1 Occupational and Educational Gender Segregation in India

Occupational and Educational Gender Segregation in India

Tushar Agrawal Assistant Professor, Centre for Development Management Indian Institute of Management Udaipur, Udaipur (India) [email protected]

Ankush Agrawal Assistant Professor, Department of Humanities and Social Sciences Indian Institute of Technology Delhi, New Delhi (India) [email protected]

Abstract This paper examines gender segregation by educational and occupational levels in India using a recent round of a nationally representative employment and unemployment survey. We employ a three-way additive decomposition of the mutual information index based on the concept of entropy for examining the effects of education and occupation on overall gender segregation. Our results show that the extent of gender segregation is higher in urban areas than that in rural areas. The decomposition analysis reveals that human capital plays a major role in explaining gender segregation in rural areas whereas in urban areas occupational choices are much more important.

1. Introduction Most studies on segregation are one-dimensional and they generally examine occupational gender segregation. There are a few studies (see for example, Borghans and Groot 1999; Mora and Ruiz-Castillo 2003; Sookram and Strobl 2009; Van Puyenbroeck et al. 2012) that analyse occupational segregation along with educational segregation. The gendered division of labour can be understood as a multidimensional phenomenon, and therefore it becomes necessary to identify which dimension is more important to undertake appropriate gender policies (Van Puyenbroeck et al. 2012). In the traditional one dimensional approach, the implicit assumption is that each variable (occupation or education) generate segregation on their own; they are independent of each other. Human capital is an important factor that can explain occupational segregation. Borghans and Groot (1999) find that educational segregation is a major cause of occupational segregation. Mora and Ruiz-Castillo (2003) also examine the links between occupational segregation and human capital characteristics and propose two conjectures. First, if education results in widening of career opportunities and occupational choices for females, then occupational segregation should differ across human capital categories. Higher educational level may result in smaller gender segregation and segregated male occupations would not necessarily need more education than female occupations. Second, women will likely to choose those occupations where their skills depreciate less if they leave for periods of time because of their family obligations. Furthermore, Spriggs and Williams (1996) find that education is important in explaining occupational segregation by gender and race, and argue that controlling for differences in human capital can be of great use to policy makers and economists when discussing race and gender inequality. However, the effect of education on occupational

1 segregation is not unambiguous. Sookram and Strobl (2009) analyse the role of educational choice on the degree of occupational segregation in Trinidad and Tobago for the period 1991-2004. They find that though educational segregation has declined substantially during this period but it has not translated into less occupational segregation. Gender segregation may arise due to different choices (fields) of education made by individuals. A study by Van Puyenbroeck et al. (2012), for the Flemish labour market, finds that choice of area of study has a larger effect than sectoral (occupational) choice on overall gender segregation. Understanding gender segregation in the Indian labour market is useful due to several reasons. In many developing countries, it has been noticed that educational attainment of females has been rising. At the same time, female labour force participation is also increasing. There are close linkages between educational credentials and labour market outcomes in modern societies both symbolically and functionally (Charles and Bradley 2002). Economic structures and opportunities, particularly female labour force participation rate, possibly affect women’s educational choices and placements. The labour market in India is quite different for women. The country has one of the lowest labour force participation rates for women in the world, and especially in urban areas the rate is very low. The recent trend suggests that female labour participation rate is declining. In urban India, it has remained stagnate despite rising educational levels and rapid economic growth. The aim of this study is to examine gender segregation in India. We use the mutual information index and employ a three-way additive decomposition to measure educational and occupational gender segregation and their effects on overall gender segregation. The decomposition allows us for discerning the role of human capital and occupational choices on the gender segregation. The analysis in the paper is based on a recent round of employment and unemployment survey conducted by the National Sample Survey Office, Government of India.

2. Measurement of segregation: mutual information index This paper uses the Mutual Information Index (MII) to measure gender segregation.1 This index is adopted from the literature on information theory and based on the concept of entropy. The index was first proposed in the segregation literature by Theil and Finizza (1971). Mora and Ruiz-Castillo (2003) develop this index to study two-dimensional gender segregation for investigating the links between occupational gender segregation and human capital. Overall segregation can be decomposed into a ‘between group’ component that measures the direct gender segregation attributable to human capital characteristics, and a ‘within group’ component that measures gender segregation within human capital categories induced by the occupation. In a recent paper, Van Puyenbroeck et al. (2012) develop this index to analyse two horizontal dimensions of gender segregation. The paper provides a novel three-way additive decomposition of the index for studying overall gender segregation as the sum of: (i) educational gender segregation, (ii) occupational gender segregation, and (iii) the interaction of both segregation. In this paper we follow the same framework.2 In the information theory, interaction information can either be positive or negative and it has a useful meaning. If it is positive, this shows a synergetic effect and if

1 Frankel and Volij (2011) present full axiomatization of the MII index using ordinal axioms in the context of multi-group school segregation. 2 The notations and methodology in this paper are taken from Van Puyenbroeck et al. (2010 and 2012). 2 negative, this shows several attributes providing overlapping, redundant information (Jakulin and Bratko 2004). In our context, a negative interaction indicates that total segregation would be overestimated when it is envisaged as being the sum of educational and occupational segregation. Therefore, there is an informational overlap in explaining gender segregation using both segregation measures. On the other side, a positive interaction term indicates synergetic effects of both educational and occupational segregation. This suggests that two partial measures alone are not sufficient in explaining total segregation.

3. Data This paper is based on the data from a nationally representative household survey conducted by the National Sample Survey Office (NSSO), Government of India. The NSSO conducts the large scale surveys on employment and unemployment once every five years. We use a recent round (68th round) of the survey which was carried out during July 2011- June 2012. The survey covers the entire country except some interior villages in some Indian states. The survey covers a total of 101,724 households enumerating a total of 456,999 individuals. The employment and unemployment survey contains detailed information on household and individual characteristics. The household characteristics include information on place of residence (rural or urban), household size, membership of a social group and religion. The individual level characteristics include age, educational attainment, gender and marital status. The survey also includes information on wages/earnings and occupation of the employed individuals. For our analysis, we classify the individuals in one of the following educational groups as follows: illiterate (including non-formal education), below primary, primary, middle, secondary, higher secondary (and diploma), graduation, and post-graduation. In the survey, the information on occupation of individuals is recorded as per the three digit National Classification of Occupations-2004 (NCO-04) code structure. We use a two-digit classification scheme which is sufficiently disaggregated level for the purpose of this study. There are 27 occupations at the two-digit level. Workers not classified by occupations are not included in the analysis.3 The study is restricted to the individuals aged 15-65 years since this age group matches well with the labour force.

4. Descriptive statistics Table 1 shows the distribution of male and female workforce across the educational levels, separately in rural and urban areas. There are large differences in educational attainment between rural and urban areas. In rural areas, more than one-third of the workforce is not literate or has only non-formal education. At the top end of the education distribution, only less than 5% of the workforce has a college degree. In case of female workforce, 57% does not have any formal education and only about 2% has graduation and above degrees. In urban areas, the share of literates in the workforce is much higher than that in rural areas. Nonetheless, the share of uneducated females is quite high in comparison to male workers. For instance, about 27% female workforce does not have formal education whereas the

3 This is a very small proportion of the workers. 3 same share for male workforce is 11%. Interestingly, we may notice that at the post- graduation level the share of females is higher than that of males. In Table 2, we show the distribution of male and female workforce across occupations that are classified using the two digit NCO-2004 code structure. A majority of the workforce in India is male-dominated and women represent relatively a small share in the workforce. Of the total workforce, about 24% in rural areas and 18% in urban areas are women. As can be seen, the main differences between the rural and urban areas also lie in the distribution of occupations. In rural areas, 58% of male workforce and 71% of female workforce are employed in agriculture and allied occupations (market oriented skilled agricultural and fishery workers, and agricultural, fishery and related labourers). In urban areas, the male workforce is mostly employed in occupations- general managers; models, sales persons and demonstrators; and labourers in mining, construction, manufacturing and transport. Female workforce is mainly employed in occupations- sales and services elementary occupations; and other craft and related trades workers.

5. Results Educational gender segregation First, we discuss results of the two-way decomposition. The gender segregation (educational or occupational), measured by the MII, is decomposed into two components: a ‘between-group’ term and a ‘within-group’ term. Table 3 shows gender segregation across educational groups.4 The last row of the first column (in rural or urban areas) shows pure educational segregation. The second column shows occupational gender segregation within each educational level. The third column, which is the sum of the first and second columns, represents the total two-dimensional gender segregation. Overall gender segregation is 9.34 in rural areas and 11.23 in urban areas. We find that in rural areas the between component is higher than the within component whereas in urban areas the within component is higher than the between component. This suggests that worker’s educational outcomes lead to a high degree of direct gender segregation in rural areas whereas in urban areas educational outcomes bring a low degree of direct gender segregation.

Occupational gender segregation Next we examine gender segregation across occupational units (using the NCO-04 codes) in Table 4. The first column (in rural or urban) in the table shows pure occupational segregation. The second column shows educational gender segregation within each occupation. The third column shows total gender segregation. In rural areas the within component (5.08) is higher than the between component (4.26) whereas in urban areas the within component (3.10) is lower than the between component (8.13). This shows that given the occupational choices, gender segregation due to differences in educational outcomes within occupations is high in rural areas and it is low in urban areas. We find that 46% of total gender segregation in rural areas is due to occupational choices whereas in urban areas 72% of the gender segregation is due to occupational choices. These results suggest that in rural areas knowing the education of an individual is on average more informative about the individual’s gender than knowledge of his/her

4 All values of the index are multiplied by 100. 4 sector of employment (occupation) given the individual’s educational level. Or put it conversely, knowing the occupation will be less informative than knowing the individual’s education, given the information on occupation.

Three-way decomposition The three-way decomposition allows for decomposing the total gender segregation into educational segregation, occupational segregation and an interaction term. We find that gender segregation is somewhat higher in urban areas than in rural areas. In rural areas, educational segregation is more than occupational segregation whereas in urban areas, occupational segregation is quite high than educational segregation. This suggests that in rural areas occupational segregation is less important than educational segregation and in urban areas educational segregation is less important than occupational segregation. We also notice that the interaction term between educational and occupational gender segregation is positive (and negligible in rural areas). The positive interaction term indicates the synergic effects between educational and occupational segregation. This shows that observed increase from educational segregation to occupational segregation could be due to an ‘enhancing effect’ of educational outcomes on occupational segregation (Van Puyenbroeck et al. 2012).

6. Conclusions This paper measures gender segregation in the Indian labour market using the entropy based mutual information index. We use the data from a recent round of a large scale nationally representative survey on employment and unemployment profiles and estimate educational gender segregation and occupational gender segregation. We find that a large part of educational segregation in both rural and urban areas is due to one category of individuals who have no schooling or have non-formal education. Occupational gender segregation in rural areas is largely driven by agricultural sector whereas in urban areas elementary occupations (sales and services) have the major impact on occupational segregation. Overall gender segregation is decomposed into three components: educational segregation, occupational segregation and their interaction. Total gender segregation is higher in urban areas than in rural areas. In rural areas, educational segregation is more pronounced than occupational segregation while in urban areas, occupational segregation is much more than educational segregation. We note a positive interaction term that indicates the synergetic effects between educational and occupational segregation. Our findings suggest that the policies should be targeted to increase the females’ participation in schooling in rural areas. This will help in reducing educational segregation as well as developing women’s human capital potential as many women in the workforce do not have formal schooling. However, in urban areas lowering educational segregation would not necessarily decrease gender segregation. There is need to increase job opportunities for females and their representation across different occupational categories. This will also result in increasing female labour force participation. Promoting non- discriminatory practices at workplaces would certainly help in increasing women’ participation.

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Tables

Table 1: Gender Distribution of Workforce across Educational Levels (%)

Educational Level Rural Urban Male Female Total F/T Male Female Total F/T Illiterate 27.98 56.76 34.89 39.10 11.39 27.29 14.20 33.95 Below Primary 12.19 10.03 11.67 20.67 6.96 7.80 7.10 19.40 Primary 14.98 11.67 14.19 19.77 11.46 10.62 11.31 16.58 Middle 19.40 10.17 17.18 14.23 17.63 11.64 16.57 12.40 Secondary 12.91 5.82 11.21 12.49 16.43 8.70 15.07 10.20 Higher Secondary 7.55 3.15 6.50 11.65 13.81 9.79 13.10 13.20 Graduation 3.93 1.69 3.39 11.97 16.09 14.99 15.90 16.65 Post-Graduation 1.06 0.71 0.98 17.45 6.24 9.16 6.76 23.95 Total 100.00 100.00 100.00 24.04 100.00 100.00 100.00 17.66 Note: F/T is the proportion of females in total workforce. Source: Authors’ calculation from the 68th round (2011-12) NSSO survey.

Table 2: Gender Distribution of Workforce across Occupations (%)

Occupation Code Rural Urban Male Female Total F/T Male Female Total F/T 11 0.05 0.01 0.04 4.92 0.38 0.35 0.38 16.41 12 4.11 2.26 3.67 14.83 16.19 10.35 15.16 12.05 13 0.04 0.01 0.03 10.83 0.43 0.17 0.38 7.78 21 0.14 0.02 0.11 3.30 2.32 1.62 2.19 13.02 22 0.17 0.13 0.16 19.25 0.68 1.62 0.85 33.74 23 0.66 0.86 0.71 29.29 1.74 6.60 2.60 44.87 24 0.91 0.33 0.77 10.36 3.29 2.46 3.14 13.84 31 0.15 0.03 0.12 6.82 1.42 0.64 1.28 8.83 32 0.24 0.46 0.30 37.25 0.54 2.34 0.86 48.00 33 0.83 1.90 1.09 41.94 0.96 6.19 1.88 58.15 34 0.63 0.16 0.51 7.40 3.16 1.57 2.88 9.61 41 0.94 0.30 0.79 9.27 4.21 4.73 4.30 19.43 42 0.10 0.05 0.08 14.34 0.80 1.09 0.85 22.60 51 1.78 1.75 1.77 23.71 4.77 6.42 5.06 22.41 52 3.92 1.63 3.37 11.61 10.69 5.36 9.75 9.71 61 36.22 39.56 37.02 25.69 3.78 4.09 3.83 18.83 62 1.49 3.11 1.88 39.69 0.09 0.18 0.11 29.53 71 6.91 2.22 5.78 9.23 6.96 2.46 6.16 7.04 72 1.60 0.14 1.25 2.76 5.14 0.51 4.33 2.06 73 0.54 0.38 0.50 18.09 1.41 1.47 1.42 18.25 74 2.20 5.78 3.06 45.39 5.46 13.85 6.94 35.22 81 0.52 0.15 0.43 8.30 1.14 0.30 0.99 5.31 82 1.01 0.63 0.92 16.60 4.00 2.17 3.67 10.41 83 2.68 0.02 2.04 0.21 5.88 0.09 4.86 0.33 91 1.59 1.89 1.66 27.30 5.04 13.99 6.62 37.32 92 21.46 31.84 23.96 31.95 1.97 4.65 2.45 33.60 93 9.11 4.39 7.98 13.23 7.55 4.74 7.06 11.87 Total 100.00 100.00 100.00 24.04 100.00 100.00 100.00 17.66 Note: See Appendix 1 for a list of occupations. F/T is the proportion of females in total workforce. Source: Authors’ calculation from the 68th round (2011-12) NSSO survey.

Table 3: Educational Gender Segregation Indices

Rural Urban Educational level Between Within Total Between Within Total Illiterate 2.800 1.299 4.099 1.562 1.132 2.694 Below 0.054 0.316 0.371 0.010 0.697 0.707 Primary Primary 0.107 0.615 0.721 0.007 0.998 1.004 Middle 0.732 0.698 1.430 0.248 1.217 1.465 Secondary 0.680 0.560 1.240 0.476 0.972 1.448 Higher 0.460 0.428 0.888 0.139 1.404 1.543 Secondary Graduation 0.227 0.213 0.440 0.008 1.594 1.602 Post-Graduation 0.018 0.132 0.150 0.122 0.646 0.768 Total 5.078 4.262 9.340 2.573 8.659 11.232 Source: Authors’ calculation from the 68th round (2011-12) NSSO survey.

Table 4: Occupational Gender Segregation Indices

Occupation Rural Urban code Between Within Total Between Within Total 11 0.008 0.002 0.010 0.000 0.004 0.005 12 0.137 0.151 0.287 0.260 0.454 0.714 13 0.003 0.013 0.015 0.022 0.033 0.056 21 0.027 0.006 0.033 0.025 0.012 0.037 22 0.001 0.008 0.010 0.091 0.014 0.105 23 0.007 0.041 0.049 0.739 0.012 0.751 24 0.068 0.013 0.082 0.024 0.033 0.057 31 0.018 0.002 0.020 0.059 0.022 0.080 32 0.019 0.019 0.037 0.300 0.052 0.352 33 0.121 0.049 0.170 1.111 0.006 1.117 34 0.071 0.003 0.074 0.107 0.015 0.122 41 0.083 0.012 0.095 0.007 0.078 0.084 42 0.004 0.011 0.015 0.010 0.016 0.026 51 0.000 0.062 0.062 0.053 0.346 0.399 52 0.240 0.087 0.327 0.354 0.294 0.647 61 0.039 2.452 2.491 0.003 0.255 0.258 62 0.163 0.323 0.486 0.007 0.012 0.018 71 0.612 0.186 0.799 0.427 0.330 0.757 72 0.325 0.036 0.361 0.784 0.041 0.825 73 0.007 0.028 0.036 0.000 0.110 0.111 74 0.478 0.065 0.543 0.879 0.041 0.920 81 0.052 0.019 0.071 0.098 0.037 0.135 82 0.022 0.072 0.094 0.109 0.107 0.216 83 0.773 0.004 0.777 1.243 0.023 1.266 91 0.007 0.153 0.160 1.034 0.477 1.511 92 0.555 1.037 1.592 0.258 0.172 0.431 93 0.419 0.227 0.646 0.129 0.102 0.232 Total 4.259 5.081 9.340 8.133 3.099 11.232 Note: See, Appendix 1 for a list of occupations. Source: Authors’ calculation from the 68th round (2011-12) NSSO survey.

Appendix 1: List of Occupations

Occupation Code Description 1 LEGISLATORS, SENIOR OFFICIALS AND MANAGERS 11 Legislators and Senior Officials 12 Corporate Managers 13 General Managers 2 PROFESSIONALS 21 Physical, Mathematical and Engineering Science Professionals 22 Life Science and Health Professionals 23 Teaching Professionals 24 Other Professionals 3 TECHNICIANS AND ASSOCIATE PROFESSIONALS 31 Physical and Engineering Science Associate Professionals 32 Life Science and Health Associate Professionals 33 Teaching Associate Professionals 34 Other Associate Professionals 4 CLERKS 41 Office Clerks 42 Customer Services Clerks 5 SERVICE WORKERS AND SHOP AND MARKET SALES WORKERS 51 Personal and Protective Service Workers 52 Models, Sales Persons and Demonstrators 6 SKILLED AGRICULTURAL AND FISHERY WORKERS 61 Market Oriented Skilled Agricultural and Fishery Workers 62 Subsistence Agricultural and Fishery Workers 7 CRAFT AND RELATED TRADES WORKERS 71 Extraction and Building Trades Workers 72 Metal, Machinery and Related Trades Workers 73 Precision, Handicraft, Printing and Related Trades Workers 74 Other Craft and Related Trades Workers 8 PLANT AND MACHINE OPERATORS AND ASSEMBLERS 81 Stationary Plant and Related Operators 82 Machine Operators and Assemblers 83 Drivers and Mobile-Plant Operators 9 ELEMENTARY OCCUPATIONS 91 Sales and Services Elementary Occupations 92 Agricultural, Fishery and Related Labourers 93 Labourers in Mining, Construction, Manufacturing and Transport X WORKERS NOT CLASSIFIED BY OCCUPATIONS Note: The entries in block letters correspond to one-digit classification.