Introduction

Regional income disparities have long been phenomena in Canada. These disparities may be due not only to cost-of-living differences across characterized regions within the country, but also to differences in human-capital related demographic characteristics. An examination of the extent in magnitude of differences among regional-income distributions—particularly that of low income—often has important policy implications, as it is the basis for evaluating existing provincial welfare policies and for developing re-distributive policies of the federal government of Canada, such as the fiscal equalization grants. As a result, how to obtain a reliable and robust regional profile is a key aspect of policy formulation, and it therefore deserves close scrutiny.

In Canada, a regional low-income profile is not officially constructed, but it can often be obtained by constructing low-income measures—such as the headcount ratio or low-income gap—based on Statistics Canada's low-income cutoffs (LICOs), as in Figure 1.¹ However, questions have always been raised concerning the robustness of the results, particularly when measurements of the welfare function and poverty itself are controversial. The use of such cutoffs is subject to arbitrary choices, with respect to the proportion of spending on necessities and what constitutes necessities. It can be argued that any revision of these standards would lead to completely different geographic distribution of low income (see, for example, Ravallion and Bidani 1994).

This paper provides a robust way to compare regional low-income profiles in Canada without arbitrarily specifying a low-income line. The empirical analysis is motivated by the theory of stochastic dominance, which can be used in examining the rankings of income distributions with multiple-poverty criteria for a wide range of low-income lines.² That is, by comparing the cumulative distribution functions of income between two regions, one may judge whether the choice of low-income line affects the conclusion about ranking. This avoids using one single low-income line to make a comparison.

The paper is also motivated by the long-standing debate in Canada over the meaning of the term 'poverty.' This is due, in part, because there is no consensus on the choice of scaling factors to make the income distributions comparable for poverty analysis. Such scaling factors include a price index that accounts for inflation, a spatial price index for cost-of-living differences and an equivalence scale that accounts for household composition. There is often criticism that Statistics Canada's LICOs provide no satisfactory index for cost-of-living across regions. Policy makers and researchers have suggested using other measures to supplement the LICOs, including the low-income measures (LIMs)—which place emphasis on a relative concept—and the market-basket measure (MBM)—which tackles cost/price-differences in necessities for a total of 48 urban centres and community sizes in the 10 provinces. ³Low-income statistics (e.g., headcounts or low-income gaps) are bound to differ among the underlying scaling factors chosen. The important question, however, is to know by how much they differ. To answer this question, tests of stochastic dominance are also performed, based on different choices of assumptions made to defining income or low income. These include the choice of spatial price deflators, equivalence scales and an absolute or a relative concept. Statistical inferences for stochastic dominance are used to account for sampling variations.

In Section 2, we briefly summarize the low-income measures used and explain the stochastic dominance approach. In Section 3, we go on to describe the data and the definition of income. Section 4 provides an empirical illustration on how tests of the stochastic dominance are implemented, using real data from two provinces—Newfoundland and Labrador and Ontario. The results for all provinces are presented and discussed in Section 5, and the conclusions are then summarized in Section 6.