Section 6
Summary and conclusions

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A number of low income lines are regularly published in Canada. Together with several aggregate indexes such as the incidence and the gap ratio, they can be used to highlight changes in the low income in Canada from different perspectives. However, public discourse, policy debates and even poverty researches often focus on a single line and a single index. It is unclear if these practices are misleading. A simple question to ask is, do we arrive at the same conclusion by using multiple lines and indexes instead of a single line and index?

In this study, we examined low income trends in Canada for the period of 1976 to 2007 under different lines using a number of distribution sensitive indexes from the Foster-Greer-Thorbecke family. We also investigated how these lines interact with each other by looking at individuals who are captured by one line but not by others. Attention was also paid to assess how well different lines capture the contribution of individuals subject to high-risk of social exclusion to low income incidence.

The results indicate that, no matter which line was employed, low income indexes moved in the same direction in the long-run and they were sensitive to the movements of economic indicators such as the unemployment rate. We found aggregate low income indexes based on a fixed Low Income Measures (LIM) tracked those of Low Income Cut-Offs (LICO) and Market Basket Measure (MBM) very well. In the short-run, however, aggregate indexes based on variable LIM behaved differently than indexes based on the other three lines, suggesting that variable LIM was able to provide additional information on the well-being of individuals from the lower tail of the income distribution.

Similarly, it was found that, under the same line, different aggregate indexes moved in the same direction in the long-run, while in the short-run, they sometimes moved in opposite directions or in the same direction with different magnitudes. For purposes of policy monitoring and development, it may be necessary to look at various indexes simultaneously. Between the lines, we found that, for individuals being captured by MBM, there was a fair chance that other low income lines failed to identify them as being in low income; while for those who were above MBM, there was little chance they would be counted as low income persons by other lines. But there was some non-trivial chance for individuals who were above the fixed LIM line to be captured by other lines. It appeared that the current MBM line would capture more individuals than the 1992 fixed LIM, suggesting that a fixed LIM line needs to be re-based periodically to maintain its relevance.

Our decomposition result suggests that disadvantaged individuals as a whole contributed more under the fixed LIM than under MBM, and the fixed LIM line was also stronger than other lines in terms of its capability to capture individuals exposed to multiple risks of social exclusion. But, the fixed as well as the variable LIM lines were relatively less able to capture individuals from families headed by recent immigrants, most likely because new immigrants tended to settle in large cities where costs of living were high. This indicates that regional differences in costs of living should be taken into consideration in future development of the LIM lines.

Finally, since all aggregate low income indexes are based on survey samples, one of the best practices is to estimate sampling standard errors for low income statistics to determine when the movements in these indicators are statistically significant.