Religiosity and Economic Performance: Micro-econometric Evidence from Tibetan area

Qin Tu1*, Erwin Bulte1 and Shuhao Tan2,3

1 Development Economics Group, Wageningen University, 6706 KN Wageningen, Netherlands.

2 School of Agricultural Economics and Rural Development, Renmin University of China, 100872, Beijing, China.

3 Land Policy Center, Nanjing Agricultural University, 210095, Nanjing, China. E-mail: qin.tu@wur.nl, Erwin.bulte@wur.nl and shuhtan68@gmail.com

(* corresponding author)

Abstract: We use results from a household survey to explore the relation between religiosity

and our proxy for income among herders in rural Tibet. Our main results are twofold. First,

there exists a positive relation between the intensity of religious beliefs (the main ‘output’ of

the religious production process) and income—beliefs about the after-life affect production

and savings decisions today. Second, and perhaps more surprising, we find an inverted U-

shaped relation between religious ‘inputs’ (time and money spent in the temple) and income.

While it is possible to use too much resources as religious inputs, we find that the great

majority of the respondents is on the upward sloping part of the curve linking economic

performance to religious inputs. We present tentative evidence that the positive impact of

religiosity and income may be explained by status (reputation) and information effects

associated with producing religiosity.

Keywords: Beliefs, religion and economics, informal institutions, social capital, Buddhism

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Religiosity and Economic Performance: Micro-econometric Evidence from Tibetan area

1. Introduction

In recent years the literature on institutions and economic development has developed

rapidly. While the majority of the work has focused on formal institutions (especially

property rights security, see for example Acemoglu et al. (2001), Acemoglu and Johnson

(2005), Rodrik et al. (2004)), it is increasingly recognized that informal institutions –

including culture, social capital and religion – are also important drivers of development

(e.g., North (1990) and Platteau (2000)). Empirical cross-country regression work by

Tabellini (2005) and Knowles and Weatherson (2006) suggests that informal institutions –

broadly defined – are probably as important to development as are formal institutions.

Knowles and Weatherston (2006) argue that informal institutions are very similar to

social capital or culture, and that most definitions of these concepts center around issues

like trust, shared norms, beliefs and conventions, and networks. From this perspective,

religion is closely related to social capital or culture as well. Religiosity ties people

together in communities, facilitates the accumulation of trust, conventions and shared

norms, and introduces mechanisms for enforcement of desirable behavior.

Economists have long considered the relation between religion and economic

performance. As in the broader literature on development and institutions, the nature of

this relation is characterized by mutual feedback mechanisms. ‘Demand for religion’ may

partly be determined by economic factors and development, but economic outcomes are not

invariant with respect to religion either. Development raises the opportunity cost of time

spent in religious participation, and “modern” lifestyles shield people from the vagaries of

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nature, possibly undermining beliefs in the supernatural. Conversely, religiosity matters for

development because time spent praying is unavailable for work-related activities (a costly

effect, operating via the “input side” of the religious process), or because religion promotes

certain norms and traits in people that may, or may not, be conducive to work and savings

(an effect via the “output side” of the religious process). Examples of traits conductive to

income are work ethic, honesty, thrift, and trust. Moreover, religion may impact on

economic outcomes via a social capital or networking mechanism. Classic texts include

John Calvin (1585) and Weber (1904), and more recent contributions include Azzi and

Ehrenberg (1975), and Anderson (1988).1

Various recent cross-country studies suggest that religion is a significant driver of

economic growth. Grier (1997) focuses on Protestantism in a broad set of former colonies,

finding it is significantly related to income and economic growth. Guiso et al. (2003) use

data from the World Values Surveys and document a positive relation between religious

beliefs and certain attitudes conducive to economic growth. Barro and McCleary (2003)

use the same religion data, and distinguish between inputs in the religious process (e.g.

church attendance) and outputs (religious beliefs—in particular beliefs in heaven and hell).

They find that inputs are negatively correlated with growth, but that the reverse is true for

beliefs. They conclude religious beliefs enhance traits that are conducive to economic

performance, but that church attendance uses up resources (notably: time) that could also

be employed productively (see also Barro and McCleary (2002), who also analyse the

reverse chain of causation – the impact of economic development on religiosity). Finally,

using national and regional religion shares as explanatory variables, Noland (2006) also

finds (mixed) evidence for a positive effect of religion on economic outcomes – both at the

1 For overviews, refer to Iannaccone (1998) and McCleary and Barro (2006).

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national (cross-country) and sub-national (within-country) level. This analysis is based on

population shares with a certain religious affiliation.

It is well-known that failure to fully account for the heterogeneity across countries

can compromise the relevance of cross-country regressions.2 This applies to the work on

(informal) institutions broadly, as well as the specific literature on religion and economic

outcomes. We set out to augment the amassing evidence on the relation between religiosity

and income with new micro-econometric evidence. We gathered unique household data on

religious inputs and outputs (beliefs) among Buddhist yak and sheep herders in the rural

Tibetan area, and relate those to a proxy for income. The micro-economic focus implies

that the risk of omitted variables looming large in cross-country studies, is attenuated

considerably.

The main objective of this paper is analyse the link between religious inputs and

beliefs on the one hand, and economic outcomes on the other. By sorting out the various

conflicting mechanisms, and determining whether religious inputs and outputs are

associated with higher or lower income for herder families in Tibetan area, we aim to

contribute to a better understanding of the role of social capital or culture in development.

Our micro-econometric evidence corroborates some existing cross-country

regression results, but qualifies others. Specifically, while prior research suggests that

religious inputs are harmful for economic outcomes, our findings suggest that

dissemination of information via temples and sending signals about one’s beliefs (status,

reputation) are conducive to improving economic performance. Confirming earlier cross-

country studies, we also find that religious beliefs tend to be positively correlated with our

income proxy. Arguably this reflects the notion that beliefs enhance the traits that facilitate

2 Panel and cross country regressions may mask important cross-country differences and suffer from measurement, statistical, and conceptual problems. For example, Pesaran and Smith (1995) raise the issue of parameter heterogeneity across countries, and Luintel and Khan (2004) show the lack of correspondence between panel estimates and country-specific parameters.

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functioning well in trading networks. Taken together, these results emphasize the role of

religiosity as a component of social capital and culture.

Note that this paper is not intended as a normative contribution—a rational choice

approach as to why Tibetans should embrace or shed their religious beliefs. Instead, this is

a positive analysis, aimed at analyzing the economic consequences of religiosity

(regardless of its origins). Also, we are cognizant of the fact that the role of religion in the

lives of Tibetans extends beyond the realm of economic performance. With these caveats

in mind, the paper proceeds as follows. Section 2 briefly introduces the Tibetan area, the

importance of Buddhism in Tibetans’ life, and analyses how Buddhism influences the

herders in allocating their available resources. Section 3 describes the data and outlines the

econometric analysis. Section 4 presents and discusses the results. The final section

concludes.

2. Buddhism in the Tibetan area

McCleary and Barro (2006, p.51) argue that “Buddhism has high salvific merit, in the sense

that following a designated path of lifetime behavior leads with a great deal of assurance to

salvation in the sense of enlightenment and knowledge.” Religious beliefs about whether or

not there is an afterlife, and about how current behavior affects one’s prospects in such an

afterlife, may have forceful consequences for behavior here and now. On the other hand,

incentives to produce and save might be attenuated by the strong sharing ethos that is also

distilled via Buddhism. The net effect of religious beliefs – output from the religious

production process – on income is therefore a priori ambiguous. The same applies to the

effect of inputs. While time spent praying or visiting temples should reduce output, other

effects may compensate for that. Higher levels of input may be accompanied with superior

access to information (one dimension of Putnam’s bonding social capital), or enhanced status

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in the community (Adam Smith’s so-called “reputational signal”). Exploring the net effect of

these conflicting mechanisms is one of the immediate aims of this paper.

We focus on the Tibetan areas, which is mainly located in Tibet-Qinghai Plateau.

This is the highest plateau in the world, also known as the “Roof of the World”. The average

altitude is more than 4,000 meters above sea level (Yang and Zheng, 2004). Historically,

Tibet-Qinghai Plateau is home to indigenous Tibetans and other ethnic groups. It is an arid

area dominated by rangelands. The traditional life style was primarily nomadic pastoralism,

with the exception of some agriculturalist in some low altitude regions (Ekvall, 1968; Miller,

1999). Feudal serfdom was practiced by the various lamaseries until 1959 (Strong, 1976;

Goldstein, 1989). Cultural Tibet (or the “Tibet regions”) consists of three major dialect

regions: U-Tsang, Amdo and Kham, in which majority inhabitants are Tibetans. U-Tsang is

the central part of cultural Tibet, making up the majority of Tibet Autonomous Region (TAR)

now, indicating the area where the Dalai lama ruled. Amdo is in the Northeast, divided

among northern Qinghai Province, southern Gansu and northeastern Sichuan Province.

Kham is in the southeast, comprised of northwestern Sichuan, northern Yunnan, southern

Qinghai and the eastern part of the TAR. According to the Fifth Population Census, there

were 5.416 million Tibetans in 2000, accounting for 0.42% of the total Chinese population

(China Population Statistics Yearbook, 2001).

Buddhism was introduced to Tibet during the 7th century, and gained a real foothold

during the 11th. Tibetan Buddhism mixes Tantric Buddhism with a good portion of Bon

religion – the native religion of Tibet consisting of beliefs in spirits, demons and ghosts in

nature, bringing good and evil. It was a key identity- and nation-building factor in Tibet, and

strongly influenced most aspects of life. Religion affected the cultural heritages via art,

literature and entertainment, but also via education and medicine. For overviews of Tibetan

history or culture and identity, refer to Goldstein (1989), and Goldstein and Kapstein (1998).

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The main influence of Buddhism on economic life is due to three teachings: (i)

attitudes to wealth and work; (ii) principles for finding happiness in life; and (iii) principles

for happiness in the afterlife. Datajeevo (2009) outlines these teachings, and the presentation

below is derived from this source. We believe they are relevant as they speak to norms of

reciprocity and trustworthiness in behavior. In the context of this study; living by the

principles sketched below implies being a reliable, and therefore preferred, trading partner.

The Attitude of Buddhists to Wealth and Work

The Buddha never prohibited wealth—he praises contentment and limited desires, but not

poverty. However, once wealthy, it should be used so as to support wholesome aims – not be

frittered away aimlessly. Resources should be spent in moderation, distinguishing between

“need” and “want,” where need is defined in terms of clothing, food, shelter, and medical

care. Such moderation contributes to economic and mental well-being. Consistent with

modern economics, Buddhism views human labor as a fundamental source of wealth.

However, Buddhism also regards work as an opportunity to utilise and develop faculties,

enabling individuals to overcome ego-centeredness by joining with other people in a

common task (Schumacher, 1973). Hence work and leisure are viewed as complementary

and inseparable parts of life.

The Principles for Finding Happiness in Life

The Buddha provided four principles for finding happiness in life: (i) diligent acquisition

(work hard and behave ethically), (ii) careful conservation (skillful saving and avoiding

conflict), (iii) having virtuous friends (be active in a network of friends endowed with faith,

self-discipline, self-sacrifice and wisdom), and (iv) living within your means. The Buddha

recommended part of one’s earnings should be applied for immediate needs, part should be

reinvested in business and the remaining part should be saved in case of an emergency.

The Principles for Finding Happiness in the Afterlife

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According to the Buddha, the four principles for happiness in the afterlife are (i)

faithfulness (confidence in the wisdom of the Buddha, translating into a willingness to

practice in his footsteps), (ii) self-discipline (including the need to improve oneself and

spend time in the temple), (iii) self-sacrifice (take pleasure in giving), and (iv) wisdom

(including awareness of the constant change in life – arising, continuity and decay). For

economic purposes, faithfulness is perhaps the main principle, resting on the conviction

that good deeds invite good returns (and, conversely, evil deeds invite retribution). To

cultivate faith, time, money and effort should be invested in spiritual activities. This

includes listening to Dhamma teachings to consolidate faith in the so-called Triple Gem

(sanbao in Chinese)—referring to Buddha, his teachings (dharma, fa in Chinese) and the

community (sangha, or seng in Chinese).

3. Empirical Analysis

The purpose of this study is to examine whether religion affects the yak and sheep herders’

decision-making on how to allocate their available resources (including time and capital, etc.),

and thus influences their economic performance in the Tibetan area. For this purpose, we

conducted a household survey among 242 rural households in the Qinghai Lake area in May

2007. Qinghai Lake is located 3,200 meters above sea level in the northern part of the Tibet-

Qinghai Plateau, between Hainan and Haibei Tibetan Autonomous Prefectures in Qinghai

Province, which are belonged to Amdo. Qinghai Lake is the largest lake in China, covering

more than 4,500 km2. It is a salt lake.

Households were randomly selected and interviewed in four rather similar and

comparable counties3 surrounding the lake. After clearing the data, the responses of 242

3 According to the Statistical Yearbook of Qinghai, more than 80% of agricultural income comes from livestock in these four counties. In 2006, average income per capita in this rural area was 2565 Chinese Yuan in Haiyan, 3033 Yuan in Gangcha, 3227 Yuan in Tianjun, and 2901Yuan in Gonghe. Average expenditure per capita in our sample is about 3300 Yuan, consistent with income data from the statistical yearbook.

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households were used in our analysis (that is: 53 from Haiyan, 71 from Gangcha, 33 from

Tianjun and 85 from Gonghe county).

Our questionnaire includes questions about communal matters, like collective

ownership of nearby grazing lands, but mostly consists of detailed questions about a range of

household characteristics. These include demographic status, asset ownership (land,

livestock, house, etc.), education, income and consumption, trust, and religion. Important for

our purposes, and following Barro and McCleary, we distinguish between religious inputs

and outputs. Three variables measure households’ input in the religion process: money (in

Chinese Yuan) spent in the (local) temple, average time (in minutes) spent on praying per

day, and the number of temple visits per year. We take logs of the three variables, and then

used factor analysis to construct an overarching index of religious input. This first principal

component explains 49% of the variation, and we use it as one of our key explanatory

variables. A Shapiro-Wilk W test reveals we cannot reject normality of this index based on

transformed inputs.

Two different questions were used to construct a proxy for religious output (“intensity

of beliefs”):4 (i) Do you believe in the “Triple Gem” of Buddhism: Buddha, Dharma and

Sangha? and (ii) Do you believe that your religious beliefs are instrumental in solving

problems in your life? Respondents were asked to indicate the intensity of their beliefs on a

5-point scale, and we constructed a dummy variable based on their responses. When both

questions were answered in the affirmative and respondents indicated a strong belief (i.e.

score 4 or 5), the household received a score equal to one on the output variable. Otherwise

it received a score of zero.

The dependent variable in our analysis is the natural logarithm of household’s

consumptive expenditures (that is: total expenditures excluding spending on house

4 These questions were composed after consultation of Dr. Kalsang Gyal, prof. in Tibetan Buddhism, Institute of World Religions, Chinese Academy of Social Sciences.

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maintenance, health and religion).5 This includes, but is not limited to, expenditures on food

and drinks, clothing, fuel, water and electricity, education, telephone, and transportation. As

is often the case, expenditures are easier to measure than income which originates from

various sources, and it may also be more stable over time (actual earning fluctuates but

households aim to smooth consumption). For example, in our sample only 77.7% of the

households was able to report income data, but all households could produce expenditure

data. On average, 83.8% of income originates from livestock, consistent with data from the

provincial statistical yearbook. Expenditures on house maintenance and health are excluded

because of their irregular nature (but including them does not fundamentally alter our

results), and expenditures on religious items are excluded because we treat those as one of

the religious “input variables” mentioned above. Table 1 shows descriptive statistics of our

variables.

<< Insert Table 1 about here >>

As is evident from Table 1, households in our study region are both poor and religious.

Annual consumptive expenditures amount to 12,300 Chinese Yuan, or some $1,600 per year

(based on the 2005 exchange rate). Note that the average number of members in the

household is almost 5, reflecting that the Chinese one-child policy does not apply to the

ethnic minorities in our sample. On average, households spent 495 Yuan in the temple in

2006, or more than 3% of total yearly expenditures. Households’ heads spent 34 minutes

praying everyday, and visited the temple 5.5 times a year on average. These three variables

were combined to construct a religious input index. Regarding the flipside of the coin, or

religious output, about 70% of our respondents indicated to be strong believers.

Turing to our control variables: the average age of households’ heads is 42 years, and

education levels are typically low (less than 2 years of schooling, on average). About 76% of

5 The density function of expenditures roughly exhibits a log-normal shape.

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our sample households are Tibetan, and the rest are Mongolians. Both Tibetans and

Mongolians are Buddhists in this region, but there are customary differences so we control

for ethnicity via a dummy variable. Respondents in our sample are all herders, and herd size

was based on the numbers of yak and sheep in households (via the simple formula that one

yak counts as four sheep – a formula reflecting the relative value of the animals, but that is

also based on the amount of forage required: one yak consumes four times as much grass as a

sheep, CMA, 2002). Traditionally, herder households build their houses at relatively low

altitudes, relatively close to roads. Grasslands close to these villages are normally used for

livestock herding in winter and spring, and called winter/spring grassland. Plots at higher

altitudes are only accessible in summer and autumn, and are called summer/autumn

grassland. We use ownership of both types of grassland, as well as any publicly accessible

grassland that might exist (in 70% of the cases), as productive assets that may explain

income. Importantly, since the allocation of grasslands was determined by township and

village leaders in the 1980s in response to perceived needs (especially household size) back

then; and grassland rental markets are hardly developed in our study region in the interim, we

treat the land variables as exogenous regressors below.6

To analyze the relationship between religion and economic performance we estimated

the following regression equation:

(1) ikE = constant + αikX + β1R1ik + β2R2ik +Ckγ + εik ,

where ikE is economic performance (consumptive expenditures, our proxy for income) of

household i in village/county k, Xik is a vector of characteristics of household i including

6 The government encouraged the settlement of people to improve living standards as it was believed that a sedentary lifestyle facilitated the efficient provision of public goods (e.g., education, health care). Similar policies have been pursued in other countries (such as the establishment of group ranches for the Maasai in Kenya and Tanzania). Land was allocated based on perceived “household needs” so larger households received larger plots. While the allocation process was supposed to be random (Tan et al., 2006), it is difficult to exclude the possibility that in certain localities the actual distribution of land also reflected power relations among herders. If so, correlations implied by regression analysis should not be interpreted as causal relations. (However, the key focus of this paper is not on the causal effects of land size.)

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ownership of productive assets and human capital, R1ik and R12k are respectively inputs and

outputs of the religion process, Ck is a vector of variables at the village/county level, and

finally εik is the error term of the equation. Controlling for various types of capital assets,

therefore, we set out to explore whether religiosity is a driver of economic performance. To

check the robustness of our results, we estimated various OLS specifications as well as a

two-stage least squares model to control for potential endogeneity of herd size and, in

particular, religious variables.

4. Results and Discussion

Our main results are provided in Table 2. Column 1 presents the results of a regression where

we include both religious inputs and output as linear terms. Both enter positively and

significantly at the 1% level, suggesting that both efforts and beliefs are associated with

higher expenditures. The other variables enter as expected: there is a positive association

between herd size and expenditures, and the relation between age and education on the one

hand, and expenditures on the other, is positive but characterized by diminishing returns.7

Chi-sq results of White's test indicate there is no heterogeneity problem in our data – we

cannot reject the null hypothesis that our regression errors are homogeneous (p-values is

0.99).

The positive relation between beliefs and expenditures is consistent with earlier

evidence from cross-section studies, arguably reflecting that beliefs about the after-life (or

certain traits promoted by the religion) is conducive to a work ethos today. The positive

relation between inputs and expenditures is more surprising. Payments to the temple come at

the cost of personal expenditures, and time spent visiting temples or praying involves an

opportunity cost. There are several explanations for the positive association. Most notably,

7 Indeed, the turning point with respect to age is within sample, and households maximize expenditures (income) when the household age is 51 years old.

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temple visits may facilitate the flowing of information (herders in our study region are

scattered across the landscape, live isolated lives, and have infrequent opportunities for

interaction). Temple visits (pilgrimage) have an important social function in addition to the

religious one. In Tibet, temple visits provide a (rare) opportunity to meet people from

different areas, to build social networks, and to explore business opportunities (e.g., Grunfeld,

1996). Personal observations in the study region indeed suggested that herders use the

opportunity to discuss various matters while visiting temples (that is: before and after

visiting—never while inside the temple). Second, visits and payments to temples may

contain a signal about one’s “type” as a good Buddhist.8 Both superior information about

prices and enhanced status within the community probably translate into a superior

bargaining position vis-à-vis suppliers and traders. Alternatively it could be argued that

praying and temple visits result in improved mental health and an overall sense of well-

being, which in turn raises the effectiveness of work effort. We return to this issue below.

Column 2 indicates that the relation between religious inputs and expenditures is

curvilinear. Indeed, the “top” of the parabola is within sample, and few herders (to be

precise: 4 households) are to the right of the top. This may reflect non-use benefits from

religious activities. In Figure 1 we illustrate the parabolic relation implied by column 2, and

have superimposed the mean (the rescaled index has a mean of zero), the minimum and

maximum value of the index, and the mean plus one standard deviation.

<< Insert Figure 1 about here >>

As mentioned, our input data are log-normally distributed. For that reason we replace

the linear and quadratic input terms by an index where the factor analysis was based on

logged input variables (time, money, visits). In Column 3 we present results for the

8 Local Tibetan officials – even Chinese Communist Party members – often stop by temples, spending several minutes to visit them and contributing some small amount of money there. Officially CP members should have no religious briefs. Personal communication revealed that such visits were conducive in gaining respect in local communities.

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parsimonious specification, and in Column 4 we add several controls to check the robustness

of our findings. In both cases we continue to find support for positive relations between

religious inputs and outputs, and our income proxy. Indeed, our extra controls are never

significant.

As suggested by Barro and McClearey (2002), some of our explanatory variables may

be endogenously determined. Specifically, one wonders about endogeneity of the livestock

variable and, more importantly for our purposes, of the religious input variable. Higher

incomes translate into larger herds if part of the income is routinely used to augment the herd

(or invest in its maintenance), and also could translate into greater monetary transfers to

temples (if religiosity is a normal good). Suitable instruments for these covariates should be

(i) exogenous and strongly related to the endogenous variables, and (ii) uncorrelated to the

error term of the income (expenditures) equation. To explore this issue further we consider

the insignificant variables of Column 4 (ethnic Tibetan, labor size in the household, variables

capturing land ownership, willingness to trade immature animals (lambs), trust, information

variables, county dummies) as excluded instruments, and do a 2SLS regression. Column 4

suggests these variables are uncorrelated with expenditures, and therefore they are unlikely to

be correlated to the error term in the augmented expenditures regression model.

Test statistics indicate that our instruments are adequate. More specifically, the first

stage results indicate the excluded instruments are correlated with the endogenous variables.

Details are as follows (further details available from the authors on request): (i) The R2 and

partial F-values of the religion input and livestock regressions, respectively, are (0.36, 6.62)

and (0.26, 4.4); and (ii) The Anderson canonical correlations likelihood ratio rejects the null

that the equation is underidentified (p = 0.01). Moreover, the exclusion restriction is

satisfied; the Sargan statistic suggest we cannot reject the null that the instruments are valid

(uncorrelated with the error term and correctly excluded from the 2nd stage regression: p =

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0.46). The instruments identify exogenous variation in the endogenous variables, and the 2nd

stage results indicate that all the earlier results go through with very little modifications –

specifically, the coefficient of the religiosity input index variable is hardly affected.

However, a Wu-Hausman F-tests and Durbin-Wu-Hausman chi-sq tests indicate that

we cannot reject the null hypothesis that the regressors are jointly and separately exogenous

(p-values of 0.98 for both the livestock and inputs variables).

Finally, we ask why religious inputs may raise expenditures. While a full analysis

requires additional data, the current survey enables us to present some suggestive albeit

tentative conjectures. As mentioned, there are three competing explanations for the positive

causal mechanism linking inputs to income: superior information, enhanced status, and

increased mental health or well-being. Ideally we would have collected data on these

variables, but that proved far from straightforward. Instead, and as sketched above, the three

distinct variables underlying our input index allow us to say something about this, based on

deduction and logical inference. Specifically: if mental health matters, we expect praying to

enter positively and significantly. If information matters, we would expect temple visits to

enter positively and significantly. And if status matters, we would expect transfers to the

temple, and probably temple visits, to enter significantly. We would not expect praying time

to pick anything up then, as praying is typically unobservable to fellow herders.

Results are reported in Table 3 for the three separate input variables.9 The results

suggest that mental health plays at best a small role, as praying time is not significantly

correlated with expenditures (Column 1). In contrast, both religious transfers and temple

visits matter. The transfer variable result in Column 2 strongly suggests that reputational

effects and status matter (see also Anderson 1988). This is corroborated by the temple visit

results in Column 3, as such visits are also an important means to send signals about one’s

9 Again, we checked all models with 2SLS (same IV as in Table 2), and again the results of Durbin-Wu-Hausman show that we cannot reject the null hypothesis of exogeneity of all three input variables.

16

type. However, there is a confounding factor, as temple visits also enable herders to pick up

information when interacting with their peers. To verify whether the information mechanism

is important over and above the reputation effect we report the results of a final regression

with both variables in Column 4. We find that both transfers and temple visits enter

significantly, suggesting that the temple visit variable captures something that is distinct from

status. This could be the dissemination of information. Additional data are required to

analyze this issue in detail.

5. Conclusion

In this paper we provide micro-econometric evidence for the result that religion matters for

economic performance. Our micro-econometric evidence corroborates some existing cross-

country regression results, but qualifies others. Our first result is that the intensity of

religious beliefs positively impacts on expenditures. This result is consistent with the

existing cross-country literature. However, unlike Barro and McCleary (2003), we also find

evidence for a positive and curvilinear effect of religious inputs on expenditures. While it is

possible to allocate so many resources as inputs in the religiosity production process that a

negative relation between inputs and economic performance eventuates (e.g. spending too

much time praying carries an opportunity cost in terms of labor time foregone), we find that

the great majority of the respondents is on the “upward sloping” part of the curve linking

expenditures (our proxy for income) to inputs.10

In theory, the positive effect of religious inputs on economic performance could be

due to mental health effects, information dissemination or enhanced status associated with

providing religious inputs. We present tentative evidence that especially information and

status (reputation) effects matter. This is consistent with personal observations while

10 Of course it is an open question whether they spend “too much” or “too little” resources in the religiosity process, from an economic perspective. Addressing this issue requires a comparison of marginal costs and benefits—beyond the purpose of the current analysis.

17

gathering data for this study. Government officials claim to visit temples for status and

reputation reasons, and we have observed herders exchanging information about a range of

issues just outside the temple. If follow up work indeed confirms the information

dissemination function of temples (i.e., if information is scarce and valuable), then perhaps

one policy recommendation would be to consider promoting the spread of information about

markets and pastures via complementary channels.

The analysis suggests the role of religion goes beyond that of “people’s opium.”

Religion is a component of culture, informal institutions and social capital. In the Tibetan

context, temples provide meeting places in a context where individuals are alone most of the

time. Religion enhances shared norms that promote the incentive compatibility of non-

contractual or legally enforceable exchange. Shared beliefs about ethical conduct, as well as

the possibility to send signals about one’s ‘type’ (in terms of trustworthiness, honesty) via

choice of religious inputs, may enable individuals to function better in a trading network and

earn higher incomes than they would otherwise do.

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20

Table 1: Descriptive statistics of the variables Variable Mean Std. Dev. Min Max

Log (expenditure)

9.42 0.784 5.39 11.35

Religion input (Index)

0.0 0.309 -0.257 2.14

Log (1 + religion input)

3.57 1.78 0.0 7.71

Log (1 + religion input 1: money spent in temple (in Chinese Yuan))

4.18 2.82 0.0 9.21

Log (1 + religion input 2: praying time per day in minutes)

2.61 1.65 0.0 5.48

Log (1 + religion input 3: number of visits a year to temple)

1.28 0.871 0.0 5.71

Religion output (Dummy)

0.707 0.456 0 1

Age of HH head (/10)

4.22 1.32 1.4 7.7

Years of education

1.93 3.02 0 13

Tibetan (Dummy)

0.760 0.428 0 1

Number of members in HH

4.80 1.40 1 11

Labor in HH

2.94 1.28 0 7

Log (1 + herd size)

5.25 1.42 0 7.31

Log (1 + area of winter grassland)

6.15 1.17 0 8.85

Log (1 + area of summer grassland)

3.00 3.07 0 8.29

Access to public grassland (Dummy)

0.743 0.438 0 1

Ownership TV (Dummy)

0.566 0.497 0 1

Ownership phone/mobile (Dummy)

0.620 0.486 0 1

Absence private summer grassland (Dummy)

0.496 0.501 0 1

GSS trust question

0.662 0.474 0 1

Sell and kill a lamb? (Dummy)

0.384 0.487 0 1

21

Table 2: Determinants of income among herders in rural Tibet Variable 1

OLS1 2

OLS2 3

OLS 3 4

OLS 4 5

2SLS Religion inputs 0.566***

(4.02) 1.033***

(4.31)

(Religion inputs)2 -0.442 (2.39)**

Log(religion inputs) 0.164 (6.64)***

0.144 (5.86)***

0.146 (2.19)**

Religious beliefs 0.319 (3.01)***

0.268 (2.50)**

0.283 (2.82)**

0.214 (2.26)**

0.208 (2.25)**

Log (herd size) 0.162 (5.08)***

0.159 (5.06)***

0.126 (4.10)***

0.159 (4.35)***

0.168 (2.07)**

Age/10 0.751 (3.75)***

0.701 (3.51)***

0.725 (3.85)***

0.784 (4.37)***

0.798 (4.65)***

Age2/100 -0.0729 (3.41)***

-0.0681 (3.20)***

-0.0692 (3.44)***

-0.0779 (4.06)***

-0.0796 (4.35)***

Education 0.0951 (2.49)**

0.0903 (2.38)**

0.0937 (2.59)**

0.0600 (1.79)*

Education2 -0.0086 (2.03)**

-0.0085 (2.03)**

-0.0087 (2.15)**

-0.0061 (1.65)*

Tibetan (dummy) -0.136 (0.99)

Number of members in household

0.0962 (2.85)**

0.0935 (2.80)**

0.0767 (2.38)**

0.0473 (1.25)

0.0475 (1.40)

Number of laborers in household

-0.0187 (0.47)

Trust -0.0098 (0.12)

Log (winter grassland) 0.0563 (1.21)

log (summer grassland) -0.0014 (0.02)

Public grassland 0.141 (1.46)

Sell lambs 0.0255 (0.57)

No summer grassland -0.0109 (0.03)

Information 1 (own TV) 0.0414 (0.49)

Information 2 (own phone/mobile)

-0.0262 (0.30)

County 1 (dummy) 0.0463 (0.35)

0.0983 (0.74)

0.205 (1.59)

0.0984 (0.62)

0.207 (1.18)

County 2 (dummy) -0.0231 (-0.22)

-0.0222 (0.21)

0.0486 (0.48)

0.0275 (0.27)

0.0576 (0.63)

County 3 (dummy) 0.365 (2.64)**

0.356 (2.60)**

0.371 (2.83)**

0.353 (2.45)**

0.326 (2.27)**

Constant 6.009 (13.0)***

6.230 (13.3)***

5.712 (12.19)***

5.472 (10.1)***

5.729 (12.1)***

Sigma - - - - - (Pseudo) R2 0.36 0.37 0.42 0.44 0.42 Number of observations 242 242 242 232 232 For column 5 excluded instruments are Tibetan dummy, education and its squared, number of laborers, log (winter grassland), log (summer grassland), public grassland, trade lambs, information1 and information 2. The Anderson Canon. Corr. LR statistic = 24.6. The Sargan statistic = 9.8.

22

Table 3 Results with separate religion input variables Variable Input 1:

Praying time Input 2: Temple

transfers Input 3: Number of visit to temples

Visits and transfers together

Age/10 0.631 (3.08)***

0.672 (3.56)***

0.633 (3.21)***

0.671 (3.61)***

Age2/100 -0.0614 (-2.79)***

-0.0637 (-3.16)***

-0.0592 (-2.80)***

-0.0622 (-3.13)***

Education 0.0896 (2.27)**

0.0863 (2.37)**

0.0821 (2.15)**

0.0817 (2.28)**

Education2 -0.0080 (-1.83)*

-0.0077 (-1.91)*

-0.0080 (-1.89)*

-0.0077 (-1.94)*

Number of members in household 0.0999 (2.86)***

0.0833 (2.58)**

0.0989 (2.94)***

0.0842 (2.65)***

Log (herd size) 0.169 (5.14)***

0.119 (3.81)***

0.166 (5.24)***

0.124 (4.00)***

Log (praying time +1) 0.0093 (0.29)

Log (money spent in temple +1) 0.0975 (6.42)***

0.0856 (5.49)***

Log (number of visit to temples +1) 0.221 (4.20)***

0.143 (2.76)***

Religious beliefs (Dummy) 0.290 (2.59)**

0.326 (3.23)***

0.275 (2.60)**

0.308 (3.09)***

County 1 (Dummy) -0.0269 (-0.19)

0.138 (1.09)

0.0659 (0.50)

0.186 (1.47)

County 2 (Dummy) 0.0094 (0.09)

0.0492 (0.49)

-0.0750 (-0.70)

-0.0103 (-0.10)

County 3 (Dummy) 0.345 (2.41)**

0.375 (2.85)***

0.431 (3.10)***

0.427 (3.25)***

Constant 6.25 (12.9)***

6.00 (13.7)***

5.97 (12.9)***

5.84 (13.4)***

R2 0.31 0.42 0.36 0.43

Number of observations 242 242 242 242

23

Figure 1: the relation between religious input (index) and income

9.2

9.3

9.4

9.5

9.6

9.7

9.8

9.9

10

10.1

10.2

-0.3 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4Religion input index

log(exp)