HUMAN CAPITAL MEASUREMENT: THEORY AND PRACTICE

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In early 2010, the US economy sits in its most precarious position since the Great Depression of the 1930s. At its height in 1933, 25% of all workers and 37% of all non-farm workers were completely out of work. The unemployment rate in the USA is now over 9.5%.1 As Figure 1 indicates, when the US economy spirals into severe recession unemployment cascades up as well.

Figure 1.

GDP and Economic Performance over Time.
Source: US Bureau of Labor Statistics, National Bureau of Economic Research for determining recession and depressions.

Periods of recession coincide with significant rise in unemployment and the two forces feed on each other, taxing economic systems. When consumer spending declines and capital (stock) markets endure significant loss unemployment spirals upward, earned income tax revenue declines, government expenditure on benefits for the unemployed increase, consumer spending declines further and sales tax revenue declines. Housing values decline in a recession which causes property tax revenue to decline which causes a shortfall in municipal tax revenue so city budgets do not balance, so more unemployment occurs, now in the public sector. The US and the world economy in general are still in the throes of such cataclysmic events even while this is written in the summer of 2009.

Europe's economy continues in a similarly dire situation with overall unemployment at 9.5% for the European Union as of May 2009.2 Interestingly Australia unemployment hovers in the 5.8% rate range.3 The crash of the US economy has reverberated throughout the world and adversely impacted virtually every other economic system on the globe. This sad fact is well known and undisputed by economists and social scientists throughout the world. The US Congress sits as this is being written (in the summer of 2009) and debates an almost 1 trillion dollar healthcare package to assist (in part) in bringing the US economy out of crisis. On February 17th of 2009, US President Obama signed into law an $800 billion plus stimulus package. Significant debate in the USA continues as to whether the immense government spending (and attendant increase in debt) has done any good. Britain, Germany and other world powers are undergoing similar investments and debates. Hundreds of millions of these dollars are earmarked for projects to increase employment and increase the human capital (HC) of the world labour force on programmes such as educational loans and subsidies.

Understanding how HC is measured is a vital step in attempting to assess the policy impact of these hundreds of millions of dollars of investments on the HC stock. The purpose of this ‘special topic’ issue of the Journal of Economic Surveys is to provide straightforward explanations on various methods used to estimate and quantify the stock of HC and also discuss the measurement of factors that impact HC. To our knowledge there has not been a robust collection of chapters presented in a parsimonious fashion of the state of the art in this important research area in some time (perhaps not since the early 1990s), although a superb survey of the literature was presented in 2003 by Le et al. Among recent work in this area, Dagum and Slottje (2000) presented a comprehensive model of HC measurement almost a decade ago. Since that time, a series of excellent papers on the rigorous measurement of HC has been written by Le et al. (2003, 2005, 2006) and Oxley et al. (2008) that examine three general approaches to HC measurement – cost based, income based and education based – and present both critical reviews of the theories and applications to data from a range of countries including Austria, Australia, Canada, Germany, USA, Sweden and New Zealand. However, only the results for Australia and New Zealand provide recent monetary measures of HC which are compared to monetary measures of physical capital stocks.

This issue follows on the stellar work of Le et al. and presents the most recent advances in HC measurement (that I am aware of) using modern econometric tools to analyse and understand how the stock of HC is measured and how various policy initiatives may impact the HC of the world's labour force in clear and understandable ways. The first paper by Antonelli et al. (2010) analyses the measurement of HC by relying on what they refer to as the ‘job competition approach’, whereby they provide a labour-demand-oriented measure of HC as defined by the amount of specific skills a firm generates through work-based training (WBT) activities. The authors argue that by merging three firm-level datasets, they are able to estimate the impact of a set of variables that are supposed to affect both the propensity to invest in WBT and the intensity of training within the Italian manufacturing industry over the period 2001–2005. The authors' estimates show that the effects of innovation on WBT are higher when the introduction of new technologies is supported by organizational innovations. When looking at the nature of WBT, the authors also investigate the different determinants of the firms' propensity to provide both in-house and outside training. The authors conclude their interesting research by estimating training intensity in terms, respectively, of the number of training activities provided, private and total training costs and the share of trainees. The second paper by Folloni and Vittadini (2010) is a survey of the literature on how economists, statisticians and other social scientists have measured HC over time. After a short history of the concept of HC in economic thought (Section 1), the authors discuss several methods for estimating the value of the stock of HC – they refer to the methodologies as ‘retrospective and prospective’ methods – and present the models in Section 2 of their paper. In their paper the authors link these methods to both the theory of HC investment as a rational choice (Section 3) and the literature analysing the contribution of HC investment to economic growth (Section 4). In the last part of the paper the authors assess the more recent literature on HC as a latent variable (Section 5) and propose a new method of estimation where HC is seen both as an unknown function of formative indicators and as a ‘latent effect’ underlying earned income (Section 6). Section 7 concludes their fine paper. The next paper by Lovaglio (2010) is on estimation of the latent variable HC at disaggregated level (worker) by available routinely institutional data flows. In particular he relies on Lombardy region administrative archive data on ‘Employment Centers of the Province of Milan’, collecting information about careers of workers in the private sector of the Milan area, and administrative flows data collecting mandatory workers' individual income tax returns, filed with the National Internal Revenue Service. Professor Lovaglio (2010) proposes and empirically estimates HC scores in a static (using 2004 as his benchmark) framework, by means of a realistic measurement model within causal relationships among endogenous and exogenous (investment) HC indicators. He also specifies a set of (concomitant) indicators that, not belonging to HC investment indicators, have causal impact on endogenous variables and on HC scores, too. In addition, he proposes a longitudinal analysis (period 2000–2004) aimed to investigate how workers' earned income growth rates vary over workers' educational levels and other personal characteristics. The empirical results of both analyses confirm that the Italian job market is marked by significant inequality, and by looking at the processes of school to work transitions, uncovers a weak incidence of educational impact on longitudinal trajectories of earned income. Lye and Hirschberg (2010) examine a segment of the literature that investigates the impact of health on HC, and which has (curiously) found a positive impact of alcohol consumption on HC. They find that there may be a number of mitigating factors influencing the findings from these studies. In particular, they review the health literature in order to conduct a meta-analysis of these findings. In this analysis the authors conclude that such factors as the estimation technique, the presence of ex-drinkers in the sample, possible sample selection bias and publication bias may all contribute to these findings. They also examine the suggestion that the positive relationship between alcohol and wages may be due to the presence of a common set of personality traits that determines drinking behaviour and also leads to higher earnings, as well as reviewing the research that has examined how alcohol consumption has been found to influence educational outcomes and the work force participation of problem drinkers. The paper by Millimet et al. (2010) builds on one of the methods of HC measurement discussed in Folloni and Vittadini (2010): the present actuarial value of expected net lifetime earnings approach. Implementation of such a measure requires accurate estimates of worklife expectancy. However, previous calculations of worklife expectancy are either several decades old or are too crude to permit accurate measurement. In this paper, the authors review various methods of worklife expectancy estimation; document the need for updated and more detailed worklife estimates; and present worklife estimates for men and women in the USA categorized by educational attainment, race, marital status, parental status and current labour force status. Race, education, marriage and parenthood are all found to be strongly associated with worklife expectancy, although the magnitudes and even the directions of some of the associations differ by gender, which ultimately means they impact the level of HC embodied in individuals as well. The paper by Tchernis (2010) presents a flexible model of post education HC accumulation. It endogenizes the job mobility decisions and shows that not only the stock of HC but also the composition and timing of HC accumulation have important effects on wage growth. I am pleased to present all these fine papers to interested readers of the Journal of Economic Surveys.

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