Rick Noack did a great job in the Washington Post today to concisely summarise the hypothesis behind the OECD’s claim that inequality holds back growth. In the case of New Zealand 15 ½ percentage points of economic growth was lost due to rising equality since the late 1980s.
According to the OECD, it is all about the ability to lower middle class and working class families to finance the human capital investments of their children. The OECD theory of inequality and lower growth is there is a financing constraint because of inequality that reduces economic growth because of less human capital accumulation by lower income families.
In an age of interest-free student loans or cheap student loans everywhere for several decades now at least, the OECD is nonetheless hanging its head on the notion that not enough has been done to ensure there is enough graduates from the lower middle class and working class families making it to university. Taylor also has the same problem as me with the OECD’s human capital and inequality nexus:
There are a few common patterns in economic growth. All high-income countries have near-universal K-12 public education to build up human capital, along with encouragement of higher education. All high-income countries have economies where most jobs are interrelated with private and public capital investment, thus leading to higher productivity and wages. All high-income economies are relatively open to foreign trade.
In addition, high-growth economies are societies that are willing to allow and even encourage a reasonable amount of disruption to existing patterns of jobs, consumption, and ownership. After all, economic growth means change.
One of the findings of the Coleman report in the 1960s, which is been pretty much backed up since then such as by top labour economists such as James Heckman, is family background is the key to skills development in children, not the quality of their schools or their access to finance for higher education.
Schools work with what families present to them in terms of innate ability, and personality traits such as to pay attention and work. There is not much difference between an average bad public school and an average good public school when it comes to getting on in life. Going to really bad public school is different from just going to an average bad public school in terms of the chaos imposes on a child’s education and upbringing. What matters is the home environment rather than the ability to access good schools and families of ordinary means to finance higher education for their teenagers.
Most of the skill gaps that are present at the age of 18 – skill gaps which substantially explain gaps in adult earnings and employment in all groups – are also present at the age of five (Cunha and Heckman 2007). There is much evidence to show that disadvantaged children have lower levels of soft skills (non-cognitive skills): motivation, persistence, self-discipline, the ability to work with others, the ability to defer gratification and plan ahead, etc. (Heckman 2008). Most of the skills that are acquired at school build on these soft skills that are moulded and reinforced within the family.
In 2002, with Pedro Carneiro, James Heckman showed that lack of access to credit is not a major constraint on the ability of young Americans to attend college. Short-term factors such as the ability to borrow to fund higher education has been found to be seriously wanting as an explanation for who and who does not go on to higher education.
Only a small percentage of young people are in any way constrained from going on to higher education because of the lack of money. This is not surprising in any society with student loans freely available at low or zero rates without any need to post collateral. Heavily subsidised tuition fees and cheap student loans have been around for several generations.
Source: James Heckman.
The biggest problem with the OECD hypothesis linking a lack of skill development within lower income and working class families is it is such an easy problem to solve for the ambitious politician of either the left or the right by throwing money at the problem. Schooling until the age of 16 has been free for a century and universities have been virtually free for at least two generations. Lack of access to a good education does not cut it as the explanation for large disparities in growth rates.
The OECD and more recently the IMF have placed a lot of weight in access to human capital as a driver of inequality because human capital accumulation is hypothesised to be a major driver of economic growth.
The evidence that human capital is a key contributor to higher economic growth is weakening rather than strengthening. If human capital accumulation is not a major driver of productivity growth and productivity disparities, the inequality and growth hypothesis of the OECD and the IMF based on access to finance for human capital accumulation does not get out of the gate. Moreover, as Aghion said:
Economists and others have proposed many channels through which education may affect growth–not merely the private returns to individuals’ greater human capital but also a variety of externalities.
For highly developed countries, the most frequently discussed externality is education investments’ fostering technological innovation, thereby making capital and labour more productive, generating income growth. Despite the enormous interest in the relationship between education and growth, the evidence is fragile at best.
The trend rate of productivity growth did not accelerate over the 20th century despite a massive rise in investments in human capital and R&D because of the rising cost of discovering and adapting new technological knowledge. The number of both R&D workers and highly educated workers increased many-fold over the 20th century in New Zealand and other OECD member countries including the global industrial leaders such as the USA, Japan and major EU member states.
Cross-country differences in total factor productivity are due to differences in the technologies that are actually used by a country and the degree in the efficiency with which these technologies are used. Differences in total factor productivity, rather than differences in the amount of human capital or physical capital per worker explain the majority of cross-country differences in per capita real incomes (Lucas 1990; Caselli 2005; Prescott 1998; Hall and Jones 1999; Jones and Romer 2010).
Differences in the skills of the individual worker or in the total stock of human capital of all workers in a country cannot explain cross national differences in value added per worker at the industry level.
- The USA competes with Japan for productivity leadership in many manufacturing industries.
- The Japanese services sector productivity can be as little as a one-third of that of the USA.
- Japanese labour productivity is almost twice Germany’s in producing automobiles and is better that Germany by a large margin for many other manufactured goods.
- The USA is uniformly more productive in services sector labour productivity. For example, British, French and German telecom workers were 38 to 56 per cent as productive as their American counter-parts.
The USA, Japan, France, the UK and Germany all have relatively well-educated, experienced and tested labour forces. For example, the 1993 McKinsey’s study inquired into the education and skills levels of Japanese and German steel workers. Comparably skilled German steel workers were half as productive as their Japanese counterparts (Prescott and Parente 2000, 2005).
The ability to finance human capital accumulation and go to good schools is a weak theory of inequality. Human capital accumulation itself is a weak theory of growth unless linked to sophisticated theories of the institutions fostering innovation and technology absorption which it now is.
To be fair, I will not point out that this period of rising inequality since 1980s so damned by the OECD and the Twitter Left in the Washington Post today coincided with the return of real wages growth in New Zealand after 20 years of wage stagnation. That would be kicking the Twitter Left when they are down. I was a sneak in a graph instead.
Data source: New Zealand Council of Trade Unions.
I will leave it for your own imagination to think of what happened to female labour force participation, the gender wage gap and female participation in higher education since the late 1980s and the onset of this horrific inequality which was mainly for men.
The failure of the Twitter Left to undertake a gender analysis of any labour force or income statistic they use is a major analytical shortcoming. Hardly any labour force statistics make any sense unless broken down by male and female outcomes.