The effects of prenatal testosterone on wages: Evidence from Russia

Accepted Manuscript Title: The Effects of Prenatal Testosterone on Wages: Evidence from Russia Author: John V.C. Nye Maksym Bryukhanov Ekaterina Kochergina Ekaterina Orel Sergiy Polyachenko Maria Yudkevich PII: DOI: Reference:

S1570-677X(16)30190-3 http://dx.doi.org/doi:10.1016/j.ehb.2016.11.003 EHB 615

To appear in:

Economics and Human Biology

Received date: Revised date: Accepted date:

15-3-2016 6-10-2016 11-11-2016

Please cite this article as: Nye, John V.C., Bryukhanov, Maksym, Kochergina, Ekaterina, Orel, Ekaterina, Polyachenko, Sergiy, Yudkevich, Maria, The Effects of Prenatal Testosterone on Wages: Evidence from Russia.Economics and Human Biology http://dx.doi.org/10.1016/j.ehb.2016.11.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The Effects of Prenatal Testosterone on Wages: Evidence from Russia1 John V.C. Nye2, Maksym Bryukhanov3, Ekaterina Kochergina4, Ekaterina Orel5, Sergiy Polyachenko6, Maria Yudkevich7.

1

The article was prepared within the framework of the Basic Research Program at the National Research

University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project “5-100”. We also grateful to all participants of Educational group at the Center of Institutional Studies for their comments and constructive critique during weekly seminars. We thank Donald Cox for his comments and suggestions. 2

National Research University Higher School of Economics(Moscow, Russia), International Laboratory for

Institutional Analysis of Economic Reforms, and George Mason University (Fairfax, Virginia, USA),; E-mail: [email protected] 3

National Research University Higher School of Economics(Moscow, Russia), International Laboratory for

Institutional Analysis of Economic Reforms,; E-mail: [email protected] 4

National Research University Higher School of Economics (Moscow, Russia), International Laboratory for

Institutional Analysis of Economic Reforms, , E-mail: [email protected] 5

National Research University Higher School of Economics (Moscow, Russia), International Laboratory for

Institutional Analysis of Economic Reforms, , E-mail: [email protected] 6

National Research University Higher School of Economics(Moscow, Russia), International Laboratory for

Institutional Analysis of Economic Reforms,; E-mail: [email protected] 7

National Research University Higher School of Economics (Moscow, Russia), International Laboratory for

Institutional Analysis of Economic Reforms, , E-mail: [email protected]

1

Highlights 1. 2. 3. 4.

Lower 2D:4D correlated with higher wages in Russia Means higher prenatal T correlates to higher wages Weak nonlinear effect not robust Matches literature on prenatal T and achievement

Abstract Is in utero exposure to testosterone correlated with earnings? The question matters for understanding determinants of wage differences that have attracted so much attention among economists in the past decade. Evidence indicates that markers for early testosterone exposure are correlated with traits like risk-taking and aggressiveness. But it is not at all clear how such findings might map into labor market success. We combine unique data from the Russian Longitudinal Monitoring Survey with measured markers (2D:4D ratios) for testosterone exposure and find that lower digit ratios (higher T) correlate with higher wages for women and for men, when controlling for age, education and occupation. There is also some evidence of a potential non-linear, inverse U-effect of digit ratios on wages but this is sensitive to choice of specification. These findings are consistent with earlier work on prenatal T and success in careers (Coates et al., 2009) but inconsistent with the work of Gielen et al. (2016) who find differing effects for men and women. Keywords: In utero, Testosterone, 2D:4D, Wages, Russia JEL: J24, J31

2

1. Introduction How important is exposure to hormones in utero for an individual’s adult life outcomes such as wages and income? Research has long suggested the importance of behavioral or psychological variables for occupational success. Nutrition in the womb is understood to have important effects on health and cognitive performance (cf. Almond and Currie, 2011 for a review on birth weight and prenatal nutrition effects; and Korenman et al., 1995, for nutrition and cognitive and behavioral outcomes. McCrae et al., 2000 discuss the importance of childhood temperament predicting later personality measures.). In turn, traits such as locus of control, selfdirection, impulsivity, Machiavellianism, self-esteem, and Emotional Intelligence, are recognized for their ability to explain differences in educational attainment and occupational success but they are often correlated with each other (e.g. Bastian et al., 2005 discuss Emotional Intelligence’s weak predictive value when controlling for cognitive and personality measures). Behavioral or psychological traits have received strong recent interest in the mainstream economic literature for their relationship with economic success (Semykina and Linz, 2007). The most recent work on non-cognitive determinants of achievement by Heckman and other scholars has established that traits such as persistence, confidence, or motivation play a clear role in market success (Heckman et al., 2006) and confirms much earlier work on the links between personality traits and income. Lindquist and Vestman, (2011) used Swedish military enlistment data to show that non-cognitive dimensions were important in explaining the unemployment of low-skilled workers, whereas cognitive ability mattered more for those earning above the median wage. We can link the two issues by studying how prenatal treatments with an influence on personality traits affect life outcomes. There is a large literature in biology and the social sciences showing that prenatal testosterone exposure as proxied by digit ratios is correlated with many attributes (including cognitive ability, personality traits, susceptibility to disease, sexual orientation, and competitive behavior. Cf. Austin et al., 2002; Voracek et al., 2010). One marker for the level of prenatal testosterone (T) which is experienced by an embryo during pregnancy is the inverse correlation of T exposure to the relative length of the second to the fourth finger (2D:4D) (Malas et al., 2006). The 2D:4D ratio is calculated by dividing the length of the index finger of one hand by the length of the ring finger of the same hand. 2D:4D ratios for right and left hands are correlated but not identical. The finger lengths form up during the first trimester in the womb and relative lengths are fully established by the second year of one’s life. There is evidence from observations of digit ratios in both humans and animals of the role of prenatal hormones in promoting dimorphic digit ratios (Berenbaum et al., 2009; Galis et al., 2010). 3

In the last few years it has been shown that exposure to prenatal androgens affects academic performance, current behavior, occupational interests and choices (Brosnan et al., 2011; Hell and Päßler, 2011; Hönekopp, 2011; Nye et al., 2012; Nye and Orel, 2015; Voracek et al., 2010). In particular, in a German study, using data from an internet survey, Hell and Päßler (Hell and Päßler, 2011) showed that 2D:4D measures are associated with occupational interests. Occupational interests were measured with a standard interest inventory (occupational interests were measured according to Holland’s hexagon-model). Hell and Päßler found significant negative correlations between digit ratios and interest in things (as opposed to social or people-oriented interests) for males, but did not find any relationships for females. However, none of this was linked to market or actual employment outcomes. Furthermore, 2D:4D measurements were self-reported. In more recent work, Nye and Orel (2015) used a large sample of individuals from the Moscow area that contained information about actual jobs. They found that females in occupations that were more stereotypically masculine (such as managers, lawyers, etc.) showed lower digit ratios (higher T) than those in more conventional jobs (secretaries, cleaning staff, etc.) using the Holland occupational classification (Nye and Orel, 2015). Moreover, prenatal hormonal exposure affects development of the male sex organ, secondary sex characteristics, as well as brain development and cognition functions. This exposure is said to have “profound, permanently masculinizing effects on human neural circuitry and peripheral tissues, which in turn partly account for a multitude of behavioral, cognitive, and health-related human sex differences seen in later postnatal life” (Voracek, 2011). Though we do not yet understand the exact biological mechanisms that link prenatal testosterone to observed digit ratios, the 2D:4D ratio still serves as one of the best and most common proxies for prenatal androgen exposure (Hönekopp et al., 2007). Because the digit measures are so exogenous to life experience, it may be the best current proxy for hormonal personality effects that one could use. 2D:4D ratios are linked to both standard measures of cognitive ability as well as non-cognitive personality traits but the studies of 2D:4D on adult performance in sports, business, or schooling have been limited. To date, there has been no large scale study linking digit ratios to general labor market outcomes although Coates, et al. (Coates et al., 2009) have established a clear link between 2D:4D and success only in the area of bond market trading. However, in a recent twins study which did not consider digit ratios but which used twinning as a proxy for differential T exposure in utero, Gielen et al. (Gielen et al., 2016) found a positive effect of prenatal T for male income but a negative one for female in a large national sample. This paper uses a sample of working-age respondents from Moscow and the Moscow 4

region to establish that greater prenatal T exposure (i.e. lower 2D4D ratios) is clearly correlated with higher earnings for both women and men once controlling for factors such as age, education, and occupation. Furthermore, there is also some evidence that these effects may be non-linear and non-monotonic – implying that too much T may result in a reduction in benefit – although these findings are weaker and not robust to different specifications.

2. Data We worked with a sample of Moscow and Moscow district households. We collected more than 2500 observations. Descriptive statistics for the variables used in our analysis are given in table A1. The data were taken from the Russian Longitudinal Monitoring Survey (RLMS-HSE), 20th wave of the survey (October 2011 – February 2012). Our data only include survey participants from Moscow and the Moscow district, whose finger measurements were taken. Finger measurements were taken from a randomly selected sample of survey households living in the Moscow region. Finger measurements were taken from respondents between the ages of 14 and 98. Digit ratio measurements were taken by a special team of trained assistants, while other information was taken from the existing survey which contains questions regarding an individual’s socioeconomic characteristics and family background. The data were anonymized before being provided to the authors for statistical analysis. The finger measurements were taken using electronic calipers. Actual measurements were made from the palmar digital crease to the fingertip of the index and ring fingers. Then measurements were rounded to the nearest millimeter. Initially we had 4338 observations on the length of the second digit of the left hand, 4340 observations on the length of the fourth digit of the left hand (correspondingly 4337 and 4338 in the case of the right hand). Those, who indicated that they have damaged fingers (246 observations), were excluded. Moreover, all persons younger than 25 and older than 60 years were excluded in the sample we chose to analyze in order to focus on those who would be in the labor market during the period surveyed (October 2011 – February 2012). Basic statistics of digit ratios of the individuals who were studied are presented in Table A1. The mean of the left hand digit ratio (“DL”) for men equals 0.9959 (standard deviation = 0.0456), the mean of the right hand digit ratio (“DR”) for men equals 0.9966 (standard deviation = 0.0454).The coefficient of correlation between DL and DR equals 0.6447 (significant at 1% significance level). It turns out that females have a bit larger digit ratios: the mean of DL = 1.001 (standard deviation = 0.046), the mean of DR = 0.9966 (standard deviation = .0454), and it is consistent with facts coming from the literature (Allaway et al., 2009; Brodaty et al., 2014), higher digit ratios are also detected in the meta-study of Voracek et al. (Voracek et al., 2010). The 5

coefficient of correlation between DL and DR in the case of females equals 0.5798 (significant at 1% significance level). However, we should admit that the standard deviations of these digit ratios are larger than those which appear in the literature. For example, in the meta-analysis by Voracek et al. (Voracek et al., 2010), and in other studies (Allaway et al., 2009; Fink et al., 2006), the standard deviation of digit ratios is around 0.03. That is why, in order to check stability of our results to the sample used we estimated regressions not only using the full sample, but also on the subsample obtained via deletion of observations below the lower and above the upper 2.5-percentiles of distribution of each studied finger’s length of each hand and digit ratios. In the text below we called them outliers. The procedure of exclusion of the observations below the lower and above the upper 2.5percentiles of fingers’ length and digit ratios was also implemented in 2D:4D studies by other researchers (Hell and Päßler, 2011). After the application of the procedure of outlier deletion8, the standard deviations of digit ratios became more comparable to those documented in existing studies. In particular, the mean and the standard deviation for males’ DL now become equal to 0.9976 and 0.0346 correspondingly; the mean and the standard deviation of males’ DR now equal to 0.9979 and 0.0329. In the case of females we find the mean of DL = 1 (standard deviation = 0.0339), and the mean of DR = 0.9979 (standard deviation = 0.033). For the analysis of earnings, we used three dependent variables, which correspond to three questions from the RLMS about respondents’ earnings: 1) the logarithm of average monthly wage for the last 12 months (“Y1”) from the primary employer9; 2) the logarithm of total income from full and part time employment in the last 30 days (“Y2”)10; 3) the logarithm of the total amount of money received in the last 30 days from all sources of income including wage and non-wage

8

Observations were deleted sequentially: firstly, the second finger of the left hand was treated, next – the

fourth finger of the left hand, next we treat fingers of the right hand, DL and, finally, we treat DR. 9

The corresponding RLMS question: “In the last 12 months how much was your average monthly wage after

taxes from this organization--regardless of whether it was paid to you on time or not?” 10

For this, we considered respondents’ employment and the following RLMS question: “How much money

did you receive in the last 30 days from your primary job after taxes? If you received all or part of the money in foreign currency, please convert that into rubles and report the total?”. To this amount we added earnings from secondary employment. The corresponding RLMS question is: “How much money did you receive in the last 30 days from this job after taxes? If you received all or part of the money not in rubles but in foreign currency, please convert that into rubles and report the total”. Finally, we added earnings of extra work, and the corresponding question is: “How much money in total in the last 30 days were you paid for this work? If payment was not money, estimate how much it would be in rubles.”

6

earnings (“Y3”)11. All computations were done in STATA 13 and (when taking logarithms) did not consider missing or zero values of earnings. There are 835 missing values (out of 2586, which is approximately 32%) in the case of the answer to the first RLMS question about average wages (318 missing observations stated by men, and the rest stated by women). The data set consists of 684 (approximately 26%) missing values of total earnings received in the last 30 days (219 of which is reported by males). There are 465 (approximately 18%) missing values of the answer to the last question about the total amount of money received in the last 30 days (58 of which is reported by males). We considered zero values more closely – information regarding them is provided in the end of Appendix A (table A3). We admit the potential endogeneity of missing values; however, their detailed accounting in regressions is beyond the scope of this paper. We only made a trial attempt to account for the big selection bias issue in the end of Appendix A (table A4). Next we added some additional controls. First of all we incorporated “Age” and its squared term and “Education” which are common in the literature (Lemieux, 2006). The original RLMS question about educational attainment is the following “Let me clarify, what is your highest educational level which is confirmed by certificate or diploma?”. It ranges from 3 – “3 classes of school, incomplete secondary education” up to the highest level 12 – “Graduate school with diploma” (see table A2). It is important to note that educational levels may themselves be correlated with measured 2D:4D, inasmuch as the conditions affecting prenatal testosterone exposure and their manifestation are related to such characteristics as cognitive ability and confidence/aggression that are likely to be important determinants of observed educational attainment. Thus, controlling for education will likely lead to an underestimate of 2D:4D’s significance if these factors are related to both prenatal T exposure and to education. Of course, measured 2D:4D cannot be endogenous to observed educational levels, though random factors (such as injury or exercise) would obviously affect digit ratios. Although these may affect finger length these effects are unlikely to be systematic and would just add noise. Nonetheless, we excluded any observations showing substantial finger injuries that might skew the ratios. We also know that there is a potential correlation of occupation with wages (Bonin et al.,

11

The corresponding RLMS question: “What is the total amount of money that you personally received in

the last 30 days. Please include everything: wages, retirement pensions, premiums, profits, material aid, incidental earnings, and other receipts, including foreign currency, but convert the currency into rubles”.

7

2007; Flabbi et al., 2008) and also a correlation of 2D:4D with occupation (Nye and Orel, 2015). To control for occupations we incorporated dummy variables representing 2-Digit ISCO codes12 according to the methodology of wage and risk analysis in (Bonin et al., 2007)13. Finally, taking into account the possible correlation of wages with industry/sector where an agent works (Gorodnichenko and Peter, 2005; Semykina and Linz, 2007), and, from another side, 2D:4D and profitability (Coates et al., 2009) we incorporated dummies, which represent industries/sectors (light industry, food industry, oil and gas industry etc.)14.

3. Results First, we show average values of the average monthly wage for the previous 12 months (and converted sums into US$) as reported by the individuals versus bins of the independent variable – 4 quantiles of 2D:4D distribution – just to see the general picture.

Just for information purposes, we document the statistically significant (at 10% significance level) difference of average males’ wages between the third and the fourth quartile (abbreviated as “3-4”). Interestingly, for females, statistically significant differences are found in the cases (only for the left hand 2D:4D): 1) “1-3” (at 10% significance level); 2) “1-4” (at 5% significance level); 3) “2-3” (at 5% significance level); 4) “2-4” (at 5% significance level). Next, we ran ordinary least squares (OLS) regressions for each independent variable (Y1, Y2, Y3), with controls for left and right digit ratios, age, age squared, education, occupation, and industry dummies, adding predictors sequentially. Regressions are estimated on the full data set (summary is provided in table 1). Then we corrected for outliers according to the methodology employed by Hell and Päßler (2011), and estimated the same regressions on the trimmed data set (summary is provided in table 2). Detailed regression output is provided in Appendix B. Tables (B1 – B12) correspond to the data set of the full sample and tables (B13 – 24) corresponds to the trimmed data set – without outliers. Huber-White, heteroscedasticity-robust standard errors are presented in parentheses. Summaries15 of regressions are provided in tables 1-2.

12

http://www.ilo.org/public/english/bureau/stat/isco/docs/draft08.pdf We also employed classification of occupations, utilized in (Nye and Orel, 2015), in regressing Y1, however, no major changes in comparison to regressions with 2-Digit ISCO codes were detected. 14 The whole list of industries correspond to RLMS question 4.1 http://www.cpc.unc.edu/projects/rlmshse/data/questionnaires/rtadult.pdf 15 For example, the first coefficient in Table 1 equals -1.4627 and it corresponds to the full regression output of table B1, column 1. The last coefficient equals - 0.7725 and it corresponds to table B12, column 5. 13

8

These regressions (in the case of Y1) showed a consistent pattern of significant results for women for both left-hand and right-hand digit ratios with varying numbers of controls. In every case, female digit ratios were significant (at least at the 10% level and usually at the five or one percent level) and negatively correlated with wages. Since 2D:4D is inversely correlated with prenatal T, this means that greater prenatal T was associated with higher wages for women in all cases. We also know that there are potential relationships between 2D:4D and occupational choice and education. Therefore, it seems that including those as controls weakens the effect of the digit ratio coefficients. For men (in the case of Y1), we see a slightly different pattern. When only controlling for digit ratios, age, and age squared, we observe negative but insignificant effects of prenatal T on wages for men. However, once controls for education and occupational dummies are included, we get the same general results for men with lower digit ratios (higher prenatal T) being correlated with higher wages. However, the result is most consistent for left hand 2D:4D and only weakly correlated with right hand 2D:4D only in the full regression. These results indicate that holding occupation and education constant, men with higher prenatal T are more likely to have higher wages. The lack of correlation when these are not controlled either means that these choices are less driven by prenatal T for men or else that some degree of non-monotonicity or inconsistency in terms of the effects of prenatal T on education, occupation, and income cause these effects to be muted when individual choices are not controlled. Interestingly the female 2D:4D correlations lose significance when looking at the recent month’s income (Y3) as the dependent variable, while for men they are mostly consistent in almost all specifications with both right hand and left hand digit ratios. We speculated that part of the reason for this is that there is more part-time work or mixed full-time and part-time work for women, which should lead to greater variance in last month’s actual income for women than men. Moreover, when we examine the underlying data we see that indeed, the variance of female income in the alternate specification is much greater (see descriptive statistics, table A1). This suggests that women may be optimizing over yearly income while varying employment within the year. In contrast, men are more likely to be working full time which would mean that their monthly income would be more closely correlated with their average monthly salary based on their yearly earnings. We also tested for the possibility of non-linear effects by estimating the same regressions but with the addition of a squared term (summaries are provided in tables 3-4).

We then tested for the joint significance of digit ratios and their squares for right and left hand separately divided by gender (tables B25-B24, Appendix B). 9

As we can see (table 3) there are significant non-linear (inverse U effects) for women in all regressions with Y1 as the dependent variable and only for the right hand digit ratios. And for men the relevant significance is only found for variable Y2 with the left hand ratio in only some instances. This suggests that for women there is an optimum level of prenatal T or digit ratio which generates the highest wages. However, these results do not seem very robust to the exclusion of outliers. Using the method of dropping the lowest and highest 2.5% of the studied fingers and the digit ratios from the sample, leads to the non-significance of the women’s ratios in all specifications. However, surprisingly, the male ratios become more significant for both Y1 and Y2 (table 4) with the left hand digit ratios, demonstrating the same inverse U shaped pattern with an optimum digit ratio around the mean. Following this we calculated variance inflation factors (VIF) of digit ratios and their squared terms and found severe multicollinearity which makes interpretation of these results difficult and weakens claims of non-linearity. Thus, while we do find limited evidence of non-linearity, it is not very robust and requires further investigation. Detailed regression output is provided in Appendix B. Tables (B1 – B12) correspond to the rough data set and tables (B13 – B24) correspond to the trimmed data set.

4. Conclusions In brief, we find that 2D:4D is negatively correlated with the logarithm of female’s average wages for a large sample of Moscow residents drawn from the full Russian longitudinal monitoring survey using either right or left hand ratios. In the case of female respondents, the results are consistently significant even after controlling for level of education, age, occupation, and industry classification. However, using earnings from the last 30 days (both professional and total Y2 and Y3) is not significant for women, likely due to the greater dispersion of their monthly earnings resulting from widespread part-time employment. For females, these results are mostly robust to the exclusion of outliers. In the male subsample it is more complicated: Male incomes are mostly correlated with lower 2D:4D (i.e. higher prenatal T) but only when controlling for education and industry. However, unlike the results for the females, this is true for all measures of male earnings and income Y1, Y2, and Y3. Moreover, these results are also robust to the exclusion of outliers. There is some evidence that women’s salaries respond non-linearly (inverse U shaped curves) to prenatal T as measured by the right hand digit ratio implying a peak in earnings at 10

intermediate levels. However, these findings are not robust to the exclusion of outliers. However, for males, the nonlinearity shows up only after exclusion of outliers. Given the high degree of multicollinearity revealed by the Variance Inflation Factors, the results for non-linearity must be deemed inconclusive. Our main goal was to understand how exposure to prenatal androgens affects labor market earnings. We use measured 2D:4D ratios as proxies for genetically dimorphic responses to prenatal testosterone, which are frequently linked in the existing literature with risk-taking behavior and a number of other personality traits. Indeed we found that the 2D:4D ratio has a significant negative effect on earnings (i.e. higher prenatal T implies greater wage) for both women and men, even when controlling for age, education, occupation, and industrial classification. This work generally supports the findings that higher prenatal leads to greater rewards in the labor market such as in Coates, et al (2009). However, our work is partially at variance with that of Gielen et al (2016) who found positive effects of greater prenatal T for men but negative for women. However, they did not use measures of digit ratios nor of overall exposure to prenatal T, but rather used differing sex twins to identify random shocks to testosterone exposure in utero. Although our non-linear findings are weak, the possibility that these effects exist might mean that it is not only enough to identify a random shock at the margin but also to identify the levels of these individuals receiving the extra T exposure. This is left for future researchers. And there is also the possibility that the cultural responses to women with personality characteristics promoted by higher prenatal T are different in Holland and Russia given differing institutional and socio-cultural characteristics. It is also interesting that the male results are not significant before controlling for education, occupation, and industry. This may be because of omitted variable bias as choice of occupation and educational attainment might both be affected by prenatal T exposure. Some of these issues are explored for educational attainment in Nye et al. (2016) It is also likely that the link between a worker's non-cognitive skills and his or her occupational attainment stems in part from the fact that personality traits appear to have labor market returns that are both occupation and gender-specific (Groves, 2005). This raises obvious questions regarding the extent to which gender differences in non-cognitive skills can account for the disparity in men's and women's relative wages. Recent research investigates this issue and generally concludes that non-cognitive skills have a significant, but rather modest, role in explaining the gender wage gap (Cobb-Clark and Tan, 2011; Semykina and Linz, 2007). Overall, our work confirms existing research on the significance of greater prenatal T for labor market success. Further work needs to be done on the precise contribution of prenatal T to 11

educational attainment and other factors relevant to human capital in order to separate the mechanisms that cause prenatal hormone exposure to affect labor market outcomes.

References

Allaway, H.C., Bloski, T.G., Pierson, R.A., Lujan, M.E., 2009. Digit ratios (2D: 4D) determined by computer-assisted analysis are more reliable than those using physical measurements, photocopies, and printed scans. Am. J. Hum. Biol. 21, 365–370. Almond, D., Currie, J., 2011. Killing me softly: The fetal origins hypothesis. J. Econ. Perspect. 25, 153–172. Austin, E.J., Manning, J.T., McInroy, K., Mathews, E., 2002. A preliminary investigation of the associations between personality, cognitive ability and digit ratio. Pers. Individ. Dif. 33, 1115–1124. Bastian, V.A., Burns, N.R., Nettelbeck, T., 2005. Emotional intelligence predicts life skills, but not as well as personality and cognitive abilities. Pers. Individ. Dif. 39, 1135–1145. Berenbaum, S.A., Bryk, K.K., Nowak, N., Quigley, C.A., Moffat, S., 2009. Fingers as a marker of prenatal androgen exposure. Endocrinology 150, 5119–5124. Bonin, H., Dohmen, T., Falk, A., Huffman, D., Sunde, U., 2007. Cross-sectional earnings risk and occupational sorting: The role of risk attitudes. Labour Econ. 14, 926–937. Brodaty, T., Gary-Bobo, R.J., Prieto, A., 2014. Do risk aversion and wages explain educational choices? J. Public Econ. 117, 125–148. Brosnan, M., Gallop, V., Iftikhar, N., Keogh, E., 2011. Digit ratio (2D: 4D), academic performance in computer science and computer-related anxiety. Pers. Individ. Dif. 51, 371–375. Coates, J.M., Gurnell, M., Rustichini, A., 2009. Second-to-fourth digit ratio predicts success among high-frequency financial traders. Proc. Natl. Acad. Sci. U. S. A. 106, 623–8. doi:10.1073/pnas.0810907106 Cobb-Clark, D.A., Tan, M., 2011. Noncognitive skills, occupational attainment, and relative wages. Labour Econ. 18, 1–13. Dougherty, C., 2007. Introduction to econometrics. oxford university press, usa. Fink, B., Manning, J.T., Neave, N., 2006. The 2nd--4th digit ratio (2D: 4D) and neck circumference: implications for risk factors in coronary heart disease. Int. J. Obes. 30, 711– 12

714. Flabbi, L., Paternostro, S., Tiongson, E.R., 2008. Returns to education in the economic transition: A systematic assessment using comparable data. Econ. Educ. Rev. 27, 724–740. Galis, F., Arntzen, J.W., Lande, R., 2010. Dollo’s law and the irreversibility of digit loss in Bachia. Evolution (N. Y). 64, 2466–2476. Gielen, A.C., Holmes, J., Myers, C., 2016. Prenatal testosterone and the earnings of men and women. J. Hum. Resour. 51, 30–61. Gorodnichenko, Y., Peter, K.S., 2005. Returns to schooling in Russia and Ukraine: A semiparametric approach to cross-country comparative analysis. J. Comp. Econ. 33, 324– 350. Groves, M.O., 2005. How important is your personality? Labor market returns to personality for women in the US and UK. J. Econ. Psychol. 26, 827–841. doi:10.1016/j.joep.2005.03.001 Heckman, J.J.J., Stixrud, J., Urzua, S., 2006. The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior, Journal of Labor Economics. doi:10.1086/504455 Hell, B., Päßler, K., 2011. Are occupational interests hormonally influenced? The 2D: 4D-interest nexus. Pers. Individ. Dif. 51, 376–380. Hönekopp, J., 2011. Relationships between digit ratio 2D: 4D and self-reported aggression and risk taking in an online study. Pers. Individ. Dif. 51, 77–80. Hönekopp, J., Bartholdt, L., Beier, L., Liebert, A., 2007. Second to fourth digit length ratio (2D: 4D) and adult sex hormone levels: new data and a meta-analytic review. Psychoneuroendocrinology 32, 313–321. Korenman, S., Miller, J.E., Sjaastad, J.E., 1995. Long-term poverty and child development in the United States: Results from the NLSY. Child. Youth Serv. Rev. 17, 127–155. Lemieux, T., 2006. The “Mincer Equation” Thirty Years after Schooling, Experience, and Earnings. Jacob Mincer, A Pioneer Mod. Labor Econ. 127–145. doi:10.1111/j.16000447.1989.tb05239.x Lindqvist, E., Vestman, R., 2011. The labor market returns to cognitive and noncognitive ability: Evidence from the Swedish enlistment. Am. Econ. J. Appl. Econ. 3, 101–128. Malas, M.A., Dogan, S., Evcil, E.H., Desdicioglu, K., 2006. Fetal development of the hand, digits and digit ratio (2D: 4D). Early Hum. Dev. 82, 469–475. McCrae, R.R., Costa Jr, P.T., Ostendorf, F., Angleitner, A., H\vreb’\ičková, M., Avia, M.D., Sanz, J., Sanchez-Bernardos, M.L., Kusdil, M.E., Woodfield, R., others, 2000. Nature over nurture: temperament, personality, and life span development. J. Pers. Soc. Psychol. 78, 173. 13

Mroz, T.A., 1987. The sensitivity of an empirical model of married women’s hours of work to economic and statistical assumptions. Econom. J. Econom. Soc. 765–799. Nye, J.V.C., Androuschak, G., Desierto, D.D., Jones, G., Yudkevich, M., 2012. 2D: 4D asymmetry and

gender

differences

in

academic

performance.

PLoS

One

7,

e46319.

doi:10.1371/journal.pone.0046319 Nye, J.V.C., Orel, E., 2015. The influence of prenatal hormones on occupational choice: 2D: 4D evidence from Moscow. Pers. Individ. Dif. 78, 39–42. Nye, J. V, Bryukhanov, M. V, Polyachenko, S., 2016. 2D: 4D and Education: Evidence from the Russian RLMS Survey. High. Sch. Econ. Res. Pap. No. WP BRP. Semykina, A., Linz, S.J., 2007. Gender differences in personality and earnings: Evidence from Russia. J. Econ. Psychol. 28, 387–410. Voracek, M., 2011. Special issue preamble: Digit ratio (2D: 4D) and individual differences research. Pers. Individ. Dif. 51, 367–370. Voracek, M., Tran, U.S., Dressler, S.G., 2010. Digit ratio (2D: 4D) and sensation seeking: New data and meta-analysis. Pers. Individ. Dif. 48, 72–77.

14

Figure 1. Variation of the average monthly wage for 12 months over the distribution of 2D:4D

15

Table 1. Summary of linear regressions of 2D:4D and wages. Coefficients on digit ratios of the left and the right hands. Full sample. OLS estimates.

Controls:

Digit ratio Digit ratio, age, age squared/100 Digit ratio, age, age squared/100, education Digit ratio, age, age squared/100, education, occupation dummies Digit ratio, age, age squared/100, education, occupation dummies, industry dummies

Females Dependent variables: Y1 Digit ratio of: The left The right hand hand (1) (2) -1.4627*** -1.2032*** (0.42) (0.43) -1.5190*** -1.3111*** (0.42) (0.43) -1.2184*** -1.1101*** (0.42) (0.41)

Y3 Digit ratio of: The left The right hand hand (5) (6) -0.1410 -0.0370 (0.60) (0.59) -0.1194 0.0868 (0.59) (0.59) 0.0313 0.2111 (0.58) (0.57)

Males Dependent variables: Y1 Digit ratio of: The left The right hand hand (7) (8) -0.4151 -0.4377 (0.43) (0.45) -0.4783 -0.5271 (0.42) (0.44) -0.8464** -0.7002 (0.41) (0.43)

Y2 Digit ratio of: The left The right hand hand (3) (4) -1.0252** -0.8586 (0.52) (0.56) -1.0246** -0.8361 (0.51) (0.57) -0.8588* -0.7295 (0.51) (0.56)

Y2 Digit ratio of: The left The right hand hand (9) (10) -0.5582 -0.5348 (0.52) (0.53) -0.5542 -0.6181 (0.51) (0.52) -0.9622* -0.8381* (0.51) (0.50)

Y3 Digit ratio of: The left The right hand hand (11) (12) -0.5877 -0.4257 (0.56) (0.54) -0.5744 -0.4912 (0.56) (0.54) -0.9726* -0.7136 (0.54) (0.52)

-0.7653* (0.40)

-0.8032** (0.40)

-0.3343 (0.49)

-0.1785 (0.48)

0.2395 (0.57)

0.2743 (0.55)

-0.8646** (0.39)

-0.6439* (0.39)

-0.9482** (0.41)

-0.8918** (0.39)

-0.8880** (0.44)

-0.7938* (0.44)

-0.5472 (0.39)

-0.6512* (0.39)

-0.3824 (0.48)

-0.4066 (0.48)

0.2701 (0.57)

0.0858 (0.55)

-0.6800* (0.39)

-0.4419 (0.39)

-0.9233** (0.43)

-0.8736** (0.41)

-0.8638* (0.46)

-0.7725* (0.46)

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses. In all regressions “Age squared” is divided by 100 in order to make regression coefficients on it easy to read.

16

Table 2. Summary of linear regressions of 2D:4D and wages. Coefficients on digit ratios of the left and the right hands. Subsample without outliers. OLS estimates.

Controls:

Digit ratio Digit ratio, age, age squared/100 Digit ratio, age, age squared/100, education Digit ratio, age, age squared/100, education, occupation dummies Digit ratio, age, age squared/100, education, occupation dummies, industry dummies

Females Dependent variables: Y1 Digit ratio of: The left The right hand hand (1) (2) -2.1511*** -1.4083** (0.67) (0.69) -2.1974*** -1.4935** (0.67) (0.69) -1.9107*** -1.4727** (0.66) (0.66)

Y3 Digit ratio of: The left The right hand hand (5) (6) -1.0175 0.3598 (0.92) (0.91) -1.0777 0.4297 (0.92) (0.91) -0.8913 0.3482 (0.91) (0.90)

Males Dependent variables: Y1 Digit ratio of: The left The right hand hand (7) (8) -0.5161 -0.8758 (0.65) (0.66) -0.6089 -1.0431 (0.65) (0.65) -1.0277 -1.3478** (0.63) (0.63)

Y2 Digit ratio of: The left The right hand hand (3) (4) -1.3295 -0.1525 (0.82) (0.90) -1.3502* -0.0764 (0.82) (0.90) -1.1890 -0.1531 (0.81) (0.87)

Y2 Digit ratio of: The left The right hand hand (9) (10) -0.7997 -0.6948 (0.89) (0.97) -0.9019 -0.9086 (0.88) (0.96) -1.4028 -1.3455 (0.87) (0.92)

Y3 Digit ratio of: The left The right hand hand (11) (12) -0.7672 -0.5360 (0.85) (0.88) -0.8121 -0.7610 (0.84) (0.87) -1.2865 -1.2300 (0.81) (0.83)

-1.1930* (0.61)

-0.7575 (0.64)

-0.3136 (0.77)

0.6010 (0.78)

0.0241 (0.89)

1.0075 (0.88)

-1.0709* (0.59)

-1.0689* (0.61)

-1.1767* (0.67)

-1.3478** (0.67)

-1.3068** (0.65)

-1.5849** (0.68)

-0.9205 (0.60)

-0.5424 (0.62)

-0.1977 (0.77)

0.5684 (0.79)

0.3802 (0.93)

1.0770 (0.91)

-0.7212 (0.61)

-0.6800 (0.61)

-1.0076 (0.69)

-1.0837 (0.70)

-1.1500* (0.67)

-1.3418* (0.70)

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses. In all regressions “Age squared” is divided by 100 in order to make regression coefficients on it easy to read.

17

Table 3. Summary of regressions with linear and quadratic term. Coefficients on linear and quadratic terms of digit ratios. Full sample. OLS estimates. #

Controls:

1

Digit ratio, digit ratio squared

2

Digit ratio, digit ratio squared , age, age squared/100

3

Digit ratio, digit ratio squared ,age, age squared/100, education Digit ratio, digit ratio squared , age, age squared/100, education, occupation dummies Digit ratio, digit Linear term 11.7162 ratio squared , age, (8.42) age squared/100, Quadratic term -6.0874 education, (4.18) occupation dummies, industry dummies

4

5

Coefficients Females Males on: Dependent variables: Dependent variables: Y1 Y2 Y3 Y1 Y2 Y3 The leftThe rightThe leftThe rightThe leftThe rightThe leftThe rightThe leftThe rightThe leftThe right hand hand hand hand hand hand hand hand hand hand hand hand (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Linear term 13.8133 21.8142** 1.9129 -3.4124 23.9586* 12.6091 9.5409 1.2256 14.9964 -5.9107 17.3943 -5.8793 (10.20) (8.82) (11.09) (10.00) (14.02) (10.79) (10.61) (12.16) (9.30) (11.35) (11.46) (14.05) Quadratic term -7.5816 -11.4083*** -1.4560 1.2593 -11.9801* -6.2428 -4.9746 -0.8322 -7.8413* 2.6856 -9.0664 2.7266 (5.08) (4.33) (5.50) (4.92) (6.94) (5.25) (5.25) (6.02) (4.71) (5.65) (5.77) (6.94) Linear term 13.5657 22.6037** 2.5147 -3.6713 24.0583* 11.5806 13.1581 1.8440 17.2965* -4.4295 19.9106* -3.9262 (10.08) (8.77) (10.99) (9.95) (13.97) (10.79) (10.65) (12.04) (8.89) (11.20) (11.19) (13.95) Quadratic term -7.4863 -11.8549*** -1.7539 1.3981 -12.0189* -5.6743 -6.8136 -1.1865 -8.9982** 1.9041 -10.3277* 1.7174 (5.01) (4.31) (5.45) (4.91) (6.92) (5.26) (5.27) (5.96) (4.50) (5.57) (5.63) (6.89) Linear term 9.1057 16.4302** -0.3605 -7.7579 19.7793 5.3939 9.0810 -6.8401 16.1322* -10.7869 18.5540* -11.0523 (9.14) (8.25) (10.39) (9.65) (13.33) (10.35) (10.87) (11.40) (8.93) (10.65) (11.13) (13.56) Quadratic term -5.1236 -8.6963** -0.2469 3.4663 -9.8164 -2.5590 -4.9597 3.0718 -8.6167* 4.9698 -9.8443* 5.1685 (4.54) (4.05) (5.15) (4.76) (6.59) (5.05) (5.39) (5.63) (4.52) (5.29) (5.62) (6.69) Linear term 12.1457 16.9933** 2.2389 -7.2114 18.4806 -0.8313 -1.1595 -10.2998 10.3942 -14.0819 13.6207 -4.2203 (8.52) (8.48) (10.01) (10.05) (12.69) (10.82) (11.29) (10.17) (8.40) (9.01) (8.90) (12.50) Quadratic term -6.4090 -8.8225** -1.2749 3.4724 -9.0459 0.5462 0.1473 4.8309 -5.7253 6.5904 -7.3248 1.7130 (4.24) (4.16) (4.97) (4.94) (6.23) (5.25) (5.65) (5.04) (4.24) (4.49) (4.47) (6.17) 23.5973*** 5.3868 (8.38) (9.49) -12.0123*** -2.8601 (4.12) (4.70)

2.7897 (10.13) -1.5783 (4.96)

21.6162* (12.66) -10.5916* (6.19)

8.9360 (10.90) -4.3733 (5.27)

6.6870 (11.27) -3.6789 (5.63)

-2.1850 (10.41) 0.8720 (5.16)

11.9970 (8.69) -6.5207 (4.39)

-9.8369 (9.48) 4.4777 (4.73)

14.4616 (9.28) -7.7359* (4.66)

-0.9863 (13.13) 0.1069 (6.48)

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses. In all regressions “Age squared” is divided by 100 in order to make regression coefficients on it easy to read .

18

Table 4. Summary of regressions with linear and quadratic term. Coefficients on linear and quadratic terms of digit ratios. Subsample without outliers. OLS estimates. #

Controls:

1

Digit ratio, digit ratio squared

2

Digit ratio, digit ratio squared , age, age squared/100

3

Digit ratio, digit ratio squared , age, age squared/100, education Digit ratio, digit ratio squared , age, age squared/100, education, occupation dummies Digit ratio, digit Linear term ratio squared , age, age squared/100, Quadratic education, term occupation dummies, industry dummies

4

5

Coefficients Females Males on: Dependent variables Dependent variables Y1 Y2 Y3 Y1 Y2 Y3 The left The right The left The right The left The right The left The right The left The right The left The right hand hand hand hand hand hand hand hand hand hand hand hand (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Linear term 33.9417 13.4261 7.8397 -20.8205 47.4983 -56.2825 63.0979** 48.2759 94.3949** 77.1548* 57.5811 28.4741 (29.27) (31.51) (38.19) (44.93) (42.61) (44.33) (28.23) (32.61) (41.20) (45.36) (41.25) (41.68) Quadratic -18.0176 -7.4012 -4.5773 10.3025 -24.2065 28.2417 -31.8253** -24.5207 -47.5963** -38.8581* -29.1895 -14.4838 term (14.69) (15.70) (19.06) (22.40) (21.15) (22.09) (14.13) (16.27) (20.70) (22.72) (20.69) (20.79) Linear term 32.0478 14.5653 5.1256 -23.3556 44.8853 -59.2401 65.5424** 41.3143 93.1721** 70.3691 61.4859 27.6675 (29.58) (31.00) (38.05) (45.10) (42.03) (44.07) (27.67) (32.47) (40.56) (46.06) (40.87) (41.76) Quadratic -17.0951 -8.0125 -3.2327 11.6043 -22.9327 29.7521 -33.0977** -21.1313 -47.0379** -35.5768 -31.1666 -14.1931 term (14.85) (15.45) (19.00) (22.50) (20.87) (21.96) (13.85) (16.22) (20.37) (23.08) (20.47) (20.82) Linear term 29.0518 0.1610 8.7599 -39.6428 42.4225 -72.1445* 66.1868** 32.7808 98.8166** 57.1338 66.5470* 14.6552 (29.19) (29.46) (37.83) (43.86) (41.36) (43.25) (26.73) (31.29) (39.32) (43.65) (39.37) (40.32) Quadratic -15.4583 -0.8152 -4.9670 19.6867 -21.6126 36.1459* -33.6298** -17.0253 -50.1126** -29.1867 -33.9372* -7.9303 term (14.66) (14.70) (18.90) (21.89) (20.53) (21.55) (13.37) (15.60) (19.73) (21.83) (19.71) (20.07) Linear term 31.9276 11.2410 13.9556 -22.9459 27.4722 -51.1828 41.3520 16.1675 40.7421 -8.2850 22.1290 -16.2277 (26.47) (27.71) (36.49) (37.59) (40.99) (42.58) (25.61) (30.52) (28.17) (31.76) (28.71) (32.01) Quadratic -16.5399 -5.9867 -7.1235 11.7425 -13.7067 26.0357 -21.2270* -8.5991 -20.9657 3.4618 -11.7203 7.3064 term (13.26) (13.80) (18.20) (18.66) (20.30) (21.10) (12.81) (15.22) (14.07) (15.85) (14.32) (15.97) 34.6443 (26.42) -17.7644 (13.21)

33.3099 (26.05) -16.8824 (12.95)

14.2114 (35.85) -7.1954 (17.86)

-8.4760 (38.15) 4.5081 (18.91)

25.2988 (41.87) -12.4469 (20.70)

-38.2155 (42.95) 19.5930 (21.24)

50.1659** (25.28) -25.4768** (12.65)

16.1305 (32.22) -8.3878 (16.06)

54.4552* (28.75) -27.7528* (14.38)

-4.3066 (32.92) 1.6087 (16.42)

31.4783 (29.18) -16.3251 (14.57)

-19.2839 (33.57) 8.9544 (16.75)

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses. In all regressions “Age squared” is divided by 100 in order to make regression coefficients on it easy to read.

19

Appendices Table A 1 Summary statistics of major variables.

Age Gender (male=1) Digit ratio, left hand (DL) Digit ratio, right had (DR) Education Logarithm of average monthly wage for the last 12 months (Y1) Logarithm of total income from full and part time employment in the last 30 days (Y2) Logarithm of total amount of money received in the last 30 days (Y3)

Mean

Standard deviation

Minimum

Maximum

40.9134 0.4196 0.9992 1.0001 17.9911

10.6109 0.4936 0.0459 0.0460 3.4747

25 0 0.7368 0.7024 3

60 1 1.2333 1.2857 23

Number observations 2586 2586 2531 2531 2572

10.1315

0.6141

6.6201

12.7657

1726

10.0987

0.7980

3.9120

13.8155

1898

10.0216

0.9030

3.9120

13.8155

2222

of

20

Table A 2 Educational attainment of individuals in the sample. Highest level of education completed Three grades Six grades Seven grades Eight grades Nine grades 7-9 grades plus vocational school (without diploma) 7-9 grades plus vocational school (with diploma) 10 grades without academic certificate of secondary education 7-9 grades plus some time spent in technical college (less than 2 years) Certificate of secondary education 10 and more grades plus some professional education without diploma 10 and more grades plus any professional education wit diploma 10 and more grades plus completion of technical college without diploma Completion of technical college with diploma 1-2 years of university education 3 years of university education Diploma of university education Graduate school (without diploma) Graduate school with diploma Total

Value 3 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 -

Frequency 1 2 3 43 67 13 34 2 2 305 4 438 11 439 73 58 995 19 63 2572

21

In the case of monthly wage in the last 12 months, 25 zero values were detected -- 4 males and 16 females were among them. All men indicated that they were currently working, and they had no wage debts, and that they were not owners or co-owners of enterprises, where they worked. Men represented the following industries: transportation and communication, light and food, civil machine construction, trade and consumer services. Nineteen women were on maternity leave. RLMS defines this as “paid leave (maternity leave or taking care of a child under 3 years of age)”. One woman worked with no wage debt, was not an owner of a enterprise, and she works in the public health industry. Interestingly, only 4 individuals (1 man and 3 women) indicated zeros for all components of total earnings received in the last 30 days. All indicated that they were not working at the time of the survey. The independent variable which comprises the largest set of zeros is the total amount of money received in the last 30 days. 160 women and 94 men reported zeros in the case of this variable.

Table A 3 Characteristics of respondents, who reported zeros for total amount of money received in the last 30 days. Employment status N of cases Wage debt N of cases Owner or co-owner of the enterprise where she works Males Currently working 18 YES 8 YES On unpaid leave 1 NO 10 NO Not working 75 Missing 76 Missing Females Currently working 19 YES 11 YES Paid leave 11 NO 18 NO Not working 129 Missing 131 Missing Missing 1

N of cases 1 17 76 1 28 131

We also made an attempt to account for the problem of sample selection bias in the case of females, and decided to apply STATA’s heckman selection procedure. The literature suggests (Dougherty, 2007; Mroz, 1987) that the presence of young children may be a possible instrument. For the first trials, just to see the general picture, we employed RLMS question: “Are there any children in the room below 10 years?”. We created a binary 22

variable – Children – which equal 1 if the answer is “Yes” and zero otherwise. Significant results were found only for Y1, for Y2 results for both hands were negative, but insignificant; for Y3 the coefficient on out instrumental variable were insignificant and positive, positive insignificant coefficient were detected on DL and on DR. We also tried another variable as an instrument – total family income for households which size is greater than 2. For Y1 results stay the same. In the case of Y2-Y3 – either computation procedure does not converge or insignificant results are detected. Results are provided in table below. Table A 4 Heckman Selection procedure, females. Dependent variable – Y1. (1) (2) (3) Y1 DL -1.0479** -0.4504 (0.42) (0.40)

(4)

(5)

(6)

-0.2659 (0.38)

Education

0.0624*** (0.01)

0.0628*** (0.01)

0.0365*** (0.01)

0.0361*** (0.01)

0.0301*** (0.01)

0.0299*** (0.01)

Age

0.0398* (0.02)

0.0412** (0.02)

0.0260 (0.02)

0.0270 (0.02)

0.0192 (0.02)

0.0200 (0.02)

Age squared/100

-0.0537** (0.03)

-0.0558** (0.03)

-0.0332 (0.02)

-0.0347 (0.02)

-0.0229 (0.02)

-0.0241 (0.02)

-1.0305** (0.43)

DR

Occupations Industry Constant

9.5005***

9.4519***

-0.6993* (0.41)

-0.5154 (0.40)

YES NO

YES NO

YES YES

YES YES

9.5066***

9.7521***

9.6617***

9.9074*** 23

select Children

Constant

(1) (0.58)

(2) (0.61)

(3) (0.56)

(4) (0.58)

(5) (0.57)

(6) (0.58)

-0.2690** (0.11)

-0.2756*** (0.11)

-0.2365** (0.10)

-0.2426** (0.10)

-0.2363** (0.10)

-0.2419** (0.10)

0.4207*** (0.04)

0.4214*** (0.04)

0.4148*** (0.04)

0.4153*** (0.04)

0.3926*** (0.04)

0.3932*** (0.04)

-1.1310*** (0.15)

-1.1254*** (0.15)

-1.2728*** (0.15)

-1.2702*** (0.15)

-1.3316*** (0.14)

-1.3248*** (0.14)

-0.3353*** (0.06) -0.8114 0.7151 -.5802 60.24 (0.00)

-0.3362*** (0.06) -0.8094 0.7145 -0.5783 59.62 (0.00)

-0.3688*** (0.05) -0.8546 0.6915 -0.5909 70.37 (0.00)

-0.3702*** (0.05) -0.8538 0.6906 -0.5897 71.87 (0.00)

-0.4008*** (0.06) -0.8696 0.6698 -0.5825 85.00 (0.00)

-0.4029*** (0.06) -0.8679 0.6684 -0.5801 84.39 (0.00)

1306

1306

1305

1305

1276

1276

athrho

lnsigma

rho sigma Lambda Wald test of Rho = 0 Chi-2 (Prob > chi2) N

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01 Robust standard errors in parentheses

24

Table B 1 Wage determinants. Full sample. Dependent variable – Y1, females. OLS estimates. (1) (2) (3) (4) (5) (6) *** *** *** * DL -1.4627 -1.5190 -1.2184 -0.7653 -0.5472 13.8133 (0.42) (0.42) (0.42) (0.40) (0.39) (10.20) Age 0.0259 0.0303* 0.0290* 0.0238 (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0386* -0.0396** -0.0352* -0.0269 (0.02) (0.02) (0.02) (0.02) Education 0.0577*** 0.0344*** 0.0290*** (0.01) (0.01) (0.01) DL squared -7.5816 (5.08) Constant 11.4319*** 11.1175*** 9.5811*** 9.5064*** 9.5857*** 3.7528 (0.42) (0.52) (0.54) (0.54) (0.56) (5.12) Occupation dummies YES YES Industry dummies R-squared 0.012 Number of observations 952

(7) 13.5657 (10.08) 0.0266 (0.02) -0.0393* (0.02)

-7.4863 (5.01) 3.5200 (5.09)

(8) 9.1057 (9.14) 0.0308* (0.02) -0.0402** (0.02) 0.0574*** (0.01) -5.1236 (4.54) 4.3873 (4.61)

(9) 12.1457 (8.52) 0.0294* (0.02) -0.0356* (0.02) 0.0341*** (0.01) -6.4090 (4.24) 3.0252 (4.32) YES

YES 0.028 952

0.106 947

0.212 946

0.269 917

(10) 11.7162 (8.42) 0.0242 (0.02) -0.0274 (0.02) 0.0288*** (0.01) -6.0874 (4.18) 3.4289 (4.26) YES YES

0.014 952

0.030 952

0.107 947

0.214 946

0.271 917

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

25

Table B 2 Wage determinants. Full sample. Dependent variable – Y1, males. OLS estimates. (1) (2) (3) (4) (5) (6) ** ** * DL -0.4151 -0.4783 -0.8464 -0.8646 -0.6800 9.5409 (0.43) (0.42) (0.41) (0.39) (0.39) (10.61) Age 0.0797*** 0.0821*** 0.0816*** 0.0861*** (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.1009*** -0.1037*** -0.1027*** -0.1053*** (0.02) (0.02) (0.02) (0.02) Education 0.0522*** 0.0339*** 0.0356*** (0.01) (0.01) (0.01) DL squared -4.9746 (5.25) Constant 10.7518*** 9.3505*** 8.7484*** 8.6778*** 8.2795*** 5.7804 (0.43) (0.56) (0.54) (0.52) (0.51) (5.35) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) 13.1581 (10.65) 0.0809*** (0.02) -0.1025*** (0.02)

-6.8136 (5.27) 2.5171 (5.42)

(8) 9.0810 (10.87) 0.0830*** (0.02) -0.1048*** (0.02) 0.0520*** (0.01) -4.9597 (5.39) 3.7767 (5.52)

(9) -1.1595 (11.29) 0.0816*** (0.02) -0.1027*** (0.02) 0.0339*** (0.01) 0.1473 (5.65) 8.8252 (5.69) YES

YES 0.001 of 738

0.034 738

0.147 733

0.275 732

0.324 716

(10) 6.6870 (11.27) 0.0867*** (0.02) -0.1060*** (0.02) 0.0355*** (0.01) -3.6789 (5.63) 4.5919 (5.67) YES YES

0.002 738

0.035 738

0.148 733

0.275 732

0.324 716

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

26

Table B 3 Wage determinants. Full sample. Dependent variable – Y1, females. OLS estimates. (1) (2) (3) (4) (5) (6) *** *** ** * DR -1.2032 -1.3111 -0.8032 -0.6512 21.8142** *** 1.1101 (0.43) (0.43) (0.41) (0.40) (0.39) (8.82) Age 0.0256 0.0301* 0.0289* 0.0236 (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0383* -0.0395** -0.0352* -0.0269 (0.02) (0.02) (0.02) (0.02) Education 0.0584*** 0.0344*** 0.0289*** (0.01) (0.01) (0.01) DR squared 11.4083*** (4.33) Constant 11.1735*** 10.9204*** 9.4656*** 9.5515*** 9.6991*** -0.4144 (0.43) (0.54) (0.57) (0.56) (0.56) (4.49) Occupation YES YES dummies Industry dummies R-squared Number observations

(7) 22.6037**

(8) (9) (10) ** ** 16.4302 16.9933 23.5973***

(8.77) 0.0241 (0.02) -0.0367* (0.02)

(8.25) 0.0290* (0.02) -0.0383** (0.02) 0.0578*** (0.01) -8.6963**

11.8549*** (4.31) (4.05) -1.0844 0.6752 (4.45) (4.18)

(8.48) 0.0277* (0.02) -0.0338* (0.02) 0.0337*** (0.01) -8.8225** (4.16) 0.6375 (4.32) YES

YES 0.008 of 952

0.024 952

0.104 947

0.212 946

0.270 917

(8.38) 0.0218 (0.02) -0.0248 (0.02) 0.0278*** (0.01) 12.0123*** (4.12) -2.4544 (4.26) YES

YES 0.012 952

0.028 952

0.106 947

0.215 946

0.274 917

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

27

Table B 4 Wage determinants. Full sample. Dependent variable – Y1, males. OLS estimates. (1) (2) (3) (4) (5) (6) * DR -0.4377 -0.5271 -0.7002 -0.6439 -0.4419 1.2256 (0.45) (0.44) (0.43) (0.39) (0.39) (12.16) Age 0.0803*** 0.0830*** 0.0824*** 0.0866*** (0.02) (0.02) (0.02) (0.02) Age squared/100 *** *** *** 0.1016 0.1046 0.1036 0.1058*** (0.02) (0.02) (0.02) (0.02) *** *** Education 0.0516 0.0335 0.0352*** (0.01) (0.01) (0.01) DR squared -0.8322 (6.02) Constant 10.7748*** 9.3861*** 8.5932*** 8.4507*** 8.0418*** 9.9454 (0.45) (0.59) (0.56) (0.52) (0.52) (6.13) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) 1.8440 (12.04) 0.0803*** (0.02) 0.1016*** (0.02)

-1.1865 (5.96) 8.2039 (6.09)

(8) -6.8401 (11.40) 0.0830*** (0.02) 0.1046*** (0.02) 0.0518*** (0.01) 3.0718 (5.63) 11.6502** (5.76)

(9) -10.2998 (10.17) 0.0824*** (0.02) -0.1036***

(10) -2.1850 (10.41) 0.0866*** (0.02) -0.1058***

(0.02) 0.0340*** (0.01) 4.8309 (5.04) 13.2574*** (5.13) YES

(0.02) 0.0352*** (0.01) 0.8720 (5.16) 8.9101* (5.25) YES

YES 0.001 of 738

0.034 738

0.146 733

0.273 732

0.322 716

YES 0.001 738

0.034 738

0.146 733

0.274 732

0.322 716

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

28

Table B 5 Wage determinants. Full sample. Dependent variable – Y2, females. OLS estimates. (1) (2) (3) (4) (5) (6) ** ** * DL -1.0252 -1.0246 -0.8588 -0.3343 -0.3824 1.9129 (0.52) (0.51) (0.51) (0.49) (0.48) (11.09) Age 0.0784*** 0.0819*** 0.0821*** 0.0836*** (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0947*** -0.0958*** -0.0921*** -0.0914*** (0.03) (0.03) (0.02) (0.02) Education 0.0522*** 0.0346*** 0.0332*** (0.01) (0.01) (0.01) DL squared -1.4560 (5.50) Constant 10.9651*** 9.4466*** 8.1893*** 7.9914*** 8.1499*** 9.4858* (0.52) (0.69) (0.71) (0.69) (0.72) (5.59) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) 2.5147 (10.99) 0.0785*** (0.02) -0.0948*** (0.03)

-1.7539 (5.45) 7.6628 (5.52)

(8) -0.3605 (10.39) 0.0819*** (0.02) -0.0958*** (0.03) 0.0522*** (0.01) -0.2469 (5.15) 7.9383 (5.24)

(9) 2.2389 (10.01) 0.0821*** (0.02) -0.0921*** (0.02) 0.0345*** (0.01) -1.2749 (4.97) 6.6976 (5.04) YES

YES 0.003 of 1019

0.015 1019

0.051 1013

0.126 980

0.170 945

(10) 5.3868 (9.49) 0.0838*** (0.02) -0.0916*** (0.02) 0.0331*** (0.01) -2.8601 (4.70) 5.2506 (4.80) YES YES

0.003 1019

0.015 1019

0.051 1013

0.126 980

0.170 945

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

29

Table B 6 Wage determinants. Full sample. Dependent variable – Y2, males. OLS estimates. (1) (2) (3) (4) (5) (6) * ** ** DL -0.5582 -0.5542 -0.9622 -0.9482 -0.9233 14.9964 (0.52) (0.51) (0.51) (0.41) (0.43) (9.30) Age 0.0868*** 0.0898*** 0.0843*** 0.0890*** (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.1075*** -0.1112*** -0.1030*** -0.1069*** (0.03) Education

(0.03) 0.0565*** (0.01)

(0.02) 0.0310*** (0.01)

(0.02) 0.0301*** (0.01)

DL squared 10.8434*** 9.1992*** (0.51) (0.70)

Constant

8.5600*** (0.67)

Occupation dummies

8.7073*** (0.57) YES

Industry dummies R-squared Number observations

8.4917*** (0.60) YES

-7.8413* (4.71) 3.1461 (4.60)

(7) 17.2965* (8.89) 0.0884*** (0.02) -0.1093***

(8) 16.1322* (8.93) 0.0912*** (0.02) -0.1129***

(9) 10.3942 (8.40) 0.0855*** (0.02) -0.1045***

(0.03)

(0.03) 0.0564*** (0.01) -8.6167* (4.52) 0.0721 (4.46)

(0.02) 0.0309*** (0.01) -5.7253 (4.24) 3.0732 (4.20) YES

-8.9982** (4.50) 0.3335 (4.44)

YES 0.001 of 835

0.021 835

0.104 830

0.238 790

0.263 774

(10) 11.9970 (8.69) 0.0904*** (0.02) 0.1085*** (0.02) 0.0301*** (0.01) -6.5207 (4.39) 2.0711 (4.32) YES YES

0.003 835

0.024 835

0.106 830

0.239 790

0.264 774

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

30

Table B 7 Wage determinants. Full sample. Dependent variable – Y2, females. OLS estimates. (1) (2) (3) (4) (5) (6) DR -0.8586 -0.8361 -0.7295 -0.1785 -0.4066 -3.4124 (0.56) (0.57) (0.56) (0.48) (0.48) (10.00) Age 0.0776*** 0.0813*** 0.0820*** 0.0833*** (0.02) (0.02) (0.02) (0.02) Age squared/100 *** *** *** 0.0940 0.0953 0.0920 0.0913*** (0.03) (0.03) (0.02) (0.02) *** *** Education 0.0525 0.0345 0.0330*** (0.01) (0.01) (0.01) DR squared 1.2593 (4.92) Constant 10.7989*** 9.2769*** 8.0686*** 7.8392*** 8.1872*** 12.0910** (0.57) (0.76) (0.78) (0.71) (0.71) (5.08) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) -3.6713 (9.95) 0.0777*** (0.02) 0.0941*** (0.03)

1.3981 (4.91) 10.7102** (4.98)

(8) -7.7579 (9.65) 0.0815*** (0.02) 0.0954*** (0.03) 0.0528*** (0.01) 3.4663 (4.76) 11.6157** (4.82)

(9) -7.2114 (10.05) 0.0821*** (0.02) 0.0921*** (0.02) 0.0349*** (0.01) 3.4724 (4.94) 11.3833** (5.05) YES

YES 0.002 of 1019

0.013 1019

0.050 1013

0.126 980

0.170 945

(10) 2.7897 (10.13) 0.0833*** (0.02) -0.0912*** (0.03) 0.0328*** (0.01) -1.5783 (4.96) 6.5772 (5.10) YES YES

0.002 1019

0.014 1019

0.051 1013

0.126 980

0.170 945

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

31

Table B 8 Wage determinants. Full sample. Dependent variable – Y2, males. OLS estimates. (1) (2) (3) (4) (5) (6) * ** ** DR -0.5348 -0.6181 -0.8381 -0.8918 -0.8736 -5.9107 (0.53) (0.52) (0.50) (0.39) (0.41) (11.35) Age 0.0881*** 0.0917*** 0.0863*** 0.0909*** (0.02) (0.02) (0.02) (0.02) Age squared/100 *** *** *** 0.1089 0.1133 0.1052 0.1089*** (0.03) (0.03) (0.02) (0.02) *** *** Education 0.0559 0.0307 0.0298*** (0.01) (0.01) (0.01) DR squared 2.6856 (5.65) Constant 10.8209*** 9.2354*** 8.4055*** 8.6225*** 8.4208*** 13.5055** (0.53) (0.71) (0.67) (0.54) (0.56) (5.70) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) -4.4295 (11.20) 0.0880*** (0.02) 0.1087*** (0.03)

1.9041 (5.57) 11.1416** (5.65)

(8) -10.7869 (10.65) 0.0913*** (0.02) 0.1128*** (0.03) 0.0562*** (0.01) 4.9698 (5.29) 13.3762** (5.37)

(9) -14.0819 (9.01) 0.0857*** (0.02) -0.1045***

(10) -9.8369 (9.48) 0.0905*** (0.02) -0.1083***

(0.02) 0.0311*** (0.01) 6.5904 (4.49) 15.2160*** (4.55) YES

(0.02) 0.0300*** (0.01) 4.4777 (4.73) 12.9044*** (4.77) YES

YES 0.001 of 835

0.021 835

0.103 830

0.237 790

0.262 774

YES 0.001 835

0.022 835

0.104 830

0.238 790

0.263 774

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

32

Table B 9 Wage determinants. Full sample. Dependent variable – Y3, females. OLS estimates. (1) (2) (3) (4) (5) (6) DL -0.1410 -0.1194 0.0313 0.2395 0.2701 23.9586* (0.60) (0.59) (0.58) (0.57) (0.57) (14.02) Age 0.0576** 0.0613*** 0.0217 0.0228 (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0588** -0.0598** -0.0091 -0.0083 (0.03) (0.03) (0.03) (0.03) Education 0.0573*** 0.0228** 0.0212** (0.01) (0.01) (0.01) DL squared -11.9801* (6.94) Constant 9.9996*** 8.6634*** 7.3256*** 8.6388*** 8.6598*** -2.0943 (0.60) (0.79) (0.78) (0.76) (0.78) (7.08) Occupation dummies YES YES Industry dummies R-squared 0.000 Number of observations 1272

(7) 24.0583* (13.97) 0.0577** (0.02) -0.0589** (0.03) -12.0189* (6.92) -3.4706 (7.06)

(8) 19.7793 (13.33) 0.0613*** (0.02) -0.0599** (0.03) 0.0567*** (0.01) -9.8164 (6.59) -2.5732 (6.74)

(9) 18.4806 (12.69) 0.0220 (0.02) -0.0094 (0.03) 0.0226** (0.01) -9.0459 (6.23) -0.5271 (6.44) YES

YES 0.011 1272

0.046 1266

0.092 1021

0.122 986

(10) 21.6162* (12.66) 0.0234 (0.02) -0.0089 (0.03) 0.0211** (0.01) -10.5916* (6.19) -2.0634 (6.42) YES YES

0.002 1272

0.014 1272

0.048 1266

0.094 1021

0.125 986

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

33

Table B 10 Wage determinants. Full sample. Dependent variable – Y3, males. OLS estimates. (1) (2) (3) (4) (5) (6) * ** * DL -0.5877 -0.5744 -0.9726 -0.8880 -0.8638 17.3943 (0.56) (0.56) (0.54) (0.44) (0.46) (11.46) Age 0.0896*** 0.0894*** 0.0774*** 0.0840*** (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.1153*** -0.1150*** -0.0934*** -0.0993*** (0.03) Education

(0.03) 0.0552*** (0.01)

(0.02) 0.0318*** (0.01)

(0.02) 0.0312*** (0.01)

DL squared 10.8259*** 9.1940*** (0.56) (0.70)

Constant

8.6301*** (0.67)

Occupation dummies

8.7850*** (0.59) YES

Industry dummies R-squared Number observations

8.5136*** (0.61) YES

-9.0664 (5.77) 1.9284 (5.69)

(7) 19.9106* (11.19) 0.0912*** (0.02) -0.1172***

(8) 18.5540* (11.13) 0.0909*** (0.02) -0.1168***

(9) 13.6207 (8.90) 0.0790*** (0.02) -0.0953***

(0.03)

(0.03) 0.0551*** (0.01) -9.8443* (5.62) -1.0588 (5.58)

(0.02) 0.0318*** (0.01) -7.3248 (4.47) 1.5789 (4.46) YES

-10.3277* (5.63) -0.9729 (5.63)

YES 0.001 of 902

0.027 902

0.098 897

0.223 798

0.247 781

(10) 14.4616 (9.28) 0.0856*** (0.02) 0.1012*** (0.02) 0.0312*** (0.01) -7.7359* (4.66) 0.8989 (4.62) YES YES

0.003 902

0.030 902

0.100 897

0.225 798

0.249 781

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

34

Table B 11 Wage determinants. Full sample. Dependent variable – Y3, females. OLS estimates. (1) (2) (3) (4) (5) (6) DR -0.0370 0.0868 0.2111 0.2743 0.0858 12.6091 (0.59) (0.59) (0.57) (0.55) (0.55) (10.79) Age 0.0581** 0.0617*** 0.0219 0.0227 (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0593** -0.0603** -0.0092 -0.0082 (0.03) (0.03) (0.03) (0.03) Education 0.0574*** 0.0229** 0.0212** (0.01) (0.01) (0.01) DR squared -6.2428 (5.25) Constant 9.8958*** 8.4480*** 7.1352*** 8.5985*** 8.8463*** 3.5054 (0.59) (0.81) (0.81) (0.76) (0.76) (5.54) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) 11.5806 (10.79) 0.0571** (0.02) -0.0581** (0.03)

-5.6743 (5.26) 2.6598 (5.49)

(8) 5.3939 (10.35) 0.0612*** (0.02) -0.0597** (0.03) 0.0571*** (0.01) -2.5590 (5.05) 4.5316 (5.26)

(9) -0.8313 (10.82) 0.0219 (0.02) -0.0092 (0.03) 0.0229** (0.01) 0.5462 (5.25) 9.1553* (5.48) YES

YES 0.000 of 1271

0.011 1271

0.046 1265

0.092 1021

0.122 986

(10) 8.9360 (10.90) 0.0225 (0.02) -0.0081 (0.03) 0.0208** (0.01) -4.3733 (5.27) 4.3911 (5.52) YES YES

0.001 1271

0.012 1271

0.046 1265

0.092 1021

0.123 986

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

35

Table B 12 Wage determinants. Full sample. Dependent variable – Y3, males. OLS estimates. (1) (2) (3) (4) (5) (6) * * DR -0.4257 -0.4912 -0.7136 -0.7938 -0.7725 -5.8793 (0.54) (0.54) (0.52) (0.44) (0.46) (14.05) Age 0.0910*** 0.0917*** 0.0791*** 0.0856*** (0.02) (0.02) (0.02) (0.02) Age squared/100 *** *** *** 0.1169 0.1175 0.0953 0.1011*** (0.03) (0.03) (0.02) (0.02) *** *** Education 0.0546 0.0315 0.0309*** (0.01) (0.01) (0.01) DR squared 2.7266 (6.94) Constant 10.6652*** 9.0817*** 8.3355*** 8.6671*** 8.4048*** 13.3867* (0.54) (0.69) (0.65) (0.57) (0.59) (7.10) Occupation dummies YES YES Industry dummies R-squared Number observations

(7) -3.9262 (13.95) 0.0909*** (0.02) 0.1167*** (0.03)

1.7174 (6.89) 10.7985 (7.04)

(8) -11.0523 (13.56) 0.0913*** (0.02) 0.1170*** (0.03) 0.0549*** (0.01) 5.1685 (6.69) 13.4977** (6.84)

(9) -4.2203 (12.50) 0.0790*** (0.02) 0.0951*** (0.02) 0.0316*** (0.01) 1.7130 (6.17) 10.3794* (6.30) YES

YES 0.001 of 902

0.027 902

0.096 897

0.222 798

0.246 781

(10) -0.9863 (13.13) 0.0856*** (0.02) -0.1011*** (0.02) 0.0309*** (0.01) 0.1069 (6.48) 8.5117 (6.61) YES YES

0.001 902

0.027 902

0.097 897

0.222 798

0.246 781

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

36

Table B 13 Wage determinants. Subsample without outliers. Dependent variable – Y1, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) *** *** *** * DL -2.1511 -2.1974 -1.9107 -1.1930 -0.9205 33.9417 32.0478 (0.67) (0.67) (0.66) (0.61) (0.60) (29.27) (29.58) Age 0.0203 0.0235 0.0235 0.0189 0.0194 (0.02) (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0324 -0.0323 -0.0292 -0.0217 -0.0313 (0.02) (0.02) (0.02) (0.02) (0.02) Education 0.0596*** 0.0368*** 0.0294*** (0.01) (0.01) (0.01) DL squared -18.0176 -17.0951 (14.69) (14.85) Constant 12.1489*** 11.9460*** 10.4061*** 10.0088*** 10.1361*** -5.9068 -5.1678 (0.67) (0.73) (0.75) (0.73) (0.76) (14.58) (14.73) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 29.0518 (29.19) 0.0227 (0.02) -0.0314 (0.02) 0.0596*** (0.01) -15.4583 (14.66) -5.0650 (14.52)

(9) 31.9276 (26.47) 0.0225 (0.02) -0.0279 (0.02) 0.0366*** (0.01) -16.5399 (13.26) -6.5215 (13.19) YES

YES 0.014 of 774

0.030 774

0.111 770

0.212 769

0.275 743

(10) 34.6443 (26.42) 0.0176 (0.02) -0.0201 (0.02) 0.0292*** (0.01) -17.7644 (13.21) -7.6032 (13.17) YES YES

0.016 774

0.032 774

0.112 770

0.214 769

0.277 743

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

37

Table B 14 Wage determinants. Subsample without outliers. Dependent variable – Y1, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) * ** DL -0.5161 -0.6089 -1.0277 -1.0709 -0.7212 63.0979 65.5424** (0.65) (0.65) (0.63) (0.59) (0.61) (28.23) (27.67) Age 0.0784*** 0.0781*** 0.0775*** 0.0828*** 0.0773*** (0.02) (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0989*** 0.0999*** 0.0993*** 0.0971*** 0.1010*** (0.02) (0.02) (0.02) (0.02) (0.02) *** *** *** Education 0.0552 0.0402 0.0410 (0.01) (0.01) (0.01) DL squared 31.8253** 33.0977** (14.13) (13.85) *** *** *** *** *** Constant 10.8330 9.4944 8.9381 8.8317 8.3459 -20.9183 -23.4910* (0.65) (0.75) (0.71) (0.67) (0.66) (14.09) (13.81) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 66.1868** (26.73) 0.0769*** (0.02) -0.0983*** (0.02) 0.0551*** (0.01) 33.6298** (13.37) -24.5754* (13.35)

(9) 41.3520 (25.61) 0.0765*** (0.02) 0.0961*** (0.02) 0.0405*** (0.01) -21.2270* (12.81) -12.3057 (12.78) YES

YES 0.001 of 548

0.039 548

0.169 544

0.304 543

0.362 531

(10) 50.1659** (25.28) 0.0815*** (0.02) -0.0997*** (0.02) 0.0415*** (0.01) 25.4768** (12.65) -16.9882 (12.59) YES YES

0.009 548

0.047 548

0.178 544

0.308 543

0.367 531

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

38

Table B 15 Wage determinants. Subsample without outliers. Dependent variable – Y1, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) ** ** ** DR -1.4083 -1.4935 -1.4727 -0.7575 -0.5424 13.4261 14.5653 (0.69) (0.69) (0.66) (0.64) (0.62) (31.51) (31.00) Age 0.0190 0.0224 0.0231 0.0185 0.0185 (0.02) (0.02) (0.02) (0.02) (0.02) Age squared/100 -0.0310 -0.0310 -0.0286 -0.0212 -0.0304 (0.02) (0.02) (0.02) (0.02) (0.02) Education 0.0608*** 0.0374*** 0.0299*** (0.01) (0.01) (0.01) DR squared -7.4012 -8.0125 (15.70) (15.45) Constant 11.4071*** 11.2697*** 9.9713*** 9.5663*** 9.7532*** 3.9817 3.2432 (0.70) (0.78) (0.78) (0.76) (0.76) (15.80) (15.56) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 0.1610 (29.46) 0.0223 (0.02) -0.0309 (0.02) 0.0608*** (0.01) -0.8152 (14.70) 9.1552 (14.75)

(9) 11.2410 (27.71) 0.0226 (0.02) -0.0281 (0.02) 0.0372*** (0.01) -5.9867 (13.80) 3.5745 (13.89) YES

YES 0.006 of 774

0.022 774

0.106 770

0.210 769

0.273 743

(10) 33.3099 (26.05) 0.0175 (0.02) -0.0200 (0.02) 0.0293*** (0.01) -16.8824 (12.95) -7.1640 (13.08) YES YES

0.006 774

0.022 774

0.106 770

0.210 769

0.275 743

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

39

Table B 16 Wage determinants. Subsample without outliers. Dependent variable – Y1, males. OLS estimates. (1) (2) (3) (4) (5) (6) (7) ** * DR -0.8758 -1.0431 -1.3478 -1.0689 -0.6800 48.2759 41.3143 (0.66) (0.65) (0.63) (0.61) (0.61) (32.61) (32.47) Age 0.0794*** 0.0793*** 0.0784*** 0.0833*** 0.0771*** (0.02) (0.02) (0.02) (0.02) (0.02) Age squared/100 *** *** *** *** 0.1011 0.1008 0.0981 0.1017 0.0986*** (0.02) (0.02) (0.02) (0.02) (0.02) *** *** *** Education 0.0551 0.0400 0.0407 (0.01) (0.01) (0.01) DR squared -24.5207 -21.1313 (16.27) (16.22) Constant 11.1927*** 9.9113*** 9.2373*** 8.8360*** 8.3116*** -13.4118 -11.2452 (0.66) (0.79) (0.74) (0.72) (0.70) (16.32) (16.19) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 32.7808 (31.29) 0.0775*** (0.02) 0.0988*** (0.02) 0.0548*** (0.01) -17.0253 (15.60) -7.8051 (15.62)

(9) 16.1675 (30.52) 0.0776*** (0.02) 0.0971*** (0.02) 0.0399*** (0.01) -8.5991 (15.22) 0.2225 (15.25) YES

YES 0.003 of 548

0.041 548

0.172 544

0.304 543

0.362 531

(10) 16.1305 (32.22) 0.0825*** (0.02) -0.1007*** (0.02) 0.0406*** (0.01) -8.3878 (16.06) -0.0832 (16.09) YES YES

0.007 548

0.044 548

0.174 544

0.304 543

0.362 531

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

40

Table B 17 Wage determinants. Subsample without outliers. Dependent variable – Y2, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) * DL -1.3295 -1.3502 -1.1890 -0.3136 -0.1977 7.8397 5.1256 (0.82) (0.82) (0.81) (0.77) (0.77) (38.19) (38.05) Age 0.0866*** 0.0897*** 0.0888*** 0.0924*** 0.0865*** (0.03) (0.03) (0.02) (0.03) (0.03) Age squared/100 -0.1052*** -0.1060*** -0.1007*** -0.1026*** -0.1051*** (0.03) (0.03) (0.03) (0.03) (0.03) Education 0.0552*** 0.0374*** 0.0353*** (0.01) (0.01) (0.01) DL squared -4.5773 -3.2327 (19.06) (19.00) Constant 11.2772*** 9.6312*** 8.3250*** 7.9027*** 7.9537*** 6.6902 6.3932 (0.82) (0.96) (0.98) (0.98) (1.02) (19.11) (18.96) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 8.7599 (37.83) 0.0896*** (0.03) -0.1059*** (0.03) 0.0553*** (0.01) -4.9670 (18.90) 3.3498 (18.87)

(9) 13.9556 (36.49) 0.0886*** (0.02) -0.1005*** (0.03) 0.0374*** (0.01) -7.1235 (18.20) 0.7718 (18.20) YES

YES 0.003 of 833

0.016 833

0.055 829

0.127 802

0.175 771

(10) 14.2114 (35.85) 0.0922*** (0.03) -0.1023*** (0.03) 0.0354*** (0.01) -7.1954 (17.86) 0.7569 (17.89) YES YES

0.003 833

0.016 833

0.055 829

0.127 802

0.175 771

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

41

Table B 18 Wage determinants. Subsample without outliers. Dependent variable – Y2, males. OLS estimates. (1) (2) (3) (4) (5) (6) (7) * ** DL -0.7997 -0.9019 -1.4028 -1.1767 -1.0076 94.3949 93.1721** (0.89) (0.88) (0.87) (0.67) (0.69) (41.20) (40.56) Age 0.0938*** 0.0958*** 0.0871*** 0.0913*** 0.0921*** (0.03) (0.03) (0.02) (0.02) (0.03) Age squared/100 -0.1151*** 0.1168*** 0.1197*** 0.1065*** 0.1097*** (0.03) (0.03) (0.03) (0.03) (0.03) *** *** *** Education 0.0602 0.0342 0.0335 (0.01) (0.01) (0.01) DL squared 47.5963** 47.0379** (20.70) (20.37) *** *** *** *** *** * Constant 11.0572 9.3982 8.8083 8.8132 8.5169 -36.4834 -37.5396* (0.88)

(1.03)

(0.97)

Occupation dummies

(0.76) YES

Industry dummies R-squared Number observations

(0.80) YES

(20.48)

(20.15)

(8) 98.8166** (39.32) 0.0939*** (0.03) -0.1179*** (0.03) 0.0605*** (0.01) 50.1126** (19.73) 41.1990** (19.53)

(9) 40.7421 (28.17) 0.0868*** (0.02) 0.1062*** (0.03) 0.0347*** (0.01) -20.9657

(10) 54.4552* (28.75) 0.0908*** (0.02) 0.1093*** (0.03) 0.0343*** (0.01) -27.7528*

(14.07) -12.0987

(14.38) -19.1334

(14.04) YES

(14.29) YES

YES 0.001 of 622

0.024 622

0.108 618

0.242 583

0.269 571

YES 0.011 622

0.034 622

0.119 618

0.244 583

0.273 571

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

42

Table B 19 Wage determinants. Subsample without outliers. Dependent variable – Y2, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) (8) DR -0.1525 -0.0764 -0.1531 0.6010 0.5684 -20.8205 -23.3556 -39.6428 (0.90) (0.90) (0.87) (0.78) (0.79) (44.93) (45.10) (43.86) Age 0.0861*** 0.0893*** 0.0892*** 0.0928*** 0.0864*** 0.0899*** (0.03) (0.03) (0.02) (0.03) (0.03) (0.03) Age squared/100 *** *** *** *** *** 0.1047 0.1056 0.1011 0.1029 0.1051 0.1062*** (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) *** *** *** Education 0.0556 0.0372 0.0351 0.0561*** (0.01) (0.01) (0.01) (0.01) DR squared 10.3025 11.6043 19.6867 (22.40) (22.50) (21.89) Constant 10.1004*** 8.3670*** 7.2911*** 6.9889*** 7.1892*** 20.4548 20.0226 27.0511 (0.90) (1.04) (1.02) (0.94) (0.96) (22.51) (22.47) (21.81) Occupation dummies YES YES Industry dummies R-squared Number observations

(9) -22.9459 (37.59) 0.0897*** (0.02) 0.1015*** (0.03) 0.0376*** (0.01) 11.7425 (18.66) 18.7589 (18.74) YES

YES 0.000 of 833

0.013 833

0.053 829

0.127 802

0.175 771

(10) -8.4760 (38.15) 0.0929*** (0.03) -0.1030*** (0.03) 0.0353*** (0.01) 4.5081 (18.91) 11.7129 (19.02) YES YES

0.000 833

0.014 833

0.054 829

0.127 802

0.175 771

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

43

Table B 20 Wage determinants. Subsample without outliers. Dependent variable – Y2, males. OLS estimates. (1) (2) (3) (4) (5) (6) (7) ** * DR -0.6948 -0.9086 -1.3455 -1.3478 -1.0837 77.1548 70.3691 (0.97) (0.96) (0.92) (0.67) (0.70) (45.36) (46.06) Age 0.0947*** 0.0971*** 0.0883*** 0.0924*** 0.0925*** (0.03) (0.03) (0.02) (0.02) (0.03) Age squared/100 *** *** *** *** 0.1179 0.1212 0.1079 0.1110 0.1153*** (0.03) (0.03) (0.03) (0.03) (0.03) *** *** *** Education 0.0599 0.0340 0.0332 (0.01) (0.01) (0.01) DR squared -38.8581* -35.5768 (22.72) (23.08) Constant 10.9529*** 9.3875*** 8.7312*** 8.9838*** 8.5923*** -27.9968 -26.2278 (0.97) (1.13) (1.05) (0.78) (0.80) (22.63) (22.88) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 57.1338 (43.65) 0.0952*** (0.03) 0.1191*** (0.03) 0.0594*** (0.01) -29.1867 (21.83) -20.4816 (21.75)

(9) -8.2850 (31.76) 0.0885*** (0.02) 0.1081*** (0.03) 0.0341*** (0.01) 3.4618 (15.85) 12.4529 (15.93) YES

YES 0.001 of 622

0.024 622

0.107 618

0.243 583

0.270 571

(10) -4.3066 (32.92) 0.0925*** (0.02) -0.1111*** (0.03) 0.0332*** (0.01) 1.6087 (16.42) 10.2030 (16.49) YES YES

0.006 622

0.028 622

0.110 618

0.243 583

0.270 571

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

44

Table B 21 Wage determinants. Subsample without outliers. Dependent variable – Y3, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) DL -1.0175 -1.0777 -0.8913 0.0241 0.3802 47.4983 44.8853 (0.92) (0.92) (0.91) (0.89) (0.93) (42.61) (42.03) Age 0.0598** 0.0633** 0.0274 0.0300 0.0585** (0.03) (0.03) (0.03) (0.03) (0.03) Age squared/100 -0.0628** -0.0642** -0.0164 -0.0171 -0.0612** (0.03) (0.03) (0.03) (0.03) (0.03) Education 0.0558*** 0.0298*** 0.0273** (0.01) (0.01) (0.01) DL squared -24.2065 -22.9327 (21.15) (20.87) Constant 10.8938*** 9.6242*** 8.2820*** 8.7193*** 8.4855*** -13.3881 -13.3552 (0.93) (1.11) (1.11) (1.13) (1.18) (21.44) (21.16) Occupation dummies YES YES Industry dummies R-squared Number of observations

(8) 42.4225 (41.36) 0.0621** (0.03) -0.0628** (0.03) 0.0557*** (0.01) -21.6126 (20.53) -13.3700 (20.84)

(9) 27.4722 (40.99) 0.0268 (0.03) -0.0157 (0.03) 0.0299*** (0.01) -13.7067 (20.30) -4.9905 (20.69) YES

YES 0.001 1008

0.010 1008

0.042 1004

0.093 830

0.128 799

(10) 25.2988 (41.87) 0.0294 (0.03) -0.0164 (0.03) 0.0274** (0.01) -12.4469 (20.70) -3.9550 (21.14) YES YES

0.003 1008

0.012 1008

0.044 1004

0.093 830

0.128 799

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

45

Table B 22 Wage determinants. Subsample without outliers. Dependent variable – Y3, males. OLS estimates. (1) (2) (3) (4) (5) (6) (7) ** * DL -0.7672 -0.8121 -1.2865 -1.3068 -1.1500 57.5811 61.4859 (0.85) (0.84) (0.81) (0.65) (0.67) (41.25) (40.87) Age 0.1003*** 0.0997*** 0.0887*** 0.0955*** 0.0991*** (0.03) (0.03) (0.02) (0.02) (0.03) Age squared/100 *** *** *** *** 0.1295 0.1293 0.1064 0.1127 0.1285*** (0.03) (0.03) (0.03) (0.03) (0.03) *** *** *** Education 0.0578 0.0350 0.0346 (0.01) (0.01) (0.01) DL squared -31.1666 29.1895 (20.69) (20.47) *** *** *** *** *** Constant 10.9763 9.2210 8.7008 8.9094 8.5600 -21.8448 18.1478 (0.84) (0.96) (0.91) (0.76) (0.79) (20.54) (20.38) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 66.5470* (39.37) 0.0984*** (0.03) 0.1282*** (0.03) 0.0580*** (0.01) -33.9372*

(9) 22.1290 (28.71) 0.0884*** (0.02) 0.1062*** (0.03) 0.0353*** (0.01) -11.7203

(10) 31.4783 (29.18) 0.0951*** (0.02) 0.1123*** (0.03) 0.0350*** (0.01) -16.3251

(19.71) -25.1273

(14.32) -2.7813

(14.57) -7.7049

(19.63)

(14.36) YES

(14.55) YES

YES 0.001 of 678

0.034 678

0.104 674

0.230 591

0.257 578

YES 0.004 678

0.037 678

0.109 674

0.231 591

0.259 578

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

46

Table B 23 Wage determinants. Subsample without outliers. Dependent variable – Y3, females. OLS estimates. (1) (2) (3) (4) (5) (6) (7) (8) DR 0.3598 0.4297 0.3482 1.0075 1.0770 -56.2825 -59.2401 -72.1445* (0.91) (0.91) (0.90) (0.88) (0.91) (44.33) (44.07) (43.25) Age 0.0598** 0.0633** 0.0281 0.0308 0.0613** 0.0651** (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) Age squared/100 -0.0629** -0.0642** -0.0170 -0.0179 -0.0647** -0.0665** (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) Education 0.0560*** 0.0294*** 0.0267** 0.0568*** (0.01) (0.01) (0.01) (0.01) DR squared 28.2417 29.7521 36.1459* (22.09) (21.96) (21.55) Constant 9.5159*** 8.1165*** 7.0387*** 7.7402*** 7.7936*** 37.8859* 37.9724* 43.2947** (0.92) (1.11) (1.10) (1.06) (1.08) (22.23) (22.04) (21.60) Occupation dummies YES YES Industry dummies R-squared Number observations

(9) -51.1828 (42.58) 0.0295 (0.03) -0.0186 (0.03) 0.0303*** (0.01) 26.0357 (21.10) 33.8061 (21.32) YES

YES 0.000 of 1008

0.009 1008

0.042 1004

0.094 830

0.129 799

(10) -38.2155 (42.95) 0.0316 (0.03) -0.0188 (0.03) 0.0275** (0.01) 19.5930 (21.24) 27.4305 (21.53) YES YES

0.002 1008

0.011 1008

0.044 1004

0.096 830

0.130 799

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

47

Table B 24 Wage determinants. Subsample without outliers. Dependent variable – Y3, males. OLS estimates. (1) (2) (3) (4) (5) (6) (7) ** * DR -0.5360 -0.7610 -1.2300 -1.5849 -1.3418 28.4741 27.6675 (0.88) (0.87) (0.83) (0.68) (0.70) (41.68) (41.76) Age 0.1014*** 0.1016*** 0.0900*** 0.0968*** 0.1007*** (0.03) (0.03) (0.02) (0.02) (0.03) Age squared/100 *** *** *** *** 0.1309 0.1315 0.1080 0.1143 0.1301*** (0.03) (0.03) (0.03) (0.03) (0.03) *** *** *** Education 0.0576 0.0348 0.0343 (0.01) (0.01) (0.01) DR squared -14.4838 -14.1931 (20.79) (20.82) Constant 10.7461*** 9.1476*** 8.6114*** 9.1862*** 8.7493*** -3.7650 -5.0570 (0.89) (1.02) (0.96) (0.81) (0.83) (20.88) (20.87) Occupation dummies YES YES Industry dummies R-squared Number observations

(8) 14.6552 (40.32) 0.1012*** (0.03) 0.1311*** (0.03) 0.0574*** (0.01) -7.9303 (20.07) 0.6759 (20.18)

(9) -16.2277 (32.01) 0.0904*** (0.02) 0.1085*** (0.03) 0.0349*** (0.01) 7.3064 (15.97) 16.5087 (16.03) YES

YES 0.000 of 678

0.033 678

0.104 674

0.231 591

0.258 578

(10) -19.2839 (33.57) 0.0974*** (0.02) -0.1150*** (0.03) 0.0343*** (0.01) 8.9544 (16.75) 17.7156 (16.79) YES YES

0.001 678

0.034 678

0.104 674

0.232 591

0.259 578

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.

48

Table B 25 Results of the tests of the joint significance of coefficients on linear and quadratic terms. Full sample. Specification number (according to table 3)

Column number (according to table 3)

Test results

Variance inflation factor (VIF) of: the linear term the quadratic term 1 2 F=9.19, H0 is rejected (at 1% sig. level) 668.77 668.77 1 5 F= 1.56, H0 is accepted 562.52 562.52 1 9 F = 1.83, H0 is accepted 530.83 530.83 2 2 F = 10.55, H0 is rejected (at 1% sig. level) 670.83 670.83 2 5 F = 1.56, H0 is accepted 562.52 562.52 2 9 F = 2.38, H0 is rejected (at 10% sig. level) 532.25 532.17 2 11 F = 2.25, H0 is accepted 541.32 541.26 3 2 F = 7.25, H0 is rejected (at 1% sig. level) 671.03 671.39 3 9 F = 3.32, H0 is rejected (at 5% sig. level) 532.41 532.27 3 11 F = 3.10, H0 is rejected (at 5% sig. level) 541.47 541.35 4 2 F = 5.30, H0 is rejected (at 1% sig. level) 680.97 681.24 5 2 F = 6.69, H0 is rejected (at 1% sig. level) 690.45 690.58 5 5 F = 1.19, H0 is accepted 557.27 557.40 5 5 F = 3.21, H0 is rejected (at 5% sig. level) 531.03 531.29 Notes: In this table we tested the null hypothesis (H0) that both coefficients –the linear and the quadratic term of digit ratios in the regressions presented in Table 3 – are zero. We performed tests for the coefficients in the case where at least one of the coefficients was significant. For example, the first line contains the result of the test of the joint significance of coefficients of the first regression specification, for the right hand of females (column 2, table 3); these coefficients are equal 21.8142 and -11.4083 correspondingly. “H0 is accepted” means that we cannot reject H0 at 1%, 5%, or 10% significance level. Additionally, for the evidence of multicollinearity variance inflation factor is computed for the linear and for the quadratic term of digit ratio.

49

Table B 26 Results of the tests of the joint significance of coefficients on linear and quadratic terms. Subsample without outliers. Specification number (according to table 4)

Column number (according to table 4)

Test results

Variance inflation factor (VIF) of: the linear term the quadratic term 1 7 F = 2.79, H0 is rejected (at 10% sig. level) 2101.42 2101.42 1 9 F = 2.71, H0 is rejected (at 10% sig. level) 2065.98 2065.98 1 10 F = 1.55, H0 is accepted 2493.28 2493.28 2 7 F = 3.28, H0 is rejected (at 5% sig. level) 2113.47 2113.88 2 9 F = 2.83, H0 is rejected (at 10% sig. level) 2072.43 2072.60 3 6 F = 1.49, H0 is accepted 2289.50 2289.55 3 7 F = 4.61, H0 is rejected (at 5% sig. level) 2115.65 2116.00 3 9 F = 3.95, H0 is rejected (at 5% sig. level) 2074.70 2074.97 3 11 F= 2.63, H0 is rejected (at 10% sig. level) 2082.74 2082.49 4 7 F=3.03, H0 is rejected (at 5% sig. level) 2239.90 2240.54 5 7 F = 2.77, H0 is rejected (at 10% sig. level) 2284.08 2286.16 5 9 F = 2.99, H0 is rejected (at 10% sig. level) 2245.79 2246.81 Notes: In this table we test null hypothesis (H0) that both coefficients – on the linear and on the quadratic term of digit ratios in regressions, presented in Table 4 – are zero. We perform tests for coefficients in the case where at least one of the coefficients was significant. For example, the first line contains result of the test of the joint significance of coefficients of the first regression specification, for the left hand of males (column 7, table 4); these coefficients are equal 63.0979 and -31.8253 correspondingly. “H0 is accepted” means that we cannot reject H0 at 1%, 5%, or 10% significance level. Additionally, for the evidence of multicollinearity variance inflation factor is computed for the linear and for the quadratic term of digit ratio.

50