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The association between skills and low incomeInsights on Canadian Society
The association between skills and low income

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by Andrew Heisz, Geranda Notten and Jerry Situ

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Introduction

Certain population characteristics such as immigrant status, being an Aboriginal person, having an activity limitation, having low education and being in a lone-parent family have been found to be concentrated among the low-income population.^{Note 1} This information could be useful to policymakers since understanding the characteristics of individuals in low income can inform the design of effective programs.

One population characteristic that has received less attention is skill level, which refers specifically to fundamental literacy, numeracy and problem-solving skills. Intuitively, skills should also be negatively correlated with low income. Presumably, persons with lower skills have lower wages and thus a higher likelihood that their income falls below the low-income threshold. However, there is little evidence on the scope of these relationships.

Other research has shown that skills have a positive effect on individual earnings even after controlling for other characteristics such as educational attainment.^{Note 2} Building on this evidence, this article examines the relationship between skills and household income. Specifically, this study examines whether having low skills increases the likelihood of living in a low-income family, and whether the effects persist after controlling for other factors known to increase the risk of low income.

The study uses data from the Longitudinal and International Study of Adults (LISA). LISA is unique in that it combines skill data from the Programme for the International Assessment of Adult Competencies (PIAAC) with household income information (see Data sources, methods and definitions). The skills data consist of an assessment in each of three domains: literacy, numeracy and problem solving in technology-rich environments. As done in other studies, the focus is on literacy scores because of a high correlation between the three domains.^{Note 3} The three domains assessed in PIAAC rely on the same core cognitive processes,^{Note 4} therefore using only numeracy or problem-solving scores would produce similar results.

LISA interviews were conducted between November 2011 and June 2012, whereas income information refers to the 2011 calendar year. This article refers to this collection period as “2012” for brevity.

Literacy skills and income

In 2012, 17% of Canadians aged 16 to 65 had a literacy score that placed them in level 1 and below (Table 1). Individuals in this category, for example, can only locate single pieces of information in shorts texts in the absence of other distracting information, or demonstrate only basic vocabulary. Remaining Canadians scored in higher categories with 13% achieving level 4 and level 5. Scoring in this category indicates a higher literacy proficiency, with individuals in it demonstrating, for example, being able to integrate information from multiple dense texts and being able to reason by inference.

Literacy skill level and household income are positively related. At $84,600, the median household income for individuals with the highest literacy proficiency (level 4 and level 5) was 70% higher than it was for those with lowest literacy proficiency (level 1 and below), and 33% higher than for those in the level 2 proficiency category.

The bottom row of Table 1 shows the low-income rates, using the after-tax Low Income Measure (LIM–AT). With the LIM–AT, individuals are considered to be in low income if they are living in a household whose income (adjusted for the size of the family) is lower than 50% of the overall median.

The results show a clear association between skill level and the incidence of low income. The low-income rate for the highest literacy proficiency group was, at 8%, one-half the level observed in skill-proficiency group 2 and nearly one-quarter the level seen in the lowest proficiency group. In contrast, the low-income rate was about 29% among those who were in skill-proficiency group 1 or below.

The subgroup with both lower skills and low income can be considered at a particular disadvantage.^{Note 5} Estimates suggest that, in 2012, this group represented about 5% of Canadians aged 16 to 65.^{Note 6}

Skills and other factors associated with low income

Research on low income has emphasised certain at-risk groups. These are demographic groups that have higher low-income rates, such as recent immigrants, Aboriginal persons, unattached non-elderly persons and people with activity limitations.

In each case, at-risk groups have a skill profile that tends to be closer to having a lower literacy level (Chart 1). For example, while 17% of all persons had a literacy score of in the lowest category, 30% of recent immigrants, 26% of Aboriginal persons, 27% of unattached non-elderly persons and 23% of people with an activity limitation had a literacy score level in the lowest category in 2012.

Education and skills are also highly correlated—the generation of skills is an important output of the educational system, and the presence of basic skills such as literacy are important prerequisites to entering higher education. For example, among individuals with a high school diploma as their highest education level, 8% scored a level 4 or higher on the literacy assessment compared with 28% among those with a university degree or higher (Chart 2).

Educational attainment, however, is not perfectly correlated with literacy levels—some people with a university degree had lower literacy scores,^{Note 7} while others had higher literacy scores despite having only a high school education.

The relationship between low income and skills after controlling for other factors

The previous section showed the relationships between skills and low income. It showed that skills were related to demographic characteristics, such as educational attainment, as well as whether the individual was a member of an at-risk group. In this section, a regression model is used to test whether the relationship between skills and low income persists once controls for other characteristics common to low-income populations are applied.

Several low-income models are tested. Each model controls for a different number of factors that are expected to affect low income such as educational attainment, immigrant status, the presence of activity limitations, family type and other demographic characteristics. The strategy is to compare the regression coefficient estimates across multiple models to see if the relationship between skills and low income persists as various control factors are added.

The descriptive statistics for the variables included in the regression models are shown in Table 2. Low income was higher for persons with less education (19% among persons with at most a high school diploma versus 9% among university degree holders). Immigrants who arrived in Canada in the 10 years preceding the interview had a low income rate of 25%, which was higher than the 15% rate for the Canadian-born. Persons with activity limitations had a low-income rate twice that of those without a limitation (27% versus 13%). Unattached persons (defined as persons not in a census family) and persons in a lone-parent family each had a low-income rate three times larger than that of couple families (about 30% versus 9%).

The results of the regression exercise are shown in Table 3. The coefficients represent the increased or decreased probability of being in low income relative to the omitted category. Five models are shown, each with a different set of independent variables.

Model 1 shows the results when including only basic demographic controls (age, sex and province) and literacy variables. It shows that the low-income rate for persons scoring level 1 and below is 21 percentage points higher than the rate for those scoring level 4 or level 5 (the omitted category), when holding basic demographic variables constant.

Model 2 adds controls for educational attainment. The addition of educational attainment to the model reduces the size of the coefficients on the skill variables. For example, the coefficient for proficiency level 1 and below drops from 0.213 to 0.162, or by nearly one-quarter.

Model 3 adds controls for immigrant status and Model 4 adds having an activity limitation and family controls. Each successive entry of control variables into the model reduces the size of the coefficient on skills with little change to the education variables.

After all controls are entered into the model, the disadvantage associated with having lower literacy (level 1 and below) drops to 12 points, which continues to be a large and significant effect, but much smaller than the 21 points obtained without controls.^{Note 8}

Model 5 includes all variables except the literacy variables. Changes in the coefficients between Model 5 and Model 4 show the effect of literacy on the other variables. Coefficients for education and immigration status are larger than in Model 4, while coefficients for activity limitation and family types are unchanged. Thus, differences in skill level help explain part of the higher incidence of low income among low educational attainment groups and recent immigrants. The disadvantage associated with having at most a high school diploma falls from 9 percentage points (Model 5) to 6 points (Model 4). For recent immigrants, it reduces the disadvantage from 15 points to 12 points.

Conclusion

This study examines the relationship between skills and low income using data from the Longitudinal and International Study of Adults (LISA). The results show that being in low income is related to having lower literacy skills. The results indicate that the low income rate among persons with literacy skills in the lowest category was, at 29%, more than three times that of those who were in the highest categories.

This study also examined whether the relationship between skills and low income persists after taking other demographic factors such as education, immigrant status and having an activity limitation because previous research has shown that these are important “risk-factors” of being in low income. It finds that skills continue to matter even after these other controls are added in. The effect of skills on low-income, however, is muted due to correlations between skills and other risk factors. Moreover, the inclusion of skills reduces the importance of educational attainment and immigrant status, indicating that part of the higher incidence of low income among these groups is associated with lower skills.

Andrew Heisz is Assistant Director with the Income Statistics Division at Statistics Canada; Jerry Situ is an analyst also with the Income Statistics Division; and Geranda Notten is Associate Professor with the Graduate School of Public and International Affairs at the University of Ottawa.

This article is an abridged version of the paper “The role of skills in understanding low income in Canada,” originally published in 2015 in Measurement of Poverty, Deprivation, and Economic Mobility.^{Note 9}

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