The Status of Women in Developing Countries: An Econometric Study

The improvement of living conditions for people in developing countries has long been recognised as one of the most central challenges facing policy makers in developing countries and the concerned bodies in the world at large. Though this problem affects almost all sections of the people, women are recognised to be among the most disadvantaged groups. Jazairy¹ persuasively summarised the status of women in developing countries. He concluded that women, as poor people in developing countries, live under the same conditions as men, but suffer additional social and policy biases.

However, the status of women varies significantly among developing countries. The main objective of this paper is to discuss and explain this variation. The paper will attempt to apply econometric methods to argue that this situation is explained by different factors which most of the developing countries share.

As mentioned in the introduction, the status of women in developing countries has been deteriorating sharply in the last few years. Jazairy² estimated the number of rural women living below the poverty line at 564 million in 1988. This represented an increase of about 47 percent above the figure in 1965.

There are many possible explanations for this worsening situation. Concentrating on the experience of my country, the Sudan, many factors (with direct or indirect implication to the status of women) can be mentioned. Education is a privilege that is still reserved particularly for men. The country's civil wars have provoked a dramatic deterioration of the national economy and spurred the diversion of limited resources disproportionately towards defence expenditure. These factors and more have had drastic effects on the quality of public services such as health and education. Constrained as they are by the political and economic situation, women in developing countries also suffer from social and cultural biases. These factors differ from country to country and can be used to explain the variation in the status of women.

Developing policies for improving the situation must take into account the effect of each of these factors. This paper will explain the quantitative method for measuring the status of women, and a description of the chosen explanatory variables will be provided. This will be followed by a discussion of the regression results and a conclusion of my findings.

This index (denoted by 'Y' in Appendix 1) was developed by the International Fund for Agricultural Development (IFAD).³ The variable is a composite indicator of women's conditions in developing countries. However, as Jazairy admitted, the index only included factors for which data were available. Gaps were bridged with the World Bank and UNDP estimates.

The WSI takes values between zero and one. The closer the index of a particular country to one, the better is the status of women in that country. From a sample of fifty developing countries, a wide variation from one country to another is observed. The essence of this analysis is to explain this variation.

This section outlines which explanatory variables will be used in the regression to explain the variation in the index. As stated earlier, several factors, quantitative and qualitative, contribute to the current situation. Though qualitative factors, for instance religion, are important to the status of women in these countries, they have been omitted to simplify the analysis, and so two quantitative variables have been chosen. The first explanatory variable, X₁, is the Gross National Product (GNP) per capita per annum. X₂ is the percentage female adult literacy rate (Appendix 1).

Since these two factors are perhaps positively related to the status of women, and indeed to the status of all groups of the population, one would expect the regression to show positive coefficients for the Xs.

In order to estimate the parameters, the following model was assumed:

Y = ß_o + ß₁X₁ + ß₂X₂ + u

The regression analysis was performed on the SPSS package (see Appendix 2a) and the multiple regression equation is as follows:

Y = 0.347 + 0.168X₁ + 0.314X₂

R² = 0.65

It is interesting to note that the coefficients of both Xs are positive, confirming my expectations. Given the t-values of 1.499 and 6.616 for ß₁ and ß_2, with probabilities of 0.1407 and 0.000 respectively, we will not accept the hypothesis that ß₂ is equal to zero at a 95% confidence level. However, we will accept the hypothesis that ß₁ is zero at the same confidence level.

The equation suggests that the effect X₂ (percent female adult literacy rate) has a greater effect on the index than that of X₁ (GNP per capita). This makes sense given the extent of the uneven distribution of incomes in developing countries. Therefore, holding other things constant, educated women should be expected to have better opportunities and, as a result, a better quality of life.

Using this equation to make inferences about the future, we can predict that a one-percentage increase in the female adult literacy rate will result in an increase in the WSI (allowing for the effect of GNP per capita, or holding it constant). If we consider countries with the same level of GNP per capita, the one with the higher rate of women adult literacy can be expected to have a higher WSI.

Of a group of countries with the same rate of women adult literacy, it was originally expected that the one with the lower GNP per capita would have a lower WSI value. However, the low t-value for GNP per capita indicates that this expectation is unfounded. Consider the WSI levels of for instance, Argentina (0.708), Barbados (0.732), and Dominica (0.723). Although the GNP per capita levels vary dramatically: 0.252, 0.601 and 0.168 respectively, the adult women literacy rate is consistently high: 0.95, 0.99 and 0.99. This illustrates the strong relationship between the WSI and the adult women literacy rate, as well as the weak relationship between the WSI and GNP per capita. Interestingly, China has the highest WSI level, though their women adult literacy rate is relatively low. Cultural equality between men and women in this country may explain the situation.

The constant term in the equation can be interpreted as the WSI level that a country with zero GNP per capita and 'no-education' for women would have. This may seem strange, but could be acceptable if we assume that they are completely dependent on outside help. It may also explain the factors that are not included in the model.

The R² is a summary measure that tells us how well the sample regression line fits the data. The result of the regression produces a value of 0.65. This tells us that about 65% of the variation in Y is explained by the variables X₁ and X₂ jointly.

It is helpful to observe the effects of the explanatory variables separately, as the multiple regression may not have distinguished the separate effects. The simple regressions equations are as follows (see Appendix 2b):

Y = 0.463 + 0.6X₁ R₁² = 0.32

Y = 0.345+ 0.355X₂ R₂² = 0.63

Although there is no change in the sign of the coefficients, these simple regression equations tell us slightly different things. The positive X₁ coefficient in the simple regression is significant (t-value = 4.796) and the X₂ variable remains significant.

The values of R² provide us with more information as to how much each of the Xs explains the variation in the index, Y. R₁², (Y regressed on X₁) is 0.32, indicating that 32% variation of Y is explained by X₁. R₂², (Y regressed on X₂) is 0.63, which means that 63% variation is explained by X₂. Thus, the results confirm the significance of X₂, and suggest that the X₁ variable is more important in explaining the variation of Y than the multiple regression suggested.

To assess whether multicollinearity exists between the explanatory variables, X₁ was regressed on X₂. The resulting R_x² (see Appendix 2c) is 0.32, implying a degree of multicollinearity that helps account for the varying significance of the X₁ variable between the multiple and simple regressions.

Regression methods were used to explain how GNP per capita and female adult literacy rates explain some of the variation of the status of women in developing countries. The analysis shows the importance of these variables to any policy directed towards improving these conditions. However, the two variables do not explain the whole situation. The picture will remain incomplete until further variables, particularly qualitative ones are included, as women in developing countries suffer from many forms of bias.