Discussion Paper No. 952

HISTORICAL EDUCATION LEVELSANDPRESENT-DAY NON-COGNITIVE SKILLS

Eiji Yamamura

December 2015

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Historical education levels and present-day non-cognitive skills

Eiji Yamamura
Seinan Gakuin University.

ABSTRACTThis study examined the extent to which education levels in the 19th century have shaped
current norms, which influence individuals’ present-day non-cognitive skills and perceptionsof life. Cross-country, individual-level data were compared with each country’s average yearsof schooling in 1870. After controlling for various country-level and individual characteristics,
the key findings were as follows: (1) people in countries with high historical education levels
place importance on hard work, ambition, and education; (2) people in countries with highhistorical education levels tend to show perseverance and have a sense of responsibility.
JEL classification: I25; D83; Z13; N30
Keywords: Historical education level, norms, non-cognitive skills

1

I.

Introduction

The economic education literature assumes that cognitive skills play a critical role in theability to achieve success in life (Hanushek and Woessmann 2008). Researchers have also
paid much attention to non-cognitive skills related to successful lifetime outcomes (Heckman
et al. 2006). The formation of both cognitive and non-cognitive skills is important for labor
quality improvement and economic development, and the process of skill formation has been
investigated by researchers (e.g., Chetty et al. 2011; Heckman et al. 2010a, 2010b, 2013). For
example, the “Perry Preschool Program” was designed to promote social competency,
planning, and organization in 3- and 4-year-old disadvantaged African-American youth.
Heckman et al. (2010a, 2010b) have shown that the program can significantly andbeneficially affect life outcomes. The program improved non-cognitive skills (reducing
externalizing behavior such as aggressive, antisocial, and rule-breaking behaviors) andacademic motivation (Heckman et al. 2013). 1 These findings indicate that early childhood
environments substantially influence non-cognitive skills and learning effort.
In terms of other individual characteristics, women are less likely than men to consider
hard work important for a better life, but women are more likely to have negative views ofcompetition (Fisman and O’Neill 2009). One possible explanation for this is that women’s
perceptions are influenced by their experience of barriers that prevent advancement tohigh-level positions in the workplace (Fisman and O’Neill 2009). In addition to being
influenced by individual life experience, non-cognitive skills and perceptions of life dependon norms or national characteristics. According to the “industrious revolution” argument, an
increase in demands for “market-supplied goods” during the Edo period in Japan caused
people to be more industrious (De Vries 1994). Norms or national characteristics are thus
thought to change in response to historical events and conditional changes. Therefore, it isuseful to investigate when and how norms or national characteristics are formed.
From a historical perspective, following the analogy of early childhood development, thespread of education in the early eras in modern nations is important in the formation of
citizens’ national characteristics and norms. 2 Shared characteristics such as cultures andnorms are transmitted from generation to generation (Bisin and Verdier 2011). National
characteristics and norms formed in past eras influence people’s present-day perceptions andbehaviors. For example, levels of education in the 19th century are linked to lower levels ofcorruption today (Uslaner and Rothstein 2015). 3 However, little is known about how
1

Education plays an important role in the formation of identity (Aspachs-Bracons et al.
2008) and attitude toward risk (Hryshko et al. 2011).
2
In the United States of America, growing education increased citizens’ attention to publicaffairs and politics (Milligan et al. 2004).
3
Early land inequality has negative effects on math and science skills even a century later2historical levels of education are related to people’s non-cognitive skills and perceptions oflife. 4
Based on these observations, we propose here a testable hypothesis that education levelsin the early stages of modern nations have improved present-day non-cognitive skills. To test
this hypothesis, we examined the effect of historical education levels on individuals’present-day views about factors important to life and on their attitudes to tasks. For thispurpose, we used data on historical education levels in 1870 for 28 countries matched withindividual-level data from the International Social Survey Program (ISSP).
The remainder of this paper is organized as follows. Section II describes the data and theempirical method. Section III presents the estimation results and their interpretation. The final
section offers some conclusions.

II. Data and Methods
A. DataThis study integrated individual-level ISSP data and country-level data, such as average yearsof schooling in 1870, levels of corruption in the residential country, number of conflicts, andGDP per capita. ISSP surveys have been conducted since 1985. The 2009 ISSP survey
included 41 countries. The same questionnaire was used for each country and included
questions about demographics, education level, economic condition, and subjective
perceptions. The present study focuses on the subjective views of factors that are important tolife given by respondents in the 41 countries. Other key variables in this study relate to
non-cognitive skills, such as sense of responsibility and perseverance. However, questions
about non-cognitive skills were only available for 15 countries and data on average years ofschooling in 1870 were not available for several countries. Therefore, this study used data on
subjective views from only 28 countries and data on non-cognitive skills from only ninecountries. Table A1 in the Appendix shows the countries from which the data used here weredrawn.
The ISSP 2009 data on subjective views were drawn from a sample of approximately
34,000 and the ISSP 2009 data on non-cognitive skills were drawn from a sample ofapproximately 12,000. The majority of respondents were aged 18 years and above. 5 The
fieldwork that produced the data was conducted between 2008 and 2010. 6(Baten and Juif 2014).
4
Compulsory schooling laws in the 1950s influence people’s views of superstition (Mocan
and Pogorelova 2014).
5
Respondents from Finland, Norway, and Sweden were aged 15–74, 19–0, and 17–79 years,
respectively. Those from Japan were 16 years and above.
6
Fieldwork in Italy was conducted between 2011 and 2012. Sampling procedures varied3In comparison with other individual-level surveys, such as the World Value Survey, the
ISSP has the advantage that it provides a range of information about respondents’ wealth,including monetary wealth (savings, stocks, or bonds) and non-monetary wealth (home). Oneof the dependent variables was proxy for the importance of family wealth. Therefore, themeasurement of economic conditions such as wealth should be included. It is thus necessary
to take individuals’ wealth into account. Furthermore, ISSP 2009 provides information about
non-cognitive skills by recording respondents’ views about tasks; previous ISSP surveys did
not contain this information. Because of these advantages of ISSP 2009, this paper used the
ISSP 2009 data.
The variables used in the regression estimations are shown in Table 1, which provides
definitions and basic statistics (mean, standard deviation). One ISSP question about
subjective views of factors important to life asked “Please show how important you think it isfor getting ahead in life.” Respondents answered in relation to 11 factors with five possible
responses, ranging from 1 (not important at all) to 5 (essential). Table 1 contains definitionsof these 11 factors. In this study, the factors were divided into three categories: individual
effort and education (HARD_WORK, AMBITION, I_EDU, P_EDU), social background(FAMILY_WEALTH, GENDER, RACE, RELIGION), and political factors(KNOW_PERSON, POLI_CONNECT, BRIBE).
With respect to non-cognitive skills, respondents were asked “How would you describe
yourself as a person?” Respondents selected one of three descriptions:(1) I work hard to complete my daily tasks, even if I am slightly sick or when there is another
legitimate reason for taking a break.
(2) I perform to the best of my ability even on a task that I do not like.
(3) I work hard to maintain my performance on a task, even if the task takes a long time tostart producing any results.
For each description, respondents could choose one of four responses, from 1 (strongly
disagree) to 4 (strongly agree). These three descriptions are related to each other. However, in
this paper, we broadly divided them into two groups. If a person does not feel a sense of
responsibility, they may not complete their work. Hence, in this study, the first description
indicated a sense of responsibility (RESPONSIBILITY). If a person dislikes a task or the task
does not provide an easy return, they might not persevere to complete the task. Accordingly,
the second and third descriptions suggested perseverance (PERSEV_1, PERSEV_2).
To measure the economic condition of the country of residence, we used SCHOOL_1870,
UNCORRUPT, CONFLICT, GINI (Gini coefficient before tax and transfers), and GDP per
capita. These were 2008 measures. The historical data on average schooling years in 1870
among individual countries: some were simple samples and others were multi-stage, stratified,random samples. The mode of interview also differed among countries: some usedface-to-face interviews; others used paper-and-pencil or postal surveys.
4

was drawn from Morrison and Murtin (2009). 7 As a proxy for institutional quality, we usedthe 2008 corruption index from the International Country Risk Guide (ICRG) produced by
the Political Risk Service Group. The ICRG corruption index values range from 0 to 6; larger
values indicate less corruption. According to the ICRG, the most common form of businesscorruption is financial corruption in the form of demands for special payments and bribes
connected with licenses. Therefore, the ICRG corruption index was considered a goodmeasure of financial corruption. Information about the occurrence of real conflict was
sourced from the Uppsala Conflict Data Program/Peace Research Institute Oslo Armed
Conflict Dataset v.4. 8 These data were used to calculate the number of conflicts between
1946 and 2008. The GINI data were sourced from World Bank data. 9 GDP information wascollected from the University of Pennsylvania’s Center for International Comparisons, Penn
World Table 7.1. 10
B. Econometric Framework and Estimation Strategy
This study attempted to examine the effect of historical education levels on both subjective
views of factors that determine success in life and non-cognitive skills. For this purpose, thefollowing estimated function of the baseline model was used:
Yik = α1 SCHOOL_1870k + α2I_SCHOOLi + X’Β+εi
X: Vector of country’s and individual’s characteristics
Β: Vector of coefficients of country’s and individual’s characteristics
where Yik represents the dependent variable for individual i and country k. To estimate thedeterminants of success in life, 11 variables were used as Yik. As mentioned earlier, these 11
variables were categorized as individual effort and education (HARD_WORK, AMBITION,
I_EDU, P_EDU), social background (FAMILY_WEALTH, GENDER, RACE, RELIGION),
and political factors (KNOW_PERSON, POLI_CONNECT, BRIBE). To estimate
non-cognitive skills, sense of responsibility (RESPONSIBILITY) and perseverance(PERSEV_1, PERSEV_2) were used as Yik. Values of Yik are ordinal numbers, and so anordered probit model was used to conduct the estimations.
Regression parameters are represented by α. The error term is represented by εi. It isreasonable to assume that the observations may be correlated within a country, as the countryof one agent may well relate to the preference of another in the same country. To control such
7

An email request for access to the data was sent to Fabrice Murtin. In response, he provided
the data set via an email attachment (February 12, 2015).
8
Data sourced from Uppsala University, Department of Peace and Conflict research:
http://www.pcr.uu.se/research/ucdp/datasets/ucdp_prio_armed_conflict_dataset/(accessed May 24, 2015).
9
See http://data.worldbank.org/indicator/SI.POV.GINI?page=1 (accessed June 12, 2013).
10
The data are available at the Penn World Table website:
https://pwt.sas.upenn.edu/php_site/pwt71/pwt71_form.php (accessed August 25, 2013).
5

correlation in line with this assumption, z-statistics were calculated using robust standard
errors clustered on a country. The advantage of this method is that the magnitude ofcorrelation can be unique to each country.
Let us turn to the control variables included in X. In relation to country characteristics,
people’s perception seems to depend on both income level and income inequality. The logs of
GDP and GINI were used to measure this. Conflict is related to economic inequality (e.g., De
Luca and Sekeris 2012; Esteban and Ray 2011; Macours 2011) and has a persistent effect onsociety (Besley and Reynal-Querol 2014). Further, people’s perception is influenced bypolitical stability and degree of corruption, and so the variables CONFLICT and
UNCORRUPT were included (Yamamura 2015).
With respect to individuals’ economic condition, respondents’ years of schooling(I_SCHOOL) were included to control for their educational background. In countries withwidespread education, individuals are likely to have high levels of education. That is,
individuals’ educational backgrounds depend on the past educational condition of theirresidential country. The SCHOOL_1870 and I_SCHOOL variables measured both the effectof a country’s historical education level and the effect of individual-level education. 11
Research shows a gender difference in perceptions of factors important to life (Fisman andO’Neill 2009); therefore, we included gender in the analysis. People tend to compare theirincome with the income of people around them, which influences their subjective perceptions(Luttmer 2005). Hence, the relative income levels in each country were considered and threedummy variables were constructed: HIGHINCOM (respondents belong to the 25th percentile
income group), MIDINCOM (respondents belong to the income group between the 25th and75th percentiles), and LOWINCOM (respondents belong to the 75th percentile income
group). In addition to income level, to control for stock ownership as part of the wealthassessment, dummy variables to measure savings, stocks, and bonds were incorporated:
DEBTSTOC and NOSTOC. To control for other types of wealth, dummy variables were
included to measure ownership and debt related to homes or apartments: DEBTHOM andNOHOM. Following a previous study that controlled for individuals’ work, religion,
demographic, and social status characteristics (Yamamura 2015), this study included
WOKGOV, WOKSELF, CATHOLIC, PROTEST, AGE, MARRI, and DIVO as independent
variables. The data set includes information about respondents’ residential areas within acountry, which range from 5 (the most urbanized area) to 1 (the most rural areas). The
characteristics of residential areas were controlled for by including dummy variables tocapture these characteristics and to control for differences in time-invariant characteristics
across residential country (in Asia, Europe, South America, and other areas). However,analyses of the characteristics of residential areas are not reported here.

III. Estimation Results
A. Factors Considered Important to Life
11

The I_SCHOOL data contained some outliers; to avoid any resultant bias, I_SCHOOL
scores above 30 were excluded from the sample used for estimations. However, even whenoutliers were included, estimation results were similar to those reported in Tables 2–5,
suggesting that the outliers did not influence the results. Results of the analysis that included
the outliers are available from the author on request.
6

The estimation results regarding factors perceived as important to life are shown in Tables 2,
3, and 4. We divided these variables into three categories. First, HARD_WORK, AMBITION,
I_EDUCA, and P_EDUCA represented the importance of individuals’ effort and education inlife. Table 2 shows the estimation results of the analysis with these variables as dependent
variables. Second, FAMILY_WEALTH, GENDER, RACE, and RELIGION represented theimportance of social background in life. Table 3 shows the estimation results of the analysis
with these variables as dependent variables. Third, KNOW_PERSON, POLI_CONNECT,
and BRIBE represented the importance of political factors in life. Table 4 shows theestimation results of the analysis with these variables as dependent variables. Each tableshows the coefficients of the independent variables. The coefficient values do not represent
the marginal effects. 12 This analysis focused mainly on the coefficients’ sign and statisticalsignificance and on results from the key independent variables.
Table 2 shows that the coefficient of SCHOOL_1870 is positive across all dependent
variables and is statistically significant in columns (1)–(3), but is not statistically significant
in column (4). Overall, the average number of schooling years in 1870 is positively related tothe belief that an individual’s effort and education is important in life. However, the average
number of schooling years in 1870 does not affect people’s view about the importance ofparental education. The perceived importance of parental education reflects circumstancesand varies according to parents’ educational level, which is not directly related to individual
ability and performance. Therefore, the fact that the effect of SCHOOL_1870 on I_EDUCA
differs from the effect of SCHOOL_1870 on P_EDUCA supports the argument that historicaleducation level is related to subjective views about the importance of individual effort andeducation, rather than to the conditions provided by parents.
The I_SCHOOL coefficient is positive in columns (1)–(4) and is statistically significant incolumns (1)–(3), but not in column (4). Therefore, the results of I_SCHOOL are similar to
those of SCHOOL_1870: even after controlling for individual educational background, thehistorical level of schooling in the residential country helps to shape individuals’ views on theimportance of individual effort and education in life. The variable WOMAN is notstatistically significant in columns (1)–(3) but shows a significantly negative relationship incolumn (4). One possible interpretation is that highly educated parents’ investment in theirchildren’s education differs according to the child’s gender. If highly educated parents are
more likely to invest in a son’s education than in a daughter’s education, daughters are less
12

The marginal effects of independent variables can be calculated for each value of thedependent variables (Greene 2008, pp. 831–835). For example, their marginal effect on theprobability that HARD_WORK is 5, their marginal effect on the probability that
HARD_WORK is 4, their marginal effect on the probability that HARD_WORK is 3, their
marginal effect on the probability that HARD_WORK is 2, and their marginal effect on theprobability that HARD_WORK is 1. The results of the marginal effects are not reported here
because of space limitations.
7likely to benefit from education.
Table 3 shows no statistical significance for the SCHOOL_1870 coefficient in any
columns. Historical educational background in 1870 is not related to people’s view about theimportance of social background in life. The variable I_SCHOOL shows a significant
relationship in column (4), suggesting that highly educated persons are less likely to consider
religion as important in life. This is consistent with the findings of Mocan and Pogorelova(2014). It is interesting to observe that GINI is positive and statistically significant at a 1%
probability level in columns (1)–(4). Present-day income inequality means that people place
importance on social background. The above results may suggest that people residing incountries characterized by income inequality believe that social background, rather thaneducational differences, is the largest cause of inequality. The WOMAN coefficient was
negative and statistically significant in columns (1) and (3). Women do not perceive family
wealth and race to be large influences on life. One possible interpretation is that women are
more likely to change their lives by marriage (they cannot change their origins). Therefore,
women are influenced less by family wealth and race than are men. However, the WOMAN
coefficient was positive and statistically significant in column (2), suggesting that women are
more likely than men to consider gender as an important influence on life. This may reflect
either differences in the importance of marriage to women and men or discrimination against
women in the labor market (e.g., Biau and Kahn 2003; Kunze 2005, 2008).
The variable SCHOOL_1870 is not statistically significant in any columns of Table 4,
indicating that historical educational level of a country is unrelated to present-day perceptionsof the importance of political factors. However, UNCORRUPT and CONFLICT showednegative and statistically significant relationships at the 1% probability level in columns (1)–(3). In terms of levels of corruption, this indicates that people living in countries with lowlevels of corruption are less likely to consider political factors as important in life. If
corruption levels are low, political factors such as knowing the “right” people, politicalconnections, and bribery are not considered important to success. Similarly, people living incountries in which conflict frequently occurs are less likely to consider political factors asimportant to life. Unstable political conditions reduce the importance of political factors. Thismight be because people’s influence and political connections are unstable under suchconditions and so the future value of these factors is less certain, compared with conditions inpolitically stable countries. The WOMAN coefficient is negative and statistically significant
in columns (1)–(3), suggesting that women are less likely than men to consider politicalfactors as important in life. It follows from this that men tend to benefit more from knowing
the “right” people, political connections, and bribery. This indicates that women are likely to
be excluded from political networks.
Some respondents did not answer all questions so data for these respondents wereincomplete. Therefore, we were able to obtain more observations by excluding some of the8control variables from the dependent variables. To check the robustness of the results inTables 2–4, we conducted alternative estimations excluding some of the key variables. As
shown in the Appendix (Tables A1–A3), the results for SCHOOL_1870 are almost the sameas in Tables 2–4, demonstrating their robustness to alternative specifications.
B. Attitude Toward Work
Table 5 shows that the SCHOOL_1870 coefficient was positive and statistically significant at
the 1% probability level in all columns. This suggests that average schooling years in 1870
were related to a greater sense of responsibility (RESPONSIBILITY) and perseverance(PERSEV_1, PERSEV_2) for tasks. However, the I_SCHOOL variable was positive andstatistically significant only in column (1), but not in columns (2) and (3), indicating that
individual years of schooling relates to responsibility but not to perseverance for tasks.
Considering jointly the results of SCHOOL_1870 and I_SCHOOL suggests that historicaleducational levels are more important than individual educational levels in the formation of
non-cognitive skills for tasks. As explained in the previous subsection, the exclusion of some
control dependent variables increased the observations. To check the robustness of Table 5,
some of the key variables were excluded from the equation. As is shown in the Appendix(Tables A4 and A5), the estimation results are almost the same as Table 5 and so robust toalternative specifications.
The UNCORRUPT and CONFLICT variables showed positive and negative relationships,
respectively and were statistically significant, with the exception of UNCORRUPT in column(1). These results indicate that people living in countries with low levels of corruption
possess more non-cognitive skills for tasks. The results also suggest that people living incountries in which conflict frequently occurs possess less cognitive skills for tasks. The
WOMAN coefficient was positive and statistically significant in columns (1) and (2),indicating that women show more cognitive skills than men.
IV. Conclusions
Individuals’ responses and behaviors under certain conditions vary according to their
non-cognitive skills and their views of what determines success in life. The long-term effectof non-cognitive skills such as perseverance and a sense of responsibility play an importantrole in economic success in life (e.g., Chetty et al., 2011; Heckman et al., 2010a, 2010b,
2013). It is thus useful to investigate how non-cognitive skills such as perseverance and asense of responsibility are formed.
This study focused on historical education levels in each country and their relationshipwith non-cognitive skills and subjective views of factors considered important to life. We
matched each country’s individual-level data with average years of schooling in 1870 andconducted empirical estimations. The key findings were: (1) people living in countries with9

high historical education levels place importance on individual effort and education; (2)people living in countries with high historical education levels tend to have a sense of
responsibility and perseverance. This paper provides evidence that the spread of education in
the 19th century led to an accumulation of human capital, measured by cognitive skills, andshaped future non-cognitive skills in the 21st century. This supports the argument that norms
or national characteristics are formed following widespread education, which has a long-term
effect on people’s present-day non-cognitive skills and views of life. More attention shouldbe paid to the long-term influence of historical developments in education on the formation ofnational characteristics.
Because of data limitations, the data set contained a restricted number of countries andmajor historical events between 1870 and 2009 were not taken into account. This resulted in alack of sufficient accuracy in the estimation results. To explore the patterns suggested by our
analysis in more depth, further research using a larger number of countries, and a broader
examination of historical events, is needed.

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12

Table 1
Variable Definitions and Descriptive Statistics
Definitions

Mean

Standard
deviation

Country
characteristics
SCHOOL_1870

Average years of schooling in 1870

2.50

1.85

UNCORRUPT

Level of corruption of residential country
1 (very corrupt) to 5 (not corrupt)
Number of conflicts after World War II

3.52

1.52

8.76

17.7

Gini coefficients before taxes and transfers in 2008
(World Bank)
Log of GDP (Millions of US dollars)

0.36

0.10

9.88

0.67

4.01

0.87

3.89

0.93

3.97

0.89

3.24

0.86

2.91

1.16

2.21

1.16

2.21

1.15

2.02

1.13

3.55

1.01

2.65

1.20

1.85

1.10

3.22

0.70

CONFLICT
GINI
Ln(GDP)

Individual characteristics: subjective views and demographic data
HARD_WORKAMBITION
I_EDU
P_EDU
FAMILY_WEALTHGENDERRACE
RELIGION
KNOW_PERSONPOLI_CONNECTBRIBERESPONSIBILITY

Subjective view of importance of hard work
1 (not important at all) to 5 (essential)
Importance of having ambition
1 (not important at all) to 5 (essential)
Importance of having a good education yourself
1 (not important at all) to 5 (essential)
Importance of having well-educated parents
1 (not important at all) to 5 (essential)
Importance of coming from a wealthy family
1 (not important at all) to 5 (essential)
Importance of being born a man or a woman
1 (not important at all) to 5 (essential)
Importance of a person’s race
1 (not important at all) to 5 (essential)
Importance of a person’s religion
1 (not important at all) to 5 (essential)
Importance of knowing the “right” people
1 (not important at all) to 5 (essential)
Importance of having political connections
1 (not important at all) 5 (essential)
Importance of giving bribes
1 (not important at all) to 5 (essential)
Self-description:
I work hard to complete my daily tasks, even if I am
slightly sick or when there is another legitimate
reason for taking a break.
1 (strongly disagree) to 4 (strongly agree)13

Self-description:
I perform to the best of my ability even on a task that
I do not like.
1 (strongly disagree) to 4 (strongly agree)
Self-description:
I work hard to maintain my performance on a task,even if the task takes a long time to start producing
any results.
1 (strongly disagree) to 4 (strongly agree)
Respondent’s years of schooling

3.18

0.69

3.19

0.67

11.8

3.88

Equals 1 if respondent works for government,
otherwise 0
Equals 1 if respondent is self-employed, otherwise 0

0.17

___

0.13

___

0.70

___

CATHOLIC

Equals 0 if WOKGOV or WOKSELF are 1,
otherwise 1
Equals 1 if respondent is a Catholic, otherwise 0

0.29

___

PROTEST

Equals 1 if respondent is a Protestant, otherwise 0

0.19

___

OTHER RELIGION

0.51

___

AGE

Equals 1 if CATHOLIC or PROTEST are 0,
otherwise 0
Respondent’s age

46.6

17.1

AGESQ

Square of AGE

___

___

MIDINCOM

Equals 1 if respondent’s household income isbetween 25th and 75th percentile of householdincome (25th percentile > = Group > = 75 thepercentile), otherwise 0
Equals 1 if respondent’s household income is higher
than 25th percentile (Group > 25 percentile),
otherwise 0
Equals 1 if respondent’s household income is lower
than 75th percentile (Group < 75th percentile),
otherwise 0
Equals 1 if respondent’s wealth (house or apartment)
is just debt, otherwise 0
Equals 1 if respondent has no wealth (house orapartment), otherwise 0
Equals 1 if DEBTHOM or NOHOM are 0, otherwise
0
Equals 1 if respondent’s wealth (savings, stocks, orbonds) is just debt, otherwise 0
Equals 1 if respondent has no wealth (savings,
stocks, or bonds), otherwise 0

0.56

___

0.29

___

0.14

___

0.04

___

0.15

___

0.81

___

0.05

___

0.19

___

PERSEV_1

PERSEV_2

I_SCHOOLWOKGOVWOKSELF
OTHER WORKHIGINCOM

LOWINCOMDEBTHOMNOHOMOTHER HOM
DEBTSTOCNOSTOC

14

0.75

___

WOMAN

Equals 1 if DEBTSTOC or NOSTOC are 0,
otherwise 0
Equals 1 if respondent is a woman, otherwise 0

0.55

___

MARRI

Equals 1 if respondent is married, otherwise 0

0.55

___

DIVO

Equals 1 if respondent is divorced, otherwise 0

0.06

___

OTHER STOC

Note: GINI data were obtained from OECD
(http://stats.oecd.org/Index.aspx?DataSetCode=IDD#. accessed June 27, 2013).
GDP and POP data were from Penn World Table 7.1(http://www.rug.nl/research/ggdc/data/penn-world-table. accessed June 27, 2013). Other
variables were sourced from ISSP 2009. The data set contained some outliers of I_SCHOOL;
to avoid possible resulting bias, I_SCHOOL scores over 30 were excluded from the sample
used for estimations.
Apart from WK_EFFORT, WK_EFFORT2, and WK_EFFORT3, the sample is equivalent tothat used in column (1) of Table 2. The sample of WK_EFFORT, WK_EFFORT2, and
WK_EFFORT3 is equivalent to that used in column (1) of Table 5.

15

Table 2
Estimation Results for Individual Effort and Education (Ordered Probit)
(1)
(2)
(3)
(4)
Dependent
HARD_WOR AMBITIONI_EDUCAP_EDUCAvariable
K
Country
characteristics
SCHOOL_1870
UNCORRUPTCONFLICTGINI
Ln (GDP)0.07**(2.08)−0.11(−1.59)0.001(0.18)
0.01(0.81)
0.05(0.29)

0.15***(3.73)−0.06(−1.16)−0.003(−1.03)0.003(0.46)−0.30**(−2.06)0.08**(2.43)
0.02(0.46)−0.002(−0.91)0.02***(3.39)−0.43***(−3.23)0.06(1.60)−0.01(−0.29)−0.01***(−3.86)0.02***(3.32)−0.48***(−4.45)0.01**(2.49)

0.01**(2.53)

0.02***(4.13)

0.01(1.64)

Individual
characteristics
I_SCHOOL<Reference group>

OTHER WORKWOKGOVWOKSELF
OTHER
RELIGIONCATHOLICPROTEST
AGE
AGESQ−0.04**(−1.99)0.12***(5.07)

0.03(1.30)−0.03*(−1.75)−0.02(−1.10)−0.01(−0.24)<Reference group>
0.08(1.42)
0.11(1.39)−0.01***(−3.50)0.0001***(3.77)

0.11*
0.04(1.88)(0.69)
0.16***−0.10*(2.64)(−1.96)−0.01−0.01***(−1.69)(−3.53)0.00030.0001***(0.98)(4.19)<Reference group>

0.06(0.98)−0.16***(−2.94)−0.002(−0.74)0.0004(1.51)

0.02(0.80)

0.04(1.13)

0.01(0.35)

MIDINCOMHIGINCOM−0.06***(−2.84)0.08***(3.99)

0.001(0.03)
16

LOWINCOM−0.02(−0.49)0.03−0.04(0.63)(−1.26)<Reference group>

0.03(1.16)−0.02(−0.46)−0.05(−1.34)−0.02
0.13***(−0.41)(3.73)−0.09
0.05(−1.58)(1.48)<Reference group>

0.13***(4.08)
0.03(0.80)OTHER HOM

DEBTHOMNOHOMOTHER STOC
DEBTSTOCNOSTOC
WOMAN
MARRI
DIVO
Log
pseudo-likelihood
Observations

0.01(0.32)−0.05(−1.27)0.02(1.04)
0.05***(3.21)
0.06***(2.72)−40.5220.01(0.24)−0.06(−1.34)−0.02(−1.21)0.01(0.67)
0.06**(2.39)−41.911−0.11*(−1.73)0.08(1.57)
0.01(1.13)
0.06***(2.91)
0.08**(2.14)−39.229−0.02(−0.43)0.11**(2.45)−0.04***(−3.94)0.03*(1.71)
0.07(1.60)−46.333

33,38533,77434,02733,793

Note: Values without parentheses are coefficients. Values in parentheses are z-statistics
calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
The degree of urbanization of residential areas was controlled for by including four dummy
variables. Dummy variables for Asia, Europe, and South America were also included;
however, these results are not reported.

17

Table 3
Estimation Results for Social Background (Ordered Probit)
(1)
(2)
(3)
Dependent
FAMILYGENDER
RACE
variable_WEALTHCountry
characteristics
SCHOOL_1870
UNCORRUPTCONFLICTGINI
Ln (GDP)(4)RELIGION−0.02(−0.76)−0.15***(−2.72)−0.01***(−9.98)0.01***(5.07)−0.19**(−2.23)−0.01(−0.42)0.01(0.37)−0.0001(−0.06)0.02***(4.49)−0.24**(−2.00)0.03(1.05)
0.03(0.78)−0.01***(−2.71)0.02***(4.25)−0.48***(−3.94)0.02(0.57)−0.01(−0.10)0.004*(1.66)
0.03***(3.93)−0.20(−1.29)−0.001(−0.44)0.01**(2.05)

0.005(1.22)−0.01**(−2.17)

Individual
characteristics
I_SCHOOL<Reference group>

OTHER WORKWOKGOVWOKSELF
OTHER
RELIGIONCATHOLICPROTEST
AGE
AGESQ−0.05**(−2.32)−0.03(−1.58)−0.02(−1.05)−0.03(−1.56)−0.03(−1.50)0.04(1.51)<Reference group>
0.02(0.66)
0.02(0.47)
0.002(0.82)−0.0003(−1.16)0.05
0.03(1.05)(0.60)
0.15**
0.10(2.58)(1.61)−0.0001−0.002(−0.06)(−0.95)0.00001
0.0002(0.70)(0.92)<Reference group>

0.13**(2.20)
0.34***(4.37)−0.01***(−2.85)0.0001***(2.92)−0.01(−0.54)0.03(1.28)

0.07***(3.30)

MIDINCOMHIGINCOM−0.03(−1.35)−0.01(−0.54)0.004(0.15)
18

LOWINCOM

0.06(1.53)

0.07**
0.06***(2.38)(2.64)<Reference group>

0.16***(4.96)

0.08**(1.97)
0.003(0.13)

0.03
0.02(0.63)(0.63)
0.04*−0.001(1.66)(−0.05)<Reference group>

0.03(0.59)
0.04*(1.85)OTHER HOM
DEBTHOMNOHOMOTHER STOC
DEBTSTOCNOSTOC
WOMAN
MARRI
DIVO

0.08**(2.38)
0.16***(3.97)−0.10***(−5.01)−0.02(−1.09)0.02(0.80)−48.8310.03(0.62)−0.01(−0.22)0.08***(3.95)−0.04*(−1.70)0.04*(1.73)−45.486

0.04(0.96 )
0.03
(0.94)−0.06***(−2.98)−0.01(−0.86)0.02(0.86)−45.416−0.03(−0.69)−0.04(−0.84)0.01(0.35)
0.01(0.68)
0.01(0.31)−42.220

Log
pseudo-likelihood
33,57832,79032,67432,847
Observations
Note: Values without parentheses are coefficients. Values in parentheses are z-statistics
calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
The degree of urbanization of residential areas was controlled for by including four dummy
variables. Dummy variables for Asia, Europe, and South America were also included;
however, these results are not reported.

19

Table 4
Estimation Results for Political Factors (Ordered Probit)(1)(2)
Dependent
KNOW
POLI
variable_PERSON_CONNECTCountry
characteristics
SCHOOL_1870
UNCORRUPTCONFLICTGINI
Ln (GDP)

(3)
BRIBE

0.03
(0.83)−0.26***(−4.86)−0.01***(−8.68)0.01*(1.93)−0.12(−0.79)−0.01(−0.53)−0.24***(−4.14)−0.01***(−5.05)0.01**(2.17)−0.08(−0.86)−0.06(−1.57)−0.28***(−3.32)−0.01***(−3.15)−0.002(−0.44)−0.18(−1.32)−0.01***(−2.59)−0.003(−0.79)0.0003(0.05)

Individual
characteristics
I_SCHOOLOTHER WORKWOKGOVWOKSELF
OTHER
RELIGIONCATHOLICPROTEST
AGE
AGESQ
MIDINCOMHIGINCOM<Reference group>−0.04(−1.36)0.05**(2.11)

0.004(0.17)−0.04*(−1.76)−0.03(−0.93)−0.03(−1.22)<Reference group>
0.01
0.08
(0.29)(1.63)
0.07
0.01(0.87)(0.21)−0.001−0.0003(−0.50)(−0.18)−0.00004−0.00003(−1.62)(−1.22)<Reference group>−0.01(−0.23)−0.11*(−1.78)0.001(0.62)−0.0001(1.63)−0.004(−0.16)−0.07**(−2.06)−0.05(−1.54)20

LOWINCOM

0.02(1.00)OTHER HOM
DEBTHOMNOHOMOTHER STOC
DEBTSTOCNOSTOC
WOMAN
MARRI
DIVO

group>
0.06(1.43)
0.04**(2.43)

0.04*(1.68)<Reference

0.04(1.06)

0.10**(2.27)−0.01(−0.68)<Reference group>−0.02(−0.48)−0.02(−0.97)0.03(1.02)
0.16***(2.97)−0.09***(−4.91)−0.03(−1.40)0.01(0.56)−45.465

0.02(0.73)
0.16***(3.16)−0.09***(−4.67)−0.01(−0.97)0.01(0.33)−47.2130.06(1.45)
0.18***(3.53)−0.14***(−6.37)−0.09***(−3.53)−0.002(−0.05)−33.223

Log
pseudo-likelihood
33,68232,64431,038Observations
Note: Values without parentheses are coefficients. Values in parentheses are z-statistics
calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
The degree of urbanization of residential areas was controlled for by including four dummy
variables. Dummy variables for Asia, Europe, and South America were also included;
however, these results are not reported.

21

Table 5
Estimation Results for Attitude Toward Work (Ordered Probit)
Dependent(1)(2)
variableRESPONSIBILIPERSEV_1
TY
Country
characteristics
SCHOOL_1870
0.22***
0.22***(10.6)(23.3)
UNCORRUPT
0.03
0.06*
(0.31)(1.82)
CONFLICT−0.14**−0.16***(−2.34)(−8.59)GINI
0.04
0.02***(1.41)(2.82)Ln (GDP)
0.34−0.58***(1.57)(−7.85)
Individual
characteristics
I_SCHOOL
0.01**
0.01(2.22)(1.34)OTHER WORK
<Reference group>
WOKGOVWOKSELF
OTHER
RELIGIONCATHOLICPROTEST
AGE
AGESQ
MIDINCOMHIGINCOM
LOWINCOM

0.10***(12.5)
0.23***(3.56)−0.23***(−6.73)0.03**(2.14)−1.03***(−8.08)0.01(1.65)−0.04
0.01(−1.29)(0.16)
0.14***
0.01(3.68)(0.46)<Reference group>−0.04(−1.01)
0.15**(2.40)

0.08
0.03(1.11)(0.50)
0.15**
0.07(2.57)(1.38)
0.01**
0.01**(2.41)(2.54)−0.0001**−0.0001**(−2.30)(−2.37)<Reference group>

0.05(0.63)
0.10(1.58)
0.01(0.87)−0.0001(−0.59)0.04**(2.58)
0.01(0.40)

0.01(0.40)
0.01(0.32)OTHER HOM
DEBTHOM(3)PERSEV_2

0.11***(2.87)

0.01(0.35)
0.03(1.45)<Reference group>
0.05*(1.67)
22

0.09**(2.49)NOHOM−0.004(−0.09)OTHER STOC
DEBTSTOCNOSTOC
WOMAN
MARRI
DIVO

0.01
(0.35)−0.04(−0.78)0.09**(2.59)
0.13***(6.52)
0.11*(1.78)−11.950

−0.01
−0.01
(−0.49)(−0.35)<Reference group>
0.04**(2.04)
0.01(0.20)
0.08***(2.89)
0.13***(3.94)
0.08***(2.62)−11.732

0.02(0.86)
0.01(0.34)
0.01(0.42)
0.05**(2.03)
0.03(0.65)−11.487

Log
pseudo-likelihood
Observations
11,931
11,951
11,616
Note: Values without parentheses are coefficients. Values in parentheses are z-statistics
calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
The degree of urbanization of residential areas was controlled for by including four dummy
variables. Dummy variables for Asia, Europe, and South America were also included;
however, these results are not reported.

23

APPENDIX
Table A1
List of Countries Included in Analysis

1
2
3
4
5
6
7
8
9
10
11

Name
Argentina
Australia
Austria*
Belgium
Bulgaria
Chile
China*
Denmark
Finland*France*Germany

12 Hungary*Name
New Zealand
NorwayPhilippines
Portugal
Russia*South Africa
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
United States of27
America
28 Venezuela
16
17
18
19
20
21
22
23
24
25
26

13 Italy*
14 Japan*
15 South Korea*
Note: * indicates countries included in the estimation results shown in Table 5.

24

Table A2
Alternative Specifications for Tables 2–4
Estimation Results for Individual Effort and Education (Ordered Probit)
Alternative specification for Table 2
Alternative specification for
Table 3
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
HARD
AMBIT
I_EDUCA P_EDUCWEALTH GENDER RACE
RELIG
A

Alternative specification for
Table 4
(9)
(10)
(11)
KNOW
POLI_C
BRIBE

Country
characteristics
SCHOOL_1870

0.06*(1.92)

0.14***(3.23)

0.09**(2.44)

0.07(1.58)−0.02(−0.65)−0.01(−0.44)0.03(1.06)

0.01(0.46)−0.01(−0.380.03(0.86))−0.05(−1.44)

Individual
characteristics
I_SCHOOL

Log
pseudo-likelihood
Observations

0.01***(2.62)

0.02**(2.57)

0.01***(3.80)

0.01(1.05)−0.01*(−1.690.01(1.59)

0.003(0.68)−0.01**(−2.26)−0.01***(−3.23)−0.01(−1.59−0.01(−0.79−40.575−41.970−39.273−46.380)−48.917−45.503−45.429−42.258−45.541)−47.292)−33.292

33,88533,77434.02733,79333,57832,79032,67432,84733,68232,64431,038

Note: WOKGOV, WOKSELF, HIGINCOM, LOWINCOM, DEBTHOM, NOHOM, DEBTSTOC, and NOSTOC were excluded fromthe set of variables used in the estimations of Tables 2–4. Values without parentheses are coefficients. Values in parentheses are
z-statistics calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
25

Table A3
Alternative Specifications for Tables 2–4: Estimation Results for Individual Effort and Education (Ordered Probit)
Alternative specification for
Alternative specification for
Alternative specification for
Table 2
Table 3
Table 4
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
HARD
AMBIT I_EDUCA P_EDUCWEALTH GENDE
RACE
RELIG
KNOW
POLI_C
BRIBE
ARCountry
characteristi
cs
SCHOOL_1
870

0.07**(1.99)−44.798
Log
pseudo-likel
ihood
Observation 37,307s

0.14***(3.18)

0.07*(1.94)

0.06(1.22−46.363−43.409)−51.072

37,14237,44537,176−0.02(−0.58−0.01(−0.350.04(1.18)

0.03(0.81−53.754)−50.214−50.014)−46.770

36,95436,07135,91036,096)0.03(0.87)−0.02(−0.67-0.06*(−1.69)−50.156)−52.020−36.963

37,05535,84834,093

Note: I_SCHOOL, MARRI, DIVO, CATHOLIC, PROTEST, WOKGOV, WOKSELF, HIGINCOM, LOWINCOM, DEBTHOM,NOHOM, DEBTSTOC, and NOSTOC were excluded from the set of variables used in the estimations of Tables 2–4. Values withoutparentheses are coefficients. Values in parentheses are z-statistics calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

26

Table A4
Alternative Specifications for Table 5: Estimation Results for Individual Effort and Education(Ordered Probit)
(1)
(2)
(3)
WK_EFFORT1 WK_EFFORT2
WK_EFFORT3
Country characteristics
SCHOOL_1870

0.22***(10.8)

0.22***(27.5)

0.10***(13.2)

0.02**(2.00)

0.01(1.30)

0.01(1.42)

Individual
characteristics
I_SCHOOL−11.970−11.735−11.504
Log pseudo-likelihood
11,931
11,951
11,616
Observations
Note: WOKGOV, WOKSELF, HIGINCOM, LOWINCOM, DEBTHOM, NOHOM,
DEBTSTOC, and NOSTOC were excluded from the set of variables used in the Table 5
estimation. Values without parentheses are coefficients. Values in parentheses are z-statistics
calculated using robust standard errors clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

27

Table A5
Alternative Specification for Table 5: Estimation Results for Individual Effort and Education(Ordered Probit)
(1)
(2)
(3)
WK_EFFORT WK_EFFORT WK_EFFORT
1
2
3
Country
characteristics
SCHOOL_1870

0.22***(8.00)−13.585

0.21***(17.9)−13.3210.09***(8.85)−12.985Log
pseudo-likelihood
13,341
13,434
13,709Observations
Note: I_SCHOOL, MARRI, DIVO, CATHOLIC, PROTEST, WOKGOV, WOKSELF,
HIGINCOM, LOWINCOM, DEBTHOM, NOHOM, DEBTSTOC, and NOSTOC were
excluded from the set of variables used in the Table 5 estimation. Values without parentheses
are coefficients. Values in parentheses are z-statistics calculated using robust standard errors
clustered for country.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

28