1. Necessary but not sufficient? Youth responses to localised returns to education Nicholas Biddle Centre for Aboriginal Economic Policy Research, ANU Conference of Economists September 2009

2. Background (I) – Returns to education and the decision to study  Those who complete additional years of education experience a range of positive outcomes throughout their lives  e.g. Higher income, better employment prospects and improved health  Returns to education  Increase in discounted future income from an additional year of schooling/education minus the cost of studying  Costs of studying include direct costs (tuition fees, equipment) as well as the opportunity costs (income foregone)  Simple human capital theory assumes that individuals will undertake additional years of education if the returns are positive  That is – benefits outweigh the costs  Evidence at the national level – School retention peaks along with youth unemployment

3. Background (I) – Returns to education and the decision to study  Those who complete additional years of education experience a range of positive outcomes throughout their lives  e.g. Higher income, better employment prospects and improved health  Returns to education  Increase in discounted future income from an additional year of schooling/education minus the cost of studying  Costs of studying include direct costs (tuition fees, equipment) as well as the opportunity costs (income foregone)  Simple human capital theory assumes that individuals will undertake additional years of education if the returns are positive  That is – benefits outweigh the costs  Evidence at the national level – School retention peaks along with youth unemployment

4. Background (I) – Returns to education and the decision to study  Those who complete additional years of education experience a range of positive outcomes throughout their lives  e.g. Higher income, better employment prospects and improved health  Returns to education  Increase in discounted future income from an additional year of schooling/education minus the cost of studying  Costs of studying include direct costs (tuition fees, equipment) as well as the opportunity costs (income foregone)  Simple human capital theory assumes that individuals will undertake additional years of education if the returns are positive  That is – benefits outweigh the costs  Evidence at the national level – School retention peaks along with youth unemployment

5. Background (II) – Variation in returns across the population  Not all individuals have the same expected or perceived return from an additional year of schooling  Cognitive and non-cognitive ability  Individuals with higher levels of innate ability or accumulated knowledge find education less costly  Social and cultural costs/benefits  Particular population sub-groups may face additional costs of education – ‘selling out’ or ‘acting white’  Uncertainty in predicted returns to education  Those who know very few people who either have or have not undertaken particular level of education may find it hard to gauge what returns will be  Opportunity cost/income foregone  Variation in support from family or ability to find part-time employment  Imperfect capital markets mean that individuals can’t borrow against future income  All of the above may be influenced by geography  In addition, with non-negative costs of migration, local labour markets and resource endowments may influence the wage premium from education

6. Background (II) – Variation in returns across the population  Not all individuals have the same expected or perceived return from an additional year of schooling  Cognitive and non-cognitive ability  Individuals with higher levels of innate ability or accumulated knowledge find education less costly  Social and cultural costs/benefits  Particular population sub-groups may face additional costs of education – ‘selling out’ or ‘acting white’  Uncertainty in predicted returns to education  Those who know very few people who either have or have not undertaken particular level of education may find it hard to gauge what returns will be  Opportunity cost/income foregone  Variation in support from family or ability to find part-time employment  Imperfect capital markets mean that individuals can’t borrow against future income  All of the above may be influenced by geography  In addition, with non-negative costs of migration, local labour markets and resource endowments may influence the wage premium from education

7. Paper overview  Research question (I) – What is the relationship between returns to education at the local level and high school participation?  Research question (II) – Does this relationship explain any of the difference between Indigenous and non-Indigenous participation?  Data – Full unit record data from the 2001 Census  Create 777 areas with a minimum of 10 Indigenous Australians aged 18 to 54  Methodology (I) – Calculate predicted lifetime employment and (discounted) lifetime income for those employed  Methodology (II) – Difference in lifetime employment/income between those who have and have not completed Year 12 taken as proxy for predicted benefits  Methodology (III) – Include predicted benefits as an explanatory variable in equation predicting high school participation of 15-17 year olds

8. Paper overview  Research question (I) – What is the relationship between returns to education at the local level and high school participation?  Research question (II) – Does this relationship explain any of the difference between Indigenous and non-Indigenous participation?  Data – Full unit record data from the 2001 Census  Create 777 areas with a minimum of 10 Indigenous Australians aged 18 to 54  Methodology (I) – Calculate predicted lifetime employment and (discounted) lifetime income for those employed  Methodology (II) – Difference in lifetime employment/income between those who have and have not completed Year 12 taken as proxy for predicted benefits  Methodology (III) – Include predicted benefits as an explanatory variable in equation predicting high school participation of 15-17 year olds

9. Paper overview  Research question (I) – What is the relationship between returns to education at the local level and high school participation?  Research question (II) – Does this relationship explain any of the difference between Indigenous and non-Indigenous participation?  Data – Full unit record data from the 2001 Census  Create 777 areas with a minimum of 10 Indigenous Australians aged 18 to 54  Methodology (I) – Calculate predicted lifetime employment and (discounted) lifetime income for those employed  Methodology (II) – Difference in lifetime employment/income between those who have and have not completed Year 12 taken as proxy for predicted benefits  Methodology (III) – Include predicted benefits as an explanatory variable in equation predicting high school participation of 15-17 year olds

10. Variation in predicted benefits of education – Method and results  Employment age profile varies by sex, Indigenous status and Year 12 completion  Estimated by MLE of probit model  Separate intercept term for each area by Indigenous status and Year 12 completion  Income estimated similarly for those aged 18 to 54 and employed (via OLS)  Separate estimate for those aged 15 to 17 to take into account income foregone

11. Variation in predicted benefits of education – Method and results  Employment age profile varies by sex, Indigenous status and Year 12 completion  Estimated by MLE of probit model  Separate intercept term for each area by Indigenous status and Year 12 completion  Income estimated similarly for those aged 18 to 54 and employed (via OLS)  Separate estimate for those aged 15 to 17 to take into account income foregone Non-IndigenousIndigenous MaleFemaleMaleFemale Average predicted benefit Employment benefit3.105.996.608.62 Income benefit for those employed99,92779,10187,90068,878 Average for non high school completers Lifetime employment29.6422.5220.1214.45 Lifetime income for those employed654,175460,848497,255421,107 Standard deviation of predicted benefit Employment benefit1.161.514.024.19 Income benefit for those employed37,23037,30082,89283,658 Standard deviation for non high school completers Lifetime employment2.032.734.254.16 Lifetime income for those employed93,85893,997125,351126,367

12. Variation in predicted benefits of education – Method and results  Employment age profile varies by sex, Indigenous status and Year 12 completion  Estimated by MLE of probit model  Separate intercept term for each area by Indigenous status and Year 12 completion  Income estimated similarly for those aged 18 to 54 and employed (via OLS)  Separate estimate for those aged 15 to 17 to take into account income foregone Non-IndigenousIndigenous MaleFemaleMaleFemale Average predicted benefit Employment benefit3.105.996.608.62 Income benefit for those employed99,92779,10187,90068,878 Average for non high school completers Lifetime employment29.6422.5220.1214.45 Lifetime income for those employed654,175460,848497,255421,107 Standard deviation of predicted benefit Employment benefit1.161.514.024.19 Income benefit for those employed37,23037,30082,89283,658 Standard deviation for non high school completers Lifetime employment2.032.734.254.16 Lifetime income for those employed93,85893,997125,351126,367

13. Variation in predicted benefits of education – Method and results  Employment age profile varies by sex, Indigenous status and Year 12 completion  Estimated by MLE of probit model  Separate intercept term for each area by Indigenous status and Year 12 completion  Income estimated similarly for those aged 18 to 54 and employed (via OLS)  Separate estimate for those aged 15 to 17 to take into account income foregone Non-IndigenousIndigenous MaleFemaleMaleFemale Average predicted benefit Employment benefit3.105.996.608.62 Income benefit for those employed99,92779,10187,90068,878 Average for non high school completers Lifetime employment29.6422.5220.1214.45 Lifetime income for those employed654,175460,848497,255421,107 Standard deviation of predicted benefit Employment benefit1.161.514.024.19 Income benefit for those employed37,23037,30082,89283,658 Standard deviation for non high school completers Lifetime employment2.032.734.254.16 Lifetime income for those employed93,85893,997125,351126,367

14. Factors associated with high school participation – Method  Population of interest – 15 to 17 year olds who have not completed Year 12  Dependent variable – Probability of being a high school student  Explanatory variables of interest – Predicted employment/income in the area for those who have not completed Year 12 and predicted benefit of completing Year 12  Controls – Individual and household factors  Estimation methodology – MLE of the probit model  Results presented as the difference in the probability from base case (aka marginal effects) Proportion of population currently a high school student – By sex, Indigenous status and age Non-IndigenousIndigenous MaleFemaleMaleFemale Aged 15-170.8070.8590.5370.588 Aged 150.9380.9540.7490.790 Aged 160.8110.8600.5100.574 Aged 170.6510.7480.3240.360 Sample size264,891251,7318,2208,123

15. Factors associated with high school participation – Results  Coefficients for individual and household controls follow a priori expectations  Probability of base case estimated using mean values for area level employment/income  ‘Marginal effects’ calculated as difference in probability from a one standard deviation increase in area level employment/income benefits after holding all else constant

16. Factors associated with high school participation – Results  Coefficients for individual and household controls follow a priori expectations  Probability of base case estimated using mean values for area level employment/income  ‘Marginal effects’ calculated as difference in probability from a one standard deviation increase in area level employment/income benefits after holding all else constant Non-Indigenous malesNon-Indigenous females EmploymentIncomeEmploymentIncome Probability of base case 0.8170.8070.8590.862 Marginal effect from increase in employment/income if not completed high school 0.0250.0160.0170.012 Marginal effect from increase in employment/income benefit of high school 0.0200.0210.0090.011

17. Factors associated with high school participation – Results  Coefficients for individual and household controls follow a priori expectations  Probability of base case estimated using mean values for area level employment/income  ‘Marginal effects’ calculated as difference in probability from a one standard deviation increase in area level employment/income benefits after holding all else constant Non-Indigenous malesNon-Indigenous females EmploymentIncomeEmploymentIncome Probability of base case 0.8170.8070.8590.862 Marginal effect from increase in employment/income if not completed high school 0.0250.0160.0170.012 Marginal effect from increase in employment/income benefit of high school 0.0200.0210.0090.011 Indigenous malesIndigenous females EmploymentIncomeEmploymentIncome Probability of base case 0.6630.6980.7310.769 Marginal effect from increase in employment/income if not completed high school n.s.0.0370.0170.060 Marginal effect from increase in employment/income benefit of high school n.s. 0.021n.s.

18. Discussion – implications, limitations and extensions  Those who lived in an area where the difference between a Year 12 completer and non-completer’s income or probability of being employed was relatively high were more likely to be participating in high school  Results did not, in general, hold for the Indigenous population.  Particularly for Indigenous males  Results a little dated – Unable to access full unit record data for 2006 Census  Census data designed to count people not measure returns to education  Income data available as grouped values, unable to control for taxes and wages/salaries not separately identified  Employment experience unobserved  Only able to measure high school participation, not high school completion  Cognitive and non-cognitive ability unobserved  Extend analysis to other types of education or other population sub-groups  Combine area level measures of returns from census with other information:  Qualitative information on youth perception of returns to education  High school completion information from longitudinal data sources or administrative data

19. Discussion – implications, limitations and extensions  Those who lived in an area where the difference between a Year 12 completer and non-completer’s income or probability of being employed was relatively high were more likely to be participating in high school  Results did not, in general, hold for the Indigenous population.  Particularly for Indigenous males  Results a little dated – Unable to access full unit record data for 2006 Census  Census data designed to count people not measure returns to education  Income data available as grouped values, unable to control for taxes and wages/salaries not separately identified  Employment experience unobserved  Only able to measure high school participation, not high school completion  Cognitive and non-cognitive ability unobserved  Extend analysis to other types of education or other population sub-groups  Combine area level measures of returns from census with other information:  Qualitative information on youth perception of returns to education  High school completion information from longitudinal data sources or administrative data

20. Discussion – implications, limitations and extensions  Those who lived in an area where the difference between a Year 12 completer and non-completer’s income or probability of being employed was relatively high were more likely to be participating in high school  Results did not, in general, hold for the Indigenous population.  Particularly for Indigenous males  Results a little dated – Unable to access full unit record data for 2006 Census  Census data designed to count people not measure returns to education  Income data available as grouped values, unable to control for taxes and wages/salaries not separately identified  Employment experience unobserved  Only able to measure high school participation, not high school completion  Cognitive and non-cognitive ability unobserved  Extend analysis to other types of education or other population sub-groups  Combine area level measures of returns from census with other information:  Qualitative information on youth perception of returns to education  High school completion information from longitudinal data sources or administrative data