17Gender & Development
Mridul Eapen
1.0. Introduction
It is well-recognised now that development policy is neither class-neutral nor gender-neutral; it impacts differentially on women and men due to their different locations in the family and in the economy. Particularly, in the context of Structural Adjustment Programmes (SAPs) since the 80s, the gender biased effects have been brought out very sharply in a number of studies. There is a growing recognition also of the impact of gender on macro policies and efforts are being made to engender macroeconomic policy. Hence the concept of gender like the concept of class is an analytical category for understanding processes of change; emphasising gender here is not to preclude its interconnectedness with other structural factors like class, race and resource distribution which lie at the root of poverty.Accurate and reliable gender-disaggregated statistics are obviously an imperative which would enable us to draw attention to problems not addressed before; identify fallacies in the way policy is designed; critique theories and tools in order to expose their inadequacy and to seek new tools of analysis As the issue of women became a focus of national and international concern since the 70s and various programmes/policies were proposed for the benefit of women, the call for reliable statistics on women became more insistent. The last two and a half decades have witnessed an upsurge in data collection by sex; however concern continues to be expressed regarding the gender blindness of our data systems.
This note attempts to briefly review the existing gender differentiated database; however, the focus is on identifying its limitations from a gender perspective. We confine ourselves primarily to some key economic aspects. This note has two sections. In Section 1 we elaborate a bit on our understanding of gender which provides greater insights into the process of development and its outcomes. Without creating a gender awareness, it would not be possible to identify the gaps. Some idea of the existing database differentiated by sex is presented in Section 2, which in the light of the need for a gendered view of development also discusses the lacunae in existing data.
IGender is largely socially constructed rather than biologically determined. Very often discussions of gender are conflated with discussions of women; however, gender should not be read as 'pertaining to women' but should be understood in terms of the socially constituted relations between women and men in which women have been systematically subordinated. It is constituted not only in the sphere of culture or ideology but is reproduced in the economic practices of making a living and in turn shapes the outcomes of such practices. It does this through stratifying society into "productive" (or "market") and "reproductive" (or "domestic" or "social reproduction" or "care economy") activities which is the basis for a fundamental division of labour between women and men. Needless to state productive activities refer to income generating activities most of which are linked to the market while reproductive activities include work for the reproduction, care and maintenance of members of the household, like food preparation, cleaning and sanitation and very often collection of fuel and water. In most societies and throughout most of history women have borne the responsibility of the "care economy" as well as contributing to productive activities while men have been primarily involved in income-earning activities.
While the possible role of biology, as for instance pregnancy and child-bearing, cannot be denied in the historical construction of the gender division of labour, it cannot explain its perpetuation nor the whole gamut of gender inequalities we observe today, revealed in a range of practices, ideas and representations, indicative of the iniquitous gender relations. Access to and control over resources situates the men in a dominant position within and outside the household while women's work which is primarily concentrated in the domestic sphere and is not paid for, is not perceived as "productive" work and contributing to national output although "productive" work may be parasitic on it. Values become embedded in tasks and who does them; hence even when women do engage in income generating activity, given the hierarchical structures associated with capitalist institutions and the ideology of domesticity, they enter the labour market as inferior bearers of gender both in terms of nature of work and earnings.
Focussing on gender, the division of labour and the fact that it is organised by asymmetric relations of power which constitute and shape the lives of women and men, introduces certain insights which strengthen the need to bring a gender lens to development discourse and development policies:
First, it redefines the scope of the economy to include unpaid domestic labour and makes women's work more "visible" thus reshaping our understanding of the conditions necessary for the functioning of the paid, productive economy.
Second, gender as a category of social stratifier influences not only the distribution of work, income, wealth and productive inputs but also the behaviour of agents. For instance a number of studies across different societies reveal that women and men exhibit different consumption patterns- that women tend to have a higher propensity to spend on goods that benefit the household which has important policy implications.
Third, it questions the assumption, underlying most development programmes, of the "household", characterised by harmony and equal control over resources and decision making, with a benign (male breadwinner) head maximising total utility ignoring the significance of power relations and the existence of both cooperation and conflict within households.
Fourth, the assumed elasticity of women's work in the reproductive economy. Most development policies take the "reproductive" economy for granted which is equivalent to assuming an unlimited supply of female labour. This has been found to be especially true of SAPs.
Fifth, institutions produce or transmit gender-biases which then permeate all economic relationships and necessitate engendering of macroeconomic performance.
Such an awareness regarding women's subordinate position in society, can contribute to generating data not only to understand gender differentiated impact of macro policy but also to formulating gender sensitive policy more capable of provisioning human needs and enhancing human capacities. In the following Section we look into the data base on some of the aspects which have emerged from our preceding discussion as critical to engendering development, that is, women's work defined broadly to include unpaid work, the dynamics of the household, and certain macroeconomic aspects like fiscal budgets.
II
As indicated earlier, since the 70s when women were emerging as a recognised constituency in the development effort, the need for reliable statistics on women was strongly reiterated, particularly by international organisations contained in three significant documents: The World Plan of Action for the Implementation of the Objectives for the International Women's Year and in the Programme of Action for the second half of the United Nations Decade for Women. In the late 80s, the United Nations International Research and Training Institute for the Advancement of Women (INSTRAW) declared its major objective as that of improving the availability of social and economic indicators for the analysis of women's status and related statistics concerning women. The effort to improve the statistics is continuing.
In India too, following the developments on the international front and the publication of the Report of the Committee on the Status of Women in 1974, considerable effort was made by official agencies to collect sex wise information wherever relevant and establish a set of indicators by which progress towards gender equality could be monitored. A national workshop was organised by the Central Statistical Organisation (CSO) on Improvement of Statistics on Gender Issues in 1994 in which demographic, legal, social and economic issues were discussed. An important observation made at the workshop was that it was not so much the lack of gender based data as its "opacity", that is a lot of the information was hidden inside detailed and convoluted tabulations and presented in a bland non-committal manner. This meant culling out the facts from diverse sources, checking for their comparability and consistency and then presenting them.
However, while substantial information has appeared from which the inferior position of women in the economic sphere can be established, even in the field of education in a 100 percent literate state like Kerala and the incidence of educated unemployment (as we show later, information on a critical issue, that is 'work', in particular unpaid work of women within which many activities are "economic" in nature, still remains inadequate. The problem lies ofcourse with the way "work" is defined. Traditional economic analysis as we know, tended to make a large proportion of women's work invisible since income-earning activities only were conceptualised as work as also "unpaid family labour" in agriculture that produced goods for the market. However, a wide range of unpaid activities for own production of goods and services for survival needs of households, including voluntary work in the community, were not, economically speaking considered as work. Given that most of such work was done by women as discussed in the preceding section, it resulted in their economic invisibility and statistical underestimation. The situation was aggravated by the gendered views regarding women's primary role and inferior position in the paid labour market; their concentration in the informal sector, especially home-based work, also tended to miss them out since such work itself was not visible due to its unrecorded character.
Let us examine our database on this issue. There are national sources of data, which give information at the state level, and state sources agencies which collect data for the state on all aspects of the economy. The two major sources of data (national in nature) on employment/unemployment for women and men in the country are the decennial Censuses and the quinquennial Rounds of the National Sample Survey Organisation since 1972-73, operating largely on the above concept of work. The situation has changed somewhat at the tenacious insistence of women working at different levels, particularly in academic circles and UN organisations to make women's work more visible at the theoretical and empirical level and improving labour force statistics. In the Indian context the NSSO has been more responsive to capturing women's unpaid work. Cultivation of crops and allied agricultural activities even if meant for own consumption is counted as work and very recently unpaid work on family enterprises (in the 1991 Census too) is also included. The coverage of the informal sector was also sought to be improved by special Rounds of the NSSO on unregistered small and micro enterprises in the informal sector. The latest Round, that is, the 55th Round for the year 1999-2000, provides detailed information on the informal sector.
However, there still remain a range of activities, outside the purely domestic chores of cooking, cleaning and child care, which are not enumerated- like husking of paddy, maintenance of kitchen garden, basket-making, tailoring, collection of fuel, fodder and water. In 1977-78 (the 32nd Round of the NSSO) the activity pattern in such unpaid work of women recorded as primarily engaged in household duties was also given and this continued for the 43rd (1987-88) and 50th (1993-94) Rounds (see Table 1). While these data provide very interesting information on the vast range of tasks women perform, which are economic in nature and strengthen the case for improving labour force statistics and incorporating unpaid work in national income accounts as some countries have done, they remain unincorporated into our data systems. The fact that a large part of women's work is unrecognised either because it is unpaid family work, or because it is irregular or because (male) heads of households refuse to regard it as work for official purposes, is reflected in the very low work participation rates of women which can be seen from Table 2; only about one-fifth of the female population (or a little over it in rural areas) is recorded as workers in Kerala.
A study done earlier using the 32nd Round data on activity pattern of housewives for all-India revealed that if the unpaid domestic labour (of an economic type) was taken into account then the work participation rates of women in rural India increased from 30.5 percent to 52.3 percent. It is necessary that attempts be made to improve both labour force and national income statistics by broadening the definition of work to include unpaid production. Concepts and methods could be borrowed from work being done elsewhere. One could adopt multiple definitions of work, for multiple uses so that different sets of data can be constructed for different purposes and women's labour force participation can be captured with greater accuracy. Work by academics and experts particularly at the UN Statistical Commission has led to recommending the construction of satellite accounts to provide estimates of the contributions of unpaid domestic work to national income. To even initiate such an exercise of estimating the value of home production it is necessary to conduct household surveys with questions on labour time spent on unpaid housework as part of a total time allocation study between paid and unpaid work for both men and women. The Department of Economics and Statistics, Tamil Nadu, recently produced a household level time use study. Such an effort is not merely to generate quantitative measurement of women's work and make it visible but also to bring out the gender inequality in the distribution of leisure and domestic work and changes that take place in domestic work with changes in nature and organisation of paid work. It is only now, after considerable pressure from women's writings/demands that the Central Statistical Organisation undertook a Time Use Survey (2000) which would enable us to measure women's real workload and gender disparity in terms of the relative time men and women spend on paid and unpaid work, a large proportion of which is "economic" in nature. The Survey, at the moment, has been done for 6 states (Kerala is not among them).
As argued earlier, if one digs for data from various sources, and reorganises it substantial information can be generated regarding the inferior position of women in education (see Table 3, 4 and 5) educated unemployment (Table 6), paid work in terms of the industrial/occupational structure1 (Table 7) and earnings (Table 8). Such data are very useful especially to trace the changes over time, particularly in the context of economic restructuring and the presumed shift towards export-oriented sectors. At the moment neither from the available production data, nor trade data, is it possible to decipher the growth in women intensive export industries. However, the 2001 Census information on employment by industry groups at a 3-digit or 4-digit level is still to be tabulated which may enable us to make inferences on post-Reform changes.
With the increasing shift towards the market in respect of social support services, and considerable restructuring in the job market, opening up employment opportunities for women in labour intensive export industries, the family/household is the only refuge where adjustments have to be made in terms of intensification of unpaid domestic work. Since households mediate so much of development programmes whose micro outcomes depend crucially on the dynamics discussed above the need to collect data at the household level has become imperative- time allocation data to understand the burden of double (or triple if one includes community level work) work for women as also the dynamics of intra household income flows and expenditures, distribution of resources, process of decision making, the growing incidence of domestic violence and mental stress among women. Both the Census and the NSSO collect information at the household level. On a sample basis, since deeper probing is required on most of these aspects, more insightful data should be generated at the household level.
We now come to the issue of engendering macro policy - gender budgeting is one such attempt rapidly gaining ground. Hardly any data are available in Kerala on gender sensitising budgets though several initiatives are underway in India. For instance gendered analyses of policies in sectors like education and health critical to women by women's movement groups, feminist scholars, NGOs; gender analyses of the Union Budget for 2001-02 and 2002-03 by the National Institute of Public Finance and Policy; gender analysis of state budgets commissioned by the Department of Women and Child Development and undertaken by NIPCCD.
Budgets determine how Governments mobilise and allocate public resources and are political instruments shaped by competing demands of different sectors and interest groups and can be powerful vehicles for the advancement of social goals. Social concerns are more visible in fiscal rather than monetary policy. Tools for gender sensitive budgets are evolving which also imply the need to generate very different type of data. For instance to do a gender disaggregated beneficiary assessment, where potential and actual beneficiaries of a government programme are asked their views as to whether existing forms of public service delivery meet their needs as they perceive them, data can be collected by using quantitative surveys (e.g. opinion polls and attitude surveys) and qualitative processes (e.g. focus groups, interviews, participant observation). A gender disaggregated public expenditure incidence analysis can be used to provide an assessment of the distribution of government expenditure of a given programme between men women, boys and girls. This tool requires considerable quantitative data in order to estimate both the unit cost of providing a particular government service and the utilisation of public expenditures by households and individuals disaggregated by gender and so on. The usefulness of generating household based data is once again brought out sharply.
Note
- It may be noted that data on the industrial/occupational distribution are available both from the Census and the NSSO; however, for the occupational structure, the Census figures appear to be more comprehensive.
References
Baneria, L (1995): "Towards a Greater Integration of Gender in Economics", World Development, Vol. 23, No.11.Banerjee, N (1994): "A Note on Collecting and Publishing Gender-oriented Data On Economic Issues", paper presented at the First National Workshop on Improvement of Statistics on Gender Issues, CSO.
Cagatay, N et. al (1995): "Introduction", World Development.
Census of India, Kerala, (various decadal issues)
Duvuury N (1989): "Women in Labour Force: A Discussion of Conceptual and Operational Biases with Reference to the Recent Indian Estimates", (source not known).
Government of India (2000): Report of the Time Use Survey, Central Statistical Organisation, (New Delhi, Central Statistical Organisation).
Government of India (Various issues) National Family Health Survey, (New Delhi, Ministry of Health and Family Welfare).
Government of Kerala (1999): Report of the Committee for Monitoring Programmes for Women, (Trivandrum, State Planning Board)
Government of Kerala (1989): Women in Kerala, (Trivandrum, Dept. of Economics and Statistics)
Krishnaraj, Maitreyi (1990): "Women's Work in Indian Census: Beginning of Change", Economic and Political Weekly, December 1-8.
National Sample Survey Organisation, Quinquennial Rounds, 1972-73, 1977-78, 1983, 1987-88, 1993-94 and 1999-2000.
Sen, G and C. Sen (1984): "Women's Domestic Work and Economic Activity: Results from the National Sample Survey", Working Paper No.197, (Trivandrum, Centre for Development Studies).
Govt. of Kerala, Statistics for Planning, (various issues) (Trivandrum, Planning Board)
Table: 1
Number of women usually engaged in household duties (principal status) & also
participating in specified activities per 1000 women engaged in household duties in Kerala:
1987-88 & 1993-94
Specified addl. Activities 1. Maint. of kitchen garden etc 2. Work in hh poultry, dairy etc 3. Free collection of fish etc 4. Free collection of firewood… Any of Items 1-4 5. Husking of paddy (own produce) 6. Grinding of foodgrains (-do-) 7. Preparation of Gur (-do-) 8. Preservation of meat etc (-do-) 9. Making baskets etc (-do-) Any of items 5-9 10. Husking paddy (acquired) 11. Grinding of food grains (-do-) 12. Preparation of gur (-do-) 13. Preservation of meat etc(-do-) 14. Making baskets etc (-do-) 15. Preparation of cowdung cakes 16. Sewing, Tailoring etc 17. Tutoring of own children 18. Bring water from outside hh pr. 185
19. Bring water from outside village Any items 1-19
Source: Results of the Fourth Quinquennial Survey on employment and unemployment: NSS 43 Round Sarvekshana, Vol XXI No 2, Jan 1992 and Survey Results on Participation of Indian Women in Household Work and Other Specified Activities, NSS 50th Round, Sarvekshana.
Table: 2
Worker Population Ratios in Kerala
(1999-00)
Source: Sarvekshana: Jan-April; Oct-Dec 1992; July-Sep 1996 and Employment and Unemployment Some Key Results, 1999-2000, NSSO, 55th Round
Table 3:
Gender Disparity (M/F) in the Different Levels of Educational Achievements of Kerala: 1971-91
Levels Primary Middle Matric Mat+high.Sec High Sec. NTDip Tdip Grad & ab I II III IV V VI
Note: NTDip: Non Technical Diploma not equal to degree
TDip : Technical Diploma not equal to degreeThis disaggregation is given only for urban areas.
I : Graduation other than technical degree
II : Post Graduation degree
III : Engineering and Technology
IV : Medicine
V : Agriculture, Dairying and Veterinary
VI : Teaching
2. Gender disparity is estimated as the ratio of male to female percentage of literates in each educational category
* Percentage to literates
Source: Census of India, Social and Cultural Tables: Kerala (various issues)Table 4:
Trade-wise Intake in Government ITIs and Private ITCs during 1995-96 (one year course)
Dat Prep & Comp... Total
Source: Kerala State Planning Board (1997) Report of the Steering Committee.
Ninth Five Year Plan: 1997-2002Table 5:
Tradewise Intake in Government ITIs and Private ITCs during 1994-95 (two year course)
Draftsman Civil