In recent years, measuring the distributive impact of growth has emerged as an important topic in the field of economic development. A wide set of analytical models have been proposed to assess the distributional impact of growth, and to understand the relationship between poverty, inequality and growth. The main instrument for this kind of analysis is the Growth Incidence Curve (GIC), plotting the mean income growth of each percentile in the distribution, between two points in time, proposed by Martin Ravaillon and Shaohua Chen (2003). While a common feature of these models has been a focus on individual achievements, such as income or consumption, a growing number of scholars and policy makers have argued in the last two decades that equity judgments should be based on opportunities rather than on observed outcomes.
In this context, the equal-opportunity framework provides a basis to see the link between the opportunities available to an agent and the initial conditions that are inherited or beyond the control of this agent. Proponents of equality of opportunity (EOp) accept the inequality of outcomes that arises from individual choices and effort, but they express aversion with respect to the inequality of outcomes caused by circumstances beyond the individual control.
We share this view, and believe that a better understanding of the relationship between growth and inequality can be obtained by coupling traditional tools with an approach that looks at the space of opportunities. If two growth processes have, say, the same impact in terms of poverty and inequality reduction, but in the first case, all members of a certain ethnic minority — or all people whose parents are illiterate — experience the lowest growth rate whereas poverty reduction in another case is uncorrelated with differences in race or family background, our current arsenal of measures does not readily allow to distinguish them.
In this spirit, in a recent paper we investigate in detail the distributional effects of growth from an opportunity egalitarian viewpoint. In particular, with reference to a given growth episode, we address the following questions: is growth reducing or increasing the degree of inequality of opportunity (IOp)? Are some socio-economic groups systematically excluded from growth?
To answer these questions we take two different approaches: the Individual Opportunity Growth Incidence Curve (individual OGIC) and type Opportunity Incidence Curve (Type OGIC). By plotting the rate of growth of the (value of the) opportunity set given to individuals, OGIC enables us to assess the pure distributional effect of growth in terms of increasing or reducing the aggregate. And by plotting the rate of economic development of each social group in the population, IOp allows us to detect the existence of possible inequality traps. While complimentary to the standard analysis of the distributive impact of growth, these two approaches provide interesting insights for the design of public policies. In particular, they may help target specific groups of the population and/or identify priorities in redistributive and social policies.
We have applied OGIC to analyze the distributional impact of growth in Italy and in Brazil, two countries that experienced different patterns of growth in the last decade. Italy was characterized by very limited growth, and a slightly increasing level of outcome inequality; whereas, Brazil was characterized by sustained and markedly progressive growth.
Using the Bank of Italy’s “Survey on Household Income and Wealth” we find that the growth process that took place in Italy in the first part of the decade — between 2002 and 2006 — was clearly progressive according to the standard equality of outcome approach.
However, this progressivity is reversed when the equality of opportunity perspective is adopted: the smoothly decreasing GIC- measuring the quantile-specific rate of economic growth — conflicts with the slightly increasing shape of the individual OGIC (see Figure 1 panel (a) and (b)). With respect to the most recent episode of growth — between 2006 and 2010 — we find instead that the burden of the crisis was borne by the weak groups of the population, as demonstrated by the regressive pattern characterizing both the individual and type OGIC. Moreover, we find that the 2006-10 growth episode dominates the 2006-10 according to the standard GIC and the individual OGIC, but it does not according to the type OGIC.
Figure 1. panel (a) GIC, panel (b) individual OGIC, panel (c) type OGIC Italy 2002-06 vs. 2006-10
Using the “Pesquisa Nacional por Amostra de Domicilios” provided by the Istituto Brazilero de Geograpia e Estatistica, we find that each part of the distribution (with the exception of the top 15%) benefited considerably from Brazil’s 2002-2005 growth period.
However, when we move to the space of opportunity, this growth appears to be less prominent. Indeed, most of the social groups suffered from a reduction in the value of their opportunity. By contrast, the growth episode that took place between 2005 and 2008 appears to have been beneficial for the whole population regardless of the focus of the analysis. This period of growth was not only generally progressive but it also led to a reduction in the IOp (decreasing individual OGIC). Furthermore, the initially disadvantaged social groups of the population benefited more from growth than those initially disadvantaged (decreasing type OGIC). When the two processes are compared, the dominance of the 2002-05 growth episode over the 2005-08 is evident for every perspective adopted.
The results of our analyses suggest that although complex and very demanding in terms of data, the analysis of growth in terms of opportunity can shed light on a number of aspects of development that traditional analytical tools cannot capture.
Figure 2. panel (a) GIC, panel (b) individual OGIC, panel (c) type OGIC Brazil 2002-05 vs. 2002-08