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Working Smarter: Driving Productivity Growth Through Skills - TPRP 08/06

How skills can drive productivity

Skills contribute to productivity in several ways

Skills applied in the workplace can increase productivity by:

  • directly increasing individuals’ productivity, enabling them to achieve more with the resources and technology available;
  • increasing the productivity of others they work with – enabling others to work more effectively with the resources and technology available;
  • enabling firms to adapt more quickly – by integrating new technologies, or adjusting to new markets and new challenges;
  • increasing people’s capacity to innovate – creating, adopting and applying new ideas and technologies.
Productivity comes from the development, supply, matching and utilisation of skills

The key stages in the process through which skills policy can drive productivity are:

  • skills development – the overall “domestic” supply of skills
  • skills supply – the skills made available to the economy
  • skills demand and utilisation – how firms behave in recruiting and deploying skills
  • skills matching – the linkages between these three domains, including processes by which skills are redeployed to activities of higher value through the labour market, and how information on firms’ skills needs feeds back into skill development and supply.
Figure 1: How skills contribute to productivity
Figure 1: How skills contribute to productivity.

Skills policy issues therefore span many areas of public policy, including: education; labour market and industry regulation; migration; tax policy; internationalisation; and social policy.

Some critical aspects of skills development, such as early childhood education and schooling, are long-term drivers where improvements will not impact on overall labour quality in the workforce for decades. Other aspects of skills policy can impact on productivity in the much shorter term – for example by boosting workforce participation of high skilled workers or increasing the efficiency of labour market matching of skills supply and demand.

Other papers in this productivity series connect skills with innovation and enterprise

This paper focuses on the “embodied human capital” or skills of individuals and how this can drive productivity. Other papers cover related issues:

Innovation and Productivity: Using Bright Ideas to Work Smarter looks in more depth at innovation as a driver of productivity growth, including the importance of the “disembodied human capital” of shared knowledge and technology, and the contribution of tertiary education institutions to New Zealand’s innovation system.

Enterprise and Productivity: Harnessing Competitive Forces includes a discussion of the importance of entrepreneurial and management skills.

The evidence that skills matter for productivity growth is stronger than ever

Investment in skills has high individual and social rates of return

There is a large body of microeconomic evidence demonstrating the importance of skills in determining the economic success of individuals, and the value of education in raising skills. The private and social rates of return from education are high.

Differences in the incomes of people with different qualifications and work experience are the simplest indicator of this:

Figure 2: Median Weekly Income by Level of Qualification
Figure 2: Median Weekly Income by Level of Qualification.
 

Internal rates of return from education can be estimated from an individual’s perspective (counting the individual’s costs and foregone earnings during study, and their future income gains) and from a public perspective (incorporating costs of education subsidies, and the net effect on lifetime tax payments). OECD estimates of the average private and public internal rates of return from gaining a qualification in New Zealand are presented in the table below.

Table 1: Public and private internal rates of return to education
When enrolling directly after completing
the previous level qualification
Upper secondary
qualification
University
degree
  Males Females Males Females
Private internal rate of return 14.1 14.9 9.3 12.9
Public internal rate of return 8.3 5.2 9.9 9.9

Source: Education at a Glance 2007: OECD Indicators tables 9.5-9.8

Some of the best evidence of high returns from investment in skills comes from studies that look at how changes in school leaving age laws have affected individuals’ incomes. These studies (for example, Oreopoulos, 2008) are valuable because they are effectively a “natural experiment” that can compare outcomes for similar individuals and groups who left school immediately before and after the introduction of a higher schooling leaving age. An extra year of schooling is found to increase lifetime income by approximately 10%.

Recent evidence shows that skills differences explain much of the difference in countries’ growth rates

Recent macroeconomic analysis is showing more clearly that differences in education and skills explain much of the international differences in long-term growth rates.

Many earlier studies of comparative growth rates suggested that human capital differences account for little if any of the differences in countries’ growth performance over time. These studies have generally used the average number of years spent at school as a proxy measure for each country’s human capital. The modest positive relationship between long-run growth and quantity of education shrinks or disappears once other variables such as fertility rates and proxies for institutional quality are added (for example, Krueger and Lindahl, 2001).

But the problems with using years of schooling as a measure of a country’s level of skills are rather obvious. This approach:

  • does not measure the actual skills of students;
  • ignores differences in the quality of education (a year of schooling is assumed to have the same impact on skills in all countries, despite huge differences between education systems); and
  • assumes, despite all the evidence to the contrary, that attending school is the only thing that contributes to people’s skills.

Recent research is overcoming this problem by measuring differences in countries’ skill levels more directly. By using data on countries’ performance in various international education surveys, it is possible to test whether differences in directly measured abilities (such as reading, numeracy and scientific literacy) are important in predicting countries’ long-term economic growth rates.

Hanushek and Wößman’s (2007) paper “The Role of Education Quality in Economic Growth”found that a much larger proportion of the variance in GDP per capita between countries can be explained by differences in human capital:

  • When educational quality (as measured by test results) is added to a simple model relating per-capita income growth and schooling, the amount of variation in economic growth explained by the model jumps from 0.25 to 0.73 (adjusted R2).
  • The authors estimate that a standard deviation increase in test scores (relative to OECD) is associated with an annual average growth rate in GDP per capita that is two percentage points per year higher over a 40 year period;
  • There is stronger evidence of a causal relationship – that higher skills contribute to higher economic growth, rather than other unobserved factors influencing both skills levels and growth rates or reverse causality (where increases in GDP enable higher expenditure on education not vice versa);
Increases both in advanced and basic skills are related to growth

Improvements in skills at all levels of ability seem to matter. Increases in both the proportion of top achievers and the proportion with at least a minimal level of literacy and numeracy are correlated with higher economic growth rates.

Some research seeks to test the theory that advanced skills will matter most in economies that are near the technological frontier, while the general skill level of the broader workforce will matter more in economies where growth comes more from imitating and adapting technologies rather than from creating new technology.

But distance from the technological frontier varies at a micro-level between sectors, between firms, and even between units within firms. It is not something that can be usefully measured on an economy-wide level. Each firm and sector will have a different optimal mix of the advanced skills needed for innovation, technical skills needed to copy and adapt technologies, and the general workforce skills needed to achieve efficient production.

We can’t choose between developing a small community of elite “top talent” and providing a high quality education for all – we have to do both.

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