Location, location, location: Examining the rural-urban skills gap in Canada

Journal of Rural Studies 72 (2019) 252–263

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Journal of Rural Studies journal homepage: www.elsevier.com/locate/jrurstud

Location, location, location: Examining the rural-urban skills gap in Canada ∗

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David Zarifa , Brad Seward , Roger Pizarro Milian

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Nipissing University, 100 College Drive, Box 5002, North Bay, ON, P1B 8L7, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Skills Rural Literacy Numeracy Longitudinal international study of adults Brain drain

The elevated demands of the new knowledge economy pose particular challenges to rural and northern regions in Canada, long acknowledged by policymakers to suffer from acute human capital deficits. Rural residents obtain lower levels of education than their urban counterparts and those that do obtain post-secondary training often migrate to urban regions offering abundant employment opportunities and higher wages. Despite an emerging consensus around over skill deficits across rural regions, Canadian researchers have yet to systematically explore contemporary rural-urban differences in human capital using refined measures of literacy and numeracy skills. We ameliorate this deficiency by mapping rural-urban disparities in skills across the working age population (16–65) using Statistics Canada's 2012 Longitudinal International Study of Adults (LISA). Our results indicate that residents from smaller population centers and rural areas within Canada show significantly lower skills proficiencies. These differences across location of residence shrink considerably when controlling for education level, underscoring the need to enhance post-secondary access in rural areas.

1. Introduction In the concluding decades of the 20th century, Canada began to transition from a resource to a knowledge-based economy (Baldwin and Beckstead, 2003; O'Hogan & Cecil, 2007; Gera and Mang, 1998) – one that is driven primarily by the “production, distribution and use of knowledge and information” (OECD, 1996, p. 7). This evolution has been enthusiastically encouraged by policymakers keen on transforming Canada into a “Northern Tiger,” capable of competing with international counterparts across lucrative high-tech industries (Policy Horizons Government of Canada, 2010). Policymakers have sought to foster Canadian competitiveness in these industries via a plethora of initiatives designed to improve the domestic production, retention and international recruitment of highly-skilled workers (Gera and Songsakul, 2007; Head and Reis, 2004; Livingstone, 2012, 2018; Metcalfe and Fenwick, 2009). In fact, by the year 2031, reports suggest that roughly 70 to 80 percent of jobs in Canada will require some form of post-secondary education (PSE), rendering skilled workers a vital commodity (see Miner, 2010, 2012). Some caution that skill “mismatches” are already emerging in the Canadian labor market, as the human capital needs of the knowledge economy outstrip the skillsets of an aging Canadian workforce (Stuckey

and Munro, 2013; Ontario Chamber of Commerce, 2012). Recent employer surveys show that approximately a third of Canadian businesses experience difficulty hiring skilled workers (ManpowerGroup, 2016), and more than half perceive skill shortages as a serious industry-level problem (Business Council of Canada, 2018).3 Industry association “white papers” have also lobbied for the revitalization of policy frameworks associated with skills development, with a view towards allowing domestic companies to successfully compete in global markets (see Deloitte & HRPA, 2012). Even among critics of the skills “crisis” narrative, there is a general acceptance that Canadian policymakers can do much to improve the synchronization of workforce skills and dynamic labor market demands (Burleton et al., 2013). The human capital demands of the new knowledge economy pose particular challenges for rural and northern regions in Canada, who have long suffered from acute skilled labor deficits (Beckley and Reimer, 1999; Bollman et al., 1992; Bollman, 1999). Research finds that rural residents aspire and obtain lower levels of education than their urban counterparts, even after controlling for a host of socio-economic factors at the individual and family levels (Finnie et al., 2015; Newbold and Mark Brown, 2015). They are less likely to attend university, as opposed to community college (Zarifa et al., 2018), and less likely to enroll in university-level science, technology, engineering and

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Corresponding author. E-mail address: [email protected] (D. Zarifa). 1 This author is now a Visiting Researcher in the Department of Leadership, Higher and Adult Education, University of Toronto, Ontario, Canada. 2 This author is now at the Conference Board of Canada, Ontario, Canada. 3 See Cross (2014) and Borwein (2015) for reviews of employment surveys done in recent years. https://doi.org/10.1016/j.jrurstud.2019.10.032 Received 29 October 2018; Received in revised form 19 August 2019; Accepted 8 October 2019 Available online 31 October 2019 0743-0167/ © 2019 Elsevier Ltd. All rights reserved.

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mathematics (STEM) fields (Hango et al., 2018). Moreover, those that do obtain post-secondary training in remote geographical areas often migrate to urban regions, which offer a greater abundance of employment opportunities and higher wages, spurring a “brain drain” that further augments rural-urban workforce skill disparities (Alasia, 2005; Corbett, 2005; Malatest and Associates, 2002; Rothwell et al., 2002; Tremblay, 2001). The skill shortages produced by these uneven migration flows hinder the economic development of many rural and northern communities, especially those that sit on natural resources (e.g. minerals, oil) whose extraction and processing requires a highlyskilled workforce (Moazzami, 2015a). Perhaps unsurprisingly, reports published by the Canadian Rural Revitalization Foundation (Lauzon et al., 2015) have branded the reversal of the rural-urban “brain drain” as a top development priority for rural communities. Despite an emerging consensus over the intensive skill demands of the new knowledge economy, and over skill deficits across northern and rural regions, researchers have yet to empirically explore contemporary rural-urban skill differences using refined measures of literacy and numeracy proficiencies. Policy reports typically use proxies like educational attainment (e.g. Bollman, 1999; Moazzami, 2015a; 2015b) and occupational categories (e.g. Alasia and Magnusson, 2005; Magnusson and Alasia, 2004) to ascertain rural-urban imbalances in human capital (Alasia, 2005). However, our review of the literature unearthed only three examinations of the Canadian rural-urban skills gaps using refined measures from the now discontinued International Adult Literacy Survey (Corbeil, 2000; Green and Riddell, 2001; Willms, 1997) and the Adult Literacy and Life Skills Survey (Green and Ridell, 2015). Our present understanding of existing rural-urban skills disparities is thus far from ideal, being based on imperfect proxies and outdated data. This state of the literature impedes effective policymaking pertaining to rural economic development. Through this study, we thus seek to extend existing research by using data from Statistics Canada's 2012 Longitudinal International Study of Adults (LISA) to examine rural-urban disparities in skills across the working age (16–65) Canadian population. More specifically, we ask: to what extent do urban and rural residents differ in their literacy and numeracy skills? Since prior research has found that rural-urban disparities in educational attainment are partly explained by socio-economic characteristics, we also examine to what extent factors like family income, education, and/or employment characteristics explain these ruralurban skills gaps.

credentials) among rural populations. Despite this litany of attacks, the perspective remains firmly entrenched in policy circles. Observers suggest we are living in an “age of human capital” (Becker, 2002, p. 3), where the success of individuals, firms and national economies are believed to be tied to investments in people (Barro and Lee, 2001; Blundell et al., 2005; Wright and Mcmahan, 2011; Heisz et al., 2015, 2016). Indeed, Canadian policymakers depict the creation of the “best-educated, most-skilled and most flexible workforce in the world” as an economic imperative for the nation (Department of Finance, 2006, p. 6). In 2018, the federal government invested $300 million into the establishment of a Future Skills Center that will endeavour to identify looming skills needs within the economy, and evaluate skills assessment and development strategies (Government of Canada, 2019). Still, attempts to use direct measures of skill in human capital research are rare (Bills, 2003). With the exception of psychologists, who have developed a battery of tests to measure intelligence (Jones and Joel Schneider, 2006), social scientists typically rely on “proxies” of human capital, such as years of education or credentials, which are only rough approximations of a person's skills or productive capacity (Bills, 2016; Hanushek, 2013; Wright and Mcmahan, 2011). The usage of such proxies, prompted by the costs associated with gathering more refined measures of skill for large samples, can be problematic for two reasons. First, though education and skills are positively correlated, they are not synonymous. Recent research finds that the actual amount of learning that takes place within education is much smaller than traditionally believed (Arum and Roksa, 2011). There is also great heterogeneity in the skillsets of individuals with credentials at similar tiers (e.g. BA, MA), or an identical number of years of education (Hanushek, 2013; Hanushek and Woessmann, 2010, 2012; Massing and Schneider, 2017a, b). A number of other factors after graduation, such as on-the-job training or community resources (e.g. libraries, cultural organizations, news media), also drive skill disparities between individuals with similar levels of attainment (Desjardins, 2003; Park and Kyei, 2011). As such, education is far from a perfect indicator of human capital. Second, scholars argue that studies employing education as a proxy for human capital ignores qualitative distinctions between the “multiple dimensions of knowledge and skills” that individuals possess (Desjardins and Albert, 2005, p. 361) – such as literacy, numeracy and problem solving. There are also a variety of types of intelligence developed outside of schooling, such as emotional or cultural intelligence (see Neisser et al., 1996), that are not effectively captured by education proxies. As Barro and Lee (2001) note, education is “at best a proxy for the component of the human capital stock obtained at schools” (p. 542). By employing education as a proxy, we are sorting for a combination of abilities and social dispositions which may facilitate educational attainment (e.g. test taking, rule following/conformity), but may have little or negative impact on productive capacities outside of the education system. The sum of these limitations has driven researchers to propose that education be supplanted within human capital research by more finely-grained measures of specific skill, such as standardized test scores (Bills, 2016).

2. Literature review 2.1. Human capital & skills in the labor market The concept of human capital gained prominence within the field of economics during the mid-20th century (see Becker, 1962; Mincer, 1958; Schultz, 1961, 1960). Since then, it has become central to academic and policy discussions of economic development. Human capital theorists posit that participation in varied activities, ranging from formal education to on-the-job training, provides skills that render individuals more productive (Becker, 2002). This, in turn, augments financial returns on their labour, and also has positive effects on the performance of the firms they work for and jurisdictions they inhabit (Becker, 2002). The logic of human capital theory (HCT) has been subject to repeated and varied criticism since its emergence. On the one hand, studies have questioned the extent to which formal credentials map directly on to the skill sets of degree- and diploma-holders, suggesting a looser linkage between skills acquired and the formal credential conferred than assumed by HCT (e.g., Berg, 1970; Collins, 1979). Other theories underscore the structural barriers that disadvantaged groups (i.e., low SES, racial and ethnic minorities, rural youth) face in their pursuit of human capital (e.g., Blaug, 1976; Bowles and Gintis, 1976; England, 1982). As such, our study embraces these criticisms by exploring skills disparities (as opposed to formal

2.2. The rural-urban human capital gap Research has repeatedly found that individuals across remote regions in Canada are less likely to enroll in PSE (Frenette, 2004, 2006; 2009a, b). It also finds that “distance deterrence” shapes their educational trajectories, and the type of post-secondary education they acquire (Finnie et al., 2015; Newbold and Mark Brown, 2015; Zarifa et al., 2018). Lower PSE participation rates among rural youth have also been observed across European nations (see Dickerson and McIntosh, 2013; Gibbons and Vignoles, 2012; Spiess and Wrohlich, 2010) and the United States (Byun et al., 2012). These observed differentials in educational attainment are routinely cited during policy discussions of skill disparities across Canadian regions (Alasia, 2005). They also inform 253

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underperform relative to their urban counterparts. Researchers argue that within the Canadian context, rural-urban gaps are not due to disparities in K-12 school quality – given that provincial funding formulas produce relative “wealth neutrality” across schools (Fazekas, 2012). Unlike in the US, where large differences exist in school quality from one neighborhood to the next, Canadian provinces generally ensure parity by funding schools according to their enrolments, student population characteristics, and school board characteristics (Davies, 2016; Li, 2008). Moreover, in those provinces where funding sources are both provincial and local, disparities between wealthy and poor districts are minimized through equalizing grants (Li, 2008). The weak effects of school resources within Canada is further evidenced by recent Statistics Canada reports which show that they account for a very limited portion of the variance in student outcomes (Frenette, Ping Chan, 2015a, 2015b). Rural schools do face difficulty attracting more experienced teachers (Cappon, 2006), but such factor has yet to be tied by researchers to student outcomes, net of other influencers. Instead, research has found that disparities in skills between rural and urban regions in Canada may be attributable to broader community-level differences (Cartwright and Allen, 2002). The ecology of rural regions is often characterized by a dearth of opportunities for learning which are more accessible in urban regions. The Canadian Council on Learning, for example, has produced a Composite Learning Index, which accounts for an array of factors, such as access to cultural resources (e.g. museums), interaction with people from other cultures, and access to other community institutions (e.g. social, religious institutions). It has found that rural regions consistently lag behind their urban counterparts in learning opportunities, as measured by this index (Cappon, 2006). Haight et al. (2014) have also found, like previous researchers (Looker and Thiessen, 2003), that Canadians in urban regions continue to have greater rates of internet access – and use the internet with greater intensity than rural counterparts. Though often taken for granted by urbanites, internet access has been found to have a transformative effect on northern cities in Canada, granting them easier access to social institutions, goods and information (Collins and Barry, 2010).

more sophisticated analyses. Moazzami (2015b), for example, developed a weighted index using educational attainment and income measures (see p. 31–32) to map human capital disparities across regions of Ontario, Canada. His analysis demonstrated that communal stocks of human capital were positively correlated with population size. In a second piece, Moazzami (2015a) used this same weighted index to examine the dispersion of human capital across northern Ontario, finding that in the north-east region of the province, stocks of human capital were negatively correlated with rurality. However, in northwest portion of the province, rural communities with weaker links to urban centers possessed larger human capital index scores. This approach to measuring human capital has been employed repeatedly by Cuddy and Moazzami (2017; 2017b) in a series of other reports examining skill deficiencies across different districts in northern Ontario. An alternative, though seemingly less popular approach within the literature, has been the use of occupational categories as a proxy for human capital (Alasia, 2005; Alasia and Magnusson, 2005; Beckstead et al., 2003; Magnusson and Alasia, 2004). Alasia & Magnusson (2005), for example, developed a skills specialization quotient to measure differentials in the type of jobs present across geographical regions. They found that there is a clustering of skill within the Canadian labour market, with managerial and professional occupations being concentrated in urban regions and unskilled occupations within rural labour markets. However, Beckstead and Vindorai (2003) note that, though rural-urban disparities persist in the distribution of knowledge workers across Canada, there has also been growth in the amount of knowledge work performed in rural areas. As such, this literature focusing on occupational categories concurs with that which uses education as a proxy of human capital – finding notable differentials between rural and urban regions. Studies using more refined measures of human capital to examine rural-urban disparities in Canada are both scarce and dated. Using the International Adult Literacy Survey (IALS), Willms (1997) found that rural residents scored lower than urban counterparts on certain types of skills (reading/quantitative). However, once their background characteristics were accounted for, they actually surpassed their urban counterparts by a margin equivalent to about one year of additional schooling. Corbeil (2000), also using the IALS, found that a “major gap” existed in adult literacy skills across urban and rural areas (p. 35–36). However, he also observed that rural effects proved statistically insignificant once variables like years of schooling, age and reading habits were accounted for. Green and Riddell (2001) also report mean differences in the literacy skills of rural and urban populations in Canada, but do not examine the factors which may account for such differences. Most recently, Green and Riddell (2013) analyzed correlates of cognitive skills using the Adult Literacy and Life Skills (ALLS) survey. Interestingly, though they suggest place of residence (rural/urban) had a statistically significant effect on skills scores, they omit this dummy variable from their regression tables, and do not specify either its effect size or direction. The most recent data available through the Statistics Canada's Longitudinal and International Study of Adults (LISA) and the Programme for the International Assessment of Adult Competencies (PIAAC), though used to study the relationship between literacy and income (Heisz et al., 2015, 2016), have yet to be employed to study rural-urban disparities in skills. Needless to say, this state of the literature provides a less than ideal picture upon which to engage in meaningful policy discussions about skills development and economic development in northern and rural Canadian regions.

3. Methods 3.1. LISA 2012 data To assess the extent to which literacy and numeracy skills proficiencies might vary regionally, this paper draws on data from the 2012 wave of Statistics Canada's Longitudinal and International Study of Adults (LISA) survey. The LISA is an optimal data source for the exploration of skills distributions for several reasons. First, the LISA in a nationally-representative survey that collects job, education, health, and family information from people across Canada. The survey employs a longitudinal design, interviewing respondents between January and May of 2011 and 2012, and then again two years after (in 2013 and 2014). The main objective of the LISA is to assess outcomes related to education and the labour market, including the long-term benefits of postsecondary education, labour market mobility, the effects of employment on the health and well-being of families, and standards of living for those in retirement. Data are collected via Computer Assisted Personal Interviews (CAPI), with additional follow-up surveys conducted by telephone if respondents were unavailable or ineligible for personal visits. Second, the LISA data overcome many of the methodological flaws in self-reported survey data (e.g., over- and under-reporting of income) because they have also been linked to administrative data, including T1FF, T4, Pension Plan, and Immigration files. Finally, of central importance to our skills focus, the first wave of LISA data has been linked to the Programme for the International Assessment of Adult Competencies (PIAAC), which includes standardized assessments of literacy, numeracy, and problem solving in

2.3. Why rurality matters? Research consistently finds that socio-economic differences between rural and urban populations account for a sizable share of the gap in educational attainment, and thus, stocks of human capital, between these populations (Cappon, 2006; Zarifa et al., 2018). However, even when one compares “apples to apples,” rural students tend to 254

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Canada, 2013: 16). Below 1: adults scoring between 0 and 175 Level 1: adults scoring between 176 and 225 Level 2: adults scoring between 226 and 275 Level 3: adults scoring between 276 and 325 Level 4: adults scoring between 326 and 375 Level 5: adults scoring between 376 and 500 In step with previous studies using PIAAC and its predecessor, the ALL (Adult Literacy and Life Skills survey), we dichotomized these measures of skill: (1) low skills, and (2) high skills (Hango, 2014; Statistics Canada et al., 2013; OECD and Statistics Canada, 2011; OECD and Statistics Canada, 2005). Respondents with “low” levels of skill are those who achieved scores below category 3 on the skill assessment scale, while respondents with “high” skill achieved scores at three and higher. Prior research suggests that on average the working populations with at least Level 3 skill proficiency are more likely to fully and productively participate in today's knowledge-intensive economies (Carey 2014; OECD and Statistics Canada, 2005). Empirically, roughly 51 percent of populations across OECD countries including Canada can successfully perform tasks at least at Level 3 (Statistics Canada, 2013).

technologically-rich environments, as well as the use of these skillsets at work and in everyday life. The two surveys share a portion of their samples, though the target populations differ in their scope. That is, the LISA includes respondents of all ages, while the PIAAC surveys only those respondents between the ages of 16 and 65 years old at the time of the initial survey. Since the PIAAC skill component is only available in the 2012 database, our analyses draw only from the first wave of LISA data. The sample therefore consists of 8500 respondents contained in the integrated PIAAC-LISA data file.4 3.2. Analytical approach To investigate the rural-urban skills gap, our statistical analyses include descriptive statistics and binary logistic regressions (Long and Freese, 2014; Long, 1997). Our regression models are used to assess literacy and numeracy skills across a number of rural-urban population categories. These models are ordered, such that the first model regresses the rural-urban variable against the dependent variable, without controls. The second model, Model 2, then introduces key sociodemographic control variables. Finally, Model 3 includes all previous variables from Models 1 and 2 with additional terms to control for the effect of level education on skill level. Given the complexity of the LISA and PIAAC surveys, all models were estimated using the REPEST package for STATA (Avvisati and Keslair, 2016). Finally, to aid in the interpretation of the binary logistic regression results, predicted probabilities for our rural-urban variable, along with 95 percent confidence intervals are calculated and presented in graphical displays.

3.4. Independent variables Since our primary goal of the analyses is to determine skills differences across rural and urban populations, our key independent variable in our analyses measures the population characteristics of the respondent's location of residence at the time of interview. This variable adheres to Statistics Canada's (2010) population center definition in the 2011 Census and is comprised of five categories: (1) rural area (less than 1000 residents); (2) small population center (1000 to 29,999 residents); (3) medium population center (30,000 to 99,999 residents); (4) large urban population center (100,000 to 499,999 residents); and (5) metropolitan population center (500,000 or greater).6 In order to assess the extent to which population size has an independent effect on skills, we include several key sociodemographic control variables in the analyses. Our review of the literature above pointed to the following key control variables to account for the array of factors that may also influence the acquisition of skills and ultimately explain regional skills gaps: age, age-squared,7 immigration status, indigenous status, sex, marital status, highest level of parental education, employment status, province of residence, and highest level of respondent's education. The natural log of income as reported on the respondent's tax file and linked to the LISA through the T1FF (i.e., total income from employment reported on 2011 T4 slips) has also been included as a control in order to consider the effect of earnings on skill levels, while also accounting for the typically skewed distribution of the variable.8 The independent variables in the following analyses are treated as categorical, with the exception of age and the natural log of income. For further details on variable definitions and categories, see

3.3. Dependent variables We examine skills gaps across rural-urban populations in Canada with two key dependent variables: (1) literacy, and (2) numeracy.5 As mentioned above, the PIAAC component of the LISA contains the standardized measures of literacy and numeracy proficiencies. For literacy, respondents in PIAAC were measured for their ability to engage with print-based and digital written texts. They were accessed in their abilities to identify and process information in a variety of different settings and across a wide range of texts. For numeracy, respondents were measured on their ability to engage with mathematical information to infer their ability to meet the mathematical demands across a range of settings. Specifically, respondents were assessed on their understanding of mathematical content and ideas (e.g., dimensions, relationships, quantities, numbers) and the representation of that content in graphs, diagrams, pictures, and objects (see Statistics Canada, 2013 for further details). The measurement scale for both literacy and numeracy domains ranged from 0 to 500. The following proficiency levels (Below 1 to 5) were also created and are commonly used to aid in the interpretation of the raw scores (adapted from Table 1.1 in Statistics 4 In our final subsample, we also restricted to those who were not self-employed (n = 7560), and had valid data across all of our analysis variables (n = 7395). Listwise deletion was used to remove 2.18 percent of cases on parents' education variable, and less than 30 missing cases on the marital status, aboriginal status, and education level variables combined. While listwise deletion can be problematic when data are not missing completely at random, in cases where missing data is small such as ours, it is often preferred over computationally intensive missing data techniques to avoid adding an additional layer of measurement error to the data (see Allison, 2001; Cheema, 2014). 5 We do not consider potential gaps in problem solving skills in technologically rich environments (PSTRE) here, since a sizable portion of the LISA subsample did not do the PSTRE assessment. The PSTRE assessment was not done if the person had insufficient computer skills, or the person opted to do a paperand-pencil-based assessment, or the person did not do the computer assessment for literacy-related reasons. As a result, there are missing values for PSTRE proficiency values that are not missing at random and addressed through the LISA imputation or weighting (see Statistics Canada, 2013).

6 Given the rather restricted definition of rural as places with a population of less than 1000, we include the five-category disaggregated version in our analyses which includes all possible categories across this variable rather than use the simple dichotomy of rural and urban. This population center variable is readily used in the Canadian Census of Population, and was defined and created by Statistics Canada in 2010 (for details, see https://www.statcan.gc.ca/eng/ subjects/standard/pcrac/2016/introduction). By this definition, a population center has a population of at least 1000 and a population density of 400 persons or more per square kilometer, and all areas outside population centers are classified as rural areas. 7 We use orthogonal polynomial contrasts for age and aged-squared in all regression models account for the collinearity between these two variables. 8 Unlike other surveys that use self-reported estimates of earnings, the LISA contains data linkages to administrative tax records. Thus, we are able to account for much more precise income figures for respondents than survey data typically allows.

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Fig. 1. Predicted probabilities of literacy proficiency by population center (without controls).

Island. Finally, 16 percent of the sample consists of those respondents who have less than a high school diploma, while 26 percent of respondents have at least a high school education. Respondents with certification in the trades make up 12 percent of the sample, while 23 percent of respondents have some form of postsecondary education below a bachelor's degree. Roughly 15 percent of the sample are bachelor graduates, while the remaining eight percent are those who have obtained a university degree above the bachelor level.

Table 1.

4. Results 4.1. Characteristics of the 2012 LISA respondents Table 1 shows the summary descriptive results of the sample used in our study. Roughly 51 percent of the sample achieved high levels of literacy, while the corresponding 49% were among those with low levels of literacy skills. At the same time, 44% of respondents have high numeracy skills, while 56% have low numeracy skills. Approximately 16 percent of the sample reside in rural locations with less than 1000 residents. Alternatively, 12 percent of respondents are from small population centers between 1000 and 29,999 residents, 9 percent are from medium centers with populations between 30,000 and 99,999, and 15 percent are in large urban centers with populations between 100,000 and 499,999. The largest number of respondents are from metropolitan centers with populations greater than 500,000 (48 percent of the sample). The average age of respondents in the sample is 40 years. Approximately 77 percent of the respondents are Canadian born, while the remaining 18 percent are established immigrants, and 5 percent are those who immigrated to Canada within five years of the time of survey. Only 3 percent of the sample are indigenous respondents. Approximately 51 percent of the sample are female, with males comprising the remaining 49 percent. Respondents who were married or common law made up 57 percent of the sample, with the remaining 43 percent those who were not married at the time of survey. Roughly 76 percent of respondents did not have at least one parent who had obtained a university degree, with the remaining 24 percent of respondents having at least one university-educated parent. Respondents who are employed comprise 74 percent of the sample, while only five percent of respondents are unemployed. Respondents who were not actively involved in the labour market make up the remaining 21 percent of the sample. The average income of respondents is $37,297. With respect to province, roughly 39 percent of respondents are from Ontario, followed by 24 percent from Quebec. Similar proportions of respondents are from British Columbia and Alberta (12 percent and 11 percent respectively). The remaining respondents are from Manitoba (4 percent), Saskatchewan (3 percent), Nova Scotia (3 percent), Newfoundland (2 percent), and New Brunswick (again, 2 percent). Less than 1 percent of the respondents are from Prince Edward

4.2. Rural-urban Literacy Skills gaps? To investigate the extent to which location of residence impacts literacy proficiencies, we estimate a series of binary logistic regressions (see Table 2). As outlined above, initial models contain only our key independent variable for population center and size. Our next model adjusts for all controls with the exception of education level, which is added to the mix in the third model, to separate out the explanatory strength of pursuing advanced levels of higher education on literacy levels. Indeed, Model 1 reveals significant skill differences across population centers. Respondents in large urban centers have higher levels of literacy, though the differences are more pronounced for large urban centers with populations between 100,000 and 499,999 (p < 0.01), than for metropolitan areas with more than 500,000 residents (p < 0.05). At the same time, respondents living in medium and smaller population centers are not statistically different from those in rural areas, suggesting that there may be literacy skill shortages in these less populated, more rural areas in comparison to largely populated areas of Canada. These differences can best be seen when comparing the predicted probabilities of literacy proficiency from Model 1 in Fig. 1. Overall, residents from rural areas show the lowest probabilities of achieving a literacy score at Level 3 or higher (0.456). In fact, with the exception of metropolitan centers, the probabilities appear to climb steadily with population center size. Those in large urban centers show the highest probabilities at .536, followed by metropolitan centers (0.518), medium population centers (0.515), and finally small population centers (0.477). In Model 2, the binary logistic regressions adjust for a number of sociodemographic factors. The results reveal that the relationships presented in the previous model hold, although the inclusion of these 256

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4.3. Rural-urban numeracy skills gaps?

variables does weaken the previously observed relationships slightly. Once again, large urban centers and metropolitan areas are significantly more likely to have higher levels of literacy. However, the significance of the large urban population centers effect weakens slightly to p < 0.05, while the coefficient for metropolitan centers increases slightly over the previous model.9 Again, medium and smaller population centers do not significantly differ from rural areas in their literacy levels. There are a number of significant control variables in the model that are worth noting. As found with previous surveys (OECD and Statistics Canada, 2011), our quadratic term for age is negatively associated with literacy proficiency, suggesting a non-linear relationship whereby literacy proficiencies first increase for younger age groups, peak, and increasingly decline for older respondents (p < 0.01). In terms of immigration status, both established immigrants (greater than 5 years) and recent immigrants (5 years or less) had lower levels of literacy than Canadian born respondents, though the skill gap was greater for recent immigrants than those who had lived in Canada for longer than 5 years.10 Indigenous respondents were also more likely to show lower literacy levels than non-indigenous respondents (p < 0.05). Respondents whose parents had acquired higher education credentials reported higher literacy levels (p < 0.001). As well, the likelihood of achieving higher literacy levels increases with one's level of income (p < 0.001). Finally, among the provinces, only respondents in Newfoundland and Quebec (p < 0.001) showed significantly lower likelihoods of achieving higher literacy levels than their counterparts in Ontario. Fig. 2 displays the predicted probabilities and their corresponding 95 percent confidence intervals of literacy proficiency by population center estimated from Model 2.11 By and large, the inclusion of sociodemographic control variables does not significantly alter the absolute values of the predicted probabilities estimated in Model 1. However, the relationship between population size and literacy proficiency appears to be more linear than before. That is, predicted probabilities consistently increase with population size, and metropolitan centers now show the highest predicted probability at .532. Model 3 includes all of the previous variables with the addition of level of education, to isolate the impact that higher education has on skill level differences between population centers. As expected, introducing respondents' level of education influences the previous findings considerably. Most importantly, our population center effect is no longer significantly associated with literacy proficiency, once level of education is introduced into the model. Therefore, it is likely that the previous literacy differences higher concentrations of individuals with postsecondary education in larger population centers. To further grasp these differences, the predicted probabilities of literacy skill levels by population center from Model 3 are plotted in Fig. 3. Interestingly, when including level of education, the probabilities of achieving Level 3 or higher literacy proficiency decrease for metropolitan centers (from 0.532 in Model 2 to 0.505 in Model 3), while medium and large urban centers show the highest probabilities at .517 and .516 respectively. Small population centers and rural areas show comparatively smaller probabilities at .489 and .488. As such, it appears that much of the difference between the literacy proficiencies of respondents may be attributable largely to the educational differences between these groups.

A similar approach was used to investigate numeracy differences among population centers. Once again, in our binary logistic regression models, variables were entered in three stages to first observe the zero order effect of population center type on numeracy skill levels before adjusting for other related factors in subsequent models. For Model 4, only population center is included in the model. Overall, the results indicate that residents of large urban centers and metropolitan areas are significantly more likely to achieve higher numeracy skills than those from rural locations (p < 0.05 and p < 0.01 respectively). As with our previous models for literacy, these findings may suggest that residents in rural locations have either fewer opportunities to develop their numeracy skills, or that such regions may have more difficulties attracting and retaining individuals with higher levels of numeracy skills. Fig. 4 shows the predicted probabilities of numeracy proficiency by population center estimated from Model 4. These unadjusted estimates indicate that residents from large urban population centers as well as metropolitan centers have the greatest chances of achieving high levels of numeracy (0.463 and 0.456 respectively). Medium population centers have the next highest probability (0.439), while small population centers, and those in rural areas, have comparatively lower probabilities (0.398 and 0.389 respectively). It is interesting to note that the overall pattern in the relative probabilities does look similar to that which was found for Model 1 above. However, much like the numeracy proficiency scores more generally in the population, the overall probabilities of achieving high numeracy levels are comparatively lower than those for literacy. When introducing sociodemographic control variables in Model 5, the population center effect weakens slightly, though respondents living in both large urban centers and metropolitan areas remain statistically significantly more likely to achieve higher levels of numeracy proficiency than those living in rural locations (p < 0.05 and p < 0.01 respectively). As with Model 4, respondents residing in medium and small population centers do not significantly differ from those living in rural locations in regard to their numeracy skills. Similar to the literacy models, a number of significant effects among the control variables are worthy of note. Immigration status was again statistically significantly related to skill level, as both recent immigrants and established immigrants (to a lesser extent) have lower levels of numeracy than Canadian born respondents (p < 0.001). Indigenous respondents have lower levels of numeracy than non-indigenous respondents (p < 0.01), while those respondents whose parents had acquired higher education have greater levels of numeracy (p < 0.001). Interestingly, women are more likely to have higher levels of numeracy than men (p < 0.001), an encouraging finding given recent policy initiatives geared towards increasing female involvement in STEM fields. Married respondents have higher levels of numeracy than those who are single (p < 0.01). In terms of age, once again our quadratic term is statistically significant (p < 0.001). Similarly, higher income individuals were also more likely to obtained higher numeracy skills (p < 0.001). Finally, numeracy skill levels varied little by province – only respondents in New Brunswick show significantly lower levels of numeracy than respondents in Ontario (p < 0.05). Fig. 5 shows the predicted probabilities of numeracy by population center for Model 5. Once again, including sociodemographic control variables weakens the probability of achieving higher numeracy levels for each of the population centers. Still, large urban centers (0.454) and metropolitan areas (0.452) have the highest probabilities of numeracy. Medium population centers respondents continue to have a greater probability (0.42) than those living small centers, and in rural areas (each with a probability of .38). Finally, in Model 6, when controlling for level of education differences between population centers, the effect of location on numeracy differences is no longer statistically significant. This suggests that the

9 Although the effect weakens slightly to p < 0.05, the coefficients indicate that once sociodemographic factors are accounted for, metropolitan areas have the highest literacy rates of the five population centers included in the model, surpassing large urban centers (the previously highest reported literacy values in the zero order models). 10 It is important to note that both effects are highly statistically significant p < 0.001. 11 In Fig. 2, the predicted probabilities are calculated by holding all control variables at their sample means and/or proportions.

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Fig. 2. Predicted probabilities of literacy proficiency by population center (with controls).

Fig. 3. Predicted probabilities of literacy proficiency by population center (with controls and level of education).

5. Discussion and conclusions

numeracy differences between large population centers and rural locations may also be the result of the greater levels of education for those individuals residing in more densely populated areas. In Fig. 6, the predicted probabilities estimated from Model 6 are shown. By including level of education in the mix, the probability of achieving high numeracy levels for metropolitan centers drops to .416 (from 0.452 in Fig. 5). The probability for large urban centers similarly weakens (though to a probability of .44, from 0.454 in the previous estimation). Residents in medium population centers have a slightly increased probability of numeracy (0.438), while those in small centers, and rural areas of Canada see an increase in the probability of numeracy (each to 0.401 and 0.412 respectively). Therefore, the change in probabilities which appears in the estimates for Model 6 suggests that much of the difference between rural areas and large urban populations can be attributed to differences in the level of education of respondents in these areas.

Our paper contributes to the existing literature and sets the stage for policy initiatives in two key ways. First, our work demonstrates that the traditional rural-urban human capital disparities identified through older reports (e.g. Bollman, 1999; Magnusson and Alasia, 2004) persist, ostensibly serving as an impediment to the full participation of rural Canadian communities within the new knowledge economy. Specifically, our study demonstrates that skills in the Canadian workforce differ across locations of varying size and density. It is important to note that our findings are a marked improvement over studies using proxies such as education levels or years of education, in that we identify more refined literacy and numeracy skills gaps. Moreover, it is interesting to note that rural and smaller population centers show markedly lower levels of literacy and numeracy proficiencies compared to medium, large, and metropolitan population centers. Our disaggregation of ruralurban to five categories also sheds new light on rural-urban differences, as the skill gaps of small- and medium-sized population centers often resemble the situation of “rural” areas. 258

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Fig. 4. Predicted probabilities of numeracy proficiency by population center (without controls).

Fig. 5. Predicted probabilities of numeracy proficiency by population center (with controls).

residents possess lower stocks of human capital (e.g. Cuddy and Moazzami, 2017a; Moazzami, 2015a), are less likely to attend university rather than college (e.g. Finnie et al., 2015; Newbold and Mark Brown, 2015; Zarifa et al., 2018), and to enter lucrative STEM fields (Hango et al. forthcoming), theoretical explanations for these documented disparities remain largely underdeveloped. Often, theorizing has evolved in response to the findings of case studies focused on small and very distinct rural communities (e.g. Corbett, 2005), rendering the generalizability of such frameworks questionable. This scarcity of broader level theorizing about recently observed rural-urban disparities in Canada and other nations is unsurprising, given that the study of rural education itself remains a relatively peripheral enterprise (Bæck, 2016; Corbett, 2014). We posit, however, that it would be fruitful to develop a nuanced theoretical understanding of student decisionmaking within rural communities as a form of socially “embedded” behavior (Coleman, 1988; Granovetter, 1985), one that is informed by and responsive to the local opportunity structures which rural folk encounter. Given the varied place-specific education and employment opportunity structures, this embedding may be highly contextual,

A second key contribution of our study is that we examine which sociodemographic factors play a critical role in explaining rural-urban differences in skills proficiencies. Overall, our analysis suggests that the sizable rural-urban skill differentials we uncover may be attributable primarily to regional disparities in educational attainment. This finding should be of import to both scholars seeking to understand rural-urban disparities in human capital, along with government actors wishing to ameliorate them through policymaking. For academic researchers, our findings should prompt an interest in the decision-making processes at the individual level that may underlie these observed population-level patterns. Moreover, while the current study brings the Canadian context to the forefront, PIAAC's skills data have been collected in partnership with the OECD in 22 other countries worldwide (see Hanushek et al., 2015). Future studies that explored literacy and numeracy skills gaps across rural-urban areas in additional countries would be beneficial in furthering our understanding of how rural-urban skills disparities might vary cross-nationally. Although studies such as this one have made much headway over the last decade, using large scale datasets to demonstrate that rural 259

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Fig. 6. Predicted probabilities of numeracy proficiency by population center (with controls and level of education).

dynamic and ‘differentiated' (Ryan, 2018). As Bæck (2016) astutely notes, geographic proximity to HE institutions, employment opportunities in regional labour markets, and gendered norms about what work is appropriate or desirable for certain individuals serves as a “prism” through which rural residents will evaluate educational pathways and make decisions. Such structures will shape rural resident's educational trajectories, producing the regional disparities that have been recently observed. Beyond mere “distance deterrence” (Sá et al., 2004, 2006), and the array of socio-economic barriers which are routinely cited as explanations for the lower educational attainment levels in rural communities (see Zarifa et al., 2018), it may be the case that rural residents make educational decisions that correspond with employment opportunities within their regions that they have intimate knowledge about, or those which have been a part of family or town traditions (e.g. fishing, mining). By contrast, they may eschew those pathways that lead to foreign urban spaces, and positions for which they lack first-hand knowledge. Successfully mapping these consequential sense-making processes will require richer data sources than are currently available, and likely entail ambitious mixed-methodological research designs. Nonetheless, such efforts will be key to developing more nuanced and comprehensive theoretical explanations for rural-urban disparities in human capital. Based on our findings, along with a review of the extant literature covered earlier in this piece, we argue that rural-urban disparities in educational attainment could be addressed by policymakers using a two-pronged strategy: (1) extending educational access across rural regions, and (2) reversing regional out-migration or “brain drains.” It is important to underscore that policy initiatives in each of these areas will necessarily be flexible in their scope and will need to take into consideration the unique and complex social, cultural, economic, and environmental contexts to account for the diversity of rural places (Lauzon et al., 2015; McManus et al., 2012). With respect to enhancing rural access to education, starting in the 1960s, provincial governments in Canada pursued this strategy through the establishment of new colleges and universities across underserved rural or remote regions. In light of the heavy infrastructure costs this strategy entails, along with the limited and shrinking supply of students outside of urban regions, provincial governments have recently sought to “reach the north” through online education. For example, Contact North, a subsidiary of the provincial government in Ontario, offers electronic access to programming from the province's 76 district school boards, 46 public colleges and universities, and 250 literacy and basic skills providers.

Through its online platform, rural residents are able to improve their skills and obtain credentials without having to leave their communities. In its efforts overcome the challenge of limited internet connectivity and access to technology in remote communities, Contact North has established a physical network of 116 learning centers which provide free access to computers with high-speed internet, along with access to audio-, video- and web-conferencing platforms. The intensification of these efforts in Ontario and other provinces could play an important role in helping to diminish rural-urban skills disparities, helping rural students overcome traditional barriers to obtaining an education. It is unlikely, however, that improving access to education and training alone will correct the regional skill disparities documented through this study. Attracting skilled migrants and encouraging new employment opportunities will also be critical to bolstering rural human capital levels. Highly-skilled individuals from remote regions in Canada will continue to migrate to urban “hubs” to secure employment which matches their credentials if comparable work is not readily available in their local community. Yet, counter-urbanization efforts in many rural communities in Manitoba have shown great success attracting immigrants by expanding their services to newcomers, through local immigration partnerships across municipalities, employers, and local service providers (Markey et al., 2015; Lauzon et al., 2015). Still, immigration settlement services are not equally distributed across western and northern Canada (Lauzon et al., 2015). At the same time, research from other countries is finding that foreign-born students with foreign-born parents often face more difficulties realizing their educational aspirations in non-urban settings (Lewith and Reilly, 2014). But, in rural places where metropolitan hubs are close by such as in Ontario, rural revitalization and counter-urbanization initiatives may be fruitful options to build and maintain human capital levels. In many countries worldwide, cheaper housing, employment opportunities, environmental amenities, and quality-of-life factors in rural areas have all shown to be important factors to lure urban residents to smaller municipalities (Wirth et al, 2016; Mitchelle, 2008; Dahms and McComb, 1999). However, in some of the more northern and remote rural areas of Canada, where metropolitan areas are distant, such urban out-migration is not possible. As such, it will be incumbent upon local, provincial and federal governments to also engage in concerted efforts to construct “business friendly” environments in rural communities for the specific types of companies that hire highly-skilled personnel – such as those operating within the advanced manufacturing and information technology

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sectors. The first, and simplest, piece of this puzzle entails expanding the supply of skilled workers, as advocated in the paragraph above, to ensure said companies that they will have a steady supply of labor. The second, and far more complex step, entails the development of a comprehensive framework of incentives to motivate companies to relocate to rural regions. This can include i) revising taxation or subsidy schemes, to provide concrete financial incentives for relocation, ii) reviewing regulatory frameworks (e.g. land use, zoning), to remove unnecessary bureaucratic hurdles for businesses operations, and iii) improving local infrastructure (e.g. transportation system), to facilitate business operations. Unfortunately, there is no “full-proof” blueprint to guide this process, though some resources (e.g. handbooks) exist in Canada to guide rural communities wishing to attract new employers. In addition, the trajectories of outlier regions that have managed to develop into world-leading hubs in technological innovation (e.g. Silicon Valley), often benefitted from historical or institutional circumstances that are not replicable, thus providing lessons which may not be useful for other communities wishing to make a similar leap. Nevertheless, though certainly difficult, producing steady demand for highly-skilled workers in rural communities will be a necessary step in correcting regional skill imbalances.

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