Human Capital and Sustainability: preliminary findings from the OECD Human Capital Project

Human Capital and Sustainability: preliminary findings from the OECD Human Capital Project Marco Mira d’Ercole Household Statistics and Progress Measurement OECD Statistics Directorate

Structure of presentation Background and motivation Genesis and features of OECD human capital project Preliminary results Planned developments and long-term challenges

1. Background and motivation Human capital/education enter policy discussions under a variety of headings, which shape the perspective taken on measurement : As determinants of economic growth (long OECD-tradition): Arnold, Bassanini, Scarpetta (2007): long-run effect on GDP of 1 additional year of education about 6-9 points (within range of estimates of private returns to schooling from micro-analyses) OECD (2010), High Costs of Low Educational Performance : an increase of one standard deviation in PISA (maths) scores (100 points) would boost GDP growth by 1.8 points (controlling for educational attainment), equivalent to increase of OECD GDP by USD 115 trillion over lifetime of the generation born in 2010 As determinant of income and earnings inequality: OECD (2008), Growing Unequal? on trends and determinants of income inequality (stressed role of market income inequality) Several Employment Outlook chapters on education and labour market performance OECD (2007), No more Failures, proposed (PISA) measures on i) “minimum standards of educational competences for students” (poverty) and ii) “personal and family circumstances to achieving educational potential” (inequality)

1. Background and motivation (2) Both of these perspectives largely rely on physical measures of human capital . The perspective is different when assessing ‘sustainability’ (inter-temporal) of development path. Some references: 2008, Joint UNECE/Eurostat/OECD WGSSD : necessary requirement for sustainability is that the (total) capital stock (per capita) in each country is not declining. Similar perspective taken in 2009 by report of the SSF Commission , which argued that we need separate indicators of “speed and gasoline left”. Making this ‘capital approach’ operational requires : measures based on a common (monetary) metric for those types of capital that can be substituted in production (man-made, financial, human capital) physical measures for those capital assets that are deemed to be ‘critical’ (i.e. non substitutable) for development (e.g. natural capital)

2. Genesis and features of OECD Project Joint UNECE/Eurostat/OECD WG SSD noted that “ human capital values are not directly observable.. but indirect methods exist for valuing them ”, suggesting the JF approach (life-time discounted income) as possible basis To explore this option, OECD and Fondazione Agnelli convened joint workshop in Turin in fall-2008. Main conclusions: Variants of the JF approach had already been implemented in several countries (Norway, Australia, Canada, United States, others) A simplified , and easier to implement, variant of this approach could rely on the grouped data (i.e. by age-bands) that are typically available at the OECD Scope for comparative exercise (based on common assumptions and databases) led by the OECD and involving countries participating on a voluntary basis

2. Genesis and features of OECD Project (2) OECD-project : launched in fall 2009 Participating countries : 16 OECD (Australia, Canada, Denmark, France, Italy, Israel, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Poland, Spain, United Kingdom, United States), 2 non-members (Russia and Romania), plus ILO and Eurostat Focus : i) formal education-system; ii) market-work (excl. leisure and non-market work); iii) ‘realised’ human capital (employment probabilities); and iv) specific type of return from education (i.e. higher earnings accruing to the individual investing in formal education) Basic data sources : grouped OECD data on number of people aged 15-64 (by gender and 5-years age groups), cross classified by (ISCED 97) attainment-level. Supplemented by survey data on (gross) earnings (by gender, age-group, educ. attainment), benchmarked on SNA values of “wages and salaries per employee”; enrolment rates (by gender and age group) survival rates (from one year to the next) for people of a given age (no differences of survival by educational attainment)

Basic methodological assumptions: An individual of a given age (s), gender and educational attainment will have in year t+1 the same earnings and employment probabilities of a person who, in year t, is one year older (s+1) but has otherwise the same characteristics (e.g. gender and educational attainment) Empirical implementation based on backward recursion: life-time income of a person aged 64 (1 year before retirement) equals current earnings, i.e. life-time income at 65 is assumed to be 0 by definition; life-time income of a person aged 63 equals current earnings plus life-time income of a person aged 64; etc.. 2. Genesis and features of OECD Project (3)

Basic methodological assumptions: T hree stages in the life cycle of each person aged between 15 to 64: (3) retirement : for person aged 65 ad over , life-time income = zero (by assumption, these persons have withdrawn from the labour market and do not receive earnings) (2) work only : for persons aged 41 to 64 , life-time income is sum of: current earnings, adjusted by probability of being employed; life-time income in the next year, adjusted by corresponding survival rate, real earnings growth and discount rate study and work : for persons aged 15 to 40 , life-time income is sum of: current earnings, adjusted by probability of being employed; life-time earnings under two assumptions: they enter the labour market with their current attainment level; or they remain in school, reach higher attainment level, and then enter the labour market with higher earnings and employment probabilities. 2. Genesis and features of OECD Project (4)

3. Preliminary results: levels Benchmark estimates of human capital per capita in 2006: common assumptions on real earnings growth (1.32%) and discount rates (4.58%)

3. Preliminary results: levels (2) Human capital (forward-looking) and physical capital (backward-looking, PIM) as a share of GDP, 2006

3. Preliminary results: levels (3) Sensitivity analysis

3. Preliminary results: distribution Measure: share of human capital of each group divided by share in population (values greater than 1 imply “richer”) By gender

3. Preliminary results: distribution (2) By educational attainment

3. Preliminary results: distribution (3) By age of individuals

3. Preliminary results: volume changes Divisia Index, weighted average of growth rates of each demographic group based on three characteristics (age, gender, education levels): allows measuring both total volume growth and contribution of various factors Total volume growth : Growth rates of human capital, population and human capital per capita, United States

3. Preliminary results: volume changes (2) Sensitivity analysis (discount rate set at 4.58%) Baseline: annual real income growth rate = 1.3% Scenario 2: annual income growth , around 1% (average over 1997-2017, OECD/MTB) Scenario 3: annual income growth rate, around 0% (low-end over 1997-2017, OECD/MTB) Scenario 4: annual income growth rate, around 4% (high-end 1997-2017, OECD/MTB)

3. Preliminary results: volume changes (3) Decomposition of total HC growth. The first order partial index by gender captures changes in population structure between men and women (i.e. doesn’t reflect population shifts among age groups or educational categories within each gender)

3. Preliminary results: volume changes (4) Decomposition of HC per capita growth . The contribution to growth of HC per capita by gender is the difference between the first order index by gender and the growth rate of population; the sum of the contributions of all partial indices (by gender, age, and educational) is a (first order) approximation to the growth rate of HC per capita

4. Developments and long-term challenges Where we stand? WP to be released by year-end, containing some of the results shown above (and more) Second phase of the project in 2011-12 (as part of OECD follow-up to the recommendations of SSF Commission). Foreseen developments: Improving estimates and extending country-coverage Constructing accumulation accounts that will explain changes in human capital in terms of investment, depreciation and revaluations Using HC estimates to construct an ‘educational account’ integrating data on the various inputs entering its production as well as outputs produced Analysing how HC measures might be used to improve analysis of different economic aggregates and accounting approaches: i) role of HC in measuring MFP-growth; ii) measurement of the output of the educational sector; iii) construction of extended household production accounts

Longer-term challenges: How to incorporate quality? Qualitative measures of the cognitive skills of the adult population will become more prominent in the future (PIAAC results available in 2012). They provide direct measure of an important set of skills, informing about both ‘average’ performance and inequality, and allowing to assess how competencies change for a given attainment level. Integrating these test scores into monetary measures of HC will be a challenge. Can we extend monetary measures of HC to non-economic returns? Monetary measures of human capital are based on view that main benefit from investing in education is in the form of higher earnings. This also applies to measures of human capital extended to non-market time (valued at opportunity costs. What these measures exclude are the non-economic benefits from education accruing to the individual and to society at large. 4. Developments and long-term challenges (2)

Two examples of importance of non-economic returns of HC: OECD (2010), Improving Health and Social Cohesion through Education Marginal effects of education on self-reported health 4. Developments and long-term challenges (3)

Marginal effects of education on political interest 4. Developments and long-term challenges (4)

Conclusion Deriving monetary estimates of human capital (education) based on a consistent methodology and assumptions is feasible: they highlight the quantitative importance of human capital, some of the factors that contribute to its accumulation, the importance of considering expenditures on them as investment rather than consumption Monetary estimates are unlikely to fully displace physical ones; some of the critical functions assured by education (e.g. better parents, better citizens, greater tolerance to diversity) are not captured by earnings-premia > physical measures of education (quantity and quality) will remain important in the future

Human Capital and Sustainability: preliminary findings from the OECD Human Capital Project

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