INTRODUCTION - THE ISSUE

The challenge of agricultural and economic policy-making and planning is to enhance productivity and output while maintaining the natural resource base, safeguarding and increasing rural incomes, generating employment and promoting the nutrition and food security status of households and individuals. There is increasing evidence and recognition that what is needed for development, even more than natural resources and physical capital, is for people to be effective and productive economic agents; in short, investment in human capital is what really matters.

Changes in the economy, especially in agriculture, affect women and men differently since the roles, responsibilities, needs and constraints of women differ from those of men. Although women play a central role in the economy, their contribution to agricultural production is largely invisible in national statistics and is thus overlooked in both economic analysis and policy formulation. This represents a significant obstacle to promoting gender-responsive sustainable development.

This paper discusses how participatory approaches and information can facilitate the formulation of gender-responsive plans and strategies. It also attempts to respond to the question of why a gender perspective is important for agricultural and economic development policy and planning. The underlying assumption is that such policy and planning would benefit from incorporating a gender dimension, yet lack of information is one of the main constraints to incorporating gender issues. Data on women are still seen as only marginally relevant to policy-making and reliable sources of such data, particularly in the agricultural sector, are generally lacking.

Gender biases are present at every stage from conceptualization and design to field interviews, analysis and implementation. This compounds the difficulties of data collection in rural areas, particularly in the informal sector. Data disaggregated by sex cannot alone provide insights into the processes that determine the differential impacts of policies on women and men.1 For policy-making purposes the analytical framework necessary to understand gender relations must accompany these data.

The current policy environment advocates "involving women", but does not necessarily promote an analysis of gender issues in policy, programme and project planning and implementation. Gender analysis studies the different roles and responsibilities of women and men, the differences in women's and men's access to and control over resources, and their consequent constraints, needs and priorities. Incorporating gender analysis into the tools of participatory agricultural planning helps policy-makers and planners to understand how the structure of policies and programmes needs to be modified if women are to be involved equally with men. It can demonstrate why some projects and policies have negative consequences for women.

Well-planned macro and sectoral policy changes have the potential for stimulating growth with equity. They also provide the opportunity and the tools for rural women to improve their productivity in production, processing and marketing activities in the rural agricultural and industrial sectors.

Gender-responsive planning means first learning about how gender shapes the opportunities and constraints that women and men face in securing their livelihoods within each cultural, political, economic and environmental setting. Because women and men have different tasks and responsibilities, and different livelihood strategies and constraints, they must each be consulted. There is overwhelming evidence that development has to address the needs and priorities of both women and men in order to be successful.

1 Data disaggregated by sex refer to the collection of data by physical attributes. Gender-disaggregated data, however, are analytical indicators built upon sex-disaggregated data on social and economic attributes. The term "gender" in this context refers to a set of statistics derived from the results of social and economic analysis.