Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 99'

' Does Enhance Labor Productivity? Cross-Country Evidence

Prof.Dr. Cuneyt Koyuncu1 Doç.Dr. İsmail Hakkı İşcan2 Bilecik Şeyh Edebali Üniversitesi, Bilecik Şeyh Edebali Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisadi ve İdari Bilimler Fakültesi

Besides the other existing determinants of labor productivity in the literature, intelligence level may play important role in the explanation of the productivity level of labor in a country. In order to empirically examine this hypothesis, this study explores the influence of intelligence level on labor productivity by using thirteen distinct labor productivity indicators. The sample contains 157 countries’ cross-section data covering the periods between 2000 and 2013. The primary finding of the study implies a strong and statistically significant positive association between intelligence and labor productivity level. This result remains valid in all models having thirteen different proxies as dependent variable for labor productivity. This finding implies that countries with higher intelligence level experience higher labor productivity level.

Key Words: Intelligence, Productivity, Cross-Section Data, Univariate Estimation, Multivariate Estimation.

Zeka İşgücü Verimliliğini Artırır mı? Kesit Veri Kanıtı

Literatürdeki işgücü verimliliğinin mevcut belirleyicileri yanında zeka, bir ülkedeki emeğin verimlilik seviyesini açıklamada önemli bir faktör olarak rol oynayabilir. Bu hipotezi ampirik olarak incelemek için bu çalışma, on üç ayrı emek verimlilik göstergesi kullanarak zeka seviyesinin emek üretkenliği üzerindeki etkisini araştırmaktadır. Çalışmanın örneklemi, 2000-2013 yılları arası 157 ülkenin kesitsel verilerini içermektedir. Çalışmanın başlıca bulgusu, zeka ve işgücü verimliliği seviyesi arasında güçlü ve istatiksel olarak anlamlı pozitif bir ilişkiyi göstermektedir. Bu sonuç, çalışmada işgücü verimliliği için kullanılan on üç farklı yakınlığa sahip bağımlı değişken için tüm modellerde geçerliliğini korumaktadır. Bu bulgu, daha yüksek zeka seviyesine sahip ülkelerin daha yüksek işgücü verimliliği seviyesine sahip olduklarını göstermektedir.

Anahtar Kelimler : Zeka, Verimlilik, Yatay Kesit Veri, Tek Değişkenli Tahmin, Çok Değişkenli Tahmin.

Introduction

Traditional economic theories, along with internal dynamics like the political and institutional quality for economic growth, emphasize the importance of external factors such as geographical and historical factors. According to the liberal approach based on Smith and Hayek, a country will prosper a lot more by realizing the optimal distribution of the capital and labor by the virtue of economic freedom in internal dynamics. Empirical studies reveal that economic freedom and the increase in this freedom will bring a high level of economic welfare (Eliot A. Jamison, Dean T. Jamison, Eric A. Hanushek 2007; Heiner Rindermann 2008). East Asian countries which have ensured economic development largely by state influence constitute an important exception. On the other hand, historical and geographical advantages of countries (e.g., natural resources, openness to overseas trade, climatic conditions etc.) are determinants of economic growth as well. Some authors regard geography as the basic reason of regional and national economic developmental difference (Douglas A. Hibbs and Ola Olsson 2004; William D. Nordhaus 2006). and (2011) exemplifying Japan as ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' 1'Bilecik Şeyh Edebali Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü Öğretim Üyesi, [email protected] ' 2'Bilecik Şeyh Edebali Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü Öğretim Üyesi, [email protected]' Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 100'

' wealthy country and Nigeria as poor country state that some countries own more natural resources while some other countries are geographically closer to the world markets. In this sense, Nigeria is much richer than Japan in terms of natural resources and Japan is geographically quite far away to Europe's industrial centers. Therefore, Nigeria must be much richer than Japan. Meanwhile, institutional structuring in a society is brought about by individuals. Primary reasons for institutional quality and its economic consequences should be sought in physical, cognitive and behavioral characteristics of the human factor. In this respect, Japan is a rich, Nigeria yet is a poor country. Forasmuch as Japan (and countries like Singapore, Taiwan and New Zealand) possess “human capital” far more than Nigeria. The importance of human capital to a country is supported by the observation of big differences among the countries in terms of labor productivity (Meisenberg and Lynn 2011, p.422). Thus, the theories on human capital set forth that individuals’ competences and intelligence supremacies by means of the improvements in the technology constitute a key factor in determining a country's wealth (Heiner Rindermann and James Thompson 2011, p.754). Recent researches in economics and psychology show that the differences in mean national intelligence levels identified by IQ tests are a determinative factor in obtaining various national economic results. Various cross-country studies exhibit that the relationship between IQ and economic growth is actually strong. A well-defined psychological relation between the IQ and personal patience indicate high saving rates and top-tier institutional quality in the countries having high level of IQ average. It is proved via diverse methods of measurement that the countries with high levels of IQ average at the same time have a higher level of savings intensity (Garett Jones, 2011a).

Importance of IQ in Economy

IQ (Intelligence Quotient) is a special indicator that compares the performance of individuals through particular tests designed to identify and evaluate the differences in mental abilities (Nathan Brody 1999, p.19). Intelligence is dispersed unevenly throughout the countries and regions. In his study, Gerhard Meisenberg (2014) by using his own computed IQ averages puts forth the regional differences in terms of IQ averages around the world. Within this context, the IQ average is 70 at sub-Saharan Africa while it is 105 at Eastern Asia. Confucian Eastern Asia countries (China, Japan, South Korea, Taiwan, Hong Kong, Singapore) are the countries with the highest IQ performance. These are followed by the Protestant Europe, mostly Europe-originated countries that speak English as the main language, the Catholic Europe, formerly socialist bloc member Eastern Europe countries, and eventually, the former Soviet Union. Countries having mid-level IQ performance are the Latin American countries; the Middle Eastern countries including predominantly Muslim countries from Morocco to Pakistan (inclusive North Africa), Pacific Islands (excluding Australia and New Zealand) and the South & South-East Asian countries from India to Indonesia and Philippines. The region with the lowest IQ level is the sub-Saharan Africa (Meisenberg 2014, p.54-55). The findings of the empirical studies indicate that individuals who have a higher IQ level in a society bear the capacity of generating a higher level of national income and are more innovative. Hence individuals with a lower IQ level contribute less to the economic development. For instance, by a verifying study of the “Smart Fraction” theory which assumes that talented and superior intelligence individuals are specifically suitable for social development, Heiner Rindermann, Michael Sailer and James Thompson (2009) reveals that societies with lower IQ levels are less influent at technological progress and in creating national income. Similarly, in the analysis conducted for 90 countries, Rindermann and Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 101'

' Thompson (2011) specified three groups with respect to cognitive adequacy levels, namely being “low”, “medium” and “high”. They compare the influence of each group’s intellectual skills on the gross domestic product. In consequence of the study, the significance of cognitive talent for the country’s wealth was found at high levels. Moreover, cognitive skills of a group can put forward the quality of the political and economic institutions in a country. The study finds cognitive sources indispensable for the development of capitalism and economic wealth. Studies implemented on the subject show that national IQ levels are saliently linked with a quite large area. The article of Richard Lynn and Tatu Vanhanen (2012a) summarizes the conclusions reached by a set of studies conducted between 2002 and 2012 on the association between national IQ level and 244 subjects. Among those associated subjects which can be enumerated are the educational level, cognitive output, per capita income, economic growth, other various economic variables, crime, political institutions, health, demographic and sociological variables, geographic and climatic variables. Moreover, Garett Jones and W. Joel Schneider (2006) by implementing hundreds of growth regressions prove the close association between economic performance and national IQ levels. Even though the studies associating intelligence level with so many subjects (i.e., 244 subjects as indicated above) exist in the literature, the relation of intelligence level with labor productivity is hardly ever studied. The existing a few studies are limited in terms of sample size and deprived from robustness. Besides the other determinants of labor productivity, intelligence level may play important role in the explanation of the productivity level of labor in a country. In order to empirically examine this hypothesis, this study explores the impact of intelligence level on labor productivity by using thirteen distinct labor productivity indicators. The sample includes 157 countries’ cross-section data covering the periods between 2000 and 2013. The primary finding of the study implies a strong and statistically significant positive association between intelligence and labor productivity level. This result remains valid in all models having thirteen different proxies as dependent variable for labor productivity. This finding implies that countries with higher intelligence level experience higher labor productivity level. To the best of our knowledge, unlike the existing a few studies, this is the first study in the literature examining the issue with so many countries (i.e., 157 countries) and checking the robustness of results with so many proxies (i.e., thirteen variables) of labor productivity by using cross-section data. The rest of the article proceeds as follows. The following section two and three reviews the literature on respectively “IQ and Economic Performance” and “IQ and Productivity”. Section four explains data and methodology. The findings are reported and discussed in fifth section. Finally the last section concludes.

IQ and Economic Performance

There is a significant amount of studies in the literature that test the correlation of the IQ level with economic variables, particularly with economic growth. These studies have become further supported with other studies such as Antonio Ciccone and Elias Papaioannou (2009), which tests the relationship of skilled labor with growth at skill-intensive industries. Indeed, more talented people will display a quicker adaptation to new technologies and contribute more to the production process; exactly like as in endogenous growth models that admit the human capital and new technologies as central elements (Paul Michael Romer 1990) or in technology-based growth models (Richard R. Nelson and Edmund S. Phelps 1966). Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 102'

' It is first time suggested by Richard Lynn and Tatu Vanhanen (2002) is that cognitive skill is a significant causative component of national wealth. Lynn and Vanhanen’s other (2006 and 2012) studies are also important in means of setting forth the causal influence of intelligence factor on an increase in national welfare level. Lynn and Vanhanen (2002) which asserts the presence of differences in intelligence levels across countries correlates those differences systematically with economic growth and examines the causality of this relation. According to the study, the primary reason for economic growth differences among countries and per capita income differences between poor and prosper countries is the intelligence level of the societies. The study makes inferences that the influence of intelligence on income is much more important than the social status of the mother and father. The research analyzing IQ and per capita relation was conducted by using two different samples, one of them has 81 countries and the other has 185 countries. As a result, the study identified the presence of a significant relation between the IQ and the per capita income for both samples. By using the same two samples as in Lynn and Vanhanen (2002), Richard E. Dickerson (2006), ascertain a high correlation between the national IQ level and national income level. The study reached the conclusion that each ten point increase at the average IQ level leads to a two-fold increase at per capita income. Garett Jones and W.Joel Schneider (2006) by utilizing countries’ economic growth rates for the period between 1960 and 1992 in 1330 regression models detected a statistically robust positive relation between national IQ and national growth rate. In his study, Rati Ram (2007) includes corporate quality and IQ scores to his growth model developed to explain economic growth. The empirical findings he reaches point out that IQ is a variable owning a far more important representative quality for human capital compared with health and education. Earl Hunt and Werner Wittmann (2008) questioned the causality between the economic wealth and intelligence levels in their study. The study reached to the conclusion that this causality is too complicated. Among the results obtained from the study is the conclusion that the relation between the IQ levels and per capita income are approximately the same in developed and developing countries, yet we will be making more mistakes in developing countries relative to developed ones when predict per capita income with IQ variable. By using cross-country sample of 168 countries, Meisenberg and Lynn (2011) in their study examined the relation of IQ level with economic, political and cultural indicators (such as economic growth, economic freedom, income distribution (Gini) index, education, corruption, political freedom and democracy). The study led the researchers to explore the presence of a remarkably close relation between these indicators and intelligence levels of countries. According to the findings of the study, there exist a positive association between IQ level and economic growth, economic freedom, and all development indicators (education, school success, freedom and democracy) whereas this figure is negative for Gini index, religiousness, and corruption. Positive relations indicate that the countries may reach economically better-developed markets with high IQ levels. It is also understood from the study that one of the ways to ensure the accomplishment of higher economic growth rates is to own high IQ levels. Rindermann (2008) in his analysis conducted for 148 countries also analyzed relationship between income distribution inequality and national IQ level by using the Gini index. The result of the negative (-0.51) correlation obtained in the study clarifies that countries with high IQ levels have a lower level of income inequality. In another study focusing on a total of 160 countries, Gerhard Meisenberg (2011) reached the result that high IQ level is not only related with high levels of per capita income and growth; but also ensures a higher level of equality at income distribution. Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 103'

' Researching the influence of cognitive skills on economic growth, Eric A. Hanushek and Ludger Woessmann (2012) express that this relation appears to be quite salient at the countries covered in the study. It is reported in the study that one standard deviation increase in cognitive skills of a country’s labor force is associated with a 2% average annual growth rate. Gregory B. Christainsen (2013) studied to gauge the effect of life conditions on IQ level via regression analysis for 130 countries. The study reaches the result that the relation between socio-economic indicators and average IQ levels is significantly stronger compared to the influence of life conditions. The study also identifies a close relation between countries’ economic welfare levels and IQ levels. Burhan et al. (2014) via conducting multiple hierarchical regression analyses detect that intellectual class is the factor that possesses the highest level of influence on economic growth. The study moreover sets forth that this class is far more beneficial than professional researchers at technological processes at production. With these findings, researchers recommend the employment of people with excellent IQ levels at research and development departments of public and private sector organizations instead of hiring people with high academic ranks or work experiences. In the analyses conducted for more than 130 countries, Meisenberg (2014) studied the relation between the IQ and various economic indicators. The study identifies a positive correlation coefficient (0.46) between cognitive intelligence and economic growth. The study states that a 10-point increase in the IQ level causes a 1.25% increase in annual economic growth. Another finding of the study points out that economic freedom in prosper countries encourages economic growth. Regarding the income distribution inequality as another economic indicator, the study revealed that a high Gini coefficient is obtained at low IQ levels (i.e., negative correlation). Meisenberg reached the conclusion that countries need to have a higher IQ level for technological competition. The studies on intelligence in the literature predominantly focus on the relation between IQ and economic growth.

IQ and Productivity

Jones and Schneider (2008) in their study denoted that a country’s average IQ score is a useful predictor of the immigrants’ wages in the U.S. without considering immigrant education. The results indicate that one IQ point predicts 1% higher wages and hence posit that IQ tests capture an important difference in cross-country worker productivity. Furthermore the study show that nearly one-sixth of the global inequality in log income can be explained by the effect of differences in national average IQ level on the private marginal product of labor. In addition, it is asserted on the basis of the calculation developed in the study that a roughly 50% increase at output per worker can be attained when IQ average increases from 5% to 95%. In his study, Jones (2011a) macro-economically questions the reasons of strong correlation between the average labor productivity and IQ level which owns a mild correlation with personal wages in a country. The study suggests that the connection between national IQ levels and productivity can be ensured by (i) better-informed voters, (ii) laborers and political elite who value cooperation in a much larger scale, (iii) higher levels of saving, (iv) access to the higher-quality production functions. The study identifies that a one-point increase in the IQ level is associated with a 1% higher wage and is also relevant with the 6-7% increase at national labor productivity. Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 104'

' In his (2011b) study, Jones suggests that, among these four channels that have an impact on the establishment of a connection between national IQ levels and productivity, intelligence is an important subject for nations rather than for individuals. Doubtlessly, each of these channels is an out of IQ factor that can enucleate productivity differences between countries. According to Jones however, economists will be able to render the permanent differences at national cognitive skills efficient in the economy through these four channels. For instance, intelligent people tend to be more patient. Growth theories propose that patient societies can save at higher levels. On the other hand, behavioral economy experiments reveal that the ones who possess higher IQ levels are more cooperative and trustworthy. Trust and trustworthiness are the keys that hold the wealth creating corporations together. National intelligence is in positive correlation with the development of operations of corporations and with more cooperative behavior. Meanwhile, according to the study, individuals with higher IQ levels seem to be more rational in terms of supporting pro-market and pro-trade policies. Consequently, smarter voters are more rational in supporting the welfare-generating policies and bringing into compliance with the requirements of the market economy. In his study, William DiPietro (2015) analyzes the positive dependence of a high level of national intelligence level and welfare level with national productivity and innovation. For that purpose, a cross-country analysis was conducted on a set that included a large number of countries around the world. The study empirically identified that national productivity and innovation is in positive correlation with national welfare and national intelligence. In the Colombia-focused study in which data regarding cognitive skills at production function were used, Attanasio et al. (2015) found that cognitive skill has a very strong relation with productivity. In this study, it was found that investments made by the parents on children who possess higher levels of cognitive skills at family level are more efficient, it is stated that this would also influence countrywide productivity in the same direction, in a strong manner. It is expressed in the study that the current status at cognitive skills will not only have a strong impact on future cognitive skills, but will also maintain an influential power on productivity for now and future.

Data and Methodology

This study examines the impact of intelligence on labor productivity in a country by using 13 different productivity indicators. Our cross-section data covers 157 countries in the widest sense and are period averages between 2000 and 2013. 3

''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' 3 The sample includes following countries: Afghanistan, Albania, Algeria, Andorra, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bermuda, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Central African Rep., Chad, Chile, Colombia, Congo (Democratic Republic of the), Costa Rica, Croatia, Cuba, Cyprus, Czech Republic, Denmark, Djibouti, Dominica, Dominican Rep., Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Finland, France, Gabon, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Honduras, Hong Kong, Hungary, Iceland, India, Indonesia, Iran (Islamic Rep. of), Ireland, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea (Rep. of), Kuwait, Kyrgyzstan, Lao P.D.R., Latvia, Lebanon, Lesotho, Liechtenstein, Lithuania, Luxembourg, Macao, Madagascar, Malawi, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Micronesia (Fed. States of), Moldova, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Nigeria, Norway, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russia, Rwanda, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovak Republic, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Swaziland, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Vanuatu, Venezuela, Yemen, Zambia, Zimbabwe. Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 105'

' By using cross-section data, we estimate the following full-logarithmic univariate regression model:

LOGPRODiii=+ββ12 LOGIQ + u (1) and the following full-logarithmic multivariate regression model:

LOGPRODii=+ββ12 LOGIQ + β 3 LOGHUMCAP i + β 4 LOGCAPSTOCK iii + β 5 LOGFDI + u (2) where i subscript stands for the i-th country’s observation value for the particular variable. All variables (i.e., dependent and independent variables) are in logarithmic forms. ui is error term of the regression. Thirteen distinct productivity indicators are used to represent labor productivity (LOGPROD) in the analyses. The main result of analyses may vary across the productivity indicators in the sense of the sign taken and significance level of the relevant coefficient; however, if the finding of analyses remains valid across those thirteen separate productivity indicators then this will be an indication of the robustness of the result. The list of thirteen productivity indicators, their definitions, and the data sources are given in Table 1 below. CTFP and CWTFP are two indicators of total factor productivity level. LPROD1, LPROD2, LPROD3, and LPROD4 are labor productivity indicators measured in either per person employed or per hour worked. PLVALADCUR is per labor value added and computed by ratio of gross value added at factor cost to persons employed. PCVALADCUR is per capita value added and computed by ratio of gross value added at factor cost to total population. GDPPEREMP is GDP in terms of per person employed. Also we computed four sectoral labor productivity indicators. MANVLADPLCUR is per labor value added in manufacturing sector and calculated by ratio of value added in manufacturing to employment in manufacturing. AGRVLADPLCUR is per labor value added in agricultural sector and computed by ratio of value added in agriculture to employment in agriculture. INDVLADPLCUR is per labor value added in industrial sector and calculated by ratio of value added in industry to employment in industry. SERVLADPLCUR is per labor value added in sector of services and calculated by ratio of value added in services to employment in services. In Table 2 below we provide names, definitions, and sources of independent variables used in regression models. IQ is average national IQ test score and a proxy for national intelligence level in a country. In a sense, intelligence represents the quality of human capital endowment of a country. Higher quality of human capital stock (i.e., higher intelligence level) may result in higher national productivity level. Thus our prior expectation for the coefficient of IQ variable is positive. Besides our primary explanatory variable IQ, we included three other covariates playing important role in the explanation of productivity into the model. The selection of covariates is made in the light of previous studies existing in the literature and our main research question. CAPSTOCK is capital stock at current PPPs(in mil. 2005US$) and a proxy for investment level in an economy. We anticipate a positive sign for coefficient of CAPSTOCK since investment in fixed capital improves both the labor productivity and total factor productivity. HUMCAP is index of human capital per person, based on years of schooling (Barro/Lee, 2012) and returns to education (Psacharopoulos, 1994). Countries investing more on human capital and improving quality of human capital may experience higher productivity level. Therefore we expect to have a positive coefficient for HUMCAP variable.

''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''

' Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 106'

' FDI is foreign direct investment in terms of percentage of gross fixed capital formation. FDI may enhance overall productivity via bringing more productive latest production technologies into the country. Hence, our prior expectation for the coefficient on FDI variable is positive. Before proceeding to evaluate the empirical results, it will be better to check the correlation between IQ variable and 13 indicators of productivity. Table 3 provides correlation coefficients and P-values for each particular variable pairs. As in the table, correlation coefficient values are positive and vary from 0.56 to 0.80. Also all of them are highly statistically significant. Once we examine the scatter plots in Figure 1, as indicated by correlation matrix, it is explicit that there is a strong positive association between IQ variable and 13 indicators of productivity.

Table 1 List of Productivity Indicators Indicator Name Definition Source CTFP Total Factor Productivity Level at Penn World Table Current PPPs CWTFP Welfare-relevant Total Factor Penn World Table Productivity Levels at Current PPPs LPROD1 Labor productivity per person employed The Conference Board in 1990 US$ (converted at Geary Khamis PPPs) LPROD2 Labor productivity per person employed The Conference Board in 2014 US$ (converted to 2014 price level with updated 2011 PPPs) LPROD3 Labor productivity per hour worked in The Conference Board 1990 US$ (converted at Geary Khamis PPPs) LPROD4 Labor productivity per hour worked in The Conference Board 2014 US$ (converted to 2014 price level with updated 2011 PPPs) PCVALADCUR {Gross value added at factor cost (current WDI US$)}/ WDI {Total Population} MANVLADPLCUR {Manufacturing, value added (current WDI US$)}/ ILO {Employment in Manufacturing (thousand of persons)x1,000} AGRVLADPLCUR {Agriculture, value added (current WDI US$)}/ WDI {(Employment in agriculture (% of total The Conference Board employment)/100)x (Persons employed (in thousands of persons)x1,000)} INDVLADPLCUR {Industry, value added (current US$)}/ WDI {(Employment in industry (% of total WDI employment)/100)x (Persons employed The Conference Board (in thousands of persons)x1,000)} SERVLADPLCUR {Services, etc., value added (current WDI US$)}/ WDI {(Employment in services (% of total The Conference Board employment)/100)x (Persons employed (in thousands of persons)x1,000)} PLVALADCUR {Gross value added at factor cost (current WDI US$)}/ The Conference Board { Persons employed (in thousands of persons)x1,000} GDPPEREMP GDP per person employed (constant WDI 1990 PPP $) Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 107'

' Table 2 List of Independent Variables Variable Name Definition Source Lynn and Vanhanen IQ Average national IQ test score (2012b) Index of human capital per person, based on years of schooling HUMCAP Penn World Table (Barro/Lee, 2012) and returns to education (Psacharopoulos, 1994) Capital stock at currentPPPs(in mil. CAPSTOCK Penn World Table 2005US$) Foreign Direct Investment, FDI Percentage of Gross Fixed Capital UNCTAD Formation 12 1.0 0.5 12

0.5 0.0 11 R

U 10 0.0 P C M

L 40.5 10 E F P P R T D F 40.5 E T A W P L C

8 C 41.0 9 P V G G

41.0 D R O O G G L L

41.5 G 8 A 41.5 O G 6 L O L 42.0 42.0 7

4 42.5 42.5 6 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8

LOGIQ LOGIQ LOGIQ LOGIQ

14 12 13 4

11 12

R 12 3 U C

1 10 2 11 3 L D D D P O O O D R R R A 10 9 10 2 L P P P L L L V D G G G N O O O I 8 9 L L L G 8 1 O L 7 8

6 6 7 0 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8

LOGIQ LOGIQ LOGIQ LOGIQ

5 12 12 12

11 4 11 R R U

10 R U C U L 4

C 10 C P D D 3 10 D D A O A L A L R L A

8 A 9 P V V L V N L C

G 2 9 A P P O

M 8 G G L G O O 6 L L 1 O 8 L 7

0 7 4 6 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8 4.0 4.2 4.4 4.6 4.8

LOGIQ LOGIQ LOGIQ LOGIQ

12

11 R U C

L 10 P D A

L 9 V R E

S 8 G O L 7

6 4.0 4.2 4.4 4.6 4.8 LOGIQ Figure 1 Graph of IQ Variable with Dependent Variables

Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 108'

' Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 109'

'

Empirical Results

Table 4 reports univariate results with White heteroskedasticity-consistent standard errors and covariance. Table 5 provides robust least square results for univariate analyses. The sign of the coefficient on IQ variable, as anticipated, is positive and statistically significant at 1% significance level across all models in Table 4 and 5. In order to see whether the finding of univariate analyses is maintained or not, we conducted multivariate analyses by adding the relevant control variables to the models. Table 6 reports multivariate results with White heteroskedasticity-consistent standard errors and covariance. Table 7 provides robust least square results for multivariate analyses. As shown by the results in Table 6, the estimated coefficient of IQ variable has positive sign and is statistically significant at 1% significance level in 8 models, at 5% significance level in 3 models, and at 10% significance level in 2 models. These figures for Table 7 are 11 models at 1% significance level and 2 models at 5% significance level. As the findings indicate, the strongly significant explanatory power of IQ variable on productivity was not drained by the inclusion of the other additional explanatory variables. Therefore, this finding implies a same direction relationship between the intelligence level and labor productivity in all models across Table 6 and 7. In respect to the other explanatory variables, the estimated coefficient of CAPSTOCK variable is positive parallel to our prior anticipation and statistically significant at least at 10% significance level in just 4 models in Table 6 while it is statistically significant at least at 5% significance level in only 4 models in Table 7. Thus, this finding more or less supports the argument that an increase in the investment of fixed capital flourishes total factor productivity and particularly labor productivity. The estimated coefficient of HUMCAP variable takes the expected positive sign and statistically significant at least at 5% significance level in all models across Table 6 and 7. Therefore we can state that increasing and improving the human capital may lead to a higher productivity level. The estimated coefficient of FDI variable gets the anticipated sign and is statistically significant at least at 10% significance level in 4 models in Table 6 and statistically significant at least at 10% significance level in 6 models in Table 7. Hence, this result in general supports the argument that an increase in FDI may augment the labor productivity level via introducing new advanced production technologies.

Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 110'

'

Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 111'

'

Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 112'

' Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 113'

'

Since our models are full-logarithmic models, the coefficients in front of the each variable reflect the elasticities. Labor productivity shows highest sensitivity against to the intelligence level in Model 9 and lowest sensitivity in Model 6 in Table 6 and 7. These figures are 1.02(the lowest)-6.39(the highest) in Table 6 and 1.04(the lowest)-6.80(the highest) in Table 7. According to the this finding, if intelligence level in a country goes up by 1% then labor productivity goes up by 6.39% in that country. Proxies of productivity variables also show highest sensitivity against to IQ variable among all independent variables. This hints how important IQ variable is in the explanation of productivity level. Meantime, in terms of robustness, our results are robust since our primary finding remains valid no matter which proxy is used for productivity level and whether estimation method is robust least square or not.

Conclusion

In fact there are many factors playing important role in the explanation of the productivity level of labor in a country. Besides them, intelligence level may affect the productivity level of labor in that country. In order to empirically examine this hypothesis, in this study we explore the impact of intelligence level on labor productivity by using thirteen distinct labor productivity indicators. The sample includes 157 countries’ cross-section data covering the periods between 2000 and 2013. The primary finding of the study implies a strong and statistically significant positive association between intelligence and labor productivity level. This result remains valid across all models having different proxies as dependent variable for labor productivity; therefore this confirms how robust the finding is. Based on the result, we may say that countries with higher intelligence level experience higher labor productivity level. To the best of our knowledge, unlike the existing a few studies, this is the first study in the literature examining the issue with so many countries (i.e., 157 countries) and checking the validity of results with so many proxies (i.e., thirteen variables) of labor productivity by using cross-section data.

References

Attanasio O., S. Cattan, E. Fitzsimons, C. Meghir, and M. Rubio-Codina. (2015). Estimating the Production Function for Human Capital: Results From a Randomized Control Trial in Colombia, Cowles Foundation Discussion Paper, No: 1987, Cowles Foundation for Research In Economics, Yale University, New Haven, Connecticut, 1-40. Brody , N. (1999). “What is intelligence ?” International Review of Psychiatry, 11(1), 19-25. Burhan, N.A.S., Mohamad, M.R., Kurniawan, Y., Sidek, A.H. (2014). “The Impact of Low, Average, and High IQ on Economic Growth and Technological Progress: Do All Individuals Contribute Equally?”, Intelligence, 46, 1-8. Christainsen, G.B. (2013). “IQ and The Wealth of Nations: How Much Reverse Causality?”, Intelligence, 41(5), 688-698. Ciccone, A. and Papaioannou, E. (2009). “Human Capital, The Structure Of Production, and Growth”, Review of Economics and Statistics, 91(1), 66–82. Dickerson, R.E. (2006). “Exponential Correlation of IQ and The Wealth Of Nations”, Intelligence, 34(3), 291-295. Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 114'

' DiPietro, W. (2015). “National Wealth and National Intelligence as Determinants of National Productivity and National Innovation”, International Economics and Business, 1(1), 21-29. Hanushek, E. A. and Woessmann, L. (2012). “Do Better Schools Lead To More Growth? Cognitive Skills, Economic Outcomes, and Causation”, Journal of Economic Growth, 17(4), 267–321. Hibbs, D.A. and Olsson, O. (2004). “Geography, Biogeography, and Why Some Countries are Rich and Others are Poor”, Proceedings of the National Academy of Sciences -PNAS, 101(10), 3715-3720. Hunt, E. and Wittmann, W. (2008). “National Intelligence and National Prosperity”, Intelligence, 36, 1–9. Jamison, E. A., Jamison, D. T. and Hanushek, E. A. (2007). “The Effects of Education Quality on Income Growth and Mortality Decline”. Economics of Education Review, 26, 772–789. Jones, G., and W.J. Schneider. (2006). “Intelligence, Human Capital, and Economic Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach”, Journal of Economic Growth, 11(1), 71–93. Jones, G. and W.J. Schneider. (2008). “IQ in the Production Function: Evidence from Immigrant Earnings”, Economic Inquiry, 48(3), 743-755. Jones, G. (2011a). “IQ and national productivity”, The New Palgrave Dictionary of Economics Online Edition, (Eds. Steven N. Durlauf and Lawrence E. Blume), Palgrave Macmillan, http://www.dictionaryofeconomics.com/article?id=pde2011_I000311, Jones, G. (2011b). “National IQ and National Productivity: The Hive Mind Across Asia”, Asian Development Review, 28(1), 51-71. Lynn, R. and Vanhanen, T. (2002). IQ and The Wealth of Nations, Westport-London: Praeger. Lynn, R. and Vanhanen, T. (2012a). “National IQs: A Review Of Their Educational, Cognitive, Economic, Political, Demographic, Sociological, Epidemiological, Geographic And Climatic Correlates, Intelligence”, Intelligence, 40(2), 226-234. Lynn, R., Vanhanen, T. (2012b). INTELLIGENCE: A Unifying Construct for the Social Sciences. Ulster Institute for Social Research, London. Meisenberg, G. (2011), “National IQ and Economic Outcomes”, Personality and Individual Differences, 53(2), 103–107. Meisenberg, G. and Lynn, R. (2011). “Intelligence: A Measure Of Human Capital in Nations”, Journal of Social, Political & Economic Studies, 36(4), 421–454. Meisenberg, G. (2014). “Cognitive Human Capital and Economic Growth in The 21st Century” In: T. Abrahams (ed), Economic Growth in the 21st Century: New Research, 49- 106, New York: Nova Publishers. Nelson, R. R. and Phelps, E. (1966). “Investment in Humans, Technology Diffusion and Economic Growth”, American Economic Review, 56(2), 69-75.

Social'Sciences'Research'Journal,'Volume'6,'Issue'4,'997115'(December'2017),'ISSN:'214775237' 115'

' Nordhaus, W.D. (2006). “Geography and Macroeconomics: New Data and New Findings”, Proceedings of the National Academy of Sciences-PNAS, 103(10), 3510-3517. Ram, R. (2007). “IQ and Economic Growth: Further Augmentation of Mankiw-Romer-Weil Model”, Economics Letters, 94(1), 7-11. Romer, P. (1990). “Endogenous Technological Change”, Journal of Political Economy 98(5), Part 2: The Problem of Development: A Conference of The Institute for the Study of Free Enterprise Systems, (Oct.), S71–S102. Rindermann, H. (2008). “Relevance of Education and Intelligence at The National Level For The Economic Welfare of People” Intelligence, 36(2), 127–142. Rindermann, H., Sailer, S. and Thompson, J. (2009). “The Impact of Smart Fractions, Cognitive Ability of Politicians and Average Competence of Peoples on Social Development”, Talent Development & Excellence, 1(1), 3–25. Rindermann, H. and Thompson, J. (2011). “Cognitive Capitalism: The Effect of Cognitive Ability on Wealth, As Mediated Through Scientific Achievement and Economic Freedom”. Psychological Science, 22(6), 754.