender roles in society determine women’s labour migration even for educated and economically better-off women.

Further, women’s prior work experience in casual work/self-employment is more rewarding in terms of facilitating job migration in comparison with women not in the labour force. This establishes that women’s prior participation in the labour force, whether or not in the workforce, is always more facilitating for their future job migration than their detachment from the labour force.

In terms of the predictive capacity of the other explanatory variables, women’s higher age, Scheduled Tribe (ST) status, higher education status, last residence at rural areas of different district (within same state of destination), rural areas of different states, and last residence at urban area of different district feature as significant in Model-2. All these factors significantly and positively impact the odds of job migration of educated middle-class women migrants.

The educated middle-class women’s propensity for job migration increases by five times and almost six times if they belong to the age group 21–34 years and 35–59 years respectively as compared to the age-group 15–20 years (Model-2). For all middle-class migrants (Model-1), the likelihood of job migration increases substantially for the age group 21–34 years. In every case, women belonging to the youngest age group are at a disadvantage in terms of job migration. Older age could mean greater work experience and higher qualifications, which would increase the chances of getting job and hence promote job migration. However, after a certain age, the employability of women might reduce, and the women might become reluctant to migrate for jobs.

Among all the caste groups, only the ST status appears to increase the chances of job migration of women migrants in both the models. As mentioned earlier, women belonging to the ST community usually enjoy relatively balanced expected gender roles and hence have greater freedom with respect to physical mobility and employment participation. A substantially high (more than four times) likelihood of job migration among educated middle-class women belonging to ST community than that of upper castes validates this point.

Higher education plays a more prominent role in determining the job migration of educated middle-class women. In Model-2, the certificate and diploma holder middle-class women are six times more likely to undertake job migration as compared to the 10th and 12th standard passed women. Women with graduate and higher level of education are 2.5 times more likely to migrate to cities for job reasons than women with 10th–12th grade education. It is not surprising to see that certificate and diploma courses increase women’s chances of job migration more than that of higher education. One of the major reasons for this is that in India, a low proportion of women are employed in high professional jobs requiring very high educational qualifications. Second, the sample of educated middle-class women considered in the study is a broad diverse group including the lower middle class and the affluent middle class. The lower middle class section of this group is likely to undertake labour migration after some diploma or certificate courses and may not go for further education.

The variable, last usual place of residence, shows that chances of job migration becomes the highest for the intestate rural to urban migration (four times of the intra-district rural to urban migration) for the educated middle-class women. It appears that the migrant’s prior residence at rural area in another state than the state of destination, enhances the job migration of educated middle-class women more than that of all middle- class women. The inter-district (within a state) rural to urban job migration is significantly higher than that of the intra-district rural to urban job migration for both groups of women.

Discussion and Conclusion

This article aims to bring forward a relatively academically disregarded phenomenon of middle-class women’s migration to cities and metro cities in the wake of economic liberalisation and globalisation in India. The structuration theoretical framework adopted in this article suggests a rise in economically driven migration of educated middle-class women under the structural changes unfolded in contemporary India. The empirical findings back this proposition as job and study-linked migration of educated middle-class women to cities come out to be considerably higher in the recent period. For metro cities, this phenomenon is even more pronounced as the job and education linked reasons not only equalise but surpass the hitherto dominant marriage and family associated reasons of migration for educated (12th standard plus) middle-class women. The undereducated middle-class women seem to lose out in the new economy job market as evident from the decline in their labour migration to cities. The middle-class women with 12th grade and above education seem to reap the benefits of urban labour market at the most. For lower class women, educational attainment does not add much value after a basic level (10th–12th grade) towards realisation of their job migration. This implies their concentration in unskilled or low-skilled jobs where higher education is not rewarded. Clearly, educational attainment and economic class play the role of mediators in tapping the urban job market opportunities in the post-liberalised era.

Further, the regression analysis unveils the socio-economic and demographic determinants of middle-class women’s migration to urban India. The agency related factors, such as, women’s acquired human capital in the form of their prior work experience and educational attainment emerge as important predictors of their labour migration to cities. Also, in the expected lines, a woman’s singlehood status increases her chances of labour migration multiple times. The findings are in sync with the patterns of women’s migration in some other developing countries. For example, in China, the individual level factors such as years of formal education and unmarried status of women strongly determine their job-related migration (Yang and Guo 1999). In the present study, a woman’s previous work experience enhances her job-related mobility much more than her acquired education. Higher academic degrees appear to be useful for educated middle-class women only. For a majority of women, a certificate/diploma post 10th or 12th standard turns out to be more rewarding than big degrees with respect to their job migration to cities. This also highlights the skewed nature of urban labour market where the majority of women workers get absorbed in low-end jobs and a handful get high-skill jobs that require higher educational qualifications.

On a concluding note, this study aims to mark two major points. First, there is a need to realise and acknowledge the changes in the nature and pattern of women’s migration in India. It is not always marriage or hunger or deprivation that pushes or pulls women to cities (destinations). A large part of women migrants in contemporary urban India that constitute the broad middle class indeed challenge this stereotyping. The educated middle-class women migrants are potential labour for the emergent new economy jobs in the metro cities. Their migration is very much economically driven beyond mere survival needs.

Second, it is important to note that given the structural forces underway in the country, the migration stream of middle-class women is likely to mature in the near future. As the job and education linked migration of middle-class women is largely independent or unassociated in nature, a sharp rise in single women’s migration is expected in Indian cities. Basi (2009) notes a disproportionately large number of single women among the call centre workers in the city of Delhi. She remarks that many of these women stay without their families in PG (paying guest) accommodations in the city. In the wake of growing crimes against women, single women living without their families might become an easy prey in a new and unfamiliar destination. Instances of physical assault and sexual assault of women are on the rise in most cities in the country (Bamzai and Doshi 2005). While on the one hand development and liberalisation emphasise the ideology of individualism, economic independence, and freedom of mobility, on the other, women have become easy targets of heinous crimes. In such a complex context, there is a great need to draw policy attention to single women’s migration and their issues and challenges in the cities of India.

Notes

1 Structuration theory by Anthony Giddens (1979, 1984) suggests that complex social processes, such as migration of women, are the result of interaction between the structures or structural properties of systems, and human agency. Structures both constrain and facilitate human action through rules and resources (structuresof domination), and norms and practices (structures of legitimation), which are drawn upon by human agency through their interpretative schema, based on their stocks of knowledge and bounded consciousness (practical and discursive) in the production of social interactions. By “agency,” Giddens (1984) implies the ability to “act otherwise” that is “being able to intervene in the world, or to refrain from such intervention, with the effect of influencing a specific process or state of affairs (p 14). Giddens’ notion of agency draws upon “people’s capabilities” to “break away from social ties and traditions that are not to their liking” (Prasad 2005: 188–89).

2 IMF World Economic Outlook (WEO) database, April 2012, http://www.imf.org/external/pubs/ft/weo/2012/01/weodata/download.aspx, viewed on May 2013.

3 The MPCE figures for 2007–08 have been multiplied (that is, deflated) by 0.69262, 0.40359 and 0.17916 for obtaining the corresponding MPCE figures for 1999–2000, 1993 and 1983 respectively. Source of the CPI-IW data is http://labourbureau.nic.in/indtab.html, accessed on 20 July 2013.

4 The reasons for individual migration are coded in NSS 2007–08 as: in search of employment–01, in search of better employment–02, business–03, to take up employment/better employment–04, transfer of service/contract–05, proximity to place of work–06, studies–07, natural disaster (drought, flood, tsunami, etc)–08, social/political problems (riots, terrorism, political refugee, bad law and order, etc)–10, displacement by development project–11, acquisition of own house/flat–12, housing problems–13, healthcare–14, post-retirement–15, marriage–16, migration of parent/earning member of the family–17, others–19.

5 The NSS state regions are the clusters of several districts of a state, sharing some common geographical characteristics. NSS does not give data at state or city level. The lowest areal unit has been the NSS state-regions.

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