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Measuring Across Countries: IQ and the Human Capital Index By Gail Heyne Hafer and R.W. Hafer

EXECUTIVE SUMMARY survey. Over the past several years, researchers have continued to It has been shown that country-level expand the boundaries of previous IQ and aggregated performance by work. A small selection of work school-age children on international done in the past few years—for sake assessment tests in math and science of brevity, we focus on economic are by-in-large capturing analogous fields—includes evaluating IQ’s indicators of the cognitive human ability to empirically explain national capital. We expand that analysis by differences in entrepreneurial comparing country-level IQ to the activity (Hafer & Jones, 2015; Hafer, The Center for Economics and the World Economic Forum’s Human 2017); corruption (Potrafke, 2012); Environment is an economics Capital Index (HCI). This index, financial development (Kodila- research center in the John W. comprised of several dozen separate Tedika & Asongu, 2015; Hafer, Hammond Institute for Free indicators, accounts for inputs and 2017); economic welfare (Hafer, Enterprise. Its focus includes policy- outcomes to measure human capital, 2017); happiness (Stolarski, et al., oriented research on the business and across age profiles and gender. Two 2015; Noklaev & Salahodjaev, economic environment, particularly of outcomes are of note. First, there is 2016); and cognitive capitalism state and local economies. a positive, significant correlation (Coyle, et al, 2016). The gist of the between IQ and the vast majority of evidence is that countries with the component indicators in the higher national average IQ tend to HCI across all age cohorts. Second, be more successful economically, because the HCI’s interpretation of have greater levels of educational attainment extends entrepreneurial activity (in general CEE Policy Series beyond formal education by and among women), less corruption, including indicators such as on-the- and a propensity toward more Number 31 job learning and other work-related democratic institutions. 2018 skills, our finding that IQ is positively correlated with these Especially in economics, attempts to measures suggests a deeper measure human capital have connection between national generally focused on either average IQ and the fundamental educational inputs (e.g., years in factors of what constitutes the school) or outcomes (e.g., test cognitive side of human capital scores). In terms of assessing the development. role of human capital in explaining differences in economic growth, 1. INTRODUCTION Mankiw, Romer and Weil’s (1992) inclusion of the rate of secondary There is a large (and expanding) school enrollment in their empirical body of work investigating the role growth model was an initial step to that IQ plays in helping to explain better understand how human economic and social outcomes. The capital affects economic growth. breadth of this work is well- Their work was followed by many represented by Lynn and similar studies: A good example is Vanhanen’s (2012) exhaustive Sala-i-Martin’s (1997) analysis

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX wherein he tried eight different to the set of explanatory variables. In what follows, we address the measures of education-based human “This dramatic decline in the following questions: capital to explain economic growth statistical significance of primary across countries. Of these he school enrollment,” Jones and • How closely correlated are IQ found that the rate of primary Schneider note, “makes the and the HCI? school enrollment was empirically performance of IQ—statistically the most robust in his exhaustive significant in 99.8% of the same • Given the structure of the HCI, search process. In contrast to using regressions—all the more surprising. is it possible to identify input-oriented measures of Not only is IQ robustly correlated specifically areas with which IQ education, others have argued that with economic growth in this is more closely related than output-based measures—e.g., sample: it is also the most robust others? That is, can we glean average scores on international math human capital measure in this any information from and science tests, such as TIMMS dataset.” (p. 88) correlating IQ with the and PISA—were more appropriate disaggregated parts of the HCI, to capture the cognitive The current paper is in the spirit of across indicators and across age development of labor. Lynn and Meisenberg (2011), who profiles? Representative work in this area show that country-level IQ and include Hanushek and Kimko aggregated performance by school- • How closely is real GDP related (2000), Hanushek and Woessmann age children on international to each of the two series? (2008, 2015), and Hanushek, et al. assessment tests in math and science (2017). Wobmann (2003) offers an are by-in-large capturing analogous The Report also provides additional assessment of how human capital indicators of the cognitive human information on other social often is calculated, though he makes capital. Our purpose here is to indicators—e.g., business no mention of Lynn and further consider the ability of perceptions of education, the Vanhanen’s national IQ measure. national-average IQ to represent a innovation environment in a broad concept of cognitive human country, and the vulnerability of The use of national IQ in an capital. To do this we compare workers in the labor market—that economic growth context was country-level IQ to a new human we also correlate with IQ. These introduced by Weede and Kampf capital construct that includes a “other” measures are not part of the (2002). They found that Lynn and cognitive component of human HCI, but they allow us to further Vanhanen’s national IQ has a large capital development and accounts gauge the ability of IQ to explain and statistically significant effect on for actual labor market outcomes. observed differences in social and economic growth, even after Not only does this measure, the economic conditions across controlling for other input-type World Economic Forum’s Human countries. education measures. It wasn’t until Capital Index (hereafter, HCI), thus Jones and Schneider (2006) that IQ consider inputs and outcomes to The point of our paper is to was tested rigorously against other measure human capital, it also consider as many facets of the social quantitative-based education provides information about these and economic environment that variables which had heretofore been aspects across a wide spectrum of may influence human capital used as proxies for human capital. age profiles and across gender. development and, in the end, Jones and Schneider found that Comprised of several dozen worker productivity. Our analysis, when pitted against a large battery separate indicators, the Forum’s we think, provides a useful of other variables, IQ was one of country-level index is constructed to extension to current understanding the most important variables in “serve as a tool for capturing the of what national IQ captures. In explaining differences in economic complexity of education and the end, our task is not to determine growth across a large sample of workforce dynamics so that various whether IQ or the HCI “beats” the countries. Even though primary stakeholders are able to take better- other in some statistical horserace: school enrollment survived their informed decisions.” (Report, p. 3) we leave that exercise to others. testing procedure and had a positive Thus, it offers a useful benchmark and significant effect on economic against which to compare IQ across The format of the paper is as growth, the number of times this countries. follows. Section II provides a brief variable achieved significance was overview of the two series. Our sharply reduced when IQ was added statistical analysis is found in

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX

Section III where we examine the alternative IQ series that focuses on educational side of human capital link between IQ and the HCI healthy Sub-Saharan populations of development account for such indicators, the “other” indicators normal socio-economic status. This factors as school enrollment, quality provided in the Report, and to real approach yields an average IQ of 80 of education, and workplace GDP. Summary remarks close the for this sample, which is learning. The other half (23) are paper in Section IV. significantly higher than that found labor market indicators, dealing with in the LV data. Lynn and labor market participation, such as 2. DATA Meisenberg (2010b) and Lynn and participation rates, and how well Vanhanen (2012) provide counter educational attainment and 2.A. National IQ arguments, and Jones and Potrafke knowledge—skills—are matched to (2014) note that the sampling bias employment. The national average IQ data are inherent in Wicherts, et al. approach taken from Lynn and Vanhanen could just as easily yield an over- What makes the HCI distinctive is (2012) (hereafter, LV). These data estimate of the average human that on top of this input-output are the most recent version of their capital level. Rindermann’s (2013) coverage it overlays a generational original IQ series (Lynn & recent analysis of African cognitive reflection by reporting the learning Vanhanen; 2002, 2006), and include measurement is worth noting in this and employment themes across adjustments due to Lynn and regard. specific age groups. In their Meisenberg (2010a, b). These data terminology, the “horizontal have been discussed in detail Partly to validate the IQ measure themes” of learning and elsewhere, so ours will be brief. and to put it into the broader employment are deployed across age The IQ data are an aggregation of perspective as a measure of human “pillars.” The Report assembles its existing cognitive testing scores capital, there has been much work index using data for the age groups from around the world, including comparing the Lynn-Vanhanen IQ Less Than 15; 15-24; 25-54; 55-64; journal articles and actual samples of to other indicators of cognitive and More Than 65. The HCI is cognitive scores from individual development, such as standardized thus constructed to account for the countries. These inputs are used to test results (Lynn & Meisenberg, multidimensional nature of how create an IQ “profile” for a country. 2010; Jones & Potrafke, 2014; and human capital is developed and When there are multiple estimates Rindermann, 2007). These results used: the accumulation of skills for a given country, LV use an (and others) suggest that IQ should acquired formally—through average. When there are multiple be considered an indicator of a education—and informally— inputs over time, LV account for nation’s labor quality—its human through workplace learning—that any potential Flynn Effects by capital—in the spirit of Hanushek aggregate into what can be adjusting the raw data for the and Kimko’s (2000) use of considered a quantitative measure of upward trend in nation-level IQ international standardized test human capital. Thus, unlike IQ, the scores. To control for this, LV scores. HCI is a broader measure of human adjust the IQ scores to bring them capital, one that encompasses more into alignment at a point in time. 2.B. The Human Capital Index than just the cognitive development For example, a country’s IQ score in component. 1960 is adjusted to make it The Human Capital Index “equivalent” to the outcome from a (hereafter, HCI) is compiled and Using data from a variety of public similar British test given in 1979. In published annually by the World sources and from survey responses, our sample of 124 countries, the Economic Forum. The first index the World Economic Forum takes mean IQ score is 86 with a standard was published in 2013, though we the raw data series and converts deviation of 11. use the 2015 index and its them into a common metric with a components in this study. The 0 to 100 scale: the higher the value Though the LV data have been used HCI focuses on both outcomes and the closer to the “ideal” for that widely across disciplines, it is not demographics. In terms of indicator. Each age group’s score is without criticism. One important outcomes, the index accounts for an unweighted average of all the issue concerns the accuracy of the both learning and employment indicators in that age pillar. The IQ statistics generated for Sub- inputs to the development of overall index is then constructed by Sahara African countries. Wicherts, human capital. Half of the weighting each age pillar according et al (2009, 2010a, b) create an indicators (23) used to measure the to its percentage share of the global

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX population in 2015. For example, of each age-specific group (rows) in the 2015 weighting scheme is, by As noted earlier, even though the Table 2, we see that the vast age group: Under 15 (26%); 15-24 broad measures are related, it is of majority of the correlations with IQ (16%); 25-54 (41%); 55-64 (9%); interest to understand why. To do are positive and statistically and 65 and over (8%). this we investigate the question significant. Looking specifically at “How IQ is correlated with the the set of variables that comprise 3. STATISTICAL ANALYSIS many indicators that comprise the the “Learning Component” theme HCI?” of the HCI, it is notable that IQ has Comparing the HCI and the IQ a positive and statistically data, we have 124 countries with The correlation between IQ and HCI significantly correlation with all the both in common. The list of Components education-related components, countries included in our sample measures of enrollment and along with their respective values Table 2 provides the evidence to attainment alike. This is consistent for IQ and the HCI is provided in answer that question. There we with previous work, which generally Appendix A. Table 1 provides report the correlations between IQ has found IQ and various summary statistics for the two and each of the 46 individual educational metrics, whether it is series. The mean IQ score is 86 and indicators included in the HCI. raw enrollment or scores on ranges from a low of 60 () to This is useful both to see where IQ standardized exams, are positively a high of 107 (). For the is or is not related to the component correlated. It also is notable that IQ HCI the mean value is 67 out of 100 parts of the HCI, and get a feel for is positively correlated with other possible points, with a range from whether such links are influenced by less formal types of learning 41 () to 86 (). age. To facilitate reading Table 2, measures, those being the quality we adopt the convention of components found in the under-15 Figure 1 provides a quick visual reporting those correlations that are age group and the 15-24 age group. answer to the question posed in the not significant at the five-percent It may not be surprising to see that introduction: How closely are IQ level of significance (or better) in countries with higher IQs also have and the HCI related? The scatter bold and underlined. higher quality educational systems. plot in Figure 1 shows that the two But the evidence in Table 2 shows series are positively related with a Looking across the five age groups that higher IQ countries tend to fair degree of closeness; that is, the (the columns) and the components have, as evidence by the results for scatter of points—each representing a country’s IQ-HCI pair—lie fairly close to the imposed best-fit regression line. The regression is significant (adj-R2 = 0.69), and the estimated slope coefficient (0.831) is not statistically different from one.

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX

the 25-54 age group, better staff The evidence in Table 2 indicates Lynn and Vanhanen (2012) provides training services and greater that the eight correlations that are an overview. Using the HCI data economic complexity. This suggests not statistically significant are we find that the IQ-unemployment that in high-IQ countries it is located exclusively in the rate correlations—again by age recognized (and implemented) that “Employment Component” of the group—tend not to be significant, continuing sources of human capital HCI. This is somewhat surprising and when significant often exhibit improvements are beneficial to given previous findings that IQ and negative signs. human capital development. broad measures of economic activity, such as real GDP, tend to But the evidence also indicates that be positively related: Chapter 4 of higher IQ countries have more

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX

“effective” labor markets. The found: Lynn and Vanhanen (2012), areas such as “skills acquired on the evidence shows that IQ is negatively Chapter 6, provide an overview. job” or “staff training services” are related to the underemployment rate related to increasing human capital across age groups. What might be surprising is finding and worker productivity, as Underemployment is based on the that IQ and the employment gender suggested by Heckman (2000), then number of people in involuntary gap are negatively and significantly the positive correlation between part-time employment; that is, related in our sample. This means them and IQ further substantiates individuals who cannot find full- that, on average, in high IQ the finding why IQ helps predict time employment, because of some countries the ratio of female to male economic growth across countries. mismatch in skills or unavailability employment (see the appendix for a of full-time opportunities. The more detailed description) is lower The correlation between IQ and negative correlation with IQ than in relatively low-IQ countries. “other” indicators suggests that countries with higher Wouldn’t one expect that in average IQ have labor markets that countries with higher average IQ To further assess the value of IQ as are able to more effectively match that women would not face a broad gauge of human capital workers with full-time jobs. In workplace disadvantages that lead to development, it is informative to addition, for the 15-24 age group, a larger gender gap? It turns out make use of the “other” indicators higher levels of IQ are associated— that this result is consistent with the that are found in World Economic again, negatively—with the measure findings of Lynn and Vanhanen Forum’s Report. These indicators of “not in employment.” For those (2012, pp. 150-152) where they are not part of the HCI, but they countries with a higher average IQ correlated IQ with the Gender offer an additional glimpse into why we would expect to find that those Inequality Index (GII), which a high-IQ country may exhibit in the 15-24 age group tend to be gauges the relative disadvantages relatively better social and economic “employed,” either it is in work- that women face in human outcomes. One such set of related fields or in education. This development. As they note, finding indicators is a collection of again suggests better functioning of no correlation between GII and responses to a survey of businesses labor markets in higher IQ IQ would imply that the people about their country’s countries. Finally, for the two disadvantages women might face in educational system, training and groups encompassing the ages 15- human development are not based talent. Though the descriptions in 54, the results indicate that higher on differences in national average Table 3 are fairly self-explanatory, IQ countries are more likely to have IQ. So, our negative correlation more compete descriptions and a lower incidence of under- suggesting that, on average, there is sources of these business education (this no doubt reflects the less parity—fewer women employed perceptions data are found in positive relationship between IQ relative to men—in high average IQ Appendix C. The correlations and the various measures of countries, is not without precedent. between IQ and these different education in the upper portion of assessments of the business- Table 2) but also economies that The results in Table 2 identified the educational environment reported in require a more skilled workforce areas most closely related to IQ: Table 3 again indicate that those in (see high-skilled and medium skilled The indicators included under the business tend to have rate the employment shares). This would Learning Component heading. This quality of education, and specifically suggest that higher levels of IQ are result provides more evidence of the of business schools, higher in those associated with higher levels of strength of the IQ-education link. countries with higher levels of IQ. worker productivity, exactly what a If educational attainment—not just This may be a post hoc ergo propter human capital measure should enrollment or years of schooling—is hoc result, but it builds upon show. an important ingredient to previous findings that suggests a improving human capital which in causal link from higher average IQ Finally, the results in Table 2 show turn raises worker productivity, then to a country’s educational outcomes. IQ and a crude measure of overall country-level IQ is a feasible In high-IQ countries businesspeople health—life expectancy at birth— measure to capture this connection. generally believe that their economy are positively related for the 55 and The results in Table 2 show that IQ is characterized by better specialized above age groups. This is what a is related to several important training services which should number of previous studies have education-related areas found in the enhance worker productivity. And HCI’s Employment Component. If there is a positive relation between

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX

IQ in one country could, all else the same, create the positive externality of improving worker productivity, which leads to greater economic success, which attracts the more productive workers from other countries.

In Table 4 we report another set of correlations using more non-HCI indicators found in the Report. These are Innovation Ecosystem, Worker Vulnerability, and Public Investment. Descriptions and sources of these indicators are found in Appendix C. The first set of correlations under the heading of Innovation Ecosystem—state of cluster development and university- business R&D collaboration—are positively and significantly related. The positive correlation found between IQ and the perceptions of university-business R&D collaboration is similar in sign to that reported by Lynn and Vanhanen (2012) where they correlated national IQ with the number of researchers in R&D per one million of population. Although there are obviously other factors at work, they argue that national IQ is a dominant factor. Their finding, and ours, reflects the underlying relation between IQ and countries’ educational success: High-IQ countries are more likely to have greater technological innovation and use of university- generated research outcomes. In contrast, we find that it is harder to start a business, on average, in higher-IQ countries. This result, which seems counter-intuitive, is not without precedent, however. Hafer and Jones (2015) investigated the role that IQ plays in explaining IQ and business perceptions of country, they suggest that higher differences in entrepreneurial business’ ability to attract and retain average IQ countries, presumably activity across countries. One talented employees. Since these with relatively more successful measure of entrepreneurship that latter two responses are couched in economies, are more likely to attract they used is a series on new terms of being able to attract and/or and retain more talented individuals. incorporations, part of the World retain talent from outside their Thus, undertaking policies to raise Bank’s Entrepreneurship Survey.

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX

They found “new incorporations” and Internet access. Given the higher regard for the educational and IQ are positively related. But findings that IQ helps explain faster systems, technological innovation once a broad set of controls are economic growth, it may be possible occurs at a higher level, and worker added to the regression, this that IQ precedes income growth vulnerability is lower. All these relationship is reduced to statistical which then leads to greater Internet conditions arguably contribute to insignificance. access. improving worker productivity. Correlations between IQ, HCI, and The second batch of correlations in Finally, the correlation between IQ Real GDP Table 4 deals with worker and public spending on education is vulnerability. In effect, these three positive, though statistically We thus far have demonstrated that indicators are an attempt to gauge insignificant. Wouldn’t one expect IQ and the HCI are closely related: the degree to which workers in a high-IQ countries to spend more of the correlation between the two country are subject to discontinuous their aggregate income on education overall series is strong, and a closer employment, which leads to relative to lower-than-average IQ examination indicates a high degree increased income uncertainty. For countries? One possible answer is of correlation between IQ and the the first two indicators that relate the finding that the level of public vast majority of the individual directly to worker vulnerability, spending on education often is not a indicators, across age groups, that countries with higher average IQ good predictor of educational comprise the HCI. As a final note, exhibit lower levels of worker success. That is, spending more on we address the question: What is vulnerability: The higher is IQ, the each pupil (in absolute or relative the relation between our two human less likely workers will find terms) does not ensure that capital measures and total economic themselves in informal and/or educational attainment is greatly output? If IQ and the HCI are discontinuous employment improved after some basic level of human capital measures, then we situations. And it is more likely that learning. Thus, this result could should expect to find that each workers living in a relatively high-IQ signify that higher IQ countries, exhibits a strong, positive country also live in a country with a while not skimping on education, correlation with output: higher social safety net—e.g., recognize the diminishing marginal levels of human capital lead to unemployment insurance—that returns to more dollars spent greater productivity and higher reduces the economic burden of relative to greater demand for levels of output. unemployment and the income educational excellence for public insecurity that accompanies it. The dollars spent. We use each country’s output using results in Table 4 corroborate the real GDP per capita, in constant US belief that higher IQ countries are To summarize the evidence to this (2011) dollars measured on a PPP more likely to be characterized by point, higher IQ countries also have basis. We use this measure for sake working conditions that enhance a higher level of human capital as of consistency: It is what is worker productivity. measured by the HCI. The main reported in the Report (2015) where reason for this, as our disaggregated our other data come from. For our The last section of Table 4 considers look at the HCI suggests, is that IQ sample of countries, the mean value the link between IQ and public and the various education-related of output is $20, 965, with a large investment in two areas related to indicators used to comprise the HCI standard deviation of $21,565. The education. One is the availability of are highly correlated. A new finding standard deviation indicates the Internet access in schools. The is that this correlation occurs across wide dispersion in the data: From a positive correlation between IQ and the age spectrum of the population. low of $781 for Malawi to a high of Internet access reflects the fact that We also find that high-IQ countries $144,427 for . Given this there is probably a greater are more likely to have more dispersion, the median value of appreciation of the need for access informal, on-the-job types of $14,350 probably is more to modern information systems in training and skill development representative of the average. countries with higher levels of IQ. opportunities for workers. And Of course, this may just be proxying looking at the non-HCI indicators We note at the outset that the for the fact that higher IQ countries available in the Report, higher results from this analysis should be also tend to be more successful average IQ countries have viewed with caution. This is economically; that is, this correlation environments in which business because both the HCI and the real reflects a relation between income uses technology more, there is a GDP per capita (hereafter, RGDP)

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX data are based on current observations: The HCI uses data around 2013-2015 and the RGDP data are for 2011. This means that both are products of an historical economic growth process that gets each country to its current state. Indeed, we would be surprised if RGDP and HCI are not highly correlated. The IQ data, on the other hand, are standardized to the early 1980s, so its relation to current RGDP may reflect more of a causal relationship.

With those caveats in mind, how is RGDP related to IQ and HCI for our sample of 124 countries? Figure 2 provides the answer. The upper panel is a scatter plot using IQ and RGDP. There is a general positive correlation, though the deviations from the superimposed regression line suggest that the relationship is not nearly as tight as that found earlier comparing IQ and the HCI. The estimated value of the slope coefficient is 1,086 (t = 7.02) which suggests that a one-unit increase in IQ is associated with a $1,086 increase in RGDP. The dispersion of points around the regression line—indicated by the adj-R2 of .28—arises because of countries with relatively a low national IQ and a high level of RGDP. For example, Qatar has a below-average IQ score of 80, but the highest RGDP ($144,427). The effect of similar to that using IQ. Once again These results do not reveal which is this one country is notable in the there is a positive relationship the preferred measure of human underlying statistics: The between the two series, and once capital. They do suggest that correlation between IQ and RGDP again there is a relative loose fit. In nation-level IQ and the HCI are is 0.537 when the sample includes this regression the slope coefficient both statistically viable constructs of Qatar, but 0.662 when Qatar is on HCI is 1,215 (t = 8.29). The human capital. The more rigorous excluded. Still, in general the estimated adj-R2 of 0.36 indicates test of how well each does in correlation between IQ and RGDP that the relationship between explaining economic growth is positive and significant, a finding RGDP and the HCI is slightly patterns across countries is, that holds with much previous tighter than that using IQ. And unfortunately, impossible: The fact works (see Lynn & Vanhanen again this statistical fit improves that the HCI data are current (2012), Chapter 4). once Qatar is omitted (adj-R2 = observations means that we are 0.47). unable to exogenize the data when The lower panel of Figure 2 plots trying to explain economic growth RGDP and the HCI. The scatter is over the past. While the HCI

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX provides a useful metric to gauge (“population”), civilian labor force (“labor the current distribution of human The bottom line is that our results force”), and civilian labor force employment (“employment”) are from the capital across countries—a measure corroborate and extend earlier work Bureau of Labor Statistics document of where countries stand in relative in which IQ was tested against a “States and selected areas: Employment terms—it is less useful in analyzing variety of education-based measures status of the civilian noninstitutionalized economic growth patterns, of the cognitive component of population, 1976 – 2016 annual averages,” something for which the IQ series human capital development. As a downloaded January 16 2018. Data for the USA is from the St. Louis Federal Reserve’s has proven able. measure of human capital, the Federal Reserve Economic Data (FRED) evidence once again points to the database, and is the civilian 4. SUMMARY positive returns to country policies noninstitutionalized population (series aimed at raising the average IQ of CNP16OV), the civilian labor force We find that it is highly correlated their citizens. While this is difficult (CLF16OV), and civilian employment level with the Human Capital Index, a in already high IQ countries, it does (CE16OV), downloaded January 16 2018. measure that was developed by the suggest that countries with lower- 3 Here we refer to data from 2015, because World Economic Forum in recent than-average IQ could improve the we will be discussing growth of RGDP along with growth of the labor force and years explicitly for the specific well-being of their citizens by the capital stock in the various states, and purpose of providing an overall engaging in policies that improve our capital stock series is only available up representation of human capital the quality and quantity of to 2015. development across countries. Not education, reduce poverty, and 4 The NCES Comparable Index only are IQ and the HCI positively improve overall health conditions, describes the prevailing wage for college- correlated, by disaggregating the among others, also will improve the educated workers who are not educators HCI into its component parts, we economic welfare of their for the period 1997 to 2005. One of the also find that IQ is mostly highly populations in years to come. authors (Lori Taylor) has extended that correlated with the educational series through 2015 here: indicators of the HCI, which should Acknowledgements 5 The capital stock data is supplied by not be too surprising. Two Steven Yamarik based on methods he developed in two paper. These are outcomes of this analysis are of We would like to thank Garett “Regional Convergence: Evidence from a note. First, the positive, significant Jones, Oasis Kodila-Tedika, Richard New State-by-State Capital Stock Series” correlation between IQ and the Lynn, Heiner Rindermann for (with Gasper Garofalo), The Review of educational indicators holds across comments and suggestions on an Economics and Statistics 84 (May 2002): all the age cohorts used to create the earlier draft, and Carlos Cabral for 316-323, and “State-Level Capital and Investment: Updates and Implications,” HCI. Second, the HCI’s his research assistance. We remain Contemporary Economic Policy (January interpretation of educational responsible for all errors and 2013): 62-72. Professor Yamarik updated attainment extends beyond formal omissions. and extended his capital stock series to education, including such indicators 2015 and provided it to one of us (Dennis as on-the-job learning and other Gail Heyne Hafer is a Professor of Jansen). A copy of that data is available work-related skills. Our results Economics at St. Louis Community upon request. show that such “educational” College–Meramec. 6 Author calculations based on state-level factors are positively related to RGDP, employment, and capital. The assumed capital share of output is .38 and national IQ. This suggests an even R.W. Hafer is Director, Center for the labor share .62. deeper connection between national Economics and the Environment, average IQ and the fundamental Hammond Institute for Free Enterprise, 7 Data from the American Community Survey (ACS) Public Use Microdata Sample components of what constitutes the and Professor of Economics, Lindenwood (PUMS) File. cognitive side of human capital University. development. Finally, we also 8 Data from the American Community Survey (ACS) Public Use Microdata Sample found that each series has a strong, NOTES (PUMS) File. positive relation with real GDP per capita. Further analysis is needed to 1 Data on US and state RGDP come from 9 We define STEM degrees, as consisting of the Bureau of Economic Analysis (the the natural sciences, computer sciences, and determine if there is independent engineering degrees. information in the HCI and IQ BEA), “Real GDP by state (millions of chained 2009 dollars), downloaded from 10 Data from the American Community series that could better explain the BEA website January 16 2018. differences in output across Survey (ACS) Public Use Microdata Sample 2 Data on state-level civilian (PUMS) File. Some numbers differ slightly countries. noninstitutionalized population

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX from those reported by the Bureau of moderates the effects of intellectual Jones, G., & Schneider, W.J. 2006. Labor Statistics. and average classes on economic Intelligence, human capital and 11 For example, see Luc Anselin, Attila productivity. Psychological Reports, economic growth: A Bayesian Varga, and Zoltan Acs. “Local geographic 119, 411-427. averaging of classical estimates spillovers between university research and (BACE) approach. Journal of high technology innovations.” Journal of Hafer, R.W. 2016. Cross-country Urban Economics, 42(3) (1997). 422-448. evidence on the link between IQ Economic Growth, 11, 71-93. 12 Bruce A. Weinberg, Jason Owen-Smith, and financial development. Jones, G., & Schneider, W.J. 2010, Rebecca F. Rosen, Lou Schwartz, Barbara Intelligence, 55, 7-13. IQ in the production function: McFadden Allen, Roy E. Weiss and Julia Evidence from immigrant earnings. Lane. “Science Funding and Short-Term Hafer, R.W. 2017a. Female Economic Inquiry, 48, 743-755. Economic Activity.” Science, 344(6179) entrepreneurship and IQ. In The (2014). 41-43. Nikolas Zolas, Nathan Wiley Handbook of Entrepreneurship, Jones, G., & Potrafke, N. 2014. Goldschlag, Ron Jarmin, Paula Stephan, Ahmetoglu, G., Chamorro- Human Capital and National Jason Owen-Smith, Rebecca F. Rosen, Barbara McFadden Allen, Burce A. Premuzic, T., Klinger, B., Karcisky, Institutional Quality: Are TIMSS, Weinberg, Julia I. Lane. “Wrapping it up in T., eds. Chichester: John Wiley & PISA, and national average IQ a person: Examining employment and Sons. robust predictors? Intelligence, 46, earnings outcomes for Ph.D. recipients. 148-155. Science, 350(6266) (2015). 1367-1371. Hafer, R.W. 2017b. New estimates Nathan Goldschlag, Sefano Bianchini, Julia on the relationship between IQ, Kodila-Tedika, O., & Asongu, S. A. Lane, Joseba Sanmartin Sola, Bruce economic growth and welfare. 2015. The effect of intelligence on Weinberg,. “Research Funding and Intelligence, 61, 92-101. financial development: Regional Economies.” NBER Working Paper Series, Working Paper 23018 (2017). Hafer, R.W., & Jones, G. 2015. Are A cross-country comparison. 1-25. entrepreneurship and cognitive skills Intelligence, 51, 1–9. 13 Downloaded January 18, 2018 here. related? Some international Lynn, R., & Meisenberg, G. 2010a. 14 Shawn Kantor and Alexander Whalley. evidence. Small Business Economics, National IQs calculated and “Knowledge Spillovers From Research 44, 283-298. validated for 108 nations. Intelligence, Universities: Evidence From Endowment Value Shocks.” Review of Economics and Hanushek, E.A., & Kimko, D. 2000. 38, 353-360. Statistics, 96(1) (2014), 171-188. Schooling, labor force quality, and the growth of nations, American Lynn, R., & Meisenberg, G. 2010b. 15 Data on appropriations come from the The average IQ of sub-Saharan Digest of Education Statistics, 2016, US Economic Review, 90, 1184–1208. Africans: Comments on Wicherts, Department of Education. Data on the Hanushek, E.A., & Woessmann, L. Dolan, and van der Maas. CPI-U come from the US Bureau of Labor Statistics. 2008. The role of cognitive skills in Intelligence, 38, 21-29. economic development, Journal of 16 Joseph H. Haslag and Michael Austin. Economic Literature, 46, 607-668. Lynn, R., & Vanhanen, T. 2002. IQ “Was Missouri Always Like This? A and the Wealth of Nations. Westport, comparison of Missouri’s Growth with that Hanushek, E. A., & Woessmann, L. Connecticut: Praeger. of The .” Show-Me Institute (2015). The knowledge capital of nations. Essay, (2017). 1-13. Education Lynn, R., & Vanhanen, T. 2006. IQ 17 See the starting salary at step 1 of the and Global Inequality. Athens: base tier in the following. For Unified and the economics of growth. Cambridge, Washington Summit. School District 500 (Kansas City, Kansas MA: MIT Press. Public Schools), see here, (page 22). For Lynn, R., & Vanhanen, T. 2012. the Kansas City School District in Kansas Hanushek, E.A., Ruhose, J., & Intelligence: A Unifying Construct for the City, MO, see here. For St Louis Public Woessmann, L. 2017. Knowledge Social Sciences. London: Ulster Schools in St. Louis, MO see here. For the capital and aggregate income Institute for Social Research. East St. Louis School District in East St. differences: Development Louis, IL see here, (page 6) Mankiw, N.G., Romer, D., & Weil, accounting for US states. American Economic Journal: Macroeconomics, 9, D. 1992. A contribution to the REFERENCES 184-224. empirics of economic growth. Quarterly Journal of Economics, 152, Coyle, T. R., Rindermann, H., & Heckman, James J. 2000. Policies to 407-437. Hancock, D. 2016. Cognitive foster human capital. Research in Meisenberg, G., & Lynn, R. 2011. capitalism: Economic freedom Economics, 54, 3-56. Intelligence: A measure of human

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MEASURING HUMAN CAPITAL ACROSS COUNTRIES: IQ AND THE HUMAN CAPITAL INDEX capital in nations. The Journal of properties, and the Flynn Effect. Social, Political and Economic Studies, Learning and Individual Differences, 20, 36, 421-454. 135-151. Meisenberg, G., & Lynn, R. 2013. Wicherts, J.M., Dolan, C.V., Cognitive human capital and Carlson, J.S., & van der Maas, H.L.J. economic growth: 2010a. A systematic literature review Defining the causal paths. The of the average IQ of sub-Saharan Journal of Social, Political and Economic Africans. Intelligence, 38, 1-20. Studies, 38, 16-54. Wicherts, J.M., Dolan, C.V., Carlson, J.S., & van der Maas, H.L.J. Nikolaev, B., & Salahodjaev, R. 2010b. Another failure to replicate 2016. The role of intelligence in the Lynn’s estimate of the average IQ of distribution of national happiness. sub-Saharan Africans. Learning and Intelligence, 56, 38-45. Individual Differences, 20, 155-157. Potrafke, N. 2012. Intelligence and Wobmann, L. 2003. Specifying corruption. Economics Letters, 114, human capital. Journal of Economic 109-112. Surveys, 17, 239- Rindermann, H. 2007. The g-factor of international cognitive ability World Economic Forum. 2015. The comparisons: The homogeneity of Human Capital Report: 2015. results with PISA, TIMSS, PIRLS and IQ-tests across nations. European Journal of Personality, 21, 667-706. Rindermann, H. 2013. African cognitive ability: Research, results, divergences and recommendations. Personality and Individual Differences, 55, 229-233.

Sala-i-Martin, X. 1997. I just ran two million regressions. American Economic Review, 87, 178–183. Stolarski, M., Jasielska, D., & Zajenkowski, M. 2015. Are all smart nations happier? Country aggregate IQ predicts happiness, but the relationship is moderated by individualism–collectivism. Intelligence, 50, 153-158. Weede, E., & Kampf, S. 2002. The impact of intelligence and institutional improvements on economic growth. Kyklos, 55, 361- 380. Wicherts, J.M., Dolan, C.V., Carlson, J.S., & van der Maas, H.L.J. 2009. Raven’s test performance of sub-Saharan Africans: Average performance, psychometric

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APPENDIX A 94.6 77.03 71.6 60.5 96.1 75.44 96.6 79.3 Country IQ1 HCI2 71 65.95 79 68.19 82 67.2 104.2 82.74 98.6 82.73 84.2 52.14 86.7 65.59 100.2 83.58 92.8 71.01 85 74.56 80 67.24 93.2 72.5 74.5 57.54 73 56.56 99.2 80.22 Korea, Rep. 104.6 76.84 89.9 68.78 99 81.02 85.6 59.31 Trinidad/Tobago 86.4 67.1 84.9 67.58 Kyrgyz Rep. 74.8 71.82 85.4 58.21 81 57.62 Lao 89 56.16 89.4 67.09 Barbados 80 65.09 95.9 78.39 71.7 57.34 99.3 81.12 66.5 54.74 94.3 76.21 78 61.11 94.3 79.33 United Arab Bolivia 87 66.46 95 78.79 Emirates 87.1 69.39 76.9 60.81 Macedonia 90.5 69.31 99.1 79.07 85.6 64.6 82 56.25 United States 97.5 79.64 93.3 72.81 Malawi 60.1 53.49 90.6 71.18 70 49.22 91.7 70.24 Venezuela 83.5 60.51 72 46.76 69.5 48.51 94 68.48 92 58.55 95.3 75.77 Yemen 80.5 40.72 64 60.75 74 42.29 74 62.5 100.4 82.88 88 66.66 ______Chad 66 41.1 87.8 68.5 89.8 71.8 92 66.81 Sources: 105.8 67.47 100 70.75 IQ: Lynn and Vanhanen (2012) 83.1 67.63 82.4 59.04 HCI: World Economic Forum (2015) 86 69.75 69.5 54.04 Côte d'Ivoire 71 49.02 85 52.97 97.8 75.37 70.4 59.09 91.8 77.33 78 55.77 Czech Rep 98.9 77.6 100.4 82.3 97.2 82.47 98.9 81.84 Dominican Rep. 82 62.79 84 60.65 82.7 61.38 71.2 47.43 78 66.89 97.2 83.84 99.7 79.88 84 52.63 68.5 50.25 80 71.01 Finland 100.9 85.78 84 65.68 98.1 80.15 84.2 68.13 98.8 78.55 86.1 71.24 69.7 62.63 96.1 77.06 93.2 73.7 94.4 74.5 79 61.34 Qatar 80.1 69.04 66.5 48.25 91 73.94 81 64.17 96.6 77.54 81 58.93 76 54.17 98.1 75.82 79.6 61.38 98.6 78.86 70.5 53.04 82.2 57.62 90.3 70.97 85.8 66.99 Singapore 107.1 78.15 85.6 63.2 Slovak Rep 98 75.48 Ireland 94.9 80.59 98 79.95

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APPENDIX B

This appendix provides a description of the HCI components, by age group. More detailed descriptions can be found in the Report, pp. 52 - 56, from which this is taken. Descriptions of sources are provided at the end of this table.

AGE GROUP/ ORIGINAL MEASURE DESCRIPTION OF MEASURE SOURCE

UNDER 15 AGE GROUP Enrolment in Education Primary enrollment rate Percentage of children in official UNESCO, primary school age range enrolled in 2013 primary or secondary.

Secondary enrollment rate Percentage of children in official UNESCO, age range enrolled in secondary 2013 education.

Basic education survival rate Percentage of students enrolled in UNESCO, lower secondary education in given 2012 school year expected to reach last grade lower secondary education.

Secondary enrollment gender gap Female-to-male ratio of enrollment rate UNESCO, in lower secondary education. Value of 2012 100 indicates gender parity; less than 100 indicates disparity toward males.

Quality of Education Quality of primary schools Response to “How would you assess the EOS, quality of primary schools in your country?” 2014-2015 1 = poor; 7 = excellent.

Vulnerability Incidence of child labor Percentage of children 5-14 years in child UNICEF, labor, including unpaid household services. latest data

15-24 AGE GROUP Enrollment in Education Tertiary enrollment rate Total enrollment in tertiary as percent of UNESCO, total population of age group that has left 2013 secondary school.

Vocational enrollment rate Tech/vocational enrollment as a percentage UNESCO, of total enrollment in secondary school. 2013

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Quality of Education Quality of educational system Survey response to “How well does the UNESCO, educational system in your country meet the 2012 or latest needs of a competitive economy?” (1 = not well; 7 = very well)

Youth literacy rate Percentage of those 15-24 who can read and UNESCO, write a short statement of their everyday life; 2015 or latest also includes ability to make simple math calculations.

Educational Attainment Primary educational attainment rate Percentage of the population with at least a Lutz; primary education, both sexes age 15-24. Barro-Lee Data is cumulative: those with secondary education and above are counted in the primary education figures.

Secondary education attainment rate Percentage of the population with at least a Lutz; secondary education, both sexes age 15-24, Barro-Lee both sexes. Data is cumulative: those with secondary education and above are counted in the primary education figures.

Economic Participation Labor force participation rate Percentage of population engaged in working ILOSTAT, or looking for work, both sexes aged 15-24. 2014 or latest

Unemployment rate Number of unemployed as a percentage of ILOPSTAT, the total number of labor force, both sexes 2014 or latest age 15-24.

Underemployment rate People in involuntary part-time employment ILOSTAT, as a percentage of the total number in 2013 or latest employment, both sexes age 15-24.

Not in employment, education or training Proportion of people age 15-24 not in ILOSTAT, employment; not in education or training. 2013 or latest

Long-term unemployment rate Number of people 15-24 unemployed ILOSTAT, for more than 12 months as a percentage 2013 or latest of total unemployed.

Skills Incidence of overeducation Mismatch between the qualification ILO, 2011 requirements of jobs held by workers or latest and the qualifications these workers possess.

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Incidence of undereducation Mismatch between the qualification ILO, 2011 requirements of jobs held by workers or latest and the qualifications these workers possess.

Skill diversity A Herfindahl-Hirschman Index (HHI) Report, of concentration of graduates among 2015 nine broad fields of study.

25-54 AGE GROUP Educational Attainment Primary education attainment rate Percentage of population with at least Lutz; primary education, both sexes age 25-54. Barro-Lee Data is cumulative: those with secondary education and above are counted in the primary education figures.

Secondary education attainment rate Percentage of the population with at least Lutz; secondary education, both sexes age 25-54. Barro-Lee Data is cumulative: those with tertiary education counted in the secondary education figures.

Tertiary education enrollment rate Percentage of the population with at least Lutz; tertiary education, both sexes age 25-54. Barro-Lee

Workplace Learning Staff training services Response to the question, “To what extent EOS, do companies in your country invest in 2014-2015 training and employee development? (1 = hardly at all, 7 = to a great extent)”

Economic complexity From the Atlas of Economic Complexity. Hausman, Attempts to measure the amount of country et al, 2012 productive knowledge and skills, as embodied in the sophistication of its exports.

Economic Participation Labor force participation rate Percentage of population actively engaged ILOSTAT, in either by working or looking for work, 2014 or latest both sexes age 25-54.

Unemployment rate Number of unemployed as a percentage of ILOPSTAT, the total number of labor force, both sexes 2014 or latest age 25-54.

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Underemployment rate People in involuntary part-time employment ILOSTAT, as a percentage of the total number in 2013 or latest employment, both sexes age 25-54.

Employment gender gap Ratio of female employment-to-population ILOSTAT, ratio over male value, people age 25-54, 2013 or latest expressed as a percentage. Value of 100 indicates gender parity.

Skills High-skilled employment share Number of all persons employed in ILOSTAT, occupations with tertiary education 2014 or latest requirements as a percentage of the total number of employed people.

Medium-skilled employment share Number of all persons employed in ILOSTAT, occupations with at least secondary 2014 or latest education requirements as a percentage of the total number of employed persons.

Ease of finding skilled employees Response to “In your country, how easy EOS, is it for companies to find employees with 2014-2015 the required skills for their business needs? (1 = extremely difficult, 7 = extremely easy)

55-64 AGE GROUP Educational attainment Primary education attainment rate Percentage of population with at least Lutz; primary education, both sexes age 55-64. Barro-Lee Data is cumulative: those with secondary education and above are counted in the primary education figures.

Secondary education attainment rate Percentage of the population with at least Lutz; secondary education, both sexes age 55-64. Barro-Lee Data is cumulative: those with tertiary education counted in the secondary education figures.

Tertiary education enrollment rate Percentage of the population with at least Lutz; tertiary education, both sexes age 55-64. Barro-Lee

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Economic Participation Labor force participation rate Percentage of population actively engaged ILOSTAT, in either by working or looking for work, 2014 or latest both sexes age 55-64.

Unemployment rate Number of unemployed as a percentage of ILOPSTAT, the total number of labor force, both sexes 2014 or latest age 55-64.

Underemployment rate People in involuntary part-time employment ILOSTAT, as a percentage of the total number in 2013 or latest employment, both sexes age55-64.

Health life expectancy Health-adjusted life expectancy developed WHO, by the World Health Organization; 2013 attempts to capture a more complete estimate of health than standard life expectancy rates. Capped at 65.

65 AND OVER AGE GROUP Educational attainment Primary education attainment rate Percentage of population with at least Lutz; primary education, both sexes age 65 Barro-Lee and over. Data is cumulative: those with secondary education and above are counted in the primary education figures.

Secondary education attainment rate Percentage of the population with at least Lutz; secondary education, both sexes age 65 Barro-Lee and over. Data is cumulative: those with tertiary education counted in the secondary education figures.

Tertiary education enrollment rate Percentage of the population with at least Lutz; tertiary education, both sexes age 65 and Barro-Lee over.

Economic Participation Labor force participation rate Percentage of population actively engaged ILOSTAT, in either by working or looking for work, 2014 or latest both sexes age 65 and over.

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Unemployment rate Number of unemployed as a percentage of ILOPSTAT, the total number of labor force, both sexes 2014 or latest age 55-64.

Underemployment rate People in involuntary part-time employment ILOSTAT, as a percentage of the total number in 2014 or latest employment, both sexes age 65 and over.

Health life expectancy beyond 65 The number of years by which a country’s WHO, health-adjusted life expectancy exceeds 2013 value of 65 years, if any.

Original Sources: Barro –Lee: Barro, R. and J.W. Lee, “A New Dataset of Educational Attainment in the World, 1950–2010”, NBER Working Paper 15902, The National Bureau of Economic Research, 2013, http://www.nber.org/papers/w15902. EOS: World Economic Forum, Executive Opinion Survey, 2014-2015. Hausmann: Hausmann, R., C. Hidalgo, et al., “The Atlas of Economic Complexity”, Centre for Economic Development at Harvard University, http://atlas.cid.harvard.edu. ILOSTAT: International Labour Organization, Annual Indicators, various years. Lutz: Lutz, W. et al. “Validation of the Wittgenstein Centre Back-projections for Populations by Age, Sex, and Six Levels of Education from 2010 to 1970”, IIASA Interim Report IR-15-008, International Institute for Applied Systems Analysis, April 2015. http://www.iiasa.ac.at/publication/more_IR-15-008.php. Report: World Economic Forum, The Human Capital Report, 2015. UNESCO: United Nations Educational, Scientific and Cultural Organization, Institute for Statistics. UNICEF: Statistics by Topic, Child Protection WHO: World Health Organization, Global Health Observatory, World Health Statistics.

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APPENDIX C

This appendix provides a description of the additional indicators as found in the Report, pp. 56-57, from which this is taken. Descriptions of sources are provided at the end of this table.

MEASURE DESCRIPTION OF MEASURE SOURCE

Business perceptions Quality of math/science education Response to question, “How would you EOS, assess the quality of math and science 2014-2015 education in your country’s schools? (1 = poor; 7 = excellent, among the best in the world)

Quality of business schools Response to question, “How would you EOS, assess the quality of management or 2014-2015 business schools in your country? (1 = poor; 7 = excellent, among the best in the world)

Specialized training services Response to the question, “In your country, EOS, to what extent are high-quality, specialized 2014-2015 training services available? (1 = not at all available, 7 = widely available)

Capacity to attract talent Response to question, “Does your country EOS, attract talented people from abroad? (1 = 2014-2015 not at all, 7 = attracts the best and brightest from around the world)

Capacity to retain talent Response to question, “Does your country EOS, retain talented people? (1 = the best and 2014-2015 brightest leave to pursue opportunities in other countries, 7 = the best and brightest stay and pursue opportunities in the country)

Innovation Ecosystem State of cluster development Response to question, “In your country, EOS, how prevalent are well-developed and deep 2014-2015 clusters (geographic concentrations of firms, suppliers, producers of related products and services and specialized institutions in a particular field)? (1 = non-existent, 7 = widespread in many fields)

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University-business R&D Response to question, “To what extent do EOS, Collaboration business and universities collaborate on research 2014-2015 and development (R&D) in your country? (1 = do not collaborate at all, 7 = collaborate extensively)

Ease of starting a business Country rank (out of 189) on the Starting a , Business pillar of the World Bank’s Doing 2014 Business report.

Vulnerability Workers in informal employment Employment in the informal sector as ILO, percentage of total non-agricultural latest year employment.

Workers in vulnerable employment Share of own-account workers, who don’t ILOSTAT, hire paid employees on a continuous basis, 2013 but may have assistance from contributing family workers (unpaid employed who usually live in same household and are related to family members) as a percentage of all persons employed.

Social safety net Response to question, “In your country, does a EOS, formal social safety net provide protection from 2014-2015 economic insecurity in the event of job loss or disability? (1 = not at all, 7 = fully)

Public Investment

Public spending on education (%of GDP) World Bank, 2013

Internet access in schools Response to question, “In your country, how EOS, widespread is Internet access in schools? 2014-2015 (1 = non-existent; 7 = extremely widespread)

Sources: EOS: World Economic Forum, Executive Opinion Survey. ILO: Women and Men in the Informal Economy: A Statistical Picture, Second Edition (2013). World Bank: World Development Indicators, Table 2.10: Education inputs (2013). http://wdi.worldbank.org/table/2.10. World Bank: Doing Business (2104). http://www.doingbusiness.org/data/exploretopics/starting-a-business

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