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The relationship between gender and trade (in two directions: from gender to trade and from trade to gender) is a new issue. Not for WIDE and a few other women’s organisations, which have been working on the topic since the mid-1990s. But it is certainly a new issue for governments, trade policy makers, the WTO, and for academic researchers. WIDE therefore has developed a tool that will help to understand, measure and monitor the relationship between trade and gender. This tool consists of three sets of indicators, which can be applied to any trading relationship between countries or trade blocks.

The need for analytical tools

Globalisation is characterised by growth in capital flows, labour migration, multinationals, and trade in goods and services. Research has indicated that costs and benefits of this global exchange of money, labour, and products are unequally distributed. North benefits more than South, skilled workers benefit more than low skilled workers, whereas the environment is a clear loser, as well as in many cases the poor. Although we know that women are the majority of the poor and low skilled workers, there is very little known on the impact of globalisation on women. Partly this is because of a lack of gender disaggregated data in trade statistics, and partly because of a lack of gender awareness in economic analyses and models. In particular, the blind spot for the unpaid care economy prevents the study of links between trade and unpaid labour. Moreover, it is difficult to distinguish trade effects from effects of global investment flows or macro-economic policies. And it is very difficult to distinguish effects of trade between two countries or trading blocks from effects of trade that each of these countries or blocks have with other countries or trade blocks.

And the issue is even more complex. In the relationship between gender and trade, there are not only impacts of trade that work out differently for men and women. There seems also to be a relationship the other way around: effects from gender inequality on trade.

Two examples of the complex gender and trade relationship:

For South Asia, for example, industrial export success depends largely on wage discrimination of women. “Thanks” to the low wages paid to women (about 75% of men’s wages), countries like Korea, Taiwan and Singapore are able to export products against low prices. Literally, this makes the discrimination of women the motor of economic growth in South Asia.

In sub-Saharan Africa, gender inequality also affects trade, but in a different way. In sub-Saharan Africa, there is a strong gender division of labour in agriculture. Women grow food crops to feed the family, and men grow cash crops to earn cash. To the surprise of the World Bank, agricultural exports remain low in Africa, despite many Structural Adjustment Programmes. It took years before the World Bank realised that the gender division of labour is the limiting factor for export growth in Africa. Female farmers refuse their labour to the cash crops of their husbands, because they do not receive anything in return, while their food production suffers from the expansion of cash crops on their plots of land. Hence, in the case of agricultural exports from sub-Saharan Africa, gender inequality reduces the success of trade. With more gender equality, exports would have been larger as well as more beneficial for women.

These two examples show how complex the relationship between gender and trade is. In order to better understand how gender inequality and exports and imports affect each other, gender analysis of trade is necessary. Moreover, in order to formulate trade policies that benefit men and women, and in which gender relations support successful trade strategies that help sustainable development, gender analysis of trade is urgently needed. Such analysis however, is only possible with appropriate analytical tools.

Tools for gender and trade analysis

A gender analysis of trade requires at least two elements. First, data on trade and the social, economic, and political position of women relative to men. Second, a theoretical framework with causal relationships, costs and benefits, and direct and indirect relationships between gender and trade. A thorough gender analysis of trade, hence, is quite complicated and extensive. Most NGO’s have not enough resources to undertake such an elaborate study. And policy makers are often not interested in such an elaborate research effort. In order to address these problems, WIDE and partner organisations in Latin America have developed some quick and simple tools for a gender analysis of trade, which consist of three sets of gender and trade indicators: (1) situational indicators, (2) indicators of political will, and (3) dynamic indicators.

Most indicators can be expressed in symbols, consisting of an acronym (like U for unemployment) and a superscript to denote the value for females or males (f or m). The dynamic indicators also include the symbol D (delta), which means ‘change’, for example the change in an indicator over a period of five years. Some measures of gender inequality however, are already widely used and can be copied from the literature, such as the Gender Development Index (GDI), which is calculated for most countries, every year, by the United Nations. The GDI measures the extent of gender inequality in the level of human development of a country: the income, the educational situation, and the health status of the people. Each gender and trade indicator will now be explained below.

1. Situational indicators

This set of indicators describes the social and economic situation of women. These may refer to women’s labour market position, their earnings, and their access to schooling and credit. This category of indicators is not necessarily related to trade but provides a general overview of the position of women in a country, as a start for a gender-aware analysis of trade.

Examples of situational indicators are:

The Gender Development Index published annually by the United Nations in the Human Development Reports: GDI
Female and male employment rates (L) and unemployment rates (U): L^f, L^m, U^f, U^m
Gender wage gap (difference in wages of men and women): w^f/w^m
Job segregation in the labour market (female dominance of some jobs, like nursing, and male dominance in other jobs, like engineering), with help of the Index of Dissimilarity: ID (the formula will not be given here but can be found in academic literature)
Unpaid labour time by women and by men: ULT^f, ULT^m
Share of export credit obtained by women: EXCR^f/EXCR

2. Political will indicators

This set of indicators measures to what extent trade policy makers are willing to take gender concerns into account, and to what extent they actually include gender equality measures in the trade agreements they negotiate with a trading partner. The indicators of political will hence point at possible inconsistencies between trading partners’ gender policies and their trading policies.

Examples of political will indicators are:

Social clauses including conditions of gender equality
Guarantee of basic labour rights as specified by the ILO, such as the freedom to form unions and the prohibition of discrimination
Share of women in official trade delegations (DEL) that represent the interests of trading partners: DEL^f/DEL

3. Dynamic indicators

This set of indicators combines data presented under the first set of indicators, the situational indicators, with data on trade. The trade data include trade tariff reductions, trading volumes, trade direction (export surplus or import surplus), and a sectoral breakdown of trade (over agriculture, industry and services and sectors within industry such as textile production or automobile assembly). The dynamic indicators have another characteristic, expressed in their name: they are able to show changes over time. This is particularly helpful in order to monitor gender-trade links over the period that a trade agreement is operating. For example, the situation before an agreement could be compared with the situation five years after the start of intensified trade. The dynamic indicators hence, will show to what extent women gain or lose from increased trade. But they may also suggest to what extent existing gender inequalities, for example in wages or in employment, influence trade volumes and patterns.

The technical formulation of the dynamic indicators is as trade elasticities. The concept of an elasticity is borrowed from micro economics, to indicate for example the change in demand for a product as a reaction to a change in the products’ price. If the relationship is elastic, its value is larger than 1, for example for haircuts: when the price of a haircut increases by 10%, many people will decide to cut their hair themselves or ask a family member to do so. Hence, the demand for haircuts may decrease a lot, say 20%. The elasticity is then calculated as follows: the change in demand divided by the change in price: - 20% / 10% = - 2. If the relationship is inelastic, the value of the elasticity is smaller than 1 and can even be zero. For example, when the price of potatoes or rice increases by 10%, many people will still buy these foodstuffs, since they have to eat anyway. They may instead buy less luxury food like meat or coffee. The decrease in demand for potatoes and rice then may be just 2%. The elasticity is then - 2% / 10% = - 0.2. In other words, the price of basic food does not have much influence on people’s demand for such food.

Going back to trade elasticities, a low elasticity means that trade has little or no influence on gender equality, whereas a high elasticity means that trade does have an impact on gender equality. This impact may either be positive (the calculated elasticity will have a positive sign) or negative (the calculated elasticity will have a negative sign). The use of elasticities requires one condition: the denominator of the elasticity, that is, the trade variable, should be big enough to make sense. If the increase in exports or imports is just 1% for example, it is not so useful to use the dynamic indicators.

Examples of dynamic indicators are:

Change in unemployment difference between women and men (U^f/U^m) divided by the change in export (EX) and import (IM) volumes. When inelastic, it means that increased trade does not help to improve women’s relative unemployment situation. When elastic, it means that increased trade parallels either an improvement or a worsening of women’s relative unemployment situation: D(U^f/U^m) / D(EX + IM).
Change in the gender wage gap divided by the change in export volume. When inelastic, it means that increased exports do not help to reduce the gender wage gap. When elastic, it means that increased exports parallel either an improvement or a worsening of the gender wage gap: D (w^f/w^m) / D EX.
Change in women’s share of unpaid labour divided by the change in export volume. When inelastic, it means that increased exports do not affect the time women spend on unpaid labour. When elastic, it means that increased exports parallel either an improvement or a worsening of women’s share in unpaid labour: D (ULT^f/ULT^m) / D EX

Use of indicators: how?

1. To assess consistency between gender policy and trade policy.

The indicators of gender and trade can show to what extent trade policy helps or hinders gender equality. Also, the indicators suggest to what extent existing gender relations affect the success of trade. If it turns out that success in trade on the one hand and gender equality on the other hand are not moving in the same direction, but the one goes at the cost of the other, trade and gender policies are not consistent with each other. In particular, the situational and political will indicators are relevant to assess policy consistency.

2. To assess gender effects of trade and trade effects of gender relations .

The dynamic indicators of gender and trade can show the size of gender effects of trade and trade effects of gender. They point out the strength of the mutual relationship between gender and trade. These quantitative indicators can be presented as a gender analysis of trade, complementary to a conventional trade review which contains other quantitative indicators on the success of trade. Hence, they provide additional information for a trade review. An example for such an additional trade review is a Sustainability Impact Analysis (SIA), which is currently discussed in the EU, in order to assess environmental and poverty effects of trade. The SIA may provide an opportunity to include gender and trade indicators as well.

3. To assess need for direct gender policy measures in trade agreements.

In the case that the relationship between gender and trade as assessed under items (1) and (2) above turns out negative, the indicators may point out in what areas of trade policy and the policy process, direct measures should be taken in order to prevent such a negative relationship. Examples of direct measures to improve the relationship between trade success and gender equality are extra efforts to increase women’s access to export credit, or an increase in the number of women on trade delegations in one or both trading partners’ delegations.

4. To assess need for indirect gender policy measures to accompany trade policy.

Again, if the relationship between gender and trade appears to be negative, the indicators may also suggest areas for indirect policy measures to accompany trade policy. Such indirect measures are not part of trade policy, but belong to other policy areas such as labour market policy or social policy. Measures may be needed in order to compensate negative effects of trade and/or to improve the relationship between trade success and gender equality over time. An example of an indirect policy measure in the field of labour policy is the implementation of core labour standards in Export Processing Zones (EPZs). Core labour standards will help to prevent companies from discriminating against women in terms of wages, unionisation, or unequal hiring, promotion and firing practices.

February 2002