بخش غیر رسمی، نابرابری درآمد و توسعه اقتصادی

This paper addresses – with the help of numerical simulations – some of the issues relating to income distribution in the context of development of an economy with an informal sector and migration of both low- and high-skilled workers from the rural to the urban area. A major aim has been to see under what conditions we do or do not get an inverted U-shaped curve of income distribution. The paper finds that the tendency always is for the Gini coefficient to rise and then decline. However, once it starts declining, it need not continuously decline; it may rise, then decline, then rise again and indeed rise above the previous peak before starting to decline again and may well end at the end of the simulation at a higher value than at the start. Any case for the redistribution of income is seen to be much stronger at the later stages of development that at earlier stages, even though at later stages, Gini coefficient may be lower than at earlier stages. The policy implications of the findings are considered.

We owe to the pioneer work of Kuznets (1955) the hypothesis that income inequality first rises and then falls with development, tracing out an inverted U-shaped curve. Kuznets argued that during the early stages of development, most of the population will be in the agricultural sector, with low per capita income but low inequality, incomes in the rural area being more equally distributed than in the urban area. As people begin to migrate to the higher-income urban area, overall inequality will increase. During the later stages of development, however, this force for inequality would be more than offset by a decline in inequality within the urban area, owing to the better adaptation of the children of rural–urban migrants to city life and “growing political power of the urban lower-income groups” to enact “a variety of protective and supportive legislation” (p17). The empirical validity of the Kuznets curve continues to remain in question. Part of the problem is methodological. Reliable time series data on the distribution of income, over any substantial period, are not available for most developing countries. Two approaches have therefore usually been taken by researchers in this area. One is to assume that different countries observed at different levels of development at a given point in time chart the path that a typical country would follow over a long period of time, and then examines whether the cross country data yield an inverted-U plot of inequality against level of development, as measured by per capita income. The other approach is to examine changes in inequality within countries over the short periods for which data are available, and see whether inequality has risen in relatively less developed countries and declined in more developed countries.1 The Kuznets curve has received support in cross-sectional studies by Paukert (1973), Cline (1975), Chenery and Syrquin (1975), Ahluwalia (1976) and Papanek and Kyn (1986), among others. However, the findings of the cross-sectional studies have been questioned, among others, by Anand and Kanbur (1993) who show that the results are very sensitive to the choice of data set and that one can get U relationship, inverted-U relationship or very little relationship at all by making different choices. In a study exemplifying the second of the two approaches mentioned earlier, Fields (1991) has shown that “inequality increases with growth as frequently in the low-income countries as in the high-income countries. There is no tendency for inequality to increase more in the early stages of economic development than in the later stages” (p45). There are others, however, who continue to hold that there is still good evidence for the Kuznets curve. The difficulty, they argue, is that many other factors, in addition to the level of development, affect a country's level of inequality. Once the analysis accounts for these factors, the Kuznets curve, as it were, “comes out of hiding”. Barro (2000), in particular, has forcefully argued for this view. Given the conflicting nature of the available empirical evidence, the scarcity of reliable time series on income inequality spanning several decades, and the multitude of factors likely to affect a country's level of inequality, the use of numerical methods would appear to be particularly appropriate here. It is, therefore, surprising that numerical methods have so rarely been employed in this area. Such methods can clearly complement both theoretical and empirical work. Numerical examples can both illustrate the important results and show how sensitive they are to changes in key parameters and initial conditions. The purpose of this paper is to use numerical methods to address some of the issues surrounding income distribution and the hypothesis of inverted-U curve. A major aim will be to highlight the key role that the informal sector plays in the evolution of income distribution. The explanations offered for the existence or non-existence of the inverted-U curve have not in general incorporated the role of the informal sector in any systematic way. In much of the theoretical literature, following Todaro, 1969 and Harris and Todaro, 1970, the informal sector is viewed as being essentially a stagnant and unproductive sector, serving merely as a refuge for the urban unemployed and as a receiving station for newly arriving rural migrants on their way to the formal sector jobs.2 In sharp contrast, the empirical literature increasingly sees the informal sector as dynamic, efficient, contributing significantly to national output and capable of attracting and sustaining labour in its own rights.3 Studies show that the share of the urban labour force engaged in informal sector activities is growing and now ranges from 30% to 70%, the average being around 50%. Empirical findings also show that many migrants from the rural to the urban area are attracted by income earning opportunities in the informal sector itself; also that there is very little job search activity by the workers in the informal sector.4 The present writer has elsewhere (Bhattacharya, 1993a, Bhattacharya, 1994 and Bhattacharya, 1998b) presented and analysed a three-sector general equilibrium model of a developing economy which systematically incorporates an informal sector and, in the dynamic version of the model, presented migration functions alternative to those employed in the Todaro and Harris–Todaro-type models. I now use this model as the base for the simulation exercises to be performed here. In the simulation model, the aim, in particular, will be to consider a number of different scenarios relating to the evolution of the primary or formal sector wage and to study the implications of these for the evolution of income distribution.5 I shall also report the results of considering the effects of migration of both low and high-skilled workers from the rural to the urban area as also the effects of skill-biased technological change. The focus will be on Lorenz curves, the evolution of Gini coefficient and to see if and under what conditions we do or do not get an inverted U-shaped curve of income distribution. We shall also consider the policy implications of the findings. This paper is organised as follows. Section 2 presents a brief outline and then sets out the final equations of the static model of the economy. Section 3 sets out the dynamic model. The simulation model and the results of the numerical solution are discussed in Section 4. Section 5 considers the policy implications and concludes. Table 1 summarises the notation used in the paper and is provided for convenient reference.

The aim of this paper has been to address – with the help of numerical simulations – some of the issues relating to income distribution in the context of development of an economy with an informal sector and migration of both low- and high-skilled workers from the rural to the urban area. In particular, we wanted to see under what conditions we do or do not get an inverted U-shaped curve of income distribution. The gap between the informal and the rural sector wage is in general seen to widen over time in our model. Changes in the formal sector wage have relatively little effects on the evolution of this gap or on the proportion of urban to total population in the economy,24 but they have major effects on Lorenz curves and the evolution of the Gini coefficient. We found the tendency always is for the Gini coefficient to rise and then decline.25 However, once it starts declining, it need not continuously decline; it may rise, then decline, then rise again and indeed rise above the previous peak before starting to decline again and may well end at the end of the simulation at a higher value than at the start. How exactly the Gini coefficient moves over time depends crucially on the evolution of the gap between the formal and the informal sector wage. And this gap can, of course, be influenced by a number of different factors such as changes in the forces emphasised in efficiency wage theories,26 insider power, government policies, trade union pressure, skill-biased technological change, and so on. (Indeed, policies aimed at reducing the power of the insiders, reducing labour turnover costs in the formal sector, improving monitoring of workers effort in the formal sector, etc., can all – by reducing the wage gap between the formal and informal sectors – help improve income distribution, besides improving efficiency in the economy). It is also clear that the expansion of the informal sector and migration by manual labourers (lm) to the informal sector play vital role in reducing income inequality in the model. Policies designed to strengthen sub-contracting relationship between the formal and informal sectors may be particularly important in this context (especially if there is skill-biased technological change tending to weaken this relationship). Clearly, attitudes need to change about the informal sector. The informal sector produces many goods and services efficiently and provides employment to a very large number of people. The informal sector enjoys a largely symbiotic relationship with the formal sector and contributes significantly to national output. It would, therefore, appear highly inappropriate to suppress or discourage the informal sector. Policies such as land-use controls and various licensing requirements, however, do have such a constraining effect. Lack of adequate credit facilities is also a major barrier to the growth of many informal sector firms. Loan programmes, training, a reduction of regulations, among others, can be used to encourage the expansion of a vibrant informal sector. Equally, barriers to entry to the formal sector should be reduced. This too would improve income distribution. The results of the paper also show that owing to the slow growth of their wage in the rural sector, the manual labourers (lm) in the rural area will continue to remain poor even at the later stages of development. There is, therefore, a case for redistribution of income. However, we found the case for redistribution to be stronger at the later stages of development than at earlier stages, even though at later stages, Gini coefficient may be lower that at earlier stages. We also examined the effects of reducing the natural growth rates of population of rural hirers and manual labourers and found that bringing these in line with the natural growth rate of population in the urban area would improve income distribution in the economy. Finally, while ours has been a simulation exercise, nevertheless the empirical evidence regarding the dynamic nature of the informal sector, the wage gap between the informal and rural sectors, the dual migration streams and the conflicting evidence on the inverted-U hypothesis would all seem to suggest that our analysis very probably does capture some important aspects of income distribution and growth in many developing countries.