# Religiosity and Innovation Attitudes: An Instrumental Variables Analysis

DUYGU BUYUKYAZICI  Duygu<EMAIL_ADDRESS>Laboratory for the Analysis of Complex Economic Systems, IMT School for
Advanced Studies, piazza San Francesco 19 - 55100 Lucca, Italy.
Francesco Serti222IMT School for Advanced Studies Lucca, Italy

###### Abstract

Estimating the influence of religion on innovation is challenging because of
both complexness and endogeneity. In order to untangle these issues, we use
several measures of religiosity, adopt an individual-level approach to
innovation and employ the instrumental variables method. We analyse the effect
of religiosity on individual attitudes that are either favourable or
unfavourable to innovation, presenting an individual’s propensity to innovate.
We instrument one’s religiosity with the average religiosity of people of the
same sex, age range, and religious affiliation who live in countries with the
same dominant religious denomination. The results strongly suggest that each
measure of religiosity has a somewhat negative effect on innovation attitudes.
The diagnostic test results and sensitivity analyses support the main
findings. We propose three causality channels from religion to innovation:
time allocation, the fear of uncertainty, and conventional roles reinforced by
religion.

JEL-Codes: Z12, O31.
Keywords: religion, innovation, religiosity, innovativeness, instrumental
variables.

##  1 Introduction

There have been numerous studies on the economic consequences of religion
since Max Weber’s The Protestant Ethic and the Spirit of Capitalism
(1905/[*]Weber), where he discusses that the emergence of modern capitalism in
Europe was a product of Protestant Reformation that fostered certain traits of
people including work ethic and thrift. Despite Weber’s thesis being more of a
verbal observation rather than an empirical argument, and many empirical
studies prove Weber to be wrong (see [58]; [11]), the idea that religion
affects economic outcomes through shaping and transforming individual
preferences and behaviours is far from being a myth. The most important
connections between economics and religion happen through the effects of
religion on economically important individual behaviours —including
consumption patterns, saving patterns, time allocation decisions, marriage,
fertility, gender roles in family and society― and traits ―such as trust,
honesty, thriftiness, tolerance to dissimilarity, willingness to work hard,
openness to strangers, being prone to crime. The present paper explores one
dimension of this connection. It examines the effect of religion on innovation
at the individual level by focusing on some economically important individual
beliefs, behaviours, and traits that we call innovation attitudes.

To our knowledge, [12] is the first empirical study at the intersection of
religion and innovation. Following their contribution, the research in this
field has begun to flourish ([52]; [16]; [13]; [37]; [18]; [5]; [55]).
Previous studies have consistently demonstrated a correlation between religion
and innovation yet recognise that observed associations probably do not
identify whether religion has a causal impact on innovation due to the
potential endogeneity of religion. Given the difficulty of defining and
measuring all potentially non-ignorable factors, some unobservable components
of the error term are likely to be correlated both with religion and
innovation, leading to an omitted variable bias. Moreover, the process of
causation is generally considered from religion to innovation. Nevertheless,
also higher levels of innovation can affect religion through economic growth
and development as conceptualised in the secularisation hypothesis ([41]),
raising the question of reverse causality.

Motivated by these premises, the present study introduces the first attempt to
untangle the endogeneity of religion with respect to innovation, aiming to
provide a causal interpretation of the effect of religion by focusing on
individual religiosity and innovation attitudes. We use eight waves ―between
2002 and 2016― of the European Social Survey (ESS), an academically driven,
cross-sectional, and an individual-level data set containing observations for
36 European countries. To our knowledge, the ESS has not been used by prior
studies on innovation and religion. We define four different measures of
religiosity —the degree of being religious, the frequency of attendance
religious activities, the frequency of praying, and religiosity index, which
is the principal component of the first three measures— since prior work has
shown that different religiosity measures are likely to have different effects
on economic outcomes ([8]). Innovation is measured at the individual level
with self-reported personal traits and beliefs related to the propensity to
innovate of an individual. These innovation attitudes are broken down into
positive and negative innovation attitudes. Positive innovation attitudes
(PIA) are creativity, being free, being different, and being adventurous;
negative innovation attitudes (NIA) are following traditions and following
rules. Using attitudes, instead of an aggregate measure of innovation output
such as patents per capita, allows the influence of institutional level
confounding factors to be mitigated. Moreover, a large set of fixed effects is
used to minimise the effect of (1) country-level economic and institutional
confounding factors, (2) individual-level unobservables such as human capital
and ability, and (3) global shocks. Finally, we instrument each measure of
religiosity with the average religiosity of people of the same sex, age range,
and religious affiliation who live in countries with the same dominant
religious denomination. In other words, the religiosity of individual $i$ from
country $c$ who belongs to a given religion (does not belong to any religion)
is instrumented by the average religiosity of people who belong to the same
religion (do not belong to any religion), share the same age range and gender
with $i$, and live in countries that have the same dominant religious
denomination with country $c$. By doing so, we aim to eliminate the effect of
individual-level unobservables. Furthermore, we use religious affiliation
along with religious intensity to mitigate reverse causality since religious
affiliation is inherited and thus is not responsive to changes in other
factors.

We start by estimating a linear model with OLS; then we apply our IV strategy.
The first-stage results show that all the excluded instruments are strongly
and significantly correlated with the religiosity measures. The OLS results
show that each measure of religiosity is significantly and positively
correlated with NIA, suggesting that religiosity has a negative effect on
innovativeness. Regarding PIA, religiosity measures are positively correlated
with creativity and being different, while negatively correlated with being
free. In the IV results, the significant and positive association remains
robust between the measures of religiosity and NIA. But the overall picture
changes with PIA. The religiosity measures are no longer significantly related
to creativity and being different. In contrast, the negative relation between
religiosity and being free persists, meaning the OLS results suggesting that
religiosity fosters some favourable attitudes to innovation may be driven by
endogeneity.

The negative effect of religiosity is robust to a series of sensitivity
analyses. First, we introduce a different dependent variable, an overall
summary measure of innovation attitudes: innosum. OLS and IV results of
innosum support the negative effect of religiosity on innovativeness. Second,
we reconsider the religiosity index (religiosity) and drop the degree of being
religious (degree) from its components, thinking that degree might be prone to
self-report bias since it is a self-evaluation of a belief, a value. In
contrast, the other components of religiosity (frequency of pray and
attendance to religious services) are measures of an activity, an action that
is difficult to over or underestimate. The estimated coefficients are pretty
close to the initial one, meaning that the initial instrument of religiosity
is not significantly affected by the potential self-report bias of degree.
Third, we re-estimate the baseline model by reducing the main sample in four
ways to analyse if minorities and/or outliers affect the main findings.
Fourth, we consider two possible violations of the exogeneity condition.
Sensitivity analyses, overall, demonstrate that coefficient values might
change depending on the specification choices, but the main results stay
robust.

Lastly, we discuss three possible causality channels from religion to
innovation: time allocation, the fear of uncertainty, and roles reinforced by
religion (individualism- collectivism and conventional gender roles). First,
we consider the opportunity cost of time spent on religious activities,
arguing that religious participation might negatively affect the human capital
formation, which is essential to innovation. Since time is scarce, if an
individual allocates a certain amount of time to religious activities, then
there will be a decrease in the maximum amount of potential time devoted to
human capital formation. Our empirical results support this argument.
Attendance in religious services is the most robust religiosity measure
throughout the analysis. Second, we argue that for religious people, personal
uncertainty is mitigated by their faith and by the support of religious
institutions such as churches, mosques, or religious social organisations.
Therefore, they have less experience dealing with risks and uncertainty
inherent to the innovation process. Third, we assert that high religiosity may
foster a collectivist culture that highly values established rules and might
leave limited space for reformist and creative endeavours, negatively
affecting innovation. We also consider the long-standing argument that
religions foster gender discrimination by imposing stricter rules for women,
influencing the access of women to education, liberty, labour force, and
social and legal rights. We empirically test these hypotheses and find
supporting evidence.

The remaining of the paper is structured as follows. Section 2.1 briefly
reviews the existing literature on religion and innovation. Section 2.2 looks
at innovation from a behavioural point of view and describes innovation as a
mindset, establishing the theoretical foundation for our innovation measures.
Section 3 extensively describes the estimation strategy and discusses possible
endogeneity channels together with our strategies to overcome them. Section 4
reports OLS and IV results and discusses the main findings. Section 5
introduces the sensitivity analyses. Section 6 discusses three possible
channels from religion to innovation. Section 7 provides concluding remarks.
In the Appendix, we present data summary tables and a detailed variable index
(Section A), the first stage results for IV estimates (Section B), the OLS
results for each innovation attitude (Section C), and the IV results for each
innovation attitude (Section D).

##  2 Literature

The present study mainly relates to the expanding literature on the effects of
religion on economic outcomes. The first subsection briefly overviews the
literature on the relation between religion and economic outcomes, emphasizing
innovation. The second subsection summarizes the literature on the individual
and behavioural aspects of innovation, presenting a theoretical framework for
our innovation measures.

###  2.1 Religion, Economic Outcomes, and Innovation

The interaction of economics and religion has long been a topic of
sociological analyses. Until recently, economists have not focused on the
topic, possibly due to a lack of reliable data on religion and potential
methodological issues such as endogeneity. Nevertheless, recent decades have
witnessed expanding literature on the economics of religion333For an in-depth
literature review, please refer to [38] and [41]. in which religion has been
integrated into economic research in three different ways: (i) applying the
methodology of economics to religion (e.g., microeconomic theory to analyse
religious behaviour of individuals, groups, or institutions); (ii) analyzing
economic outcomes of religion (e.g., the effect of religiosity on economic
growth); (iii) making use of holy books and theological norms to praise or
criticize economic behaviours and economic policies ([38]), (e.g., Islamic
economics is critical about interest rates since Islamic law, Sharia,
prohibits any interest paid on loans of money). The present study mainly
contributes to the second category by examining the effect of religion on
innovation.

The work of [12], to our knowledge, is the first empirical analysis of
religion and innovation, while qualitative studies were present before ([43];
[54]). Using the World Intellectual Policy Organization (WIPO) data, they
measure innovation with patents per capita. The two religiosity measures,
namely, belief in God and being religious, are retrieved from the World Values
Survey (WVS). Both cross-country and within-country (USA) analyses indicate a
negative relationship between religiosity and innovativeness. In a follow-up
paper, [13] enlarge the analysis by focusing on the innovation attitudes at
the individual level instead of patents per capita which is an ex-post and
macro-level measure. They define eleven different innovation attitudes and
five alternative measures of religiosity that are retrieved from the WVS. They
find an overall negative relation across 52 model specifications. Almost in
every specification, greater religiosity is significantly associated with less
favourable attitudes toward innovation. Though both papers do not directly try
to deal with endogeneity, the latter study decreases the influence of
institutional-level confounding factors by using individual-level data.

[52] examines the relationship between religious affiliation and
innovativeness in Russia. Innovation is measured by the number of patents
issued by the Russian Federal Service for Intellectual Property (Rospatent).
He finds that only Hindu, Buddhist, and Jewish faiths exhibit significant and
negative effects on innovativeness. Christians, Muslims, Jews, and Atheists do
not display any significant impact at all.

[16]444This paper follows [47] and [46] that find a positive relationship
between the propensity to gamble and risky, lottery-like financial market
investments. analyse the relation between local gambling culture and corporate
innovation. “Many innovative endeavours, i.e., attempts to come up with new
products, services and methods, represent gambles because they promise
relatively small probabilities of large success and large probabilities of
failure” ([1], pp. 229). They use religious beliefs as a measure of gambling
preferences of firms’ local communities. Taste for gambling is addressed by a
higher Catholic-to-Protestant ratio assuming that Catholics are more likely to
take risks. They find that the firms headquartered in areas with a taste for
gambling tend to be more innovative.

[18] analyse the effect of religious tolerance on innovation in Prussia by
assuming that tolerance and diversity are conducive to technological
creativity and innovation. Religious tolerance is proxied by the index of the
religious diversity of the population across 1278 cities in Prussia.
Innovation is measured by 1740 patents issued in Prussia between 1877 and
1890. They find that higher levels of religious tolerance have a strong
positive impact on innovation during the second industrial revolution. They
also show that the culture of tolerance did not stem from a particular
denomination but rather from the presence of different denominations and
churches.

The research at the intersection of religion and innovation has begun to
flourish following the aforementioned studies ([37]; [5]; [55]). However, no
empirical study has considered the potential endogeneity of religion so far,
despite reverse causality and identification problems have been recognized in
the literature ([32]).

The process of causation is generally considered as religion affects economic
outcomes by playing an integral role in shaping culture, individual
preferences, traits, and beliefs. However, the direction does not have to be
from religion to economic outcomes. The secularization hypothesis suggests
that economic outcomes, such as individual and country-level income and
growth, may affect religious behaviour, presenting a possible reverse
causality channel for our study. The core argument of secularization is that
religiosity decreases as a nation gets richer. There has been a long debate on
whether the secularization hypothesis holds. Many economists have analysed the
main channels that might facilitate the secularization effect, yet it is
unlikely to say there is a consensus in the literature. [57] argue that
modernization reduces the need for religious reassurance because it enables
the creation of more securities ―whether it be financial, social, or
political― for the population, thus decreasing the level of religiosity. [39]
provides valuable insights by analyzing the secularization with retrospective
questions from 30 nations that stretch from the 1920s through the 1980s. She
finds that secularization cannot explain the continued vitality of religion in
the USA, one of the world’s most modernized nations. On the contrary, she
finds some favourable evidence for secularization in Britain, France, and
Germany.

Apart from religion, there is a relatively vast literature on culture and
innovation that has adopted the Hofstede model (1980/[*]Hofstede2001) ([60],
[*]Shane1993, [*]Shane1995; [30]; [TaylorWilson]; [53]; [17]; [45]). Hofstede
analyses the differences in national cultures using the data of business
employees from more than 50 countries. He empirically identifies and validates
five independent dimensions affecting economic attitudes and outcomes ―namely,
power distance, uncertainty avoidance, individualism versus collectivism,
masculinity versus femininity, long-term versus short-term orientation― on
which national cultures differentiate 555Hofstede initially defines four
dimensions. In the second edition of the book ([*]Hofstede2001), he adds long-
term versus short-term orientation as the fifth dimension.. These five
dimensions together form a model of differences among national cultures and
have been pretty influential in the literature on culture and economic
outcomes. [44] review 180, and [64] reviews 61 published studies that used
Hofstede’s dimensions, and they both confirmed their relevance to the
differences in national cultures. In this study, we consider uncertainty
avoidance and individualism-collectivism as potential causality channels from
religion to innovation (see Section 6).

###  2.2  Innovation as a Mindset

Innovation can be defined in diverse forms. [68] review the literature on
innovation and propose five different innovation types that jointly determine
the overall innovativeness of an organisation: (1) product innovativeness
(newness, novelty, originality, or uniqueness of products), (2) market
innovativeness (newness of approaches that companies adopt to enter and
exploit the targeted market), (3) process innovativeness (introduction of new
production methods, new technology, and new management approaches that can be
used to improve management and production processes), (4) strategic
innovativeness (an organisation’s ability to manage ambitious organisational
objectives and to identify a mismatch of these ambitions and existing
resources to use limited resources creatively) and (5) behavioural
innovativeness (enables the formation of an innovative culture, the general
internal capacity for new ideas and innovation and screens through teams,
individuals, and management). Different types of innovation are expected to be
related to different individual and organisational traits. [15] name these
traits as innovation enablers, i.e., a set of general capabilities driven by
social capital that contributes to favour innovation. They associate with
innovation enablers and innovation types as follows: goal alignment and
knowledge enhancement for product innovation; knowledge enhancement and
cooperation for process innovation; cooperation and associability for
strategic innovation; associability, risk-taking and creative environment for
market innovation; risk-taking, creative environment, communication and
information flow for behavioural innovation. Among the innovation types,
behavioural innovativeness is not only crucial for overall innovativeness but
also for the other types of innovation, given that it enables the formation of
an innovative culture by being present at the various levels for individuals,
teams, and management ([68]). Cultivating an innovative culture through
behavioural innovativeness induces other types of innovation mainly by
creating an environment where new ideas are supported, failure is tolerated,
creativity is promoted, and risk-taking is encouraged.

Many studies underline the importance of innovative culture and mindset by
focusing on behavioural aspects of innovation. [29] argues that what separated
the East from the West in the early modern world was not capitalism nor
rationalisation of institutions; it was the willingness to innovate, which was
fostered by revolting from orthodoxy and by cultivating tolerance to internal
diversity, which enhanced openness to taking risks. He compares three
historical crises in the 17th-century —the Stuart king crisis in England,
Celali revolts in Ottoman Turkey, and revolts against the Ming dynasty in
China—that have economic roots. He argues that England fostered tolerance to
diversity and openness to take risks by adopting a relatively new, risk-taking
path, which resulted in a higher propensity to innovate. On the contrary,
Ottoman Turkey and China followed cultural orthodoxy and suppressed
alternatives, resulting in an unfavourable environment to innovate. England,
therefore, reached dynamism and growth, while Ottoman Turkey and China had
stagnation. Even though the analysis of Goldstone is at the macro level, the
cultural traits mentioned ―tolerance to diversity, openness to taking risks,
and revolting from tradition― are perfectly plausible at the individual level
as well.

[21] review the extensive literature on organisational innovation indicators
published between 1980 and 2015. They find that innovation culture has been
one of the most used company-specific innovation indicators. Innovation
culture is addressed by different indicator categories such as creativity,
attitudes toward science and technology, social innovation climate and trust,
support of new ideas, openness to new fields, the openness of the company
towards change and innovation, resistance to change, willingness to exchange
ideas, tolerance for innovation failures, et cetera. All these indicators are
related to behavioural aspects of innovation.

[42] underlines that innovation should be recognized as three different
things: (1) innovation is an outcome (product innovation, process innovation,
marketing innovation, business model innovation, supply chain innovation,
organizational innovation), (2) innovation is a process (innovation process,
product development process), and (3) innovation is a mindset (individual
mindset, organization culture). “Mindset aligns employees and manifests the
culture needed for innovation to happen. Encompassing a mindset that
predisposes individuals and organisations to be risk-taking, cross-
disciplinary, and open to varied ways of thinking helps establish the state
necessary for innovation; state implies something habitual and lasting. It is
about instilling and ingraining a mindset that prepares the individual and
organisation for innovation so that there is proper engagement in the
innovation process to achieve the desired innovation outcome.” ([42], pp.
459).

The literature mentioned above demonstrates that innovation is strongly
associated with individual traits such as tolerance to diversity, risk-
acceptance, creativity, and deviating from traditional paths and rules, which
jointly form innovative cultures of teams, groups, and organisations.
Nevertheless, previous research has generally measured innovation as an
outcome, using patents, R&D expenditure and technology improvements’
expenditure at the country or firm level. There might be a couple of drawbacks
in using patents as a sole measure of innovation outcome. [50] argues that
almost a thousand empirical studies have used patent counts to address
innovation, mostly without controlling for variation across industries and
over time. However, the percentage of patents varies significantly across
industries. Furthermore, relying on the historical evidence, the majority of
innovations were outside of the patent system, even in countries with a
developed patent system, such as the mid 19th century USA ([51]). He uses the
exhibition data of 8079 innovations between 1851 and 1915 and underlines that
89% of British innovations and 85% of American innovations were not patented.
The probability of an innovation/invention being patented mainly depends on
the country’s patent system and law, among many other determinants.
Furthermore, inventors do not have to use patents to protect their invention;
they may use other property rights such as lead-time advantage and secrecy.
Hence, patents as an indicator of innovation would be a biased measure in
cross-country and cross-industry studies.

Another critical issue is that patents are ex-post measures of innovation that
reflect country-specific institutional constraints. In other words, patents
measure some portion of occurred innovation. However, they have little to say
about the propensity to innovate, mainly determined by the behavioural and
cultural traits mentioned above. One cannot address unhappened, i.e., blocked
by unfavourable institutional constraints or cultural and religious traits,
innovation with an ex-post measure. Many papers in this regard address culture
and religion by individual measures ―belief in god, degree of religiosity,
willingness to take a risk, tolerance to diversity, et cetera― but address
innovation by country-level patents, which is a macro and ex-post measure.
Therefore some economists ( [32], [*]Guisoetal2006; [22], [*]Estebanetal2018,
[*]Estebanetal2019) suggest using individual attitudes as a measure of
innovation rather than aggregate macro outcomes such as patents.

##  3 Estimation Strategy

###  3.1 Data

We use pooled cross-sectional data from the European Social Survey (ESS). The
ESS is a biennial, academically-driven, and individual-level survey that
includes large groups of observations about preferences, beliefs, and
attitudes toward many different subjects, including immigration, religion,
political choices, trust, and markets. The ESS is available for 36666Albania,
Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,
Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy,
Kosovo, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal,
Romania, Russia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey,
Ukraine, and United Kingdom. We have dropped Israel from the sample since it
is not located in mainland Europe and is the only dominantly Jewish country.
countries for nine waves from 2002 to 2018. We use the first eight waves that
result in more than 380 000 observations. The survey does not homogeneously
include all countries across all waves, meaning that some countries are not
surveyed in certain waves and thus are represented in different proportions.
Table A1 displays the surveyed countries across the waves.

The main variables we are interested in are presented in different groups
below. Detailed descriptions of all variables are presented in Appendix A.
Descriptive statistics of the variables are presented in Table A2.

###  3.2 Measuring Innovation

Innovation is a complex phenomenon as religion and is challenging to measure
due to its many dimensions. It is important to underline that innovation
mainly occurs in two phases: initiation and implementation of innovation
([28]; [70]). The first phase is about creating ideas which is, in general, an
individual task. On the other hand, the second phase might include teams,
groups, and management activities, requiring an innovative organisational
culture and mindset. Most papers on innovation literature focused on the
second phase by using patents as the only indicator of innovation, as
underlined in Section 2.2. On the contrary, our innovation measures are mainly
related to the first phase. Nonetheless, individual innovation measures are
likely to stay vital in the second phase as well.

Following [32], we address innovation by the individual propensity to innovate
by introducing innovation attitudes that are designated under the guidance of
the literature briefly reviewed in Section 2. In this regard, we exploit
several variables in the ESS: creative (important to be creative), different
(important to be different), free (important to be free), adventurous
(important to be adventurous), traditions (important to follow traditions),
rules (important to follow rules). We categorise these variables as positive
or negative innovation attitudes. Positive innovation attitudes (PIA) are
essential behavioural conditions and favourable personal traits to innovate.
Negative innovation attitudes (NIA) are personal traits that contradict with
an innovative mindset, thus are unfavourable to innovation. We nominate four
favourable —creative, different, free, adventurous— and two unfavourable
—traditions, rules— traits to innovation.

We create two average measures: inposav and innegav. Inposav, average positive
innovation attitudes, is the mean of iprcrtiv, different, free and rules;
while innegav, average negative innovation attitudes, is the mean of
traditions and rules. Innosum is a summary measure of innovation attitudes
computed as the sum of creative, different, free, adventurous minus traditions
and rules.

Inposav and innegav are the primary dependent variables in our analysis, yet
we also analyse each attitude that composes them. The corresponding tables of
OLS and IV estimates of each attitude are presented in Appendix C and D for
the sake of brevity. Detailed information on how we construct innovation
variables is presented in Appendix A.

###  3.3 Measuring and Instrumenting Religiosity

The variables belonging, pbelonging, denomination, pdenomination, degree,
attendance, pray are religion-related observations in the ESS. Belonging,
pbelonging stand for respectively present and past belonging to a religion;
denomination, pdenomination present respectively current and past denomination
of an individual; degree is the degree of religiosity, attendance is the
frequency of attendance to religious activities, pray is the frequency of
praying. Belonging, pbelonging, denomination, pdenomination indicate religious
affiliation —belonging to a particular religion or denomination— while degree,
attendance, pray reveal religious intensity that we simply name religiosity.

Different measures of religiosity are likely to have different effects on
socioeconomic variables, reflecting the multi-dimensional nature of religion
(see [8]). For instance, the religion market model 777Developed by [26], [25].
suggests that if a state has a formal religion, imposing the state religion
and limiting the other religions, participation in religious services is
likely to decrease because the variety of services is subject to suffer ([8]).
Nevertheless, people might keep firm religious beliefs without attending
state-regulated formal religious services. Accordingly, low attendance may be
caused by the supply side of the religion market and may not mean low
religiosity. Motivated by this reasoning, we use three different measures of
religious intensity (degree, attendance, pray) as explanatory variables. We
also create an index of general religiosity, religiosity, that is the
principal component of the three religious intensity variables.

Our IV strategy is inspired by [24] where they examine the role of
religiosity, along with personal liberties, in influencing the decision of
labour effort. They construct an instrumental variable for religious intensity
by computing the average religious intensity of people of the same sex, age
bracket, and religious denomination in neighbouring countries, assuming that
religiosity is a cultural trait shared by people over national borders. By
doing so, they could eliminate the possible omitted variable problem for
individual unobservables, such as ability, because the instrument is other
people’s average religiosity, which is likely to be uncorrelated with
individual $i$’s personal characteristics. We partly adopt this strategy to
build instruments for religious intensity variables by computing the average
religiosity of people of the same sex, age range, and religious affiliation
who live in countries with the same dominant religious denomination. In other
words, the degree of religiosity of individual $i$ from country $x$ who
belongs to a religion (does not belong to a religion) is instrumented by the
average religiosity of people—who belong to a religion (do not belong to a
religion), share the same age range and gender with $i$ —who live in the
countries that have the same dominant religious denomination with the country
$x$ has.

Briefly, our instruments are constructed by taking into account four elements:
age range, gender, religious affiliation, and dominant religious denomination.
We use 11 age ranges (15-20,…, 60-65, 65+)888We have also tried 6 age ranges
(15-25, 25-35…55-65, 65+). The estimation results were pretty similar to those
estimated with 11 age ranges.. Gender is a dummy variable 0= male and
1=female. Religious affiliation is based on variable belonging which is a
dummy, 0=does not belong to a religion and 1=belongs to a religion. We
determine dominant religious denominations with the help of variables
denomination and country after weighted by population size weights. We
calculate the percentages of each religious denomination in each country in
the sample and identify the religious denomination that has the highest
percentage in a country as the dominant religious denomination. The dominant
religious denomination of each country and corresponding percentages are
presented in Table A1 in Appendix. There are five dominant religious
denominations: Roman Catholics, Protestants, Eastern Orthodox, Muslims, and
Atheists (do not belong to a religion).

The rationale behind our IV strategy emerges from the following points. [32]
show that individuals who were raised in a particular religious environment
possess common preferences and beliefs even though they refuse to belong to
any religion as adults. The dominant religious denomination in a county is the
leading actor that forms the characteristics of the religious environment in
which individual preferences and beliefs are being shaped. Some individuals
inherit more, some less, but the dominant religious denomination determines
the rules. Thus, instrumenting individual $i$’s religiosity by the average
religiosity of people who are subject to the same dominant religious
denomination in their country (along with the same age range, gender, and
affiliation) means that individual $i$’s average religiosity is instrumented
by the average religiosity of people who live in a similar religious
environment as that of $i$. Here we assume that, as Esteban et al.,
religiosity is not a national trait but a cultural trait that transcends
national borders. People raised in a particular religious environment are
likely to share a significant part of their culture with those raised in a
similar religious environment regardless of national borders. However, they do
not necessarily share the same institutional environment. We, therefore, use a
large set of fixed effects to eliminate country-level institutional
differences. Moreover, our sample consists of European countries that share
many common values and customs.

We use religious affiliation variables, along with religious intensity
variables when constructing instruments. Religious affiliation generally
passes down from generation to generation and is thus inherited and subject to
slow change. The ESS data justify this proposition. Only 9.8% of individuals
in the sample have changed their religious denomination999Calculated by using
the observations of denomination and pdenomination., meaning that religious
affiliation is pretty stable throughout one’s lifetime and does not
significantly change along with changes in other factors, including
innovation. On the other hand, religious intensity is more likely to change
over one’s lifetime, making it more vulnerable to reverse causality 101010[14]
and [33] underline that not only religious affiliation but also religious
practices are modified only over centuries; thus any aspect of religion can
largely be assumed invariant over one’s lifetime.. Moreover, it is expected
that the preferences and beliefs of an individual who belongs to a religion
would be affected by the religious environment around her more than a person
who does not belong to a religion.

Another crucial point is that the average religiosity of people who belong to
a particular religious denomination may inherently be more intense than those
who belong to another religious denomination. For instance, Muslims may be
inherently more religious than Protestants111111The data support this
proposition. For instance, Table A3 in Appendix A shows that Muslim countries
exhibit significantly higher means for the religiosity measures. We,
therefore, use religious denomination fixed effects to be able to compare the
religiosity of people from different religious denominations.

In our IV strategy, the exclusion restriction would be violated if belonging
to a religion is not inherited from past generations but chosen during one’s
lifetime. In this case, belonging to a religion would be correlated with
individual unobservables and thus cannot be considered exogenous.
Nevertheless, as mentioned above, only 9.8% of our sample have changed their
religious affiliation (once-believers). As a robustness check, we drop once-
believers from the main sample and reestimate the main specifications (Table
9, columns 7 and 8). The results are very similar to the full sample. Another
scenario in which our instruments violate the exclusion restriction is that
countries with the same dominant religious denomination are subject to
correlated shocks. We use survey year fixed effects to capture global shocks
along with country and country-survey year interaction fixed effects.

It is likely that within-group observations are correlated. In our case, those
groups are the five elements we use to construct instruments: age, gender,
religious affiliation, denominations, and country. For instance, the
religiosity of a Catholic woman at age 36 from a dominantly Catholic country
could be correlated to the religiosities of other middle-aged Catholic women
from dominantly Catholic countries. It is known that ignoring within-group
correlations leads to understated standard errors ([63]). We thus use
clustered standard errors at the instruments’ level to avoid within-group
correlations in error terms.

###  3.4 Dealing with Endogeneity

Religious beliefs are embedded in social behaviour and culture, meaning that
extracting the pure effect of religion can be challenging. One, therefore,
must extensively consider endogeneity when working on religion. We ultimately
address the possible inherent endogeneity of religion by employing the
instrumental variable strategy. In addition, we consider the primary sources
of endogeneity below and discuss how we overcome them.

Omitted variables. Religion is a complex phenomenon. Therefore, it is
plausible to assume that possible confounding factors and unobservable
components could be added to the error term when including religion-related
variables into regression, causing omitted variable bias. To minimize the
effect of confounding factors, we employ a multi-way fixed effects strategy
along with controls of individual characteristics.121212See [31] and [19] for
further information on multi-way fixed effects in linear models. We control
for the following fixed effects: country, survey year, country and survey year
interaction, religious denominations, occupation categories, and income level.
Country and survey year fixed effects eliminate the impact of institutional
features and country-level economic determinants, which are the same for the
entire country. Occupation and income level fixed effects aim to minimize the
differences of individual-level unobservables across respondents; religious
denomination fixed effects help us compare the religiosity of people who
belong to different religious denominations. Controlling for a large set of
fixed effects conveys the possibility of underestimating the impact of
religion, given that religion affects many determinants whose effects might be
partially absorbed by fixed effects. Therefore, our estimates can be
interpreted as a lower bound of the effect of religiosity on innovation
attitudes.

In our model, one possible omitted variable is human capital. It is reasonable
to assume that human capital has a feature somewhat unobservable, which gives
rise to the omitted variable problem. Occupation and income level fixed
effects can eliminate, to some degree, human capital differences among
respondents, thereby reducing the possible omitted variable bias. Furthermore,
we follow [7] and use educational attainment, namely competed years of
education, as a measure of human capital. We also control for father’s and
mother’s education levels, which are essential to human capital formation,
especially at younger ages. [2] examine a large data set on innovation and
inventors collected for the period between 1880 and 1940 in the USA. They
uncover many micro and macro stylized facts at the individual level. One of
them is that father’s education is a crucial determinant of being an inventor,
especially through the channel of a child’s education. Hence we control for
the father’s and the mother’s education level since the mother’s education is
likely to be equally important, if not more, given that mothers spend more
time with children in most cultures. Another uncovered fact by Akcigit et al.
is that inventors tend to migrate from their birth state to more eligible
states for innovation. We, therefore, control for the born-in country of
respondent.

Reverse causality. The direction of causation may go both ways when working on
religion. We aim to measure the effect of religiosity on innovation. However,
changes in innovation may affect one’s religiosity as well, mainly through
economic growth and development, which is conceptualized in the secularization
hypothesis mentioned in Section 2. To deal with reverse causality in the case
of culture and religion-related research, [33] suggest focusing only on
inherited dimensions of culture, such as religious affiliation and ethnicity,
rather than accumulated ones such as social capital. We follow Guiso et al.
and use religious affiliation along with religious intensity when measuring
religiosity, as explained above.

Another strategy to mitigate reverse causality is to focus on economic
attitudes rather than outcomes, given that economic outcomes are partly
determined by the institutional and political environment, which is rather
challenging to account for ([32], [*]Guisoetal2006). Economic attitudes reveal
individual propensity to something; in other words, they give insights into
the possibility of a particular economic outcome being real. As [32] pointed
out, asking somebody if she has ever cheated on taxes is different from asking
somebody her opinion on cheating on taxes.

Measurement error. Data may be measured with errors, such as coding and
reporting errors. When an independent variable is measured with error,
endogeneity arises since it violates the zero conditional mean assumption. Our
data source is the ESS, a comparative cross-national survey that collects
measures of individuals’ preferences and social and political attitudes across
36 European countries. These measures may contain errors due to differences in
concepts measured across the participating countries. The ESS aims to minimize
such measurement errors and improve data reliability, validity, and
comparability; therefore, it undertakes a range of data quality assessment
activities, including the Multitrait-Multimethod (MTMM) and Survey Quality
Predictor (SQP). MTMM is an experimental project in which the same respondents
are asked three survey questions twice in different concepts by using
different response scales each time.131313The detailed test data obtained from
MTMM is available online:
$https://www.europeansocialsurvey.org/data/download_{m}tmm.html$ MTMM measures
the measurement quality of individual questions, and it was implemented for
all the waves of the ESS. SQP is an open-source database on which the complete
set of the ESS questions is evaluated through MTMM design. Due to the rigorous
approach of the ESS, we may say that it is unlikely that our independent
variables suffer from measurement errors.

Furthermore, the ESS imposes a minimum target response rate of 70% in each
country to minimize non-response bias which may lead over or under-
representation of some individuals with certain characteristics. To adjust for
non-response bias and others, the ESS data come with three weighting
variables: design weights (dweight), post-stratification weights (pspwght) and
population size weights (pweight). Design weights correct for sample selection
bias, given that some countries use complex sampling designs and respondents
have different probabilities of being a part of the sample. Post-
stratification weights adjust for uneven representation of sub-groups and sub-
populations with certain characteristics, correcting for sample and non-
response biases. Population size weights are used when data from more than two
countries are combined. Since each country has a different population size but
a similar sample size, pweight corrects for over or under-representation of
countries concerning their population. We analyse more than one country and
total averages of countries in some cases, hence we must use a combination of
either dweight and pweight or pspwght and pweight. Given that pspwght includes
dweight, we use the combination of pspwght and pweight by generating a new
variable gweight= pwght $\ast$ pweight.141414For detailed information on
weighting the ESS data, please refer to the guide “Weighting European Social
Survey Data”.

###  3.5 Model

Based on the strategy above, we aim to estimate a causal relationship that
goes from religion to innovation. We first estimate equation 1 with OLS. We
then instrument religiosity measures, $R_{i}$, with $Riv_{i}$ and estimate the
baseline model with IV/2SLS 151515We use “ivreghdfe” command of Stata which
allows IV/2SLS estimation with multi-way fixed effects.. The first stage model
is specified in equation 2. All models include standard demographic controls
and fixed effects. Control variables are gender (gender), age (age), completed
years of education (education), parent’s highest education level (mothere,
fathere), born in country (bornc), paid work status (paidwork), whether had a
paid job before (pwbefore) whether have a partner or wife living in the house
(partner), whether have a child living in the house (child), subjective health
status (health). Fixed effects are country dummies (country), survey year
(essround), country and survey year interactions (cness), religious
denominations (denomination), occupation categories (occupation), and income
level (income1 & income2).

$I_{i}=\alpha_{0}+\beta
R_{i}+\theta\mathbf{X}_{i}+\delta\mathbf{F}+\varepsilon_{i}$ (1)

where $I$ denotes innovation attitudes (creative, different, free,
adventurous, traditions, rules, inposav, innegav) of individual $i$, $R$
stands for religiosity variables (degree, attendance, pray, religiosity),
$\beta$ is the coefficient of interest, the vector $X$ denotes control
variables, the vector $F$ represents the complete set of fixed effects
absorbed into estimates, $\varepsilon$ is the unobserved error term.

$R_{i}=\alpha_{1}+\zeta
Riv_{i}+\vartheta\mathbf{X}_{i}+\eta\mathbf{F}+\epsilon_{i}$ (2)

where $Riv$ denotes instrumental variables that we use as an exogenous
predictor of each religiosity measure.

##  4 Results

###  4.1 OLS Estimates

We begin by examining the relationship between religiosity and innovation
attitudes estimated with OLS. All specifications include multi-level fixed
effects that are fully absorbed into all specifications. We gradually control
for socio-demographic indicators. Regression errors are likely correlated
within groups since we expect endogeneous religiosity variables. Moreover, the
data set we use has a grouped structure, i.e., countries. We, therefore,
report robust standard errors clustered at the level of instruments, namely
clustered for the country, gender, age, religious affiliation and denomination
groups. We also performed OLS estimates with robust standard errors and found
very little difference so that the significance levels of the coefficients did
not change. However, we prefer to report the clustered version due to the
unity of the analyses throughout the study.

Table 1 presents the variations in inposav concerning different measures of
religiosity. Religiosity is the independent variable in the first three
specifications. The first specification, column 1, includes control variables
for gender, age, age squared, education, and the complete set of fixed
effects. The insignificant correlation coefficient in column 1 endures in
columns 2 and 3 after adding other control variables. Other measures of
religiosity display different patterns. Degree is statistically significant at
the 99% level in column 4 and stays robust to the further controls in column
5. On the contrary to the other religiosity measures, attendance shows a
negative relationship with inposav in column 6. It stays robust when we add
further controls in column 7. Pray is positive and significant at the 99%
level in column 8 and stays robust to the further controls, yet the
coefficient and significance level decrease in column 9.

The detailed OLS estimates, broken down into each measure of innovation
attitudes, are presented in Appendix C. Table C1 reports a stable, positive,
and significant relation between importance of creativity and the religiosity
measures, except for attendance, which has insignificant coefficients. In
Table C2, all religiosity measures are positively and significantly associated
with importance of being different, including attendance. Differently,
importance of being free is negatively related to all religiosity measures in
Table C3. As Table C4 displays, all religiosity measures have insignificant
coefficients for importance of being adventurous, except attendance, which is
negative and statistically significant at the 90% level. When compared to
Table 1, the positive coefficients for degree (0.008) and pray (0.006) seem to
be driven by importance of creativity and importance of being different as
evidenced in Tables C1 and C2. The negative relation between attendance and
inposav (-0.012) is apparently fostered by the negative relation between
attendance and importance of being free in Table C3.

Table 1: OLS Estimates: Religiosity and Average Positive Innovation Attitudes

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
---|---|---|---|---|---|---|---|---|---
| inposav | inposav | inposav | inposav | inposav | inposav | inposav | inposav | inposav
religiosity | 0.002 | 0.006 | 0.003 |  |  |  |  |  |
| (0.004) | (0.004) | (0.004) |  |  |  |  |  |
degree |  |  |  | 0.009∗∗∗ | 0.008∗∗∗ |  |  |  |
|  |  |  | (0.003) | (0.003) |  |  |  |
attendance |  |  |  |  |  | -0.010∗∗∗ | -0.012∗∗∗ |  |
|  |  |  |  |  | (0.004) | (0.004) |  |
pray |  |  |  |  |  |  |  | 0.008∗∗∗ | 0.006∗∗
|  |  |  |  |  |  |  | (0.003) | (0.003)
gender | -0.025∗∗∗ | -0.023∗∗∗ | -0.023∗∗∗ | -0.023∗∗∗ | -0.023∗∗∗ | -0.022∗∗∗ | -0.023∗∗∗ | -0.023∗∗∗ | -0.024∗∗∗
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
age | -0.005∗∗∗ | -0.003∗∗∗ | -0.002∗∗∗ | -0.003∗∗∗ | -0.002∗∗∗ | -0.003∗∗∗ | -0.002∗∗∗ | -0.003∗∗∗ | -0.002∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000∗∗∗ | 0.000∗∗ | 0.000 | 0.000∗∗ | 0.000 | 0.000∗∗ | 0.000 | 0.000∗∗ | 0.000
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | 0.004∗∗∗ | 0.003∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork |  | -0.001 | 0.001 | -0.000 | 0.001 | -0.001 | 0.001 | -0.000 | 0.001
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore |  | -0.115∗∗ | -0.069∗∗∗ | -0.115∗∗ | -0.068∗∗∗ | -0.121∗∗ | -0.077∗∗∗ | -0.115∗∗ | -0.069∗∗∗
|  | (0.051) | (0.013) | (0.052) | (0.011) | (0.049) | (0.013) | (0.050) | (0.012)
partner |  | -0.019∗∗∗ | -0.018∗∗∗ | -0.020∗∗∗ | -0.018∗∗∗ | -0.019∗∗∗ | -0.018∗∗∗ | -0.019∗∗∗ | -0.018∗∗∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
health |  | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗
|  | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child |  | -0.019∗∗∗ | -0.020∗∗∗ | -0.019∗∗∗ | -0.020∗∗∗ | -0.019∗∗∗ | -0.020∗∗∗ | -0.019∗∗∗ | -0.020∗∗∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
bornc |  |  | -0.010∗∗∗ |  | -0.009∗∗∗ |  | -0.011∗∗∗ |  | -0.009∗∗∗
|  |  | (0.003) |  | (0.003) |  | (0.003) |  | (0.003)
fathere |  |  | 0.005∗∗∗ |  | 0.005∗∗∗ |  | 0.005∗∗∗ |  | 0.005∗∗∗
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
mothere |  |  | 0.004∗∗∗ |  | 0.004∗∗∗ |  | 0.004∗∗∗ |  | 0.004∗∗∗
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
cons | 0.749∗∗∗ | 0.767∗∗∗ | 0.698∗∗∗ | 0.761∗∗∗ | 0.691∗∗∗ | 0.773∗∗∗ | 0.707∗∗∗ | 0.764∗∗∗ | 0.694∗∗∗
| (0.010) | (0.051) | (0.017) | (0.053) | (0.016) | (0.050) | (0.018) | (0.051) | (0.017)
$N$ | 234,528 | 228,327 | 202,300 | 231,306 | 204,788 | 231,733 | 205,176 | 229,606 | 203,384
Adj. $R^{2}$ | 0.135 | 0.146 | 0.150 | 0.147 | 0.151 | 0.147 | 0.151 | 0.147 | 0.150

  * •

Notes: OLS estimates for alternative measures of religiosity are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

Overall, the OLS results show that higher values of degree and pray have a
somewhat positive relationship with innovation through importance of
creativity and importance of being different, while attendance is negatively
related to innovation. Religiosity shows no significant relationship with
inposav, which is expected to some degree, given that religiosity is the
principal component of other three measures (degree, attendance, pray) that
exhibit both negative and positive relationships, yielding to cancel out each
effect when the measures combined.

In contrast with Table 1, Table 2 displays a pretty stable relationship
between different measures of religiosity and innegav. Each specification is
statistically significant at the 99% level and positively related to innegav,
regardless of the gradual inclusion of controls. Another salient result is
that all coefficients are substantially higher in absolute value than those in
Table 1, suggesting that the religiosity measures are more strongly associated
with NIA than with PIA. In other words, religiosity seems to foster individual
traits that are unfavourable to innovation.

Table 2: OLS Estimates: Religiosity and Average Negative Innovation Attitudes

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
---|---|---|---|---|---|---|---|---|---
| innegav | innegav | innegav | innegav | innegav | innegav | innegav | innegav | innegav
religiosity | 0.180∗∗∗ | 0.176∗∗∗ | 0.175∗∗∗ |  |  |  |  |  |
| (0.005) | (0.005) | (0.005) |  |  |  |  |  |
degree |  |  |  | 0.132∗∗∗ | 0.130∗∗∗ |  |  |  |
|  |  |  | (0.005) | (0.005) |  |  |  |
attendance |  |  |  |  |  | 0.129∗∗∗ | 0.129∗∗∗ |  |
|  |  |  |  |  | (0.005) | (0.005) |  |
pray |  |  |  |  |  |  |  | 0.077∗∗∗ | 0.075∗∗∗
|  |  |  |  |  |  |  | (0.003) | (0.003)
gender | -0.009∗∗∗ | -0.007∗∗∗ | -0.007∗∗∗ | -0.004∗ | -0.004∗ | 0.000 | 0.000 | -0.005∗∗ | -0.005∗∗
| (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003)
age | 0.001∗∗∗ | -0.000 | -0.000 | -0.000 | -0.001 | -0.000 | -0.001 | -0.000 | -0.001
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000 | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork |  | 0.003 | 0.005∗∗ | 0.002 | 0.004∗ | 0.002 | 0.004∗ | 0.003 | 0.004∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore |  | -0.244∗∗∗ | -0.209∗∗∗ | -0.256∗∗∗ | -0.218∗∗∗ | -0.253∗∗∗ | -0.221∗∗∗ | -0.276∗∗∗ | -0.257∗∗∗
|  | (0.034) | (0.031) | (0.037) | (0.034) | (0.032) | (0.032) | (0.027) | (0.034)
partner |  | 0.025∗∗∗ | 0.023∗∗∗ | 0.026∗∗∗ | 0.024∗∗∗ | 0.025∗∗∗ | 0.022∗∗∗ | 0.027∗∗∗ | 0.025∗∗∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
health |  | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.005∗∗∗ | 0.005∗∗∗
|  | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child |  | 0.003 | 0.001 | 0.004∗∗ | 0.002 | 0.004∗∗ | 0.003 | 0.004∗∗ | 0.002
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
bornc |  |  | -0.032∗∗∗ |  | -0.036∗∗∗ |  | -0.037∗∗∗ |  | -0.035∗∗∗
|  |  | (0.004) |  | (0.003) |  | (0.003) |  | (0.004)
fathere |  |  | -0.001 |  | -0.001 |  | -0.001 |  | -0.001
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
mothere |  |  | -0.004∗∗∗ |  | -0.004∗∗∗ |  | -0.004∗∗∗ |  | -0.004∗∗∗
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
cons | 0.510∗∗∗ | 0.756∗∗∗ | 0.776∗∗∗ | 0.765∗∗∗ | 0.787∗∗∗ | 0.793∗∗∗ | 0.823∗∗∗ | 0.818∗∗∗ | 0.859∗∗∗
| (0.010) | (0.036) | (0.032) | (0.038) | (0.035) | (0.033) | (0.033) | (0.028) | (0.035)
$N$ | 234,387 | 228,192 | 202,191 | 231,156 | 204,668 | 231,582 | 205,055 | 229,462 | 203,268
Adj. $R^{2}$ | 0.239 | 0.242 | 0.246 | 0.236 | 0.240 | 0.231 | 0.236 | 0.231 | 0.235

  * •

Notes: OLS estimates for alternative measures of religiosity are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

Tables C5 and C6 display detailed results for the two NIA. All religiosity
measures are positively and significantly (at the 99% level) associated with
both following traditions and following rules, but the coefficients are almost
four times higher for following traditions.

Table 3 shows the OLS estimates broken down into various religious
denominations and innovation attitudes. The dependent variables are PIA for
the first four columns, NIA for columns 5 and 6, and average innovation
attitudes for the last two columns. The complete set of control variables and
fixed effects are included in each specification. Briefly, Roman Catholics,
Protestants and Other Christians exhibit a negative relation with inposav,
while Jewish, Other Christian and Eastern religions are positively correlated.
When it comes to innegav, there is a consistent relationship across
denominations, each of them is positively and significantly correlated with
innegav, meaning that all kinds of religions are highly correlated to the
unfavourable individual traits to innovation.

Table 3: OLS Estimates: Religious Denominations and Innovation Attitudes

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
| creative | different | free | adventurous | traditions | rules | inposav | innegav
Roman Catholic | -0.006∗ | -0.007∗∗ | -0.016∗∗∗ | -0.025∗∗∗ | 0.171∗∗∗ | 0.048∗∗∗ | -0.014∗∗∗ | 0.110∗∗∗
| (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | (0.003) | (0.003)
Protestant | -0.009∗∗ | -0.018∗∗∗ | -0.017∗∗∗ | -0.027∗∗∗ | 0.151∗∗∗ | 0.059∗∗∗ | -0.018∗∗∗ | 0.105∗∗∗
| (0.004) | (0.004) | (0.004) | (0.003) | (0.005) | (0.005) | (0.003) | (0.005)
Eastern Orthodox | 0.008 | 0.005 | 0.003 | -0.000 | 0.111∗∗∗ | 0.034∗∗∗ | 0.003 | 0.072∗∗∗
| (0.009) | (0.008) | (0.009) | (0.008) | (0.006) | (0.007) | (0.006) | (0.005)
Other Christian | 0.007 | -0.000 | -0.027∗∗∗ | -0.026∗∗∗ | 0.111∗∗∗ | 0.075∗∗∗ | -0.011∗ | 0.093∗∗∗
| (0.009) | (0.010) | (0.009) | (0.010) | (0.010) | (0.011) | (0.007) | (0.008)
Jewish | 0.051 | 0.074∗∗ | 0.011 | 0.073∗∗ | 0.205∗∗∗ | -0.044 | 0.052∗ | 0.079∗∗∗
| (0.033) | (0.033) | (0.029) | (0.036) | (0.033) | (0.045) | (0.027) | (0.021)
Muslim | -0.006 | 0.005 | 0.001 | -0.013∗ | 0.224∗∗∗ | 0.090∗∗∗ | -0.003 | 0.157∗∗∗
| (0.009) | (0.009) | (0.010) | (0.008) | (0.009) | (0.009) | (0.007) | (0.007)
Eastern Religions | 0.015 | 0.022 | 0.015 | 0.045∗∗∗ | 0.141∗∗∗ | 0.000 | 0.024∗∗ | 0.070∗∗∗
| (0.014) | (0.015) | (0.013) | (0.015) | (0.016) | (0.020) | (0.010) | (0.015)
Other Non-Christian | 0.055∗∗∗ | 0.013 | -0.009 | 0.030 | 0.127∗∗∗ | -0.025 | 0.021∗ | 0.051∗∗∗
| (0.016) | (0.018) | (0.016) | (0.022) | (0.018) | (0.019) | (0.012) | (0.013)
Not declared | 0.005 | 0.009 | -0.015 | -0.006 | 0.143∗∗∗ | 0.041∗∗∗ | -0.002 | 0.092∗∗∗
| (0.012) | (0.009) | (0.009) | (0.011) | (0.013) | (0.010) | (0.007) | (0.010)
$N$ | 204,634 | 204,801 | 204,940 | 204,732 | 204,960 | 204,087 | 205,773 | 205,647
Adj. $R^{2}$ | 0.082 | 0.081 | 0.074 | 0.174 | 0.194 | 0.134 | 0.151 | 0.222

  * •

Notes: OLS estimates for religious denominations are reported. Robust standard
errors clustered at the level of instruments are in parentheses. All
regressions include controls for age, age squared, gender, education, paid
work status, children, health, mother’s and father’s, education, born-in
country and the following fixed effects: country, survey year, country-survey
year, religious denomination, occupation and income level. * $p<0.10$;
**$p<0.05$; ***$p<0.01$.

###  4.2 Instrumental Variables Estimates

The OLS estimates reported above indicate both negative and positive
associations between religiosity and innovation attitudes, though the negative
relation is more substantial. Nevertheless, the potential endogeneity of
religion makes it challenging to interpret the results as causal links.
Therefore, we use instruments to cope with endogeneity fundamentally. Tables 4
and 5 report the main results estimated with IV. The first stage results of
each specification can be found in the corresponding columns of Tables B1 and
B2 in Appendix B. The OLS version of each specification is presented in the
corresponding columns of Tables 1 and 2, meaning that, for instance, column 4
in Table 1 is the OLS version of the specification in column 4 in Table 4. All
specifications include the complete set of fixed effects.

We report a couple of post-estimation diagnostic tests. Kleibergen-Paap rk LM
statistic (idp) is for under-identification. Kleibergen-Paap rk Wald F
statistic (widstat), that is F test of excluded instruments valid in non-i.i.d
case, and Cragg-Donald Wald F statistic (cdf) are diagnostics for weak
identification. Widstat is equivalent to the first stage F statistic.
Anderson-Rubin Wald F statistic (AR test), which is weak-instrument-robust,
tests if the coefficients of endogenous variables are equal to zero in the
reduced form estimation. We do not need to consider the over-identification
problem because there is only one instrument for each endogenous religiosity
variable in all estimates; thus, our model is just-identified. All the results
and statistics are efficient only for homoscedasticity since all are estimated
with heteroscedasticity-robust options.

Table 4: IV Estimates: Religiosity and Average Positive Innovation Attitudes

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
---|---|---|---|---|---|---|---|---|---
| inposav | inposav | inposav | inposav | inposav | inposav | inposav | inposav | inposav
religiosity | -0.155∗∗ | -0.151∗∗ | -0.198∗∗∗ |  |  |  |  |  |
| (0.070) | (0.065) | (0.062) |  |  |  |  |  |
degree |  |  |  | -0.099 | -0.177∗ |  |  |  |
|  |  |  | (0.099) | (0.096) |  |  |  |
attendance |  |  |  |  |  | -0.157∗∗∗ | -0.185∗∗∗ |  |
|  |  |  |  |  | (0.052) | (0.050) |  |
pray |  |  |  |  |  |  |  | -0.080∗∗ | -0.119∗∗∗
|  |  |  |  |  |  |  | (0.040) | (0.039)
gender | -0.015∗∗∗ | -0.014∗∗∗ | -0.011∗∗∗ | -0.017∗∗∗ | -0.012∗∗ | -0.019∗∗∗ | -0.019∗∗∗ | -0.013∗∗∗ | -0.009∗∗
| (0.004) | (0.004) | (0.004) | (0.006) | (0.005) | (0.003) | (0.003) | (0.005) | (0.005)
age | -0.005∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗ | 0.000∗∗∗ | 0.000∗∗ | 0.000∗∗∗ | 0.000∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | 0.004∗∗∗ | 0.003∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork |  | -0.001 | -0.000 | -0.001 | 0.001 | -0.001 | 0.000 | -0.001 | -0.000
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore |  | -0.158∗∗∗ | -0.148∗∗∗ | -0.146∗∗∗ | -0.152∗∗∗ | -0.167∗∗∗ | -0.151∗∗∗ | -0.135∗∗∗ | -0.102∗∗∗
|  | (0.040) | (0.037) | (0.045) | (0.055) | (0.038) | (0.034) | (0.048) | (0.019)
partner |  | -0.017∗∗∗ | -0.015∗∗∗ | -0.018∗∗∗ | -0.016∗∗∗ | -0.016∗∗∗ | -0.014∗∗∗ | -0.019∗∗∗ | -0.017∗∗∗
|  | (0.002) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.002) | (0.002)
health |  | 0.017∗∗∗ | 0.017∗∗∗ | 0.018∗∗∗ | 0.017∗∗∗ | 0.019∗∗∗ | 0.018∗∗∗ | 0.017∗∗∗ | 0.016∗∗∗
|  | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child |  | -0.016∗∗∗ | -0.017∗∗∗ | -0.017∗∗∗ | -0.017∗∗∗ | -0.017∗∗∗ | -0.018∗∗∗ | -0.017∗∗∗ | -0.017∗∗∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
bornc |  |  | -0.024∗∗∗ |  | -0.021∗∗∗ |  | -0.019∗∗∗ |  | -0.023∗∗∗
|  |  | (0.006) |  | (0.007) |  | (0.004) |  | (0.005)
fathere |  |  | 0.005∗∗∗ |  | 0.005∗∗∗ |  | 0.006∗∗∗ |  | 0.006∗∗∗
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
mothere |  |  | 0.004∗∗∗ |  | 0.004∗∗∗ |  | 0.004∗∗∗ |  | 0.004∗∗∗
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
N | 234,528 | 228,327 | 202,300 | 231,306 | 204,788 | 231,733 | 205,176 | 229,606 | 203,384
idp | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
cdf | 2,338 | 2,380 | 2,161 | 846 | 771 | 2,732 | 2,533 | 2,012 | 1,730
widstat | 187 | 194 | 160 | 84 | 79 | 141 | 131 | 192 | 160
AR test | 0.022 | 0.016 | 0.001 | 0.297 | 0.052 | 0.002 | 0.000 | 0.046 | 0.002

  * •

Notes: IV estimates for alternative measures of religiosity are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. Kleibergen-Paap rk LM statistic (idp), Kleibergen-Paap rk Wald F
statistic (widstat), Cragg-Donald Wald F statistic (cdf), and Anderson-Rubin
Wald F statistic (AR test) are reported. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

Table 4 displays the IV results for inposav with respect to different measures
of religiosity. The first stage results in Table B1 show that all the excluded
instruments are strongly and positively correlated with the corresponding
religiosity measures, with a correlation coefficient between $0.581$ and
$0.784$. P-values of Kleibergen-Paap rk LM test (idp) show that the null of
under-identification is rejected, and the full rank condition is satisfied in
all estimates. For all IV estimates, the first stage F statistics (widstat)
are well above Stock-Yogo critical values of weak identification. The AR test
results indicate that the instruments are relevant, except for degree, which
we will discuss later.

The IV results for inposav exhibit a somewhat different pattern than the
corresponding OLS results in Table 1. It is essential to underline that the
OLS and IV results are not directly comparable since OLS estimates average
treatment effect (ATE), while IV/2SLS estimates local average treatment effect
(LATE) ([4]). However, we emphasize the main differences to discuss the
effects of vanishing endogeneity. First of all, all religiosity measures
display substantial increase in the coefficients, which is also the case in
other studies using a more aggregated instrument than endogenous variable
([40]; [20]). Religiosity becomes significant and negative in column 3; it is
positive and insignificant with OLS. Degree gets negative and less significant
in columns 5 and 6. Attendance keeps the sign of OLS estimates, which is
negative. Pray in columns 8 and 9, changes the sign to be negative as well.

Innegav reported in Table 5, in contrast to inposav, displays pretty stable
and consistent patterns. All religiosity measures are positively and
significantly correlated to innegav, as they are in the corresponding OLS
estimates in Table 2, with the increase in the coefficients. The corresponding
first stage results, reported in Table B2, show that all the excluded
instruments are strongly correlated to the religiosity measures in each
specification, with a correlation coefficient between $0.581$ and $0.784$.

Appendix D provides the IV estimates broken down into each measure of
innovation attitudes. Tables D1 and D2 indicate that importance of creativity
and importance of being different are negatively affected by attendance, while
other religiosity measures are insignificant. Table D3 shows that all
religiosity measures are negatively related to importance of being free,
yielding the highest coefficients among IV estimates. Religiosity does not
affect importance of being adventurous as demonstrated in Table D4. These
results signal that the estimated positive associations between religiosity
and PIA in OLS Tables C1 (importance of creativity) and C2 (importance of
being different) are driven by endogeneity. Tables D5 and D6 show that
religiosity measures positively affect NIA, similar to the OLS estimates.
Overall, it is plausible to argue that higher degrees of religiosity are not
advantageous for innovation since it fosters some personal traits unconducive
to an innovative mindset. Moreover, it falls away from conductive personal
traits such as freedom.

Apart from the main coefficients of interest, the control variables also
provide insightful results. The effect of religiosity on importance of
creativity, importance of being adventurous, and following rules are stronger
for men (gender); while it is stronger for women regarding importance of being
different and following traditions. Age is negatively related to importance of
being different, importance of being adventurous, and following rules and is
insignificant for importance of creativity, importance of being free, and
following traditions. Education seem to foster PIA (see Tables D1-D4) and
mitigate NIA (see Tables D5 and

Table 5: IV Estimates: Religiosity and Average Negative Innovation Attitudes

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
---|---|---|---|---|---|---|---|---|---
| innegav | innegav | innegav | innegav | innegav | innegav | innegav | innegav | innegav
religiosity | 0.139∗∗∗ | 0.177∗∗∗ | 0.210∗∗∗ |  |  |  |  |  |
| (0.052) | (0.050) | (0.055) |  |  |  |  |  |
degree |  |  |  | 0.182∗∗∗ | 0.223∗∗∗ |  |  |  |
|  |  |  | (0.067) | (0.077) |  |  |  |
attendance |  |  |  |  |  | 0.205∗∗∗ | 0.216∗∗∗ |  |
|  |  |  |  |  | (0.044) | (0.047) |  |
pray |  |  |  |  |  |  |  | 0.088∗∗ | 0.118∗∗∗
|  |  |  |  |  |  |  | (0.036) | (0.040)
gender | -0.006 | -0.007∗ | -0.009∗∗ | -0.007 | -0.010∗ | -0.001 | -0.002 | -0.007 | -0.010∗∗
| (0.004) | (0.004) | (0.004) | (0.005) | (0.005) | (0.003) | (0.003) | (0.005) | (0.005)
age | 0.001∗∗∗ | -0.000 | -0.000 | -0.000 | -0.000 | -0.000 | -0.000 | -0.000 | -0.001
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork |  | 0.003 | 0.005∗∗ | 0.003 | 0.004∗ | 0.003 | 0.004∗ | 0.003 | 0.005∗∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore |  | -0.243∗∗∗ | -0.195∗∗∗ | -0.242∗∗∗ | -0.176∗∗∗ | -0.230∗∗∗ | -0.184∗∗∗ | -0.273∗∗∗ | -0.246∗∗∗
|  | (0.037) | (0.037) | (0.049) | (0.050) | (0.039) | (0.036) | (0.028) | (0.034)
partner |  | 0.025∗∗∗ | 0.022∗∗∗ | 0.025∗∗∗ | 0.022∗∗∗ | 0.023∗∗∗ | 0.020∗∗∗ | 0.027∗∗∗ | 0.024∗∗∗
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
health |  | 0.004∗∗∗ | 0.003∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.002∗∗ | 0.002∗∗ | 0.005∗∗∗ | 0.005∗∗∗
|  | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child |  | 0.003 | 0.000 | 0.003 | 0.000 | 0.003∗ | 0.002 | 0.004∗ | 0.001
|  | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
bornc |  |  | -0.029∗∗∗ |  | -0.030∗∗∗ |  | -0.033∗∗∗ |  | -0.031∗∗∗
|  |  | (0.005) |  | (0.006) |  | (0.004) |  | (0.005)
fathere |  |  | -0.001 |  | -0.001 |  | -0.001 |  | -0.001
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
mothere |  |  | -0.004∗∗∗ |  | -0.004∗∗∗ |  | -0.004∗∗∗ |  | -0.004∗∗∗
|  |  | (0.001) |  | (0.001) |  | (0.001) |  | (0.001)
N | 234,387 | 228,192 | 202,191 | 231,156 | 204,668 | 231,582 | 205,055 | 229,462 | 203,268
idp | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
cdf | 2,327 | 2,369 | 2,147 | 839 | 763 | 2,729 | 2,525 | 2,001 | 1,722
widstat | 186 | 193 | 159 | 82 | 77 | 141 | 130 | 190 | 159
AR test | 0.001 | 0.000 | 0.000 | 0.007 | 0.004 | 0.000 | 0.000 | 0.012 | 0.002

  * •

Notes: IV estimates for alternative measures of religiosity are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. Kleibergen-Paap rk LM statistic (idp), Kleibergen-Paap rk Wald F
statistic (widstat), Cragg-Donald Wald F statistic (cdf), and Anderson-Rubin
Wald F statistic (AR test) are reported. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

D6). Having paid work in the last seven days (paidwork) is mostly
insignificant. Having paid work before (pwbefore) yields the highest
coefficients among control variables, implying that individuals with work
experience are less affected by each measure of religiosity. Individuals
living with a partner are less affected by religiosity with respect to PIA
(except for importance of creativity), while they are more affected for NIA.
Health and born-in country (bornc) do not introduce heterogeneous effects for
PIA and NIA. Health is positively related to all innovation attitudes. Bornc
is negative in all specifications, indicating the fact that being born in a
different country is a factor that increases the effect of religiosity on
innovation attitudes. Interestingly, individuals living with a child (child)
are less affected by religiosity regarding PIA, while it does not matter for
NIA. Both mother’s (mothere) and father’s education (fathere) are positively
related to PIA, while only (mothere) significantly and negatively relates with
NIA.

Overall, the negative effect of religiosity on innovativeness is in line with
the findings of prior studies ([12], [*]Benabouetal2015). Using OLS estimates,
[13] find that religiosity is positively related to creativity, which they
describe as “puzzling”. We contribute to this literature by uncovering that
the positive relationship between religiosity and creativity may be driven by
endogeneity. We find a negative relationship between religiosity and
creativity once we rule out endogeneity by applying the IV method.

The question is that, compared to OLS estimates, why does inposav change its
behaviour with respect to the religiosity measures while innegav stays stable?
The answer is hidden in the relationship between religious belonging
(belonging) and other religiosity measures, which are summarized in Tables C7
and C8 in Appendix C.

In the ESS, a respondent answers the questions regarding degree, attendance
and pray regardless of being belong to a religion. In other words, even though
the respondent does not believe in a religion, she still answers the questions
such as “How religious are you?”, “How frequently do you pray?”, “How
frequently do you attend religious services?” Interestingly, the answers are
generally not zero. Table C7 displays the mean values of all religiosity
measures, broken down into religious belonging. For instance, the mean value
of religiosity is $0.51$ for believers —people who belong to a religion at
present (belonging=1 & belongingp=1)— , while it is $0.14$ for never-believers
—people who have never belonged to a religion (belonging=0 & belongingp=0 )— ,
and is $0.18$ for once-believers —people who do not belong to a religion at
present but used to belong at past (belonging=0 & belongingp=1).

Table C8 shows disaggregated OLS results for religious belonging. For each
sub-sample, all religiosity measures are positively and significantly
associated with innegav. This pattern disappears when we look at inposav. For
the sub-sample of believers, only attendance is significantly and negatively
related to inposav, meaning that other religiosity measures of believers are
not associated with inposav. On the contrary, all religiosity measures of
never-believers and once-believers are positively and significantly related to
inposav. Under the light of this disaggregation, one can see that the OLS
results in Table 1, namely, the positive correlations between degree, pray,
and inposav are seemed to be driven by the sub-sample of non-believers,
raising the concerns for a spurious correlation. We, therefore, consider
religious belonging in our instrumental variables strategy and construct the
instruments by taking it into account. We instrument the religiosity of a
believer with the average religiosity of believers, along with other measures
explained in Section 3. By doing so, we address the differences between the
religiosities of believers and non-believers. Thus, the IV results for inposav
in Table 4 display different signs than the overall OLS results.

Table 6: Disaggregate IV Results for Age and Gender

inposav | (15-25) | (25-35) | (35-45) | (45-55) | (55-65) | (65+) | (female) | (male)
---|---|---|---|---|---|---|---|---
| (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV)
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
religiosity | -0.014 | -0.087 | -0.004 | -0.178 | -0.004 | -0.191 | 0.027*** | -0.241** | 0.012 | -0.243** | -0.004 | -0.192 | 0.000 | -0.154* | 0.010** | -0.213
| (0.013) | (0.143) | (0.009) | (0.173) | (0.008) | (0.133) | (0.008) | (0.100) | (0.008) | (0.097) | (0.008) | (0.119) | (0.005) | (0.084) | (0.005) | (0.149)
| 16,883 | 16,898 | 33,172 | 33,200 | 38,457 | 38,493 | 38,165 | 38,199 | 35,236 | 35,269 | 40,387 | 40,404 | 104,447 | 104,447 | 98,016 | 98,016
| 0.065 | 0.542 | 0.094 | 0.312 | 0.086 | 0.127 | 0.72 | 0.010 | 0.096 | 0.011 | 0.154 | 0.076 | 0.160 | 0.057 | 0.130 | 0.115
degree | -0.000 | 0.020 | 0.002 | 1.110 | 0.006 | 0.458 | 0.024*** | -0.158 | 0.018** | -0.282** | -0.003 | -0.160 | 0.006 | -0.148 | 0.012*** | -0.054
| (0.010) | (0.122) | (0.007) | (3.310) | (0.007) | (0.338) | (0.007) | (0.191) | (0.007) | (0.122) | (0.007) | (0.135) | (0.004) | (0.111) | (0.004) | (0.158)
| 17,031 | 17,047 | 33,607 | 33,636 | 38,944 | 38,982 | 38,641 | 38,675 | 35,695 | 35,729 | 40,870 | 40,887 | 105,725 | 105,725 | 99,231 | 99,231
| 0.067 | 0.869 | 0.093 | 0.464 | 0.086 | 0.035 | 0.073 | 0.411 | 0.097 | 0.053 | 0.155 | 0.198 | 0.161 | 0.146 | 0.131 | 0.727
attendance | -0.025** | -0.145 | -0.011 | -0.111 | -0.026*** | -0.329* | -0.001 | -0.250** | -0.012 | -0.181** | -0.008 | -0.266* | -0.017*** | -0.172** | -0.005 | -0.304**
| (0.012) | (0.113) | (0.008) | (0.125) | (0.008) | (0.177) | (0.009) | (0.099) | (0.008) | (0.091) | (0.007) | (0.101) | (0.005) | (0.080) | (0.005) | (0.147)
| 17,063 | 17,079 | 33,621 | 33,651 | 39,005 | 39,043 | 38,721 | 38,756 | 35,799 | 35,833 | 40,967 | 40,984 | 105,912 | 105,912 | 99,434 | 99,434
| 0.068 | 0.120 | 0.094 | 0.308 | 0.087 | 0.013 | 0.074 | 0.002 | 0.097 | 0.029 | 0.154 | 0.030 | 0.161 | 0.026 | 0.130 | 0.019
pray | -0.001 | 0.059 | -0.001 | -0.195** | 0.006 | -0.107 | 0.024*** | -0.144** | 0.011** | -0.229** | 0.000 | -0.108 | 0.007** | -0.011 | 0.009*** | -0.176
| (0.008) | (0.182) | (0.005) | (0.097) | (0.005) | (0.089) | (0.005) | (0.059) | (0.005) | (0.149) | (0.005) | (0.113) | (0.003) | (0.076) | (0.003) | (0.109)
| 16,970 | 16,985 | 33,335 | 33,365 | 38,643 | 38,679 | 38,369 | 38,404 | 35,448 | 35,481 | 40,619 | 40,636 | 104,992 | 104,992 | 98,558 | 98,558
| 0.066 | 0.741 | 0.093 | 0.065 | 0.085 | 0.219 | 0.073 | 0.009 | 0.095 | 0.007 | 0.155 | 0.313 | 0.161 | 0.887 | 0.130 | 237
| (15-25) | (25-35) | (35-45) | (45-55) | (55-65) | (65+) | (female) | (male)
innegav | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV) | (OLS) | (IV)
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
religiosity | 0.220*** | -0.244* | 0.216*** | 0.323** | 0.184*** | 0.647*** | 0.167*** | 0.354*** | 0.153*** | 0.378** | 0.151*** | 0.609** | 0.170*** | 0.292*** | 0.182*** | 0.189
| (0.015) | (0.144) | (0.011) | (0.163) | (0.009) | (0.161) | (0.009) | (0.128) | (0.009) | (0.154) | (0.008) | (0.286) | (0.006) | (0.113) | (0.006) | (0.138)
| 16,879 | 16,894 | 33,154 | 33,182 | 38,443 | 38,479 | 38,149 | 38,183 | 35,208 | 35,241 | 40,358 | 40,375 | 104,396 | 104,396 | 97,958 | 97 958
| 0.240 | 0.073 | 0.236 | 0.029 | 0.240 | 0.000 | 0.242 | 0.001 | 0.220 | 0.001 | 0.167 | 0.003 | 0.248 | 0.007 | 0.248 | 0.174
degree | 0.168*** | 0.033 | 0.141*** | 2.274 | 0.134*** | 0.860* | 0.118*** | 0.352 | 0.116*** | 0.477* | 0.123*** | 0.546*** | 0.122*** | 0.142 | 0.136*** | 0.305**
| (0.012) | (0.155) | (0.009) | (3.875) | (0.008) | (0.462) | (0.008) | (0.223) | (0.008) | (0.248) | (0.007) | (0.192) | (0.005) | (0.113) | (0.005) | (0.154)
| 17,027 | 17,043 | 33,587 | 33,616 | 38,926 | 38,964 | 38,623 | 38,657 | 35,665 | 35,699 | 40,840 | 40,857 | 105,671 | 105,671 | 99,165 | 99,165
| 0.231 | 0.830 | 0.229 | 0.115 | 0.232 | 0.001 | 0.236 | 0.110 | 0.213 | 0.011 | 0.163 | 0.002 | 0.241 | 0.197 | 0.243 | 0.058
attendance | 0.143*** | -0.236* | 0.168*** | 0.221** | 0.140*** | 0.649*** | 0.136*** | 0.323** | 0.125*** | 0.332** | 0.093*** | 0.622* | 0.133*** | 0.332*** | 0.132*** | 0.198
| (0.015) | (0.125) | (0.010) | (0.108) | (0.009) | (0.232) | (0.009) | (0.126) | (0.009) | (0.139) | (0.007) | (0.317) | (0.005) | (0.091) | (0.006) | (0.130)
| 17,058 | 17,074 | 33,601 | 33,631 | 38,987 | 39,025 | 38,704 | 38,739 | 35,770 | 35,804 | 40,935 | 40,952 | 105,858 | 105,858 | 99,367 | 99,367
| 0.218 | 0.021 | 0.227 | 0.006 | 0.227 | 0.000 | 0.236 | 0.000 | 0.212 | 0.000 | 0.155 | 0.001 | 0.240 | 0.000 | 0.236 | 0.123
pray | 0.103*** | -0.371* | 0.101*** | 0.081 | 0.079*** | 0.317*** | 0.066*** | 0.175** | 0.057*** | 0.264* | 0.071*** | 0.435 | 0.068*** | 0.268** | 0.084*** | 0.119
| (0.010) | (0.217) | (0.007) | (0.111) | (0.006) | (0.094) | (0.005) | (0.074) | (0.006) | (0.141) | (0.005) | (0.347) | (0.003) | (0.121) | (0.004) | (0.113)
| 16,965 | 16,980 | 33,317 | 33,347 | 38,627 | 38,663 | 38,352 | 38,387 | 35,420 | 35,453 | 40,587 | 40,604 | 104,939 | 104,939 | 98,495 | 98,495
| 0.222 | 0.028 | 0.226 | 0.455 | 0.228 | 0.000 | 0.231 | 0.009 | 0.209 | 0.021 | 0.159 | 0.051 | 0.237 | 0.011 | 0.240 | 0.279

  * •

Notes: Disaggregated OLS and IV estimates for age and gender are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. The observation numbers are below standard errors, followed by
adjusted R squared for OLS and Anderson-Rubin Wald F statistic (AR test) for
IV estimates. All regressions include controls for age, gender, education,
paid work status, partner, children, health, born in country, mother’s and
father’s, education and the following fixed effects: country, survey year,
country-survey year, religious denomination, occupation and income level. *
$p<0.10$; **$p<0.05$; ***$p<0.01$.

###  4.3 Heterogeneity of Religiosity: Gender and Age

We reestimate the baseline specifications to examine whether the main findings
hold for different sub-samples of age and gender. Table 6 presents the
results. Columns $1$-$12$ show OLS and IV estimates disaggregated by six age
brackets161616The instruments are built with eleven age brackets. Here we only
consider six age brackets for brevity. The model is estimated also with eleven
age brackets, but the overall results do not change significantly.. The last
four columns ($13$-$16$) display the baseline OLS and IV estimates
disaggregated by gender. The dependent variable is inposav on the upper part
of the table and innegav on the lower part. Each coefficient comes from a
different regression and includes the complete set of control variables and
fixed effects which are not reported due to space constraints. The number of
observations is reported below standard errors, followed by adjusted $R^{2}$
for OLS and AR test for IV estimates.

Considering age, OLS results show that the religiosity measures, when
statistically significant, are negatively related to inposav in early and
lower-middle ages, i.e., 15-25 and 35-45, but are positively related in middle
and upper-middle ages, i.e., 45-55 and 55-65. On the other hand, IV results
are always negatively related to inposav, regardless of age bracket. As
mentioned above, this alteration probably stems from the vanishment of
spurious correlations thanks to the indirect inclusion of religious belonging
via the instruments.

The OLS and IV results in the lower part of Table 6 show that the religiosity
measures are always positively associated with innegav for each age bracket
except for the 15-25, which is the only negative association between
religiosity measures and innegav throughout this paper. One explanation is
that young adults are generally open to questioning the values and beliefs
they inherited from their parents as well as from the culture in which they
live, assuming that in the early ages of life, people make up their minds, see
the world from their own eyes, form their own beliefs and values. Moreover,
fulfilling religious duties —such as going to church or mosque— to meet the
expectations of parents and society is not uncommon in the early ages, yet,
being exposed to religion by doing so might make them question traditions and
rules more, which can explain the negative association between attendance and
innegav.

Regarding gender, the OLS results for inposav show that attendance is
negatively and pray is positively correlated for women, while higher
religiosity favour inposav for men. In the IV results, only attendance is
significant for both genders, introducing a higher negative effect for men.

The overall picture changes when we consider innegav as the dependent
variable. Considering OLS estimates, each measure of religiosity is positively
and significantly correlated to innegav with relatively high coefficients for
both genders. IV results suggest that this pattern stays the same for women
except for degree, while degree is the only significant effect for men. These
findings are consistent with the existing literature on gender. Religions,
generally, have different approaches and prescribed behavioural rules for men
and women, imposing various restrictions on women’s liberty and rights. In
contrast, men face very little of them, if not none. Therefore, the results
are reasonable, suggesting that higher values of attendance and pray causes
women to follow traditions and established rules more.

##  5 Sensitivity Analysis

###  5.1 Alternative Dependent Variable

So far, we have analysed innovation attitudes separately, concluding that
higher religiosity is negatively related to PIA and positively correlated to
NIA, which means that being religious is not a catalyser for innovation-
related traits and is possibly an obstacle. In order to assess

Table 7: Religiosity and Summary Innovation Attitudes

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
| innosum | innosum | innosum | innosum | innosum | innosum | innosum | innosum
religiosity | -0.056∗∗∗ |  |  |  | -0.193∗∗∗ |  |  |
| (0.003) |  |  |  | (0.042) |  |  |
degree |  | -0.038∗∗∗ |  |  |  | -0.176∗∗∗ |  |
|  | (0.003) |  |  |  | (0.065) |  |
attendance |  |  | -0.051∗∗∗ |  |  |  | -0.185∗∗∗ |
|  |  | (0.003) |  |  |  | (0.038) |
pray |  |  |  | -0.021∗∗∗ |  |  |  | -0.112∗∗∗
|  |  |  | (0.002) |  |  |  | (0.027)
gender | -0.012∗∗∗ | -0.014∗∗∗ | -0.015∗∗∗ | -0.013∗∗∗ | -0.005 | -0.006 | -0.012∗∗∗ | -0.003
| (0.002) | (0.002) | (0.002) | (0.002) | (0.003) | (0.004) | (0.002) | (0.003)
age | -0.002∗∗∗ | -0.002∗∗∗ | -0.002∗∗∗ | -0.001∗∗∗ | -0.002∗∗∗ | -0.002∗∗∗ | -0.002∗∗∗ | -0.002∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | -0.000 | -0.000 | -0.000 | -0.000 | 0.000 | 0.000 | 0.000 | 0.000
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork | -0.000 | 0.000 | -0.000 | -0.000 | -0.001 | -0.000 | -0.001 | -0.001
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore | 0.022∗ | 0.026∗∗ | 0.021∗ | 0.039∗∗∗ | -0.031 | -0.036 | -0.036 | 0.015
| (0.012) | (0.013) | (0.012) | (0.011) | (0.025) | (0.039) | (0.024) | (0.012)
partner | -0.020∗∗∗ | -0.021∗∗∗ | -0.020∗∗∗ | -0.021∗∗∗ | -0.018∗∗∗ | -0.019∗∗∗ | -0.017∗∗∗ | -0.020∗∗∗
| (0.002) | (0.001) | (0.001) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
health | 0.010∗∗∗ | 0.010∗∗∗ | 0.010∗∗∗ | 0.009∗∗∗ | 0.010∗∗∗ | 0.010∗∗∗ | 0.011∗∗∗ | 0.009∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child | -0.014∗∗∗ | -0.014∗∗∗ | -0.014∗∗∗ | -0.014∗∗∗ | -0.011∗∗∗ | -0.012∗∗∗ | -0.012∗∗∗ | -0.012∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.002) | (0.002) | (0.002) | (0.002)
bornc | 0.004∗ | 0.006∗∗∗ | 0.005∗∗ | 0.006∗∗ | -0.005 | -0.003 | -0.001 | -0.004
| (0.002) | (0.002) | (0.002) | (0.002) | (0.004) | (0.005) | (0.003) | (0.004)
fathere | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
mothere | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
cons | 0.546∗∗∗ | 0.538∗∗∗ | 0.536∗∗∗ | 0.516∗∗∗ |  |  |  |
| (0.014) | (0.015) | (0.014) | (0.013) |  |  |  |
N | 198,145 | 200,453 | 200,808 | 199,128 | 198,145 | 200,453 | 200,808 | 199,128
Adj. $R^{2}$ | 0.242 | 0.241 | 0.242 | 0.239 |  |  |  |
idp |  |  |  |  | 0.000 | 0.000 | 0.000 | 0.000
cdf |  |  |  |  | 2,080 | 716 | 2,426 | 1,711
widstat |  |  |  |  | 158 | 68 | 130 | 167
AR test |  |  |  |  | 0.000 | 0.003 | 0.000 | 0.000

  * •

Notes: OLS and IV estimates for alternative measures of religiosity are
reported. Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. Kleibergen-Paap rk LM statistic (idp), Kleibergen-Paap rk Wald F
statistic (widstat), Cragg-Donald Wald F statistic (cdf), and Anderson-Rubin
Wald F statistic (AR test) are reported. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

the sensitivity of this proposition, we introduce an overall, summary measure
of innovation attitudes: innosum= creative + free + different + adventurous -
traditions - rules, which is inspired by [66]171717[66] introduces a summary
variable of cultural traits which is the sum of the three positive beliefs
(control, respect, trust) minus the negative belief (obedience).. Innosum is
rescaled to be between 0 (low propensity to innovate) and 1 (high propensity
to innovate); thus, higher values favour innovation.

We re-estimate the baseline specifications with OLS and IV by identifying
innosum as the dependent variable. The results are presented in Table 7.
Columns 1-4 are estimated with OLS, while columns 5-8 are estimated with IV.
All specifications include the complete set of control variables and fixed
effects. The OLS results favour the negative association between religiosity
and innovation attitudes through each measure of religiosity. The IV results
are also in line with the previous results.

Table 8: Alternative Model Specifications

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| inposav | inposav | inposav | innosum | inposav
religiosity | -0.204∗∗∗ | -0.204∗∗∗ | -0.204∗∗∗ | -0.201∗∗∗ | -0.201∗∗∗
| (0.058) | (0.058) | (0.058) | (0.041) | (0.059)
gender | -0.011∗∗∗ | -0.011∗∗∗ | -0.011∗∗∗ | -0.004 | -0.011∗∗∗
| (0.004) | (0.004) | (0.004) | (0.003) | (0.004)
age | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.002∗∗∗ | -0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000 | 0.000∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | 0.002∗∗∗ | 0.002∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.002∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork | -0.000 | -0.000 | -0.000 | -0.001 | -0.000
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore | -0.151∗∗∗ | -0.151∗∗∗ | -0.151∗∗∗ | -0.035 | -0.149∗∗∗
| (0.036) | (0.036) | (0.036) | (0.025) | (0.036)
partner | -0.015∗∗∗ | -0.015∗∗∗ | -0.015∗∗∗ | -0.018∗∗∗ | -0.015∗∗∗
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
health | 0.017∗∗∗ | 0.017∗∗∗ | 0.017∗∗∗ | 0.010∗∗∗ | 0.017∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child | -0.017∗∗∗ | -0.017∗∗∗ | -0.017∗∗∗ | -0.011∗∗∗ | -0.017∗∗∗
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
bornc | -0.024∗∗∗ | -0.024∗∗∗ | -0.024∗∗∗ | -0.006 | -0.024∗∗∗
| (0.005) | (0.005) | (0.005) | (0.004) | (0.005)
fathere | 0.006∗∗∗ | 0.006∗∗∗ | 0.006∗∗∗ | 0.004∗∗∗ | 0.005∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
mothere | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
N | 202,300 | 202,300 | 202,300 | 198,145 | 202,300
idp | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
cdf | 1,100 | 1,100 | 1,100 | 1,064 | 738
widstat | 83 | 83 | 83 | 82 | 58
AR test | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
Hansen J | 0.480 | 0.480 | 0.480 | 0.195 | 0.612

  * •

Notes: Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. Kleibergen-Paap rk LM statistic (idp), Kleibergen-Paap rk Wald F
statistic (widstat), Cragg-Donald Wald F statistic (cdf), Anderson-Rubin Wald
F statistic (AR test), and Sargan-Hansen test Hansen J are reported. *
$p<0.10$; **$p<0.05$; ***$p<0.01$.

###  5.2 Alternative Model Specifications

Religiosity is defined as the principal component of three measures: degree,
attendance, and pray. However, degree differs from the other two in one
important dimension. Pray and attendance are measures of an activity, an
action, while degree is self-evaluation of a belief, a value. It is plausible
to think that one cannot easily overestimate or underestimate the frequency of
an activity but can do so to evaluate a belief. Therefore an interpersonal
comparison of degree might be biased since some individuals may overvalue or
undervalue their religiosity. This point is especially crucial to our
instrumental variables, given that we instrument individual $i$’s degree by
the average degree of other people. If many respondents tend to misvalue their
degree, then this self-report bias will lead to weak instruments. Indeed, the
diagnostic tests’ results for degree in Tables 4 and 5 support this
evaluation. Furthermore, degree is a component of religiosity; it thus
endangers the instrument for religiosity as well. In order to see if the
results are sensitive to the possible self-report bias of degree, we modify
the IV strategy. Table 8 displays the results.

The first column is estimated with 2SLS and religiosity is instrumented with
attendance and pray. Columns 2 and 3 follow the specification of column 1 but
are estimated with k-class estimators. Prior studies suggest that Limited
Information Maximum Likelihood Method (LIML) and Fuller edited LIML have a
better finite-sample performance than 2SLS in the presence of weak instruments
([9]). Accordingly, in case of strong instruments, the estimates of 2SLS, LIML
and Fuller should yield very similar results. Column 2 is estimated with LIML,
and column 3 is with Fuller edited LIML with Fuller parameter $k=1$. Column 4
is estimated with 2SLS, but the dependent variable is innosum and the
instruments are attendance and pray. In specification 5, we add iv_degree as
the third instrument for religiosity. The results of 2SLS, LIML, and Fuller
estimates are the same, suggesting that our instruments are strong. The
coefficients of religiosity in columns 1, 2, and 3 are qualitatively and
quantitatively similar to the corresponding coefficient of religiosity
(-0.198) in column 3 in Table 4, indicating that the initial instrument of
religiosity (iv_religiosity) is not significantly affected by the potential
self-report bias of degree.

###  5.3 Reduced Sample Estimates

Table 9 displays the estimates from four different sub-samples. Each
coefficient comes from a different IV estimate. The number of observations is
below standard errors, which is followed by Cragg-Donald Wald F statistic
(cdf), Kleibergen-Paap rk Wald F statistic (widstat), and Anderson-Rubin Wald
F statistic (AR test) results.

First, we drop Muslim countries. Turkey, Albania, and Kosova are the only
dominantly Muslim counties in the ESS. The rest of the sample consists of
dominantly Christian countries, if not dominantly Atheist181818The second
dominant religion is Christianity in each dominantly Atheist countries..
Despite controlling for country and denomination fixed effects, combining
Muslim and Christian countries may complicate the interpretation since we
consider inter-religion religiosity regardless of denomination. Moreover,
Muslim countries exhibit outlier values for religiosity, especially for
degree, such as $0.7$-$0.73$ on average (see Table A3), which is pretty high
given that the overall cross-country average of degree is $0.46$. Therefore,
we exclude Muslim countries from the sample and re-estimate the baseline
specification as a robustness check. Columns 1 and 2 report the results. The
overall negative effect of attendance increases compared to the full sample
results; from $-0.185$ to $-0.256$ on inposav and from $0.216$ to $0.281$ on
innegav. On the other hand, the effect of degree on innegav becomes less
significant, which is not surprising since the Muslim countries exhibit
outlier values of degree. All in all, the overall picture stays robust.

Table 9: Reduced Sample Estimates

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10)
---|---|---|---|---|---|---|---|---|---|---
| inposav | innegav | inposav | innegav | inposav | innegav | inposav | innegav | inposav | innegav
religiosity | -0.214∗∗∗ | 0.199∗∗∗ | -0.235*** | 0.212*** | -0.259*** | 0.192*** | -0.162*** | 0.200*** | -0.153** | 0.206***
| (0.065) | (0.058) | (0.056) | (0.062) | (0.059) | (0.065) | (0.061) | (0.055) | (0.063) | (0.057)
| 199,429 | 199,324 | 184,551 | 184,458 | 181,680 | 181,591 | 181,332 | 184,230 | 174,626 | 174,533
| 2,041 | 2,028 | 1,632 | 1,624 | 1,530 | 1,523 | 1,999 | 1,987 | 1,916 | 1,903
| 151 | 150 | 105 | 105 | 99 | 98 | 155 | 156 | 133 | 133
| 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.003 | 0.004 | 0.001 | 0.012 | 0.000
degree | -0.178* | 0.164** | -0.251*** | 0.332***: | -0.259*** | 0.220** | -0.157* | 0.228*** | -0.125 | 0.236***
| (0.093) | (0.074) | (0.091) | (0.107) | (0.056) | (0.093) | (0.088) | (0.074) | (0.106) | (0.081)
| 201,906 | 201,790 | 186,578 | 186,474 | 183,696 | 183,596 | 183,690 | 183,577 | 176,822 | 176,718
| 877 | 868 | 358 | 354 | 415 | 412 | 866 | 857 | 615 | 609
| 101 | 99 | 45 | 44 | 57 | 56 | 98 | 97 | 57 | 57
| 0.043 | 0.031 | 0.004 | 0.001 | 0.002 | 0.019 | 0.061 | 0.002 | 0.228 | 0.006
attendance | -0.256∗∗∗ | 0.281∗∗∗ | -0.196*** | 0.192*** | -0.287*** | 0.256*** | -0.140*** | 0.194*** | -0.161*** | 0.192***
| (0.066) | (0.060) | (0.048) | (0.048) | (0.062) | (0.064) | (0.051) | (0.047) | (0.052) | (0.049)
| 202,287 | 202,170 | 187,023 | 186,920 | 184,134 | 184, 035 | 184,024 | 183,910 | 177,171 | 177,067
| 1,653 | 1,646 | 2,165 | 2,159 | 1,328 | 1,322 | 2,248 | 2,243 | 2,262 | 2,253
| 113 | 113 | 100 | 99 | 81 | 81 | 120 | 120 | 108 | 108
| 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.003 | 0.000 | 0.001 | 0.000
pray | -0.117∗∗∗ | 0.104*** | -0.148*** | 0.105** | -0.147*** | 0.084* | -0.099** | 0.121*** | -0.072* | 0.123***
| (0.039) | (0.040) | (0.039) | (0.044) | (0.038) | (0.044) | (0.039) | (0.041) | (0.040) | (0.042)
| 200,499 | 200,387 | 185,498 | 185,399 | 182,613 | 182,518 | 182,319 | 182,210 | 175,562 | 175,462
| 1,776 | 1,769 | 1,411 | 1,406 | 1,457 | 1,452 | 1,586 | 1,580 | 1,645 | 1,638
| 162 | 161 | 113 | 113 | 115 | 115 | 152 | 152 | 156 | 155
| 0.002 | 0.008 | 0.000 | 0.014 | 0.000 | 0.049 | 0.008 | 0.002 | 0.067 | 0.002

  * •

Notes: Robust standard errors clustered at the level of instruments are in
parentheses and followed by the number of observations, Cragg-Donald Wald F
statistic (cdf), Kleibergen-Paap rk Wald F statistic (widstat), and Anderson-
Rubin Wald F statistic (AR test). All specifications include the full set of
control variables and fixed effects. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

Second, we exclude Russia and Norway from the main sample because the
percentages of the first and the second dominant denominations among
respondents are pretty close, less than 1%, for those countries. The majority
of respondents from Norway are Atheists with $47.02\%$; Protestants come
second with $46.36\%$. For Russia, the majority is Eastern Orthodox with
$46.03\%$; Atheists come second with $45.83\%$. Since the dominant
denomination is one of the elements we construct instruments, any
misidentification would cause bias in the instruments of individuals who share
the misidentified dominant denomination. We thus drop Russia and Norway and
re-estimate the baseline specifications to assess the difference. Columns 3
and 4 in Table 9 report the results. Column 3 shows that the coefficient of
each religiosity measure increases for inposav and degree becomes significant
at the 99% level compared to the full sample results in Table 4.

Third, we exclude Muslim countries along with Russia and Norway to obtain a
relatively homogeneous sample. Columns 5 and 6 display the results.
Coefficients of all religiosity measures substantially increase for inposav
and degree becomes more significant compared to Table 4. For innegav, degree
and pray become less significant compared to the full sample.

Fourth, we exclude once-believers. Non-believers are split into two categories
among themselves: never-believers and once-believers. Table C7 shows that
once-believers exhibit slightly higher religiosity means. The reason behind is
that religiosity tends to be an absorbed value. Even though a person has
stopped believing in a religion, there might be persistent effects, let alone
the fact that personal values and traits might have been mostly grown when the
person was a believer. Thus, to examine whether the higher religiosity of
once-believers substantially affects the overall results, we drop once-
believers from the full sample and leave the respondents who have been either
an always-believer or never-believer. The corresponding results are presented
in columns 7 and 8 in Table 9. Column 8 shows that the overall results for
innegav do not change substantially compared to Table 5. However, the
coefficient of each religiosity measure somewhat decreases in column 7
compared to Table 4.

Lastly, we exclude second-generation immigrants. Immigrants191919Note that
immigrants are controlled for in the baseline specifications with variable
bornc: born-in country. and their descendants might possess peculiar traits
due to different cultural and institutional environments of their origin
country. Including second-generation immigrants in the sample might introduce
some unobservable effects regarding innovation attitudes. Columns 9 and 10
report the results. The coefficients for inposav slightly decrease and degree
turns to be insignificant compared to Table 4. On the contrary, the results of
innegav are pretty stable and similar to Table 5.

###  5.4 Possible Violations of Exogeneity

The dominant denomination is one of the factors by which we construct the
instruments.As Table A1 indicates, a very high percentage of the population
belongs to the dominant religion in many countries, while some countries have
relatively low percentages of the dominant religion. For instance, the
prevailing denomination is atheism in Switzerland (34%) and Germany (38%), yet
they comprise roughly one-third of the population. Apparently, there are other
religions in those countries with a significant presence. However, the
significant presence of religions that are different from the dominant
religion of a country, and their combination, might introduce some
unobservable traits related to innovation attitudes. Suppose the instruments
for religiosity are computed on a group of individuals that exhibit some
peculiar values of the unobservable traits (that stem from the presence of
different religion combinations in the country) related to innovation
attitudes. In that case, the exogeneity condition may not be met. Since the
instrument is the average religiosity of a group of countries sharing the same
dominant denomination, it is unlikely that such unobservable traits prevail in
every country in the group.

Table 10: IV Estimates with Fixed Effects for Religion Combinations

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
| inposav | inposav | inposav | inposav | innegav | innegav | innegav | innegav
religiosity | -0.193∗∗∗ |  |  |  | 0.180∗∗∗ |  |  |
| (0.074) |  |  |  | (0.066) |  |  |
degree |  | -0.227∗ |  |  |  | 0.205∗∗ |  |
|  | (0.123) |  |  |  | (0.093) |  |
attendance |  |  | -0.140∗∗ |  |  |  | 0.161∗∗∗ |
|  |  | (0.055) |  |  |  | (0.054) |
pray |  |  |  | -0.118∗∗∗ |  |  |  | 0.110∗∗
|  |  |  | (0.043) |  |  |  | (0.045)
gender | -0.011∗∗ | -0.009 | -0.019∗∗∗ | -0.009∗ | -0.008∗ | -0.009 | -0.001 | -0.009∗
| (0.004) | (0.007) | (0.002) | (0.005) | (0.005) | (0.006) | (0.003) | (0.006)
age | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.000 | -0.000 | -0.001 | -0.001
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗ | 0.000∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | 0.002∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork | -0.000 | 0.001 | 0.000 | -0.000 | 0.005∗∗ | 0.004∗ | 0.004∗ | 0.005∗∗
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore | -0.139∗∗∗ | -0.171∗∗ | -0.127∗∗∗ | -0.093∗∗∗ | -0.224∗∗∗ | -0.198∗∗∗ | -0.223∗∗∗ | -0.267∗∗∗
| (0.042) | (0.070) | (0.035) | (0.022) | (0.046) | (0.060) | (0.044) | (0.040)
partner | -0.015∗∗∗ | -0.015∗∗∗ | -0.015∗∗∗ | -0.017∗∗∗ | 0.022∗∗∗ | 0.022∗∗∗ | 0.021∗∗∗ | 0.024∗∗∗
| (0.003) | (0.003) | (0.003) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
health | 0.017∗∗∗ | 0.017∗∗∗ | 0.018∗∗∗ | 0.016∗∗∗ | 0.004∗∗∗ | 0.004∗∗∗ | 0.003∗∗ | 0.005∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child | -0.017∗∗∗ | -0.017∗∗∗ | -0.018∗∗∗ | -0.017∗∗∗ | 0.001 | 0.001 | 0.002 | 0.001
| (0.002) | (0.003) | (0.002) | (0.002) | (0.002) | (0.003) | (0.002) | (0.002)
bornc | -0.023∗∗∗ | -0.023∗∗∗ | -0.017∗∗∗ | -0.023∗∗∗ | -0.029∗∗∗ | -0.029∗∗∗ | -0.033∗∗∗ | -0.029∗∗∗
| (0.006) | (0.007) | (0.004) | (0.005) | (0.005) | (0.006) | (0.004) | (0.005)
fathere | 0.006∗∗∗ | 0.005∗∗∗ | 0.006∗∗∗ | 0.006∗∗∗ | -0.001 | -0.001 | -0.001 | -0.001
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
mothere | 0.004∗∗∗ | 0.004∗∗∗ | 0.005∗∗∗ | 0.004∗∗∗ | -0.004∗∗∗ | -0.004∗∗∗ | -0.004∗∗∗ | -0.004∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
N | 202,298 | 204,787 | 205,175 | 203,383 | 202,189 | 204,667 | 205,054 | 203,267
idp | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
cdf | 1,768 | 584 | 2,356 | 1,536 | 1,755 | 575 | 2348 | 1528
widstat | 126 | 51 | 114 | 139 | 125 | 50 | 113 | 138
AR test | 0.003 | 0.027 | 0.003 | 0.004 | 0.008 | 0.101 | 0.008 | 0.010

  * •

Notes: IV estimates for alternative measures of religiosity are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. Kleibergen-Paap rk LM statistic (idp), Kleibergen-Paap rk Wald F
statistic (widstat), Cragg-Donald Wald F statistic (cdf), and Anderson-Rubin
Wald F statistic (AR test) are reported. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

Nevertheless, as a robustness check, we include fixed effects for each pair of
religion-the dominant religion to account for diverse religious environments
resulting from different combinations of religions in a country. The results
are presented in Table 10. Despite moderate decreases in the coefficients, the
results are pretty similar to the baseline specifications reported in Tables 4
and 5, suggesting that the instruments are relevant and representative of
various religious environments.

Table 11: IV Estimates with Instruments of Non-Neighbouring Countries

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
---|---|---|---|---|---|---|---|---
| inposav | inposav | inposav | inposav | innegav | innegav | innegav | innegav
religiosity | -0.253∗∗∗ |  |  |  | 0.270∗∗∗ |  |  |
| (0.071) |  |  |  | (0.062) |  |  |
degree |  | -0.271∗∗ |  |  |  | 0.324∗∗∗ |  |
|  | (0.133) |  |  |  | (0.098) |  |
attendance |  |  | -0.251∗∗∗ |  |  |  | 0.259∗∗∗ |
|  |  | (0.065) |  |  |  | (0.057) |
pray |  |  |  | -0.127∗∗∗ |  |  |  | 0.160∗∗∗
|  |  |  | (0.042) |  |  |  | (0.043)
gender | -0.008∗ | -0.007 | -0.017∗∗∗ | -0.008∗ | -0.013∗∗∗ | -0.016∗∗ | -0.003 | -0.015∗∗∗
| (0.004) | (0.007) | (0.003) | (0.005) | (0.004) | (0.006) | (0.003) | (0.006)
age | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.000 | -0.000 | -0.000 | -0.000
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
age2 | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗ | 0.000∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
education | 0.002∗∗∗ | 0.002∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗ | -0.003∗∗∗
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
paidwork | -0.001 | 0.000 | -0.000 | -0.000 | 0.005∗∗ | 0.004∗ | 0.005∗∗ | 0.005∗∗
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002)
pwbefore | -0.170∗∗∗ | -0.194∗∗∗ | -0.180∗∗∗ | -0.104∗∗∗ | -0.171∗∗∗ | -0.131∗∗ | -0.165∗∗∗ | -0.235∗∗∗
| (0.043) | (0.075) | (0.042) | (0.019) | (0.038) | (0.061) | (0.039) | (0.033)
partner | -0.014∗∗∗ | -0.014∗∗∗ | -0.013∗∗∗ | -0.017∗∗∗ | 0.021∗∗∗ | 0.021∗∗∗ | 0.020∗∗∗ | 0.024∗∗∗
| (0.003) | (0.003) | (0.003) | (0.002) | (0.002) | (0.003) | (0.002) | (0.002)
health | 0.017∗∗∗ | 0.017∗∗∗ | 0.019∗∗∗ | 0.016∗∗∗ | 0.003∗∗∗ | 0.003∗∗∗ | 0.002 | 0.005∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
child | -0.016∗∗∗ | -0.016∗∗∗ | -0.017∗∗∗ | -0.017∗∗∗ | -0.001 | -0.001 | 0.001 | 0.000
| (0.002) | (0.003) | (0.002) | (0.002) | (0.002) | (0.003) | (0.002) | (0.002)
bornc | -0.027∗∗∗ | -0.026∗∗∗ | -0.022∗∗∗ | -0.023∗∗∗ | -0.025∗∗∗ | -0.024∗∗∗ | -0.031∗∗∗ | -0.026∗∗∗
| (0.006) | (0.009) | (0.005) | (0.006) | (0.005) | (0.007) | (0.004) | (0.006)
fathere | 0.006∗∗∗ | 0.005∗∗∗ | 0.006∗∗∗ | 0.006∗∗∗ | -0.001 | -0.000 | -0.001 | -0.001
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
mothere | 0.004∗∗∗ | 0.004∗∗∗ | 0.005∗∗∗ | 0.004∗∗∗ | -0.004∗∗∗ | -0.004∗∗∗ | -0.004∗∗∗ | -0.003∗∗∗
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001)
N | 202,300 | 204,788 | 205,176 | 203,384 | 202,191 | 204,668 | 205,055 | 203,268
idp | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
cdf | 1,621 | 469 | 1,823 | 1,408 | 1,610 | 463 | 1,815 | 1,401
widstat | 121 | 42 | 95 | 144 | 120 | 42 | 94 | 144
AR test | 0.000 | 0.024 | 0.000 | 0.002 | 0.000 | 0.001 | 0.000 | 0.000

  * •

Notes: IV estimates for alternative measures of religiosity are reported.
Robust standard errors clustered at the level of instruments are in
parentheses. All regressions include the following fixed effects: country,
survey year, country-survey year, religious denomination, occupation, and
income level. Kleibergen-Paap rk LM statistic (idp), Kleibergen-Paap rk Wald F
statistic (widstat), Cragg-Donald Wald F statistic (cdf), and Anderson-Rubin
Wald F statistic (AR test) are reported. * $p<0.10$; **$p<0.05$; ***$p<0.01$.

Another possible violation of exogeneity might stem from using neighbouring
countries when computing the instruments. We argue that religiosity is a
transnational trait; therefore, one’s religiosity can be instrumented by other
people’s religiosity who live in a similar religious environment. However,
suppose there are substantial similarities in the institutional environments
of countries that share the same dominant denomination. In that case,
individual innovation attitudes could be affected by these similarities in a
particular way, violating the exogeneity assumption. We, therefore, use a
large set of fixed effects to eliminate the institutional level factors.
However, using neighbouring countries when computing instruments might
introduce another channel of institutional similarity, given that sharing a
border and spatial proximity can reinforce a similar institutional
environment. Motivated by this reasoning, we change the computation of
instruments as a robustness check. We build instruments for religious
intensity variables by computing the average religiosity of people of the same
sex, age range, and religious affiliation who live in countries with the same
dominant religious denomination that are not neighbours, i.e., do not share a
border. Table 11 presents the results. All coefficients for inposav and
innegav are increased compared to the main results in Tables 4 and 5. The
negative effect of religion on innovation attitudes persists.

##  6 Possible Channels of Causality

###  6.1 Time Allocation

Starting from Gary Becker’s pioneering work “A Theory of the Allocation of
Time” ([10]), numerous papers have analysed agents’ time allocation among
various activities. The work of [6] is the first paper that considers
religious participation in the context of household members’ time allocation,
presenting the first model of consumer choice in religious markets. They use a
microeconomic approach to examine the demand of religion, introducing a multi-
period utility-maximising model of household behaviour. In the model,
religious participation is a part of household members’ utility function and
is a time-consuming activity as well as an investment in after-life
consumption. Household members are men and women who have different
opportunity costs of time due to different wages they are subject to in the
labour market. They allocate their time by considering not only their wage but
also the marginal utilities of after-life and in-life consumption. Suppose
household members allocate more time for religious participation, favouring
after-life consumption. In that case, they will have less time for productive
activities for in-life consumption, resulting in lower total household
production.

The point we are interested in the model of Azzi and Ehrenberg is the
opportunity cost of time spent on religious activities, given that religious
participation is a time-consuming activity and time is scarce. The concept of
opportunity cost of religious participation can also be considered for other
relevant time-consuming activities such as human capital formation, which
requires many years of education, a lifelong habit of reading, and attending
scientific and intellectual activities. All these activities demand time, and
time is limited. If an individual is religious and allocates a certain amount
of time to religious activities, then there will be a decrease in the maximum
amount of potential time that could be devoted to human capital formation. It
is well-known that human capital is one of the most critical drivers of
innovation. Thus, any unfavourable activity to human capital formation can be
plausibly assumed unfavourable to innovation.

On the contrary, the social capital approach is a perspective that contrasts
with the negative effect of time spent on religious activities. The main idea
is that participating in religious services is a form of networking and helps
the attendant build social capital. [8] underline that social capital
formation through religious participation is favourable for the economy only
if it fosters economically relevant individual traits such as work ethic,
thrifty, honesty, and trust.

”The social capital and cultural aspects of religion (communal services,
rituals, religious schools) are significant only to the extent that they
influence beliefs and, hence, behaviour. In fact, for given beliefs, more time
spent on communal activities would tend to be an economic drag, at least as
measured by market output (GDP).” ([8], pp. 51).

Consequently, the net effect of religious participation, positive or negative,
on economic outcomes would be determined by taking into account the
comparative consequences of the two approaches mentioned: the opportunity cost
of time spent on religious activities and the social capital formation through
religious participation. In our case, it is somewhat arguable to claim that
social capital formation fosters favourable individual traits for innovation.
As mentioned in Section 2.2, taking risks, being different, adventurous, and
creative are the main individual traits that are considered favourable to
innovation. However, as shown by our analyses, religious participation does
not seem to foster any of them. Moreover, it is likely to impose traditional
values and rules. The results throughout the present paper support this
argument. Attendance has been the most robust religiosity measure across
different specifications, and it negatively affects innovation attitudes.

###  6.2 The Fear of Uncertainty

As mentioned in Section 2, Hofstede (1980/[*]Hofstede2001) identifies
uncertainty avoidance as one of the five dimensions on which national cultures
differ. Innovation requires newness and risk tolerance, thus is related to
uncertainty which hints at unpredictability and a lack of structure and
information ([56]). Individuals or the decision-making unit involved in the
first stages of the innovation process would be naturally unsure of the new
idea’s results and face the inherent uncertainty of newness. Therefore, an
individual’s propensity to innovate is expected to be positively related to
the ability to deal with uncertainty202020Uncertainty and risk are initially
different concepts. Risk is the perceived probability that a particular event
will happen. Uncertainty is about ambiguity. Any event may happen, and there
is no probability attached to it ([36]). However, for simplicity, we use
uncertainty as inclusive of risk since any degree of uncertainty contains
risk, whether with a known or unknown probability. Hence, we use the term “the
fear of uncertainty” instead of “uncertainty avoidance” to distinguish it from
Hofstede’s concept.. Low levels of fear of uncertainty refer to more openness
toward new ideas and change, more willingness to take risks, and less fear of
novelty.

[61] shows that uncertainty avoidance is one of the values that have a
negative impact on a country’s overall innovativeness, and it explains, to
some degree, the variation in national rates of innovativeness across
countries. [62] uses the data of 4405 individuals in 43 organizations from 68
different countries and examines the relationship between uncertainty
avoidance and individual preferences for four innovation championing roles:
the organizational maverick, the network facilitator, the transformational
leader, and the organizational buffer. He shows that individuals from
uncertainty-avoiding cultures are less likely to prefer championing roles.
“One might argue that uncertainty-accepting societies are more innovative
([61]) because championing roles which overcome organizational inertia to
innovation are more likely to be accepted in those societies.” ([62], pp. 64).

[16] find that higher levels of uncertainty avoidance have negative effects on
corporate innovation. Firms located in countries with higher levels of
uncertainty avoidance generate fewer and less critical patents, and their R&D
expenditures are less efficient. [70] show that uncertainty avoidance has a
negative effect on economic creativity which facilitates innovation
implementation.

Religion is one of the ways to cope with uncertainty (Hofstede
[*]Hofstede2001). It can be argued that religious people tend to fear
uncertainty more than irreligious people. The first empirical study that used
risk analysis in the context of religiosity is that of [48]. They consider
religious acceptance a risk-averse behaviour and the rejection of religious
beliefs as risk-taking behaviour. One salient feature of religion is to
provide a sort of protection, both materially and mentally. Religion mitigates
the uncertainties and risks of daily life by providing spiritually rewarding
networks of welfare activities for the community, such as charity for the
poor, assistance for individuals who experience personal disasters, elder
care, medical assistance, orphanages, and education ([27]; [8]; [59]). Those
activities are provided by religious organizations (e.g., churches, temples,
mosques, or synagogues) and may be crucial in encouraging individuals to
attend religious activities and be a part of religious organizations. Religion
also helps one deal with uncertainty and fear mentally by introducing the
salvation motive and after-life rewards. Therefore, religious people are used
to being protected by divine power and religious organizations. They have
preset answers for the unknown, meaning they have less experience dealing with
uncertainty than irreligious people. In order to test this hypothesis, we
examine the relationship between religiosity and the fear of uncertainty as
follows.

We nominate five indicators from the ESS that are related to the fear of
uncertainty; then, we examine their relation with religiosity measures. Each
indicator corresponds to one or more differences between weak and strong
uncertainty avoidance societies in Hofstede’s model. For instance, societies
with strong uncertainty avoidance exhibit the following behaviours: “The
uncertainty inherent in life is felt like a continuous threat that must be
fought.”, “Higher stress, emotionality, anxiety, neuroticism.”, “Staying in
jobs even if disliked.” ([35], pp.10). More broadly, Hofstede defines three
components of uncertainty avoidance: rule orientation, employment stability,
and stress. The variables unemployed (ever been unemployed and seeking job
more than 3 months), securejob (important when choosing job: secure job) refer
to employment stability; trust (most people can be trusted ), safe (important
to live in secure surroundings ), government (important: government ensures
safety) refer to stress. Apart from these measures, following rules, one of
the NIA, also refers to rule orientation.

Table 12: IV Estimates: The Fear of Uncertainty

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| unemployed | securejob | trust | safe | government
religiosity | -0.128∗∗∗ | 0.028∗∗ | 0.027∗∗∗ | 0.109∗∗∗ | 0.098∗∗∗