Gender Discrimination in Quality of Employment and Wages ...



Gender Discrimination in Quality of Employment and Wages in Unorganized Manufacturing Sector of India

Anupama

Abstract

The concept of ‘Gender Discrimination’ has much wider coverage in the Indian economy. It is apparent in our population structure (generally defined by declining proportion of the females in total population), political structure and social structure. The underlying causes of these inequalities are centuries old traditions and the economic structure which is based on formal and informal practices\norms. Even elevated status of education as well as health does not let the women to make a free choice in their important decisions of life until they are economically dependent. This social discrimination is closely linked with the economic sphere as well. That is why, the women are employed in low paid, insecure and low status activities. Due to social hindrances, women have very low horizontal as well as vertical mobility and have to remain contended with lower wages.

However, it is expected that globalization and restructuring of the economy may benefit the women as there may be larger absorption of women into paid work. Since under the name of globalization, more and more flexibility in labour laws is being injected in the Indian economy, the size of unorganized labour force is swelling. Therefore, present paper focuses on the unorganized manufacturing sector as a case study for the analysis of sex-segregation of jobs, their quality and the earnings therein, during the period of nineties. An attempt has been made to check whether the gender inequalities have diminished or not in this particular sector in the era of globalization. For this analysis, OLS regression model will be applied on NSSO data apart from use of Ginni Coefficients and occupational Gender Segregation Measure (which is used in various studies on gender discrimination).

* Department of Economics, Punjabi University Regional Centre, Bathinda, Punjab.

anupamauppal@

Gender Discrimination in Quality of Employment and Wages in Unorganized Manufacturing Sector of India

I. Introduction: It is generally propounded that globalization has provided a vast arena of challenges as well as opportunities.

“The era of global integration has been associated with far reaching changes in the structure of employment, including pressures for increased flexibility, episodes of jobless growth, growing informalisation and casualisation, expanding opportunities for the highly skilled but the vanishing opportunities for the less skilled…….” – Heintz (2006).

Thus, globalization is being presented as a picture with two sides, depending upon the fact which side is being highlighted-the rosy one or the dark one. However, as far as the women are concerned, it is being observed that the economic opportunities available to them have grown (Cagatay and Ozler, 1995; Beneria and Feldman, 1992), though these opportunities may not be equal. The women participation in paid employment has reached an unprecedented scale. But the point is whether the employment has grown qualitatively or not, because in the era of globalization, when informalisation of work increases, it would be imperative to know how the formal and informal jobs are being divided among men and women. So, a gendered analysis of the growing employment opportunities is necessary.

Actually, the division of work among men and women is deep rooted in our societies with patriarchal outlook. It is centuries old tradition that women everywhere have primary responsibilities for non-market (unpaid) house work and caring jobs, which leads to family constraints on their choice in terms of labour force participation and their access to paid employment, both formal and informal (Beneria, 2003). So, when non market work becomes more important, women frequently have less paid work experience often leading to lower earnings, low paid, unstable and poor quality employment. Sometimes it is argued that the ‘care time’ is a temporary phase in the life of a woman

worker* and its impact can be mitigated in the long period. But equal strong is the view that even short period gender inequalities can have long term consequences on economic growth and human development (Ranis, Stewart and Ramirez, 2000), so it would be crucial to give gender dimension to the employment analysis.

Up to early eighties, it was assumed in the developing world that with economic growth, the informal economy would be absorbed in the modern industrial economy and the benefits of growth would trickle down. However, the hopes shattered thereafter as the size of the informal economy go on swelling even outside the agricultural sector. In developing countries informal employment in non-agricultural sector represents one half to three fourth of the total employment in this sector (ILO, 2002). When the rate of informalisation is increasing, the rate of female work participation is also increasing as if the women are taking over men’s jobs in the process of being informalised. So, our concern is to analyze these trends in the unorganized manufacturing sector of India in the post liberalisation phase. In this perspective, present paper attempts to find out the determinants of female employment and its quality in the unorganized manufacturing sector. It tries to find out whether the decent work deficit for the females in the unorganized manufacturing sector has grown or diminished during the phase of liberalisation. The paper is structured as follows. Apart from this introductory section, the paper will have four more sections. Section II will give the review of relevant literature, Section III deals with the data sources and methodology, Section IV discusses the sex-segregation of employment in the unorganized manufacturing sector and quality of female employment therein. This section tries to explore the relationship of female employment with various determinants in the unorganized manufacturing sector. Section V gives conclusions derived from the analysis and some suggestions.

Section II

Review of Relevant Literature

The era of liberalisation, no doubt, has registered an increase in labour force participation by women, everywhere in the world. These trends are largely being _______________________________________________________________

*though it may invoke criticism as care work includes the care of elder family members and any other ailing member of the family apart from caring the young ones and so may continue throughout life.

recognized as ‘feminisation of labour’ (ILO, 2004). At the same time, it is also being observed that women are concentrated in low quality and low-paid informal jobs (Heintz, op.cit.), specially in developing countries, women are less likely to be employed as wage and salary workers as compared to men (Chen et al, 2005; ILO, 2004). Many studies have shown gender gap in earnings even for same level of education, age and job tenure of women as compared to men (OECD, 2004; Mehra and Gammage, 1999; Elson, 1999). Since women have to devote more time to care work and less to remunerative activities than men, the total income from their employment falls (Chen et al, 2005; OECD, 2001; Folbre, 1994, UNRISD, 2000; Beneria, 2003). This has long term implications on women empowerment who can not establish themselves as independent ‘bread winners’ and continue to just supplement the earnings of the male members of the family. This may also adversely affect the families’ investment in education of their female children as the returns of this investment will be low or may not be enjoyed by the investing family. Thus, a link exists between quality of female employment, their economic empowerment and investment in their human capital formation (Sen, 1992). If women are concentrated in low paid and unprotected forms of employment, then an increase in their share of such employment does not represent an increase in gender equality (Chen et al, 2005). Moreover, due to low quality and instability of informal employment women frequently face a higher risk of poverty (Chad, 2003). But it is a fact that in virtually all countries of the world, as women bear the primary responsibility of providing care (Nussbaum, 2005), they themselves are more likely to find a irregular job in the informal sector, but it is also a fact that with increasing informalisation, when ‘feminisation of workforce’ is being observed, ‘feminisation of poverty’ coincides with it as well (Chen, Vanek and Heintz, 2006).

Section III

Data Sources and Methodology

After going through several studies on employment and wages in Indian labour market and other related studies, present paper tries to explore the impact of various factors on female employment and wages in unorganized manufacturing sector of India. NSSO defines the unorganized sector in terms of all unincorporated proprietary enterprises and partnership enterprises (GOI, 2001). NSSO provides extensive data on unorganized manufacturing sector under three categories, viz. Own Account Enterprises (OAMEs), Non Directory Manufacturing Enterprises (NDMEs) and Directory Manufacturing Enterprises (DMEs).1

Present study uses NSSO data on two digit industrial classification for about 24 sub-sectors of unorganized manufacturing units. Taking these sub-sectors as observations, cross- sectional OLS models are run on NSSO data for 1994-95 and 2000-01 for determining wages and female employment in the unorganized manufacturing in India in post reform phase. The data for both of the years relate to all types of enterprises in unorganized sector (i.e. for OAMEs, NDMEs and DMEs) and for rural and urban areas separately. The data for 1994-95 were not available for the unorganized sector as a whole. So, in order to arrive at it, we had to add the data for rural and urban areas for each enterprise type, so that it can be appropriately compared with the data for 2000-01. Since, the NSSO data does not provide data on wages paid to its workers separately for males and females, simply a regression equation is run to know the effect of number of female employees on total wages paid in an enterprise.

Apart from the regression analysis, for measuring gender segregation in the unorganized manufacturing sector, the Index of Dissimilarity (ID) and KM Index (Karmal and Maclachlan Index) are calculated for both the time periods on NSSO data.

Section IV

Gender Segregation and Quality of Employment in the Unorganized Manufacturing Sector of India

The unorganized manufacturing sector employment in India has grown in the phase of liberalisation due to stagnation in the organized sector employment. The share of unorganized manufacturing in total manufacturing employment has reached to 82.3 per cent during 2001-02 as compared to 80 per cent in 1993-94 (GOI, 2003). Under this sector, it is being observed that the share of female workers has also increased during the liberalisation phase. Depending upon the NSSO data, it is observed that the share of female employment has increased from 31 per cent of total unorganized manufacturing sector employment during 1994-95 to 34 per cent during 2000-01. Though, the overall picture shows that employment in this particular sector is being feminised, the attention diverts to the quality of female employment i.e. on the types of jobs, earnings and benefits etc. and how the jobs with some positive qualities are distributed among men and women. So, firstly, we will discuss how far the employment in unorganized manufacturing sector is segregated by gender. Though, sex segregation in employment can be defined as horizontal segregation (i.e. by different occupational groups) and vertical segregation (i.e. by types of jobs within the same group), here we are dealing with horizontal segregation. We have used here the Index of Dissimilarity (ID) as suggested by Watts (1998). The simple ID is given as follows:

[pic]

where, fj and mj denote the number of female and male employees in the jth occupation and f and m are total female and male employment, respectively.

Later, Watts himself explained that the analysis of sex-segregation in any occupation would not be complete until the respective shares of both males and females in total labour force participation are not taken into account (Watts, 2001). So, he favours Karmal and Maclachlan Index (KM Index) which is given as follows:

[pic]

where T, a, fj, mj are defined as total employment, the overall female share of employment and female and male employment in the jth occupation, respectively. The index denotes the fraction of total employment that would have to be relocated between occupations to achieve zero gender segregation. This index is a measure of difference between the integrated and actual distribution of employment by gender. Overtime, both the occupational shares and overall gender shares of employment typically change. KM index being sensitive to these interrelated changes, is considered a better measure than the simple ID.

It is observed that the horizontal sex-segregation has declined in 2000-01 as compared to 1994-95. Table: 1 clearly shows decline in both the indices during this period, especially in case of KM index, thus showing that very small proportion of labour force is needed to be shifted to reach zero gender segregation. Given the aggregate nature of data, the value of these indices is very low, showing hardly any significant gender-wise difference of employment in various enterprises.

Table: 1 Sex-Segregation of Employment in Unorganized Manufacturing Sector of India

|Enterprise Type by | | |

|location |ID |KM Index |

| |1994-95 |2000-01 |1994-95 |2000-01 |

|Rural |

|OAMEs |0.292 |0.230 |0.142 |0.112 |

|NDMEs |0.416 |0.260 |0.090 |0.06 |

|DMEs |0.258 |0.188 |0.105 |0.07 |

|Total |0.271 |0.217 |0.128 |0.104 |

|Urban |

|OAMEs |0.475 |0.321 |0.199 |0.157 |

|NDMEs |0.454 |0.300 |0.045 |0.04 |

|DMEs |0.344 |0.245 |0.062 |0.05 |

|Total |0.428 |0.305 |0.124 |0.147 |

|Combined |

|OAMEs |0.300 |0.245 |0.141 |0.121 |

|NDMEs |0.447 |0.312 |0.067 |0.052 |

|DMEs |0.361 |0.237 |0.107 |0.07 |

|Total |0.304 |0.236 |0.130 |0.105 |

Source: Calculated from NSSO (1995, 1998a, 1998b 2002a, 2002b).

However, this is the aggregate picture. Our concern is how the positive and negative qualities of this employment are distributed among the males and females in this particular sector. So, Table: 2 gives a detail of the quality characteristics of female employment in the unorganized manufacturing sector of India. The table has depicted some of the negative and positive characteristics of female employment in this particular sector. It is being observed that there is a remarkable imbalance in the share of females in various types of employment as compared to their overall share in total employment in any type of enterprise, indicating a sort of decent work deficit. For example, during 1994-95, it is observed that whereas the share of females in total employment is 31 percent, this percentage is as high as 36 per cent in total unpaid employment and as low as 14 per cent in full time employment, 18 per cent of total working owners and only 15 percent of total hired workers. This shows that in negative type of employment, the females are over-represented and in positive types, they are under-represented. The irony is that by the year 2000-01, the negative qualities of employment have outgrown the positive ones in case of female workers in this sector. The table: 2 shows that while the share of female workers in total employment is 34 per cent during 2000-01, it is 56 per cent in unpaid employment, 62 per cent in part time employment and only 16 percent, 28 per cent and 32 per cent of total hired workers, full time workers and working owners, respectively in this particular sector. This shows that whereas the share of women in positive qualities of employment has increased in 2000-01 as compared to 1994-95, the negative types of employment have also absorbed more of women than men during this period. It is worth noting that the proportions in negative types of employment largely differ from the comparable proportions in total employment in urban areas as compared to the rural ones. The type of enterprise also affects this difference e.g. in case of DMEs, though the working owners are being less represented by the females as compared to other types of enterprises, there is hardly any significant difference in case of full time workers. But here, too, the conditions have deteriorated as compared to 1994-95 as in case of full employment in DMEs, the share of females was greater than their share in total employment (by 8 percentage points) during 1994-95 but by 2000-01, there is a deficit of 1 percentage point. Actually, the quality characteristic table, does not give a clear pattern as both the deficit in case of positive qualities has reduced and the negative qualities of employment are being more represented by the female workers as the liberalisation of economy matured. So, there is a need to establish a functional relationship of various determinants with female employment during this period. For this a cross sectional OLS

Table: 2 Quality Characteristics of Female Employment in Unorganized Manufacturing Sector of India (share in % out of total employment in each category)

|Enterprise type |1994-95 |2000-01 |

| |

|Variable |Rural |Urban |Combined |

| |

|Variable |Rural |Urban |Combined |

| |OAMEs |NDMEs |

| |Rural |Urban |Combined |Rural |Urban |Combined |

|LPRO |0.694 (2.467)* |0.391 (2.274)** |0.535 (2.66)* |0.635 (4.60)* |0.802 (2.652)** |0.824 (4.279)* |

|FW |-0.266 |-0.155 |-0.185 |-0.110 (1.492)** |-0.648 |-0.503 |

| |(-1.561) |(-1.843) *** |(-0.927) *** | |(-0.916) *** |(-1.232)** |

|Constant |0.356 (0.608) |1.473 (0.891)* |1.012 (2.453)** |1.227 (1.926)* |0.717 (1.342) |1.253 (3.23)* |

|R2 |0.66 |0.806 |0.711 |0.758 |0.58 |0.886 |

Source: Calculated from NSSO (1995, 1998a, 1998b 2002a, 2002b).

* significant at 1 per cent level of significance.

** significant at 1 per cent level of significance.

*** significant at 1 per cent level of significance

In this model we have combined results of NDMEs and DMEs, excluding the OAMEs because by definition, OAMEs do not have any hired worker on a fairly regular basis. The model shows that there is a significant positive relationship of wages with labour productivity (combined for males and females). On the other hand a significant negative relationship is observed between the ratio of female workers in total employment and the wages in an enterprise. The Table: 4 shows that whereas a one percent increase in labour productivity would have increased wages by 0.53 per cent in 1994-95, it increased to 0.82 per cent by 2000-01. However, this positive impact has become greater in urban areas as compared to the rural areas in 2000-01, while the relationship was quite opposite in 1994-95. It is observed that while the total workers are getting a larger share of their productivity in terms of wages, the female workers are losing their bargaining strength as with one per cent increase in ratio of female employment out of total employment, the total wages fell by 0.19 per cent in 1994-95 and by 0.50 per cent in 2000-01. During 2000-01, the females employed in urban areas are more vulnerable as a one per cent increase in their share in total employment, reduces their wages by 0.11 per cent in rural areas and by 0.65 per cent in urban areas. This tendency may be understood in the context of previous table which show that women are being over represented in part time jobs, even though their ratio in unpaid employment has declined. This tendency explains the fact why employed women are more likely to be poor than employed men, as according to ILO data for 2003, women accounted for 60 per cent of working poor even though they comprised just 40 per cent of total employment (ILO, 2004). The impact of lower earnings of the female workers has wide implications on the economic as well as social status of women in society, which weakens in this process.

Section V

Conclusions and Policy Suggestions

Above analysis shows that although the female labour force participation has increased and sex segregation of total employment has declined in the unorganized manufacturing sector of India, yet their access to decent economic opportunities is frequently constrained, particularly in paid employment category. The women are being over represented in unpaid and part time jobs as compared to the jobs with positive characteristics under the unorganized manufacturing sector of India. It is a fact that employment as well its quality is significant by which growth can be translated to the empowerment of the marginalized classes, including women. However, our regression analysis points towards certain dark areas of increased women employment in the unorganized manufacturing sector of India. It is observed that though, female employment is positively related to the output variable, but it is negatively affected by the number of male workers in an enterprise. Under increasing employment conditions in unorganized manufacturing sector, this negative relationship would have meant substitution of females by more than the number of new male workers employed, and so pointing to ‘defeminisation of labour force’ in this sector. On the other hand a negative relationship of wages with ratio of female workers in total employment shows that as number of female workers increases the total expenditure of an enterprise on total emoluments declines indicating that females are being paid lower than their male counterparts due to their low quality jobs or due to low bargaining strength. This confirms the general trend of females being underpaid in the unorganized sector of India and so ‘feminisation of poverty’. However, there is a respite that during this period (from 1994-95 to 2000-01), the impact of unpaid employment on total female employment is diminishing.

These results require the planners\policy makers to look into the ways in which the gender intersects with other sources of disadvantage for working class. Any policies regarding women’s employment must keep in mind, the division of female work time in paid and unpaid activities. As women can devote lesser of their total working time to paid activities as compared to men, they earn less in the same type of job. However, when even for same number of working hours, same level of education and skill and experience, women are earning less than the men, then there to seriously look into the matter and ensure the implementation of ‘Equal Remuneration Act’ and any deviation from it should be strictly dealt with. As women are now largely contributing to the earnings of their family and helping to come through the poverty, it is important that their capacities should be enhanced by providing them more decent employment opportunities. Only then the poverty and gender inequality will diminish.

Finally, organizing women is the critical element in their economic and social empowerment. Responsible women organizations can enable them to defend their interests and maintain pulls and pressures in formulating appropriate policies and don’t let the policy makers ignore their gender interests. But the problem is that the mainstream trade unions are male dominated and do not address the specific problems of women workers, especially of those working in the unorganized sector. There is a need to organize these women workers neglected by the mainstream trade unions. There is a model of Korean Women’s Trade Union (KWTU) apart from our country’s SEWA, which are organizing women workers neglected by mainstream trade unions. Such unions should keep their own autonomy while co-operating with the mainstream trade unions on issues of common interests. Even the mainstream unions should also form separate women’s sections to deal with specific problems of the women workers. Whatever may be the case, the point is that women organizations are most important for women workers in the informal sector to counter the forces that contribute their impoverishment. Moreover, there is no substitute of the voice raised by the sufferers themselves! [pic][pic][pic]

Notes

1. OAMEs are the enterprises which employ no hired worker on a fairly regular basis, NDMEs are the enterprises with at least one hired worker and less than six total workers, while DMEs hire at least one workers and six or more total workers (NSSO 2002).

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