Culture and Development

Jon R. Jellema*

Department of Economics, U.C. Berkeley

November 2008

Job Market Paper - Preliminary

Abstract Whether culture affects development is one of the most fundamental questions in

economics, but sample, measurement, and direction-of-causation issues hinder empirical

analysis. Making use of advances in empirical anthropology and population genetics, I

provide robust solutions to these problems. I assemble measures of cultural behavior

collected systematically from more than 1200 anthropological case studies. I describe

the generation of cultural variety without invoking previously existing institutions or

tendencies. I exploit the parallel random mutation and long-term persistence of genetic

and cultural information in an instrumental variables framework where I demonstrate

that predictable variation in neutral genetic information (and not genetic inheritance of

social information) provides a valid and powerful instrument for culture. I show that

within this instrumental variables framework class stratification, inheritance rights, and

other cultural technologies can explain up to 115 percent of a standard deviation in

output, or approximately the size of the gap in per-capita GDP between Thailand and

Ireland.

* Contact information: 508-1 Evans Hall #3880; University of California, Berkeley; Berkeley, CA 94720-3880.

Email: jrjellema@econ.berkeley.edu.

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1. Introduction

Economic exchange is a social transaction. The outcomes resulting from it, expectations

surrounding it, and its prescribed structure cannot be isolated from the shared norms and

mandated behaviors that accompany all social transactions. The role of culture in economic

development, in other words, is fundamental.

However, social behaviors, rules, norms, and standards can logically be determinants or

results of economic exchange, making empirical identification of the developmental effects of

culture problematic. Culture itself is difficult to measure as it requires observation of social

interaction which is multi-faceted and not amenable to summary by indicators like price or

quantity; neither are data capturing singular opinions or beliefs precise guides to social activity.

In this paper, I assemble two global datasets that offer novel and robust solutions to these

identification and measurement problems in order to demonstrate empirically the causal

relationship between cultural behavior and economic development.

Specifically, I show that cultural technologies that promote division of labor and

specialization, informal education or research and development, or property rights lead to cross-

sectional increases of up to 70 percent of a standard deviation of economic development.

Increases in all of them simultaneously leads to increases of up to 115 percent of a standard

deviation of economic development. Cross-sectional differences in current real GDP per capita

roughly this size can be found between, for example, Thailand and Ireland, the Czech Republic

and Belgium, Colombia and Japan, or Bhutan and Chile. These relationships remain visible

within geographic regions and production technologies and each cultural practice remains

individually predictive when the others are held constant.

2

To measure culture convincingly, I use observations from detailed ethnographies

covering more than 1200 populations worldwide. This dataset of cultural practices is richer in

both detail and scope, more objective, less prone to perception bias, and contains less non-

random noise than other cultural datasets. Culture is observed closely and recorded as behavior

rather than belief. The sample of populations is world-wide and includes groups and areas, like

Pacific Island societies, often missing from empirical studies; there is no oversampling of

populations by income, geographic location, or colonial history.

I describe the discovery and long-term persistence of cultural practices among

populations choosing new environments to in which to settle. Variation in cultural technologies

is generated by partially-random solutions to problems concerning the management of group

resources, or the aggregate of fixed environmental endowments and the labor of individual

members. The physical environment, choice of subsistence activity, and the risk stemming from

the interaction of these two variables force decisions regarding how labor will be managed.

Chance mutation also plays a role, so similar endowments do not necessarily generate similar

cultures.1 All of these variables in addition to culture determine economic development

simultaneously.

The event generating variation in cultural technologies (or, more precisely, variation in

the constraints under which technologies are developed) is the serial migration of modern

humans across the globe that began roughly 100,000 years before present and ended roughly

10,000 years before present. This event also left a remarkable signature on the human genome

visible at the population level: genetic variety, or the number of potential genomic possibilities

found in a population, is inversely proportional to distance from East Africa, the original start

1 Though developed consciously and purposefully, cultural outcomes are not always predictable, nor is the decision

to adopt any norm always observable. In both senses, the discovery of cultural practices is partially random.

3

line for this era of migration that eventually brought humans to the extreme southern end of

South America and virtually every habitable locale in between.

This information has been measured across a large portion of the human genome that

does not code for the production proteins and is not associated with observable behaviors or

physical characteristics. Variety calculated from this information records only neutral genetic

diversity, or that portion of overall genetic diversity that is not under natural selection and is

mostly not a result of gene flow between populations. Because it is not a likely cause of any

observable behavior or characteristic, it is not an object of choice or optimization for economic

actors. Variation in this information is analogous to results from successive random draws (at

the population level) without replacement from the original pool of genetic material, leaving

populations near the source with the most variety and those farthest away with the least.

I demonstrate a significant and robust correlation between the candidate instrument and

specific cultural technologies. Since both genetic and cultural information are transmitted with

modification from parent to offspring across generations, this result is expected. I exploit the

empirical correlation and the assumption that variation in genetic heterogeneity is exogenous to

income generation to pursue an instrumental variable strategy that demonstrates empirically the

main hypothesis of this paper: culture creates economic incentives and human capital that affect

development in a robust and economically significant manner.

More generally, I provide empirical support for the hypothesis that preferences and

incentives are endogenously determined by social interaction and the unpredictable development

and adoption of cultural norms.2 It amplifies the notion that luck in the endowment of

technologies and culture has played a significant role in economic development.3 From an

2 See Akerlof and Kranton (2005) for the former and Roland (2004) for the latter.

3 See Diamond (1997) for physical technologies and Tabellini (2007) for cultural technologies.

4

economic policy perspective, the key finding is that informal, uncodified, and often-invisible

institutions create economic incentives. This suggests that formal institutions should be adapted

to local conditions rather than transplanted wholesale.

The plan of the paper is as follows: Section 2 reviews the literature on culture and

economic development, focusing on empirical treatments with careful identification strategies.

Section 3 provides the historical background and a detailed description of the mechanism linking

migration, cultural variation, genetic variety, and development. Section 4 describes data sources.

Section 5 presents the main empirical specifications and results, brief discussions of the

pathways between specific cultural behaviors and development, and examples of the cultural

behaviors at work. In addition, Section 5 includes tests of the validity of the instrument and a

discussion of the robustness of the empirical findings where I demonstrate that they hold across

several different subsamples and alternative measures of economic development. Section 6

offers concluding remarks. Extended robustness testing, discussions of the relationships between

cultural technologies and development, and specific examples of culture at work are found in the

appendices at the end of the paper.

2. Literature Review

Empirical analysis of the link between cultural variables and economic outcomes is a relatively

new research program. Some early examples like Knack and Keefer (1997), Temple and

Johnson (1998), and Hall and Jones (1999), while they do not explicitly observe culture, use

indices of ―social infrastructure‖ which likely contain latent elements of culture. These early

empirical analyses are in agreement: variation in social infrastructure predicts variation in

economic outcomes. Roland and Jellema (2006) produce evidence that culture and political

5

institutions are complements in income generation and that culture remains a significant

predictor of income when other institutions are held constant.

Identifying historical eras or chronologically distant events to utilize as a source of

plausibly exogenous variation in formal or informal institutions is also a relatively new avenue of

research. Acemoglu, Johnson, and Robinson (2001, 2002) exploit variation in the disease

environment confronting colonial settlers arriving in the 1500s to identify variation in current

levels of the risk of expropriation. Nunn (2008) finds that variation in the intensity with which

nations participated in the 13th

through 15th

century slave trades predicts variation in current

income.4 Galor and Moav (2007) suggests that years since the introduction of agriculture can

explain contemporary variation in life expectancy; they hypothesize that the Neolithic revolution

brought cultural and biological changes which in turn made longer life expectancy not only

possible but optimal.5

Recent examples combining both explicitly cultural variables and plausibly exogenous

sources of historical variation include Tabellini (2007), Licht, Goldschmidt, and Schwartz

(2007), and Guiso, Sapienza, and Zingales (2008). The first two combine cross-national

observations of beliefs or opinions and an identification strategy that uses variation in linguistic

rules determined by ―distant traditions‖.6 Each describes an interaction between a set of beliefs

(as recorded by survey) and formal institutional outcomes like the rule of law or the quality of

government. The subsequent interaction between institutions and economic outcomes is not

4 Though institutions are not statistically identified or tested in Nunn (2008), the author suggests them as

intermediaries through which slave-trade participation might operate on current income. 5 Olson and Hibbs (2005) also begins at the Neolithic revolution but does not include an independent effect of

institutions. But see also Putterman (2008), which replicates Olson and Hibbs (2005) with corrections for the

diffusion of populations and technologies, including social practices. 6 See also Alesina and Fuchs-Schündeln (2005), Giuliano (2007), Fernandez and Fogli (2007), or Munshi and

Wilson (2008), all of which use the not-too-distant traditions of first-generation immigrants as instruments for the

cultural practices of second- or third- generation offspring of those immigrants. These later generations receive a

cultural package developed for a substantially different environment and variation in outcomes among them is

correlated with this cultural variation.

6

statistically tested but is assumed given the wealth of evidence available from other studies. The

latter finds that historical experience with independent city states in Italy is associated with

greater social capital (measured by civic participation). The authors hypothesize that the cultural

norm of cooperation reaffirmed by that historical experience has persisted and produces higher

social capital and income today.

Different versions of the genetic data underlying this paper‘s proposed instrument has

been used before. Two studies, Spolaore and Wacziarg (2009) and Ashraf and Galor (2008),

estimate a direct effect of genetic dissimilarity on economic outcomes.7 Using a condensed

version of the genetic data, Spolaore and Wacziarg (2009) take pairwise genetic differences at

the country level and estimate the contribution of these differences to pairwise differences in

income. They hypothesize that pairwise genetic dissimilarity is a proxy for differences in

―characteristics, including cultural traits‖ which act as barriers to the diffusion of innovation.

Ashraf and Galor (2008) estimate the direct effect of expected variation in genetic material on

national population densities in 1500 or earlier. The authors take genetic diversity as a proxy for

intra-population cultural dissimilarity and hypothesize two competing effects on population

density: genetic diversity hinders the transmission of ―society-specific human capital‖ but

encourages the ―accumulation of universally-applicable human capital‖.8 This leads to a non-

monotonic and indeterminate effect of genetic diversity on population density.

7 One of the first examples of genetic information used as an instrument in a two-stage least squares framework is

Fletcher and Lehrer (2008). The authors use variation in the presence of specific genes known to impact multiple

health outcomes to isolate the effects of poor health on educational outcomes. Though I use genetic information that

does not lead to differences in phenotype (observable characteristics, attributes, or behaviors), my empirical strategy

is much the same as Fletcher and Lehrer (2008) as I rely on the inability of economic actors to make genetic

information a choice variable. 8 Spolaore and Wacziarg (2009) and Ashraf and Galor (2008) both suggest a relationship between variation in

genotype, or the unobservable genetic makeup of a person, and variation in the observable behaviors they believe

lead to economic growth, but these relationships are not tested empirically.

7

This paper, with its emphasis on the local generation of observable behaviors and their

effects on local development, offers the following improvements to analyses discussed above:

the candidate instrument is unobservable by economic actors; I do not rely on previously existing

cultures, institutions, or external innovations to explain developmental or cultural variety; and I

am able to show which cultural behaviors matter for local output.

3. Instrument, Hypothesis and Historical Background

Economists have long suspected that culture and development covary - see Smith (1759) or

Weber (1905) - and empirical analyses have lent broad support to this hypothesis, but

measurement and identification issues are inescapable. In Sections 5 and 6 I provide a

discussion of why certain cultural technologies might cause development (see also Appendices B

and C), describe cultural measurement, and provide additional details on the candidate

instrument, including its ability to identify exogenous cultural variation. Following directly

below is a description of the candidate instrument and a summary of its attractive features. I then

discuss the mechanism generating both cultural and instrumental variation.

3.1. Candidate Instrument

The instrument I propose is population-level variety in neutral genetic information.9 Along the

human genome there are many thousands of sites where different versions of a gene, nucleotide

group, or a single nucleotide10

can potentially occur. An individual carries at most one variant at

any site where multiple variants can occur. If an individual is carrying a particular variant in the

9 Effectively, this means I use variation-in-variation to estimate the developmental effects of culture.

10 Nucleotides are the building blocks of DNA, which is itself the material of which genes are made. Nucleotides

are the discrete chemical compounds that line up along the familiar double helix in which DNA is arranged.

8

section of the human genome that determines blood type, for example, then she will end up with

blood type A. A different variant at the same site in another individual leads to blood type B and

another possible variant to blood type O. Three individuals, each with a different variant, will

carry three different blood types.

Across a population, however, all variants are observed more or less often and

frequencies can be calculated. Individuals with varieties that produce blood types A and B are

frequent in the Ainu of Japan and nearly nonexistent in the Zuni of New Mexico, for example:

population frequencies for A, B, and O blood types are approximately 26, 20, and 54 percent and

1, 6, and 93 percent for the Ainu and Zuni respectively (Cavalli-Sforza, Menozzi, and Piazza

1994). At this site, therefore, the Ainu are more heterogeneous than the Zuni.

This population genetic variation may arise from natural selection, or the differential

reproductive success of individuals (and therefore genomes) with or without certain varieties.

The gene variety that codes for the sickling of red blood cells, for example, occurs more

frequently in populations living in environments where malaria is or was common and where that

variant confers a reproductive advantage (resistance to malaria). Sexual selection may also

produce genetic variation: if varieties lead to observable characteristics or behaviors that increase

the likelihood of sexual reproduction by increasing the probability of mating, those may also

become more frequent.

Genetic information that is not advantaged selectively, however, also exhibits varying

population frequencies due to what is essentially sampling error: endogamous populations carry

only those varieties their ancestors carried. More precisely, they carry only those varieties

transmitted by the reproductive individuals within their ancestral group. Endogamous

populations with different ancestors will therefore be carrying differing sets of gene variants and

9

not every ancestor will have contributed genetic information. This compound sampling error

leads to variation in overall genetic variety at the population level even in the absence of clearly

advantageous varieties. Blood type is not selectively advantageous for the Ainu or Zuni, but

sampling error and endogamy have created divergent gene frequencies.

In this paper I use variety in genetic information that does not confer a selective

advantage. The information is averaged from up to 1000 sites where multiple variants can occur.

Each site is not known to specify the production of any protein nor is it associated with any

observable behaviors or physical characteristics.11

Furthermore, using a well-known and

repeatedly-confirmed result from population genetics (discussed in Sections 4 and 5), I am able

to exploit that portion of genetic information that varies as a result of the compound sampling

error discussed above.

Imagine that each population is associated with 20 pages, drawn randomly, one for each

population member, from a phonebook containing all possible 10-digit phone numbers.12

When

two members reproduce, each of their pages is copied automatically and given to offspring.

These phonebooks have two special properties: first, the numbers, if dialed, would not call up

anyone – they are ―non-coding‖. Second, neither the numbers nor the copying are observable by

any group member. The analogous variation I might propose as an instrument would not be the

presence or absence of any particular number or group of numbers. Instead, the population

frequency of varieties would be observed (count the numbers with area code 510) and from that

count a summary measure that describes overall heterogeneity in phone numbers (does area code

11

Though a large portion of the human genome is ―non-coding‖ in this way, these sequences are not necessarily all

non-functional. 12

Following the rules for US exchanges (no zeros or ones in the first digit of the area code or prefix, no quadruple

zeros in the suffix), there are just under 6.4 x 109 possible phone numbers.

10

510 occur frequently or not at all?) would be calculated; this heterogeneity could be averaged

over all digit locations.

Taking an average of information at several hundred sites produces a statistic

summarizing variety in total neutral genetic information. Populations can be classified based on

this average total variety and it has been shown to be distributed around the world in a

predictable manner (see Sections 4 and 5). I take advantage of this known distribution and

suggest that population differences in average total variety make up an appropriate instrumental

variable for culture.13

It is important to note that this variety is always based on genetic

information (which nucleotide varieties are present) and never on the physical outcomes

(phenotypic variety) that genetic information sometimes instructs.

Genetic variety calculated in this manner is unobservable, does not confer a selective

advantage, and could not have been a matter of choice or optimization. The generation of variety

by repeated sampling error makes the accumulation of population genetic variety analogous to

random draws from the original pool of genetic information. Furthermore, taking average

variety over several hundred sites effectively dampens the signal from the genetic information at

any one site. Therefore, even if some sites from which information is taken are discovered to be

under selection, the average information remains neutral.14

Finally, genetic information, like cultural information, is reproduced and persists by

sexual reproduction and vertical transmission from parent to offspring. In other words, each time

the non-coding phonebooks are passed on, an additional set of phonebooks with cultural codes

are also replicated and internalized. With observations from both sets of information for several

13

Exploiting the known distribution of genetic variety allows me to avoid capturing in my instrument that portion of

genetic information that varies as a result of gene flow between neighboring populations. 14

This is true also for gene flow, or the exchange of genetic material between neighboring populations through

exogamy: a signal that happens to be the product of exogamy will be dampened by averaging over many sites.

11

distinct populations, it is possible to identify the cultural codes that have persisted by vertical

transmission across generations by their correlations with the neutral genetic codes.

3.2. Historical Diffusion of Human Populations

Figure 1 shows routes taken by modern humans to all currently inhabited areas of the globe. The

dates shown are estimated arrival times in years before present, but confidence intervals on

proposed dates are not narrow. For example, the evolutionary events leading to the

establishment of modern humans in Africa are thought to have occurred between 200,000 and

150,000 years ago; expansion out of Africa between 100,000 and 65,000 years ago; and the first

FIGURE 1 – ANCIENT MIGRATIONS of MODERN HUMANS

Note: Figure 1 shows hypothesized routes to and dates of arrival in all currently inhabited areas. Dates shown

are years before present and are inexact. Adapated from Cavalli-Sforza and Feldman (2003).

12

arrival of humans in North America between 40,000 and 10,000 years ago (Mellars 2006). Intra-

continental migration dates and arrival times are similarly imprecise (Lahr and Foley 1994).

There are two important features of these migrations the empirical analysis exploits. The

first is the ―Out of Africa‖ or ―Recent Single Origin‖ hypothesis, which states that there was one

site-specific beginning to the diffusion and expansion of modern humans; the available evidence

suggests the site was East Africa (Harpending and Rogers 2000). The second concerns the

composition of successive migrating groups: each outmigrant group was a genetically non-

representative subset of the stay-at-home population (Prugnolle, Manica, and Balloux 2005). A

recent single origin links every human population to the founding population while serial

selection of genetically non-representative migrating groups produces population variation in the

human genome.15

3.3. Cultural Innovation and Maintenance

Each migrating group confronted a distinct and unfamiliar environment requiring new strategies

and technologies for subsistence and reproduction.16

These strategies and techniques include

ideas about how members will relate to one another socially and familiarly, collective actions

and goals, taboos, leisure, and myriad other norms and conventions all of which can properly be

called culture. Some ideas may be explicitly optimal solutions to unambiguous problems,17

15

Though evidence from archaeology, linguistics, anthropology, and population genetics supports an East African

origin and serial migration, a multi-site model of the origin of modern humans has not been conclusively ruled out.

Though it would make interpretation of the patterns present in population genetic information less straightforward, a

multi-site model would not invalidate either the hypothesis of cultural innovation or the empirical results discussed

below. 16

Lahr and Foley (1998) argue that these ―myriad local histories‖, rather than population exchange, are responsible

for most of the linguistic, cultural, morphological, and genetic diversity among modern human populations. 17

Also likely is toleration of ideas that are not welfare-improving but that are maintained by collective action or

coordination failures (Olson 1965).

13

some may be the result of extensive trial and error, and some may have occurred

serendipitously.18

All locally-developed solutions need not have been a result of local inputs and local

experience alone. Human capital (including culture) and physical capital (tools, production

techniques) acquired during earlier eras may have been locally useful.19

This ability to re-

optimize and co-adapt to a broad range of ecological niches is unique (historically if not

biologically) to humans, and the available evidence suggests that the elements necessary for this

relentless innovation were in place before populations moved from Africa. The cognitive,

neurological, cultural, and technological changes that occurred before expansion out of Africa

may be directly associated with the evolution of anatomically and genetically modern

populations (Mellars 2006).

Once developed, local solutions to social problems will be transmitted from generation to

generation and parent to child (Bisin and Verdier 2001; Cavalli-Sforza and Feldman 1981). The

vertical transmission of local cultural solutions results in a relatively stable set of cultural values

which are slow to change and become fixed for several generations (Roland 2004). Though not

necessarily developed for this purpose, some parts of the cultural apparatus may prove

advantageous as populations grow and begin to produce above subsistence levels.20

Larger

populations and production above subsistence in turn make possible specialization, intra-group

18

Both Roland (2004) and Tabellini (2008) accommodate randomness or luck in the history of ideas, as no norms

concerning human interaction are accepted everywhere. Diamond (1997) leaves room for chance in the worldwide

distribution of subsistence practices and technologies by noting both that the most productive subsistence strategy,

sedentary agriculture, did not arise first in the areas most suited for it and also that the original development of

subsistence agriculture was probably accidental. 19

Roland (2004) defines the accumulated stock of embodied knowledge as technology. I will also treat cultural

strategies as part of the technological endowment that determines how a given society functions. 20

As discussed below, the cultural apparatus is determined at least partially by randomness in the generation and

adoption of ideas. The generation of ideas is analogous to the generation of mutant genotypes in the sense that it is

unpredictable and unobservable; the adoption of ideas may appear random and unpredictable but a complete model

of human interaction could yield reasonable forecasts regarding the adoption of previously unknown ideas even if

the act of adoption is unobservable.

14

trade, and group-wide improvements in production scale and scope, all of which lead to greater

incomes or levels of development.

3.4. Schematic: Cultural Innovation, Persistence, and Income

The preceding arguments concerning human diffusion, cultural innovation, cultural

maintenance, and economic development are captured in the following set of equations

governing the distribution of population income over the long run:

Yj = k(Cj, Ej, Aj, H) + εj, (1)

Cjt = (2)

H = α * m( , S). (3)

In equation (1), development Y is determined by cultural strategies C, environment E, subsistence

activity A, and technology H, which is an accumulated stock including physical and human

capital. Location is indexed by j, generations by t; ε, η, and υ are all random shocks; σ is the

share of output devoted to durable physical capital and α is a constant that describes the rate at

which the stock of capital decays. S (discussed below) represents time elapsed since settlement.

Equation (2) describes the mechanism generating the innovation and diffusion of cultural

strategies. Migration brings a people to a new homeland, where cultural strategies develop

randomly under constraints to meet the demands of social organization and subsistence

production in new environments. Once migration has ended and location is fixed, transmission

with modification across generations leads to relative stability in cultural technologies.

The distribution of culture generated by equation (2) includes error terms capturing

random shocks to culture that might, for example, be generated by contact with a neighboring

15

group, the discovery of a new food source, a re-interpretation of a sacred myth, or any event that

leads to either the insertion of new or the deletion of old cultural strategies. The presence of

these shocks means that two groups with similar initial endowments E, A, H, andY may not end

up culturally similar.

Since there are likely shocks affecting both income and culture, ε, η, and υ may contain

common elements. Consider a prolonged drought which reduces production and also leads to the

adoption of new methods by which authorities are chosen. A common shock produces

predictable (reduced output) and unpredictable (a new cultural norm) outcomes. The

unpredictable element is perpetuated by the vertical transmission of cultural behaviors where it

may be further modified.21

The cultural innovation may eventually affect income if, in addition

to specifying a new mode of political organization, it creates incentives to produce, or leads to

increased property security.

The persistence of cultural technologies also leads to stabilization in the broad technology

measure H (equation (3)) which contains S, capturing the gradual improvements in technology

that come from extensive trial and error or familiarity with the local environment, including

knowledge of other groups. During those eras when location is fixed, cultural strategies are re-

generated each period t. This means some of the depreciation α of the stock of accumulated

knowledge is counterbalanced by generational renewal.

An example of these processes is in Figure 2 which shows the worldwide distribution of

evil-eye belief for the populations I use in the empirical analysis (see Section 4.2.1). Evil-eye

belief is the belief that a look, touch, or verbal expression of envy or excessive praise can cause

21

Durham (1991) provides several examples of the transmission with modification of cultural behaviors adopted

after environmental shocks to subsistence production.

16

FIGURE 2 – EVIL EYE BELIEF WORLDWIDE

Sub-Saharan Africa

Circum-Mediterranean

East Asia

17

FIGURE 2 CONTINUED– EVIL EYE BELIEF WORLDWIDE

Note: Evil eye belief (dark circles) for 175 societies in Sub-Saharan Africa, Circum-Mediterranean, East Asia,

the Pacific, North America, and South America (consecutively from top left). See the accompanying text for

definitions.

The Pacific

North America

South America

18

material harm like sickness, loss of vitality or even death.22

In Sub-Saharan Africa, it is

distributed mostly evenly, though its absence from the west is conspicuous. In the Circum-

Mediterranean (North Africa, Europe, and the near Middle East), evil eye belief is ubiquitous.

Then, moving through East Asia, the farthest east where it is seen is Bhutan; it is absent from Sri

Lanka through Southeast Asia, China, Japan, and far east Russia. In the Pacific, where it is

occurs with the least frequency, it is seen at least as far east as Fiji. Finally, in North America,

evil eye belief is confined to the west coastal corridor and in South America it has not spread east

or south.

Tracing the diffusion of any specific behavior is beyond the scope of this paper, but

Figure 2 shows discontinuities in both the frequency and spatial distribution of cultural

technologies. These discontinuities suggest that migration and endogamy generate unpredictable

variation in cultural strategies. Significant physical boundaries or long distances produce

clusters of groups with similar technologies, but groups that have crossed such boundaries do not

predictably choose those technologies their ancestors had.

Equations (2) and (3) highlight the tendency of cultural strategies to evolve randomly

under local constraints at any j and then to persist over long periods of time after location has

become fixed. Equation (1) proposes that these same behaviors affect production. Genetic

information is conspicuously absent from the schematic model in equations (1) through (3) by

design: there is no direct mechanical link or singular mapping from genetic to cultural

information. Nonetheless there is robust global correlation between the two sets arising from

evolution under common constraints.

22

Evil-eye belief is not predictive of development – see section 5.5.1.

19

3.5 Human Diffusion and the Generation of Cultural and Genetic Diversity

Why is neutral genetic variation correlated with variation in social behaviors? Both are

generated by the process of innovation, diffusion, and permanence described above. The

particular manner in which early migrations were achieved (serially, by genetically non-

representative subsets, from a single origin) led to founder effects and genetic drift, resulting in

long-lived variation in overall genetic information at the population level (Lahr and Foley 1998;

Ramachandran et al. 2005).

A ―founder effect‖ describes the loss of genetic variation that occurs when a new society

is established by a small number of individuals who by necessity carry only a subset of genetic

information from the originally available pool. ―Genetic drift‖ is the probabilistic deviation of

genetic information due to random variations in which members of any population actually

reproduce. During the long history of human diffusion, ―Out of Africa‖ and serial migration led

to repeated founder effects and drift within endogamous founding populations; the effect is a

regular decrease in genetic heterogeneity from populations near East Africa to populations

further away (Li et al. 2008). As groups migrated, coalesced, and sent new migrants further

along, each successive group carried with it increasingly smaller subsets of genetic information

from the originally available pool. Once settled, genetic drift operated on each endogamous

population, causing further divergence in genetic information from original populations.

There was likely further adjustment in overall genetic variability depending on the

amount of gene flow achieved by sexual reproduction between neighboring populations.

However, genetic variability was only partially replenished by gene flow from neighboring

groups, so the set of local variants by and large remained local and were perpetuated across

generations through local, endogamous sexual reproduction (Lahr and Foley 1998).

20

The generation of cultural and developmental variability was described as follows:

culture responds to environment including the stock of previously existing knowledge. The

partially random response persists by transmission from parent to offspring. These behavioral

changes in turn induce development outcomes. Analogously, neutral information in the human

genome varies randomly in relation to local conditions23

and random genetic variability achieves

stability by vertical transmission from parent to offspring. Thus, the simultaneous transmission

of two sets of information allows a description of one set (cultural technologies) by the other

(genetic information).24

4. Data

Available cross-sectional data permit an instrumental variables estimation of equation (1), which

describes the effect of culture on levels of development. I describe that data here; Section 5

explains the estimated specifications, demonstrates a robust correlation between cultural

variation and neutral genetic information and identifies the effect of cultural strategies on

development.

4.1. Cultural Data

Cultural technologies are observed and recorded in the Standard Cross Cultural Sample (SCCS)

(Murdock and White 1969). The observations in the SCCS are extracted and coded from the

23

Neutral genetic variability is not a response to local conditions but a by-product of the repeated sampling error

that describes the migration of groups to local habitats (Lahr and Foley 1998). 24

Of course, cultural reproduction does not proceed with the same fidelity as does genetic replication. This should

caution against any hypothesis that asserts a causal link between genes and cultural behavior. It should also, in an

empirical treatment of equation (1) with genetic information as an instrument for culture, bias first-stage coefficients

towards zero.

21

Ethnographic Atlas (Murdock 1967), a compilation of over 1200 ethnographies that collectively

cover virtually all modern and many pre-modern societies.

The SCCS selects populations from the Atlas, each pinpointed to the smallest identifiable

subgroup, to achieve a distribution of human groups with independent histories, homes, and

cultures. Murdock and White (1969) describes the sample selection mechanism as follows: the

original universe of over 1200 well-described populations was partitioned into ―groups of

societies with cultures so similar…that no world sample should include more than one of them.‖

These clusters were then grouped into roughly 200 sampling provinces ―where linguistic and

cultural evidence reveals similarities of a lesser order but still sufficient to raise the presumption

of historical connection…‖ Finally, one population from each sampling province of related

cultures was chosen; the independence of each unit in terms of historical origin and cultural

diffusion is maximal with respect to the other societies in the sample. The observations that

make up this cross-section of world populations were not all recorded the same year or even the

same decade. By design the date of observation (focus year) is that of the earliest high quality

ethnographic description; 85 percent of the observations are recorded between 1850 and 1965.25

Table 1 gives SCCS descriptive statistics. The SCCS is drawn evenly from all world

regions, including Africa and the Pacific. All subsistence strategies are represented at the world

level, but there are no pastoralists in either the Pacific or North America; no foragers in the

Circum-Mediterranean; and relatively few agriculturalists in the Americas, especially North

America.26

25

Often ethnographic information is based on interviews with informants who describe historical practices. This

contributes to the selection of a sample relatively free of the influence of colonization by European powers. 26

This does not mean there is no pastoralism in the Pacific or foraging in the Circum-Mediterranean, only that no

society in those regions gets a majority of subsistence production from those activities.

22

TABLE 1 — DESCRIPTIVE STATISTICS

World

Sub-Saharan Circum- East Insular North South

Africa Mediterranean Asia Pacific America America

Number of societies 186 28 28 34 31 33 32

Agriculturalists (%) 39 57 50 53 42 12 25

Pastoralists (%) 6 4 21 12 0 0 3

Foragers (%) 31 11 0 21 23 70 53

Mixed (%) 24 29 29 15 35 18 19

Population density 5 to 5 to 26 to 26 to 26 to 1 to 1 to

(median range, per square mile) 25 25 100 100 100 5 5

Total population 10 to 100 to 100 to 100 to 1 to 1 to 1 to

(median range, thousands) 100 1000 1000 1000 10 10 10

Community Size/Urbanization 200 to 200 to 1000 to 200 to 100 to 100 to 50 to

(mean size range, persons) 399 399 5000 399 199 199 99

Climate equatorial equatorial

arid/ temperate/ equatorial temperate equatorial

(median Köppen -Geiger type) temperate equatorial

Pathogen stress 50th 85th 50th 50th 40th 10th 60th

(world percentile)

Observation year 1915 1920 1920 1930 1930 1870 1928

(median)

Note: The Insular Pacific includes societies from countries with East Asian elements that are not connected to the Asian landmass (Indonesia, Malaysia,

Philippines, and Taiwan) as well as aboriginal societies Australia and New Zealand. The Circum-Mediterranean includes societies from Europe, the

Middle East, and North Africa. Societies from Iran and countries east of Iran are included in East Asia. Russia is split between the Circum-Mediterranean

(Russians) and East Asia (Chukchee, Gilyak, Yukaghir, and Samoyed). North America includes Mexico. For subsistence activity categories, a society is

defined as being a type if more than 55 percent of subsistence production comes from that activity. Foragers must get more than 65 percent of subsistence

production from any combination of hunting, gathering, and fishing. Mixed economies get less than 55 percent of subsistence from any one category.

Pathogen stress is a cumulative index of the presence of 7 separate pathogens.

23

Environmental variables show the poor physical characteristics that some regions were

dealt (Sub-Saharan Africa‘s high pathogen stress) but also indicate that development is not fully

determined by geography (East Asia‘s equatorial climate, North America‘s low pathogen stress).

Interestingly, all regions are within one-third of a standard deviation of worldwide average

agricultural potential (determined by land slope, soil quality, and climate); the largest deviation

is in Sub-Saharan Africa and it is towards better agricultural potential. I return to the importance

of environmental and geographical determinates of economic development in Section 5.3 below.

Sub-Saharan Africa has relatively large societies spread thinly while the Pacific has very dense

societies that nonetheless are quite small. East Asia‘s dense populations are distributed evenly

into smaller communities, while Circum-Mediterranean populations (also dense) are more often

concentrated in a few large towns.27

This suggests that neither total population, population

density, nor urbanization are appropriate proxies for income everywhere. Instead I construct

and describe here an income proxy that directly measures production of human and physical

capital.

The SCCS does not observe prices or consumption, but does contain enough output

measures to construct society-wide wealth. The income proxy I calculate is an index aggregating

physical capital, improvements in administrative capacity, and financial market depth. Table 2

presents the index components and their range; Figure 2 presents a map marking the location of

all SCCS societies with development proxy levels indicated by marker size.28

27

Acemoglu, Johnson, and Robinson (2002) make a theoretical and empirical case for urbanization as a proxy for

income. Ashraf and Galor (2008) develop a case for total population as an income proxy (in Malthusian eras).

Maddison (2001, 2005) uses both urbanization and population densities to construct income-per-capita from the year

0. Empirical results (discussed below) hold when either urbanization or total population are substituted for the

development proxy described below. 28

Another technology, boat building, was observed but not included in the development index in order not to

handicap locations where water transport is not feasible. Including boat building in the development index while

dropping those societies for which water transport is infeasible does not change results or rankings. The

24

development index including boat building is nearly perfectly correlated (ρ = 0.98) with the index excluding boat

building.

TABLE 2 — DEVELOPMENT PROXY COMPONENTS

Minimum

Maximum

World World

Median Mean

Land transport: human motorized human pack

method carriers vehicles carriers animals

Land transport: unimproved paved unimproved improved

routes trails roads trails trails

Large or impressive none

public,

ceremonial, none

personal

structures

military, or

industrial

residence

Craft none

potters,

weavers,

potters,

weavers, potters,

weavers,

specialization

and metalwork

or metalwork

or metalwork

Writing and none

true writing mnemonic mnemonic

records and records devices devices

Money none true alien domestic

money currency articles

Credit source friends,

banks friends, internal

relatives relatives money

Note: To create the development proxy, categorical values for these seven variables are

summed. The number of categories for each variable are as follows: land transport:

method, writing and records, and money: 5; land transport: routes and credit source: 4;

large or impressive structures and craft specialization: 3. The minimum and maximum

values of the index are 7 and 29.

25

FIGURE 3 — SCCS SOCIETIES & DEVELOPMENT

Note: Larger marker size indicates greater levels of development as measured by the proxy described in the Table 2 and accompanying text. Lighter marker

shading indicates lower levels of expected genetic heterozygosity as described in Figure 3 and accompanying text in Section 4.2.

26

There are nearly 2000 variables recorded in the SCCS, from weaning practices to sources

of political authority. Though certain variables record beliefs (in a god or gods, for example),

the object of all observations is actual cultural practice rather than opinions, forecasts, or moral

judgments. This distinction is important if the goal is to identify an effect of variation in social

behavior on variation in development. A more detailed discussion of the SCCS cultural

variables used in the empirical analysis appears in Section 5.

4.2. Genetic Data

For approximately 60 percent of SCCS societies I match information on population gene

frequencies from Cavalli-Sforza, Menozzi, and Piazza (1994). The History and Geography of

Human Genes observes frequencies for nearly 2000 populations on every inhabited continent and

several islands in the Pacific Ocean.

From variant frequencies a population-wide measure of total genetic variety

(heterozygosity) can be calculated. With I loci (sites where variants occur), J possible alleles

(variants), and Pj the population frequency of each possible allele, heterozygosity is equal to:

. (4)

The more frequently any particular allele j occurs, the less frequently other potential alleles occur

and heterozygosity decreases. If all potential alleles occur with equal frequency, heterozygosity

increases.

Information from only a small number of loci is available from The History and

Geography of Human Genes for a limited number of SCCS societies. Using a result from

population genetics, I calculate for all SCCS societies a version of expected heterozygosity

calculated from alleles at over 750 loci. The measure, which is based on the previously

27

described linear decay of genetic heterozygosity as migratory distance from East Africa

increases, summarizes the evolution of these migrations: they occurred serially, with genetically

non-representative population subsets, from a single origin.

Figure 3 demonstrates this relationship for two samples: populations from the Human

Genome Diversity Cell Line Panel used to confirm the relationship between heterozygosity and

distance from East Africa (Ramachandran et al. (2005), Prugnolle, Manica, and Balloux (2005),

Liu et al. (2006), Li et al. (2008)), and a subsample of SCCS populations.29

The previous Figure

2 exhibits expected heterozygosities obtained from applying Ramachandran et al. (2005) slope

coefficients (dark dashed line in Figure 3) to SCCS societies - lighter marker shading means less

genetic heterozygosity.30

Expected heterozygosity has attractive properties beyond SCCS-wide availability. It

captures the minimum effect that genetic drift due to a serial founder effect has on variation in

total genetic information and does not contain the portion produced by selection or post-

migration gene flow between populations (Ramachandran et al. (2005)). This same portion of

demographic history is what drives cultural mutation and permanence described in equation (2).

It is also the portion of demographic history the SCCS sample selection mechanism attempts to

capture. Furthermore, using many loci that are in non-coding regions31

helps ensure that the

signal from neutral genetic information dominates and that information from any alleles under

selection is muted.

29

Intercepts vary due to the number of loci considered There is one population common to both samples: Chinese at

roughly 10500 kilometers from Addis Ababa. 30

The expected SCCS heterozygosity shown in Figure 2 is positively correlated with actual SCCS heterozygosity

based on 3 loci (shown as light diamonds in Figure 3) with a ρ approximately equal to 0.75. 31

Number of poly-allelic loci (300+, 750+, 1000+) considered changes intercepts and slope coefficients, but not the

general conclusions demonstrated in Figure 3. Number of loci underlying expected heterozygosity does not

substantively change instrument relevance or two-stage least squares coefficients in Tables 3 through 7 upcoming.

28

FIGURE 3 — EXPECTED HETEROZYGOSITY FOR TWO SAMPLES

Note: Expected genetic heterozygosity (dashed lines) versus migratory distance from East Africa for two different samples. Dark circles are actual heterozygosities

calculated over 783 loci for all populations in the Human Genome Diversity Cell Line Panel (HGDP-CEPH). Light diamonds are actual heterozygosities calculated

over 3 loci for a subset of populations in the SCCS. The only common population is Chinese at approximately 10500 kilometers from Addis Ababa. Migratory

waypoints are those used in Ramachandran et al. (2005). HDGP-CEPH data and figure are adapted from Ramachandran et al. (2005); SCCS data and figure are

author‘s calculation. When applying HGDP-CEPH slope coefficients to all SCCS societies, expected heterozygosity decays as shown in the previous Figure 2, where

lighter shading indicates less heterozygosity.

slope = -6.5 x 10-6

slope = -9.9 x 10-6

29

5. Empirical Results

5.1. Specification

Table A.1 in the first appendix lists unconditional correlations among regressors32

used in

estimating the following equation:

Yj = λy + βCj + δyAj + τyEj + σyXj + εy, (5)

where Y is the development proxy (see Table 2), C is a vector of cultural behaviors taken from

the SCCS (discussed below), A and E are vectors describing subsistence activity and

environmental conditions, respectively, X is a vector of additional controls, and λ is a constant.

Given the description of developmental and cultural change in equations (1) through (3),

empirical estimation of (5) should explicitly incorporate the partial effect of Yj on Cj,. This is

unobservable and direct estimation of (5) may produce inconsistent coefficients, so I use genetic

heterozygosity as an instrumental variable. When heterozygosity Gj satisfies the relevance

assumption and exclusion restriction, a first-stage estimation of the reduced-form equation for Cj,

Cj = λc + ΨGj + δcAj + τcEj + σcXj + εc, (6)

combined with a second-stage estimation of (5) using predicted values of culture from (6) in

place of observed Cj will just identify β for any cultural behavior. I show that Gj satisfies the

relevance assumption using standard statistical tests; overidentification tests and regional

empirical patterns suggest Gj is correctly excluded from equation (5) and therefore uncorrelated

with εy.

32

There are significant correlations between subsistence activities, environment, and cultural behaviors. Agriculture

is not always found in the most agriculturally productive environments and agriculturally productive land is

frequently situated in equatorial climates with relatively high levels of rainfall and pathogen stress. Intuitively, these

characteristics dovetail with the account of early sedentary agriculture in Diamond (1987), which argues it initially

led to worse health, nutrition, and demographic outcomes.

30

5.2. What Might Class, Inheritance Rights, and Game Complexity be Good For?

The pathways between Table A.1 cultural behaviors and development are straightforward. More

detail is given in Appendix A, but a convenient shorthand is the following: class stratification

coordinates the division of labor, games are education, and inheritance rights are an early form of

property rights.

Class stratification produces division of labor and specialization by providing rules for

the separation of the larger population into subpopulations based on culturally-specific

determinations of superiority and inferiority. Regardless of whether values like honor or purity

are eventually replaced by pecuniary success as the basis of class divisions, this division into

subpopulations, once crystallized, encourages the transmission of group-specific skills,

behaviors, and information. Not only does this facilitate specialization and the decentralized

coordination of information relevant to production, but also the formation of group identity and

the creation of markets for the wares or symbols associated with groups, all of which promote

economic development.

Games and play behavior function much as formal education or research and

development: they encourage cognitive development and human capital acquisition by providing

a consequence-free environment in which experimentation, trial and error, and the spontaneous

recombination of known quantities or methods can provide novel and better solutions to social

problems. The SCCS observes game complexity and not time spent in play; the interaction

described suggests that higher levels of game complexity are analogous to higher levels of

formal schooling.

Inheritance rights are an early form of property rights giving the property owner the

freedom to allocate his property once he can no longer use it personally. The SCCS observes

31

rights in both portable and permanent property and while they are correlated, they are not

collinear. The developmental response to secure property rights is familiar and will not be

discussed at length here; further details and references are included in Appendix A.

5.3. Instrumental Variables Results

5.3.1. Cultural Behaviors and Development

Table 3 presents results from two-stage least squares estimation of equations (5) and (6) with

expected genetic heterozygosity as an instrumental variable candidate. Three unique cultural

behaviors are included as regressors. Columns (1) and (2) take class stratification as the

regressor. Class stratification is an ordered categorical variable with five different values:

―absence among freemen‖, ―incipient wealth distinctions‖, ―elite‖ (where control over scarce

resources distinguishes a propertied class), ―dual (hereditary aristocracy)‖, and ―complex (social

classes)‖. The categories are ordered, so that higher numbers on this variable represent more

complex, crystallized, and widespread stratification. The information recorded excludes purely

political and religious statuses and individual-level variation in ―repute achieved through skill,

valor, piety, or wisdom.‖33

Columns (3) and (4) use inheritance rights as the cultural regressor.

Inheritance rights describe the ―rule or practice governing the disposition or transmission‖ of

permanent and portable property.34

Finally, columns (5) and (6) take game complexity as the

33

Both ―caste stratification‖ and the form and prevalence of ―slave status‖ is recorded and could add dimension to

the class variable, but neither castes nor slavery are common: nearly 90 percent of SCCS societies with income data

do not have castes and nearly 80 percent do not have hereditary slavery. 34

The SCCS includes information on who receives property (offspring, kin, in-laws), but I use the information to

establish the presence or absence of rights in inheritance.

32

TABLE 3 — IV REGRESSIONS OF DEVELOPMENT PROXY

(1) (2) (3) (4) (5) (6)

Panel A: IV Second stage Cultural Technology Regressor is:

Class stratification Inheritance rights Game complexity

Culture 2.740*** 2.076* 3.036** 3.117+ 5.183*** 3.575*

(0.61) (1.04) (1.11) (1.67) (1.34) (1.73)

Agriculture 0.585* 0.721* 0.665*

(0.27) (0.32) (0.28)

Foraging -0.081 -0.031 -0.378*

(0.26) (0.27) (0.19)

Nomadic 1.591 0.756 0.574

(1.28) (1.41) (1.00)

Pathogen stress -0.281* -0.256 -0.263+

(0.13) (0.16) (0.15)

Food variability 0.311 -0.864 -1.336

(0.71) (0.93) (0.99)

Agricultural potential -0.001 0.108 0.034

(0.09) (0.14) (0.10)

Rainfall -0.199 -0.192 0.158

(0.18) (0.31) (0.27)

Climate? no yes no yes no yes

Focus year? no yes no yes no yes

Panel B: IV First stage Instrument: Genetic Heterozygosity

Expected heterozygosity 1.816*** 1.101* 1.195*** 0.932*** 1.067*** 0.855**

(0.38) (0.40) (0.23) (0.23) (0.22) (0.29)

R2 0.12 0.32 0.20 0.52 0.13 0.21

Panel C: Ordinary Least Squares

Culture 2.122*** 1.595*** 3.385*** 2.071** 2.460*** 1.593**

(0.28) (0.27) (0.52) (0.65) (0.53) (0.47)

R2 0.36 0.55 0.19 0.46 0.13 0.47

Number of observations 153 144 120 113 145 136

Legend: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Note: The dependent variable is the development proxy (see Table 2). Cultural technology regressors used are listed

at the top of each column. Panel A reports the second stage results from IV regressions with the respective cultural

variable and all listed environmental and subsistence activity control variables, instrumenting for each cultural

technology with expected genetic heterozygosity; Panel B reports corresponding first stage results (first stage

coefficients on other excluded variables have been suppressed to save space); Panel C reports coefficients from an

OLS regression of the dependent variable on the respective cultural technology plus the same controls listed in Panel

A (OLS coefficients on those controls have been suppressed to save space). Robust standard errors are in

parentheses. In regressions with climate dummies, the dummy for temperate climates is omitted. In regressions

with focus year dummies, the dummy for focus years 1936-1960 is omitted. Only societies with focus years greater

than 1750 are included

33

cultural regressor. Game complexity measures which combination of the following three

elements are involved in competitive contests35

: physical skill, chance, and strategy.

Panel A presents second-stage results for each cultural behavior, first for the

unconditional correlation of development with the behavior and then with a complete set of

subsistence production, environmental, climate, and focus year controls. Panel B presents the

corresponding first-stage coefficients on expected genetic heterozygosity and confirms that it

satisfies the relevance assumption in the first stage. Panel C presents OLS coefficients from a

direct estimation of equation (5); Panel C controls are precisely the same as those in Panel A. In

Table 3 and all subsequent specifications, sample standard errors are calculated using the Huber-

White estimator which is robust to heteroskedasticity in the residuals εy and εc.36

The developmental benefits of these cultural behaviors are substantial: a one category

increase in class stratification, inheritance rights, or game complexity leads to an increase of 40,

60, and 70 percent of a standard deviation of development, respectively. Or, from the lowest

levels of class, inheritance rights, and games to the highest, individual increases of roughly 1.6,

1.2, and 2.0 standard deviations of development. The development proxy is not a standard

measure of income and most SCCS societies are unfamiliar, so the economic meaning of a

standard-deviation increase in development is not transparent. However, comparing societies

near the bottom of the distributions of all three cultural technologies and development to

societies near the middle to societies near the top involves comparing, for example, the Yahgan

(South American foragers) or Kung! (South African foragers) to Natchez (North American

35

Only games with an outcome, i.e., a winner and a loser, are included in the coded information. 36

Though the SCCS sampling frame goes some way toward producing relatively independent observations, there is

still likely to be some autocorrelation in the cultural variables along geographic, historical, or ancestral distances.

The Huber-White variance estimator is robust to such patterns.

34

foragers/agriculturalist) or Somali (East African pastoralists) to the Irish (Western European

mixed agriculturalists/pastoralists) or Chinese (Western Asian agriculturalists).

Results in Table 3 summarize a naïve statistical exercise: if expected heterozygosity is a

valid instrument for class stratification, say, it cannot technically be so for inheritance rights

because it will remain correlated with development after inheritance rights are held constant.37

Table 4 presents specifications including combinations of class stratification, inheritance rights,

and game complexity and otherwise similar to Table 3. Columns (1) and (2) use the first latent

variable extracted from a factor analysis of all three cultural behaviors38

; columns (3) and (4) use

an index created by adding scores from all three cultural behaviors; and columns (5) and (6)

include all three independently and simultaneously.

From second-stage results in Panel A, a standard-deviation increase in the first factor or

the additive index lead to increases of approximately 50 percent of a standard deviation of

development; a simultaneous one standard deviation increase in all three cultural technologies

working through the first factor increases development by approximately 115 percent of a

standard deviation. The latter number is approximately the current gap in log GDP per capita

between Mongolia and Turkey, Thailand and Ireland, or Colombia and Japan. When all three

technologies are entered independently, OLS coefficients (Panel B) on class stratification,

inheritance rights, and game complexity are reduced approximately 14, 46, and 48 percent from

Table 4 columns 2, 4, and 6 respectively, but each remains statistically significant at the 10

percent level or better. A simultaneous one-unit change in all three produces an increase of two-

37

Formally, in an IV estimation of equation (5) with inheritance rights as the culture Cj , genetic heterozygosity will

be correlated with the error εy through class stratification. I thank David Levine and Chad Jones for independently

suggesting this naivety. 38

The correlation between class and either inheritance rights or game complexity is approximately 0.45; that

between inheritance rights and game complexity approximately 0.17. The first factor extracted from the three

technologies leaves a substantial amount of variation in each unexplained.

35

TABLE 4 — IV REGRESSIONS OF DEVELOPMENT PROXY

(1) (2) (3) (4) (5) (6)

Panel A: IV Second stage Cultural Technology Regressor is:

First factor Additive index All independently

Cultural Index 3.323*** 3.335* 1.069** 1.070+

(0.97) (1.41) (0.31) (0.45)

Table 3 controls? no yes no yes no yes

Panel B: IV First stage Instrument: Genetic Heterozygosity

Expected heterozygosity 1.256*** 1.056*** 3.905*** 3.290***

(0.23) (0.24) (0.71) (0.77)

R2 0.19 0.48 0.18 0.46

Panel C: Ordinary Least Squares

Cultural index 4.728*** 3.880*** 1.49*** 1.207***

(0.54) (0.61) (0.17) (0.19)

Class stratification 1.706*** 1.387***

(0.35) (0.30)

Inheritance rights 1.551** 1.114+

(0.52) (0.59)

Game complexity 0.816+ 0.832+

(0.50) (0.48)

R2 0.43 0.61 0.43 0.61 0.42 0.60

Number of observations 115 108 115 108 115 108

Legend: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Note: The dependent variable is the development proxy (see Table 2). Cultural technology regressors used

are listed at the top of each column. First factor is the first latent variable extracted from a factor analysis

of Class stratification, Inheritance rights, and Game complexity. Additive index is a simple sum of scores

on the same three cultural technologies. Panel A reports the second stage results from IV regressions with

the respective cultural variable and all listed environmental and subsistence activity control variables

(coefficients on those controls have been suppressed to save space), instrumenting for each cultural

technology with expected genetic heterozygosity. There are no first- or second-stage results for the

specifications in columns (5) and (6) because instruments for all three cultural technologies are not

available simultaneously. Panel B reports corresponding first stage results (first stage coefficients on other

excluded variables have been suppressed to save space); Panel C reports coefficients from an OLS

regression of the dependent variable on the respective cultural technology plus the same controls listed in

Panel A (OLS coefficients on those controls have been suppressed to save space). Robust standard errors

are in parentheses. In regressions with climate dummies, the dummy for temperate climates is omitted. In

regressions with focus year dummies, the dummy for focus years 1936-1960 is omitted. Only societies

with focus years greater than 1750 are included.

36

thirds of a standard deviation of development.39

In Table 3, OLS coefficients from Panel C are mostly smaller than 2SLS coefficients

from Panel A while in Table 4 the opposite is true. This is consistent with attenuation in OLS

coefficients from measurement error in the cultural variables, which attempt to capture all

relevant cultural variation in singular indicators. Thus the first factor, which by design searches

for common variance among all the cultural technologies, is capturing a less noisy share of

―developmental culture‖ than any singular technology. The additive index may do the same but

without suppressing information that predicts development – columns 5 and 6 in Table 4

demonstrate each cultural technology has an effect beyond its association with other cultural

technologies.

Since it is a by necessity a chronological as well as a geographic summary of populations,

heterozygosity may contain the omitted variable S (time since settlement – see equation (3))

which captures gradual improvements that are a result of extensive trial and error or familiarity

with the local environment. As discussed in Section 5.5.3 below, time since the introduction of

technologies is a determinate of income at the national level. If the principle applies at the

population level, 2SLS coefficients on singular cultural behaviors (Table 3) should increase if

heterozygosity and S are correlated. However, gradual improvements in the collection of

technologies would also be expected, so the decrease in 2SLS coefficients (relative to OLS) in

Table 4 are difficult to reconcile if the omitted variable S is driving results. Furthermore, in OLS

specifications with both cultural technologies and heterozygosity (expected or actual) entered

simultaneously, the heterozygosity coefficient is not significant and occasionally negative.

39

Attempting to instrument for any two of these cultural strategies simultaneously produces weak first-stage results

for one of them.

37

5.3.2. Culture, Crops and Development

Diamond (1997) argues, in contrast to the thesis of this paper, that long-run income differences

can be traced to the particular features of settlement environments interacting with coadaptation

and domestication of potent plant and animal species. The accidental development of higher net

energy ―food packages‖ allowed societies to support more members; these demographic changes

in turn led directly to higher group incomes. Table 5 provides an empirical amplification of his

hypotheses by testing the effects of variation in animal and crop domesticates. I do not include

information concerning the ease with which locally-developed food production technologies

might have been transferred to neighboring populations, so Table 5 contains incomplete

specifications of Diamond (1997) hypotheses.

The most potent of all domesticated plants and animals were the cereals (for their annual

production of large, edible seeds) and bovines (for their edible fat, proteins, and milk, and

hauling power). The SCCS records the principal crops (10 cereals, 6 roots or tubers, and 3 tree

fruits) and domesticated animals (pigs, ovides, equines, reindeer, camel, and bovines) each

society maintains. These indicators are included in Table 5, which presents 2SLS estimates from

specifications otherwise similar to columns (2), (4), and (6) of Table 3.

Second-stage coefficients on cultural technologies are similar in economic significance to

Table 3 coefficients even when indicators for both cereals and bovines are included.40

Controlling for multiple plant and animal domesticates simultaneously (results not presented)

does not change cultural coefficient size or significance level appreciably. Most control variables

have broadly the same sign and significance patterns in Table 5 as in Table 3 and cultural

40

First-stage coefficients on expected genetic heterozygosity are also similar in size and significance to Table 3,

Panel B coefficients in columns (2), (4), and (6). Second-stage coefficients on the first factor increase by

approximately 60 percent from Table 4, column (2), while those on the additive cultural index decrease by the same

amount from Table 4, column (4). Both remain significant at the 5 percent level or better.

38

TABLE 5 — IV REGRESSIONS OF DEVELOPMENT PROXY WITH FOOD PACKAGE CONTROLS

(1) (2) (3) (4) (5) (6)

IV Second stage Cultural Technology Regressor is:

Class stratification Inheritance rights Game complexity

Culture 2.427* 2.097* 3.499* 2.874 4.454* 3.941*

(0.98) (1.03) (1.54) (1.81) (2.03) (1.94)

Wheat/Barley 4.266** 3.572* 5.277** 3.752* 3.441 2.581

(1.53) (1.39) (1.86) (1.78) (2.23) (2.11)

Rice 2.110 4.367* 2.254

(1.52) (1.68) (1.87)

Bovines 1.051 1.653 1.963

(1.08) (1.21) (1.43)

Agriculture 0.606* 0.632* 0.606+ 0.733+ 0.731* 0.946***

(0.29) (0.26) (0.32) (0.40) (0.32) (0.22)

Foraging 0.123 0.209 -0.201

(0.25) (0.26) (0.20)

Animal Husbandry -0.052 -0.091 0.118

(0.23) (0.26) (0.24)

Nomadic 1.903 2.165+ 0.574 1.513 0.608 1.162

(1.23) (1.14) (1.16) (1.24) (1.08) (1.00)

Pathogen stress 0.122 0.176 -1.285 -1.004 -1.863+ -1.572

(0.71) (0.70) (0.85) (0.87) (1.05) (1.14)

Food variability -0.255+ -0.304* -0.132 -0.267 -0.269+ -0.345*

(0.13) (0.12) (0.14) (0.17) (0.15) (0.14)

Agricultural potential -0.024 -0.023 0.117 0.078 0.022 0.008

(0.10) (0.09) (0.16) (0.15) (0.11) (0.11)

Rainfall -0.134 -0.033 -0.283 -0.023 0.301 0.344

(0.19) (0.19) (0.27) (0.28) (0.31) (0.27)

Climate? yes yes yes yes yes yes

Focus year? yes yes yes yes yes yes

Number of observations 142 142 113 113 134 134

Legend: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Note: The dependent variable in columns (1)-(6) is the development proxy (see Table 2 and accompanying text).

Cultural technology regressors used are listed at the top of each column. Panel A reports the second stage results

from IV regressions with the respective cultural variable and all listed environmental and subsistence activity control

variables, instrumenting for each cultural technology with expected genetic heterozygosity. Panel A includes

indicators for crops (Wheat/Barley and Rice) and animal domesticates (Bovines) discussed in the accompanying

text. First stage and OLS results, though not reported, are similar to the relevant specification in columns (2), (4),

and (6) from Table 3. Robust standard errors are in parentheses. The dummy for temperate climates and the

dummy for focus years 1936-1960 are omitted. Only societies with focus years greater than 1750 are included.

39

coefficients are similar in size, though less precisely estimated, when those societies not

receiving any plant or animal domesticates are excluded (results not presented).

Table 5 suggests that the distribution of plants and animals does affect development. But

cultural technologies, which are also unpredictable and persistent, continue to have a statistically

and economically significant effect on development when plant and animal distributions are held

constant. There is no indication that a geographic or environmental profile completely

determines income (or culture). In other words, there are multiple and complementary sources of

luck in long-run development.

5.4. Cultural Variation at Work

Are the developmental benefits accruing to these cultural technologies evident in pairwise

comparisons? The Olmec of Mesoamerica developed rapidly from a simple village-agricultural

lifestyle to a complex and highly productive civilization requiring management of prodigious

amounts of labor for its products and in turn benefitting from regionally-unknown outputs like

hydraulic technologies. Further south in Peru, the Chavín likewise developed rapidly from

simpler modes of organization. Meanwhile, even though societies in the area between Olmec

and Chavín homelands were also organized into village-agricultural units; were in contact with

Olmec and Chavín cultures; participated in the same technical traditions; and inhabited land

similarly rich in climatic, altitudinal, and vegetational variety and natural resources, those

intermediate societies did not develop either Olmec or Chavín versions, or any other version, of

40

sophisticated civilization. Both Coe (1968) and Willey (1962) suggest the Olmec and Chavín

―genius‖ is a result of cultural changes in class stratification and the division of labor.41

What about within the SCCS universe?42

Consider the Amhara, Aymara, and Haitians:

though in different regions and opposite sides of the equator (East Africa, 13 degrees latitude;

South America, -16 degrees latitude; and the Caribbean, 19 degrees latitude, respectively), these

are all agriculturalists who have received some of the most potent cereals available. They are

also similar in all of the environmental variables in Tables 3 through 5. But the Aymara are a

middle-income group while the Amhara and the Haitians are in the top ten percent of all SCCS

societies and only the Amhara and Haitians have complex class stratification. In addition, there

is evidence that when both Haitians and Aymara experimented with different forms of class

stratification, income responded in the manner predicted by Table 3 through 5 coefficients (see

Appendix B).

The Turks, Koreans, and Mapuche are even more similarly situated geographically,

productively, and in terms of environmental endowments (they are all rice or wheat growers at

approximately 38 degrees absolute latitude). Like the Amhara, Aymara, and Haitians, these

three societies all have inheritance rights and complex games. But the Mapuche did not develop

high levels of class stratification while the Koreans and Turks did. The Mapuche are in the 80th

percentile of the SCCS income distribution while the Koreans are in the 90th

and the Turks in the

95th

percentile, respectively.

41

Both also suggest the mutation that produced stratification was an increase in production devoted to the prevailing

secular religion. Willey (1962) says ―It does us no good to deny the sudden mutation of creative change to the

aborigines of America. It is no easier to explain elsewhere than it is here. What we are seeking is probably in New

World soil, but genius must arise from preconditions which to our eyes do not foreshadow it.‖ 42

Each of the following comparisons is explored at greater length in Appendix B. The summary in Section 5.4 is

meant only as brief sketch.

41

The Chiricahua Apache and the Pomo are both foragers living in Western North America

(-110 and -123 degrees longitude, respectively). Neither society is well-off absolutely, but the

Pomo are one of the richest North American foraging populations while the Chiricahua are tied

for third poorest. Pomo have inheritance rights while the Chiricahua instead destroy virtually all

property associated with a deceased member. The Kutenai, the most developed North American

foragers (also living in the West at -117 degrees longitude), have likewise developed inheritance

rights.43

5.5. Robustness

5.5.1. Overidentification Tests

The validity of the candidate instrument is crucial: identification of the true partial effect of

culture on development requires an instrument uncorrelated with the error εy in equation (5). If

genetic heterozygosity affects development directly, then cultural coefficients may be capturing

these effects and IV specifications will produce biased estimates. An empirical test of the

exclusion restriction for genetic heterozygosity alone is not available because true errors εy are

unobservable. An alternative, based on 2SLS coefficient comparisons when the endogenous

variable is over-identified, is presented in Table 6.

Overidentification tests indicate whether genetic heterozygosity alone provides 2SLS

coefficients similar to 2SLS coefficients from a larger set of proposed instruments. They do not

establish whether any single instrument candidate is itself correctly excluded from equation (5),

but indicate whether estimation with a smaller subset of instruments produces 2SLS coefficients

43

In environmental endowments the Pomo were slightly better endowed than the Chiricahua while the Kutenai are

intermediate between Chiricahua and Pomo.

42

TABLE 6 — OVERIDENTIFICATION TESTS

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A: IV Second stage Cultural Technology Regressor is:

First factor Additive index Class stratification Inheritance rights

Culture 4.466* 5.533 1.430* 1.784 3.184*** 3.596+ 6.005* 9.726

(1.96) (4.25) (0.63) (1.38) (0.95) (2.95) (3.03) (10.25)

Production,

Environment, Climate,

& Focus year?

no yes no yes no yes no yes

Panel B: IV First stage Instrument: Evil eye belief

Evil eye belief 0.419** 0.176 1.307** 0.548 0.787** 0.400 0.328* 0.120

(0.14) (0.15) (0.45) (0.48) (0.25) (0.26) (0.13) (0.48)

R2 0.07 0.40 0.07 0.38 0.06 0.37 0.05 0.50

Panel C: Overidentification Tests Additional Excluded Variable: Heterozygosity (Expected or Actual)

p-value: Expected {0.52} {0.70} {0.52} {0.69} {0.57} {0.41} {0.25} {0.46}

Actual {0.45} {0.41} {0.78} {0.82}

Number of Expected 115 108 115 108 153 144 120 113

observations: Actual 65 65 79 66

Legend: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Note: The dependent variable in columns (1)-(8) is the development proxy (see Table 2 and accompanying text).

Panel A reports the second stage results from IV regressions with the respective cultural variable. Production,

environment, climate, and focus year comprise exactly the set of controls listed in Table 3 (second stage coefficients

on those excluded variables have been suppressed to save space). Panel B reports corresponding first stage results for

the instrumental variable candidate evil-eye belief (see accompanying text). First stage coefficients on other excluded

variables have been suppressed to save space. Panel C reports in braces the p-value for the null hypothesis that the

coefficient on the cultural technology in the second stage (Panel A) is the same as when instrumented using the Panel

B instrument plus genetic heterozygosity. Robust standard errors are in parentheses. In regressions with climate

dummies, the dummy for temperate climates is omitted. In regressions with focus year dummies, focus years 1936-

1960 is omitted. Only societies with focus years greater than 1750 are included.

that differ (by more than expected sampling error) from coefficients produced with all candidate

instruments. Conditional on the assumption that at least one of the candidates from the larger set

is correctly excluded, a failure to reject the null hypothesis that coefficients differ only by

sampling error gives some confidence in the exogeneity of all candidates.

Table 6 provides results from testing overidentifying restrictions on class stratification.

The additional candidate instrument is evil-eye belief, discussed in Section 3.4 and Figure 2.

The worldwide correlation evil eye belief and development is small and positive (ρ = 0.16), but

43

like heterozygosity (see below) there is regional variation that suggests there is no direct

relationship. For Sub-Saharan Africa, East Asia, the Pacific, North and South America,

correlations are -0.16, -0.14, 0.11, 0.18 and 0.46 respectively.44

Panel C in Table 6 demonstrates that for this candidate instruments and expected or

actual genetic heterozygosity, the overidentifying restrictions for cultural technologies are not

rejected, meaning that first-stage coefficients do not differ markedly when a larger subset of

instrument candidates is used.45 Panels B and A show that when the evil eye belief is used in

place of expected heterozygosity, first- and second-stage coefficients are not always precisely

estimated.46

These results show no evidence that genetic heterozygosity has a direct effect on

development and therefore suggest the exclusion of this variable from equation (5) is a valid

assumption.

5.5.2. Regional Irregularities in Expected Heterozygosity

There are regional irregularities in the heterozygosity-development relationship providing

additional evidence that heterozygosity is correctly excluded from equation (5). Intra-region or

intra-subsistence activity correlations between cultural technologies and development mirror

world-wide correlations: class stratification, property rights, and game complexity are always

positively correlated with development. The regional correlations of heterozygosity and

development do not. For Sub-Saharan Africa, the Circum-Mediterranean, East Asia, the Pacific,

North America, and South America, unconditional correlations are roughly -0.5, -0.3, -0.1, 0.3,

44

There are only 4 South American societies observed after 1750 with evil eye belief. The positive correlation for

South America is driven almost entirely by Haiti. In specifications similar to those in Table 4, evil-eye belief is

never statistically significant when any of the other cultural behaviors are held constant. 45

For game complexity no candidate instrument was found to be a good first-stage predictor, leading to weak

instruments and potentially severe inconsistency in second-stage estimates. 46

Estimates of the statistics underlying the tests are heteroskedasticity-robust in both the first- and second-stage.

44

-0.2, and 0.4 respectively.

This regional pattern would entail the following for a direct effect of neutral genetic

variety on development: it was drawn down efficiently among the first migrants, but as the pool

of neutral genetic material continued to shrink absolutely, carrying more, then less, then more of

it became efficient in places further away. If the regular decay of neutral genetic material

happens to be correlated with the population frequency of genetic information that is shown to

directly affect development, it remains difficult to explain the regional heterozygosity-

development correlation pattern above. The genetic information useful for development would

occur less frequently in a regular and monotone manner (see Figure 2 or 3) while its effect on

development would fluctuate depending on region.

5.5.3. Alternative subsamples and alternative outcomes

Table 7 presents 2SLS and OLS regression results from specifications similar to those in

Table 4 columns (2), (4), and (6) over several different SCCS subsamples. In addition to the

regular outcome variable in panels A and B, in panels C and D an alternative measure of

development, urbanization, is specified for each subsample (including the ordinary Table 3

sample). Though urbanization is not a perfect income proxy47

, it is more familiar (see

Acemoglu, Johnson, and Robinson (2002)) than the development proxy calculated in this paper

and therefore more intuitively approachable.

The SCCS selection rules described earlier produce a set of ―independent as possible‖

observations. In addition, SCCS societies received identification numbers with the property

47

The SCCS-wide correlation of the development proxy and urbanization is approximately 0.75, but this average

hides significant regional variation: among the richer regions (East Asia and the Circum-Mediterranean), the

correlation is approximately 0.87; among the Americas, the correlation is 0.66; and among Sub-Saharan Africa and

the Pacific, the correlation is approximately 0.42. Sub-Saharan Africa is the 3rd

richest region ranked by

urbanization but 2nd

poorest ranked by the development proxy.

45

TABLE 7 — IV REGRESSIONS: DIFFERENT SAMPLES AND DIFFERENT OUTCOMES

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Cultural Technology: Class stratification Inheritance rights Game complexity

sample: Ordinary With

ancients

Diffuse:

seed 1

No Sub-

Sah. Afr. Ordinary

With

ancients

Diffuse:

seed 2

No

Circ.-

Med.

Ordinary With

ancients

No

agri.

No N.

Amer.

Dependent Variable is Development Proxy

Panel A: IV Second Stage

Culture 2.076* 3.236** 1.377 4.091** 3.117+ 3.985* 4.305+ 3.363+ 3.575* 5.528* 3.064+ 3.599+

(1.04) (1.18) (1.01) (1.36) (1.67) (1.66) (2.42) (1.70) (1.73) (2.64) (1.83) (1.96)

Panel B: Ordinary Least Squares

Culture 1.595*** 1.773*** 1.434*** 1.734*** 2.071** 2.411*** 1.959* 1.828** 1.593** 1.558** 1.483* 1.589**

(0.27) (0.26) (0.37) (0.32) (0.65) (0.65) (0.93) (0.65) (0.47) (0.47) (0.57) (0.51)

R2 0.54 0.56 0.60 0.59 0.46 0.49 0.36 96 0.47 0.47 0.48 0.50

Obs 144 154 74 120 113 118 54 0.45 136 144 90 108

Dependent Variable is Urbanization

Panel C: IV Second Stage

Culture 1.691* 1.707* 2.457* 2.223* 1.973+ 1.757+ 0.521 1.373 3.173+ 3.663 2.576* 4.231

(0.76) (0.70) (1.09) (0.97) (1.09) (1.01) (1.49) (0.89) (1.80) (2.48) (1.17) (2.92)

Panel D: Ordinary Least Squares

Culture 0.569*** 0.647*** 0.758*** 0.634*** 0.851** 0.906** 0.279 0.839** 0.831*** 0.845*** 0.938** 0.870***

(0.12) (0.12) (0.19) (0.14) (0.32) (0.32) (0.39) (0.31) (0.22) (0.22) (0.32) (0.25)

R2 0.55 0.58 0.64 0.59 0.50 0.53 0.42 0.48 0.55 0.57 0.59 0.54

Obs 127 136 60 105 101 106 52 83 123 130 75 103

Legend: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Note: In Panels A and B, the dependent variable is the development proxy (see Table 2). In Panels C and D, the dependent variable is urbanization (see accompanying text).

Cultural technology regressors used are listed at the top of each column. Panels A and C report second stage IV coefficients, and Panels B and D OLS coefficients, from

regressing the dependent variable on the respective cultural variable and all production, environmental, climate, and focus year controls from Table 3 (second stage and OLS

coefficients on these controls have been suppressed to save space). The cultural technology is instrumented in Panels A and C with expected genetic heterozygosity. The

"Ordinary" samples replicate those in Table 3: all SCCS societies with focus years greater than 1750. The "With ancients" sample adds back SCCS societies with focus years

of 1750 or earlier. The "Diffuse: seed 1" and "Diffuse: seed 2" samples skip every other "Ordinary" sample SCCS society (starting with society 1 or 2, respectively) in order

to achieve a subsample of societies more culturally, historically, and geographically independent (see accompanying text). The ―No agri.‖ sample excludes societies

producing more than 56 percent of subsistence output from agriculture. The "No Sub-Sah. Afr.", "No Circ.-Med.", "No S. Amer.", and "No N. Amer." samples drop all

SCCS from Sub-Saharan Africa, the Circum-Mediterranean, South America, and North America respectively. Robust standard errors are in parentheses. The dummy for

temperate climates and for focus years 1936-1960 are always omitted.

46

that two societies closest in number were judged to be closest historically, ancestrally, and

culturally (and often, but not necessarily, geographically). By using this numbering scheme to

select every second society, the most similar pairs can be dropped in a methodical manner and

the independence of each data point from all others increased. Two different seed numbers were

used to select two non-overlapping subsets of SCCS societies: these are the ―Diffuse: seed 1‖

and ―Diffuse: seed 2‖ samples in Table 7. The ―With ancients‖ samples add back in all of those

SCCS societies with focus years of 1750 or earlier. Though only about 6 percent of all SCCS

societies have focus years from this era, the set includes well-known cultures like the

Babylonians, Hebrews, Romans, Khmer, Aztecs, and Incas. The other subsamples are achieved

by dropping entire regions or production strategies from the ordinary Table 3 sample.

For the ordinary sample, increasing class stratification, inheritance rights, and game

complexity are associated with greater urbanization.48

Both OLS and IV coefficients on cultural

technologies typically increase when older SCCS societies are included (columns (2), (6), and

(10)). Choosing a culturally-, historically-, and ancestrally-diffuse sample (column (3) or (7))

does affect estimate precision and first-stage instrument fit (results not presented), but the overall

picture remains the same. Similarly, dropping entire regions or production strategies (column

(4), (8), (11), or (12)) often changes estimate size and precision, but the general conclusions

remain unchanged.

48

Urbanization in the SCCS is recorded as an ordered categorical variable, but the underlying data is log-linear so

coefficients between development proxy and urbanization specifications are not directly comparable. However, a

one-unit change in class, inheritance, or games produces a 72, 84, or 135 percent of standard deviation change in

urbanization categories (from a 2SLS specification). OLS results do not change if either ordered logit or interval

regression models are specified: from a direct interval regression of equation (5) with log(urbanization) as the Yj, a

one-unit change in class, inheritance, or games produces an approximately 35, 40, or 50 percent of standard

deviation change in log(urbanization).

47

5.5.4. Alternative controls and alternative behaviors

There are alternative ways to code most of the control variables, but alternative codings do not

change results substantially. For instance, production can be entered as 0/1 indicators for

whether a majority of production comes from that activity. Both the pathogen stress and

agricultural potential indices can be disaggregated and the components entered individually.

Absolute latitude can be entered in place of Köppen-Geiger climate classifications. The time

trend can be divided into more or fewer intervals. In results not presented, I try various

combinations of these alternative codings and find no substantial change to results or

conclusions.

Table A.2 in the appendix A presents several specifications with regional environmental

characterizations given by a measure of distance from the equator (absolute latitude) and dummy

variables for continental regions. The local environmental controls included in Tables 3 through

7 more closely capture conditions under which production takes place; Table A.2 includes

instead variables that are more familiar to researchers. Given the discussion in Section 5.5.2, it is

not surprising that regional dummies soak up much of the useful variation in heterozygosity and

consequently render it a weak first-stage instrument. However, the OLS coefficients are

remarkably stable even when all Table 3 through 7 controls are included in addition to regional

dummies and absolute latitude. When first-stage relationships are not weak, the second-stage IV

coefficients are generally larger with regional dummies in place.

There are three variables that contain information germane to the evolution of cultures

and incomes that should be included, but which are recorded at the nation-state level and are

therefore quite noisy in the SCCS sample. State history (Bockstette, Chanda, and Putterman

2002), years since transition to agriculture (Putterman 2008), or colonial identifiers are mapped

48

from just two values (for USA and Canada) to over 30 North American societies, for example.

In specifications including each of these regional variables one by one and otherwise similar to

Table 3 (columns (2), (4), or (6)), there is little change to either OLS or IV coefficients on any of

the cultural technologies (results not presented).

State history and years since transition to agriculture are often statistically significant.

Given the state-level resolution of these variable, this indicates that being surrounded by a

relatively advanced state is beneficial for development, which is sensible given the enhanced

opportunities for trade and labor mobility and the regularization of economic activity that

statehood brings.49

Indicators for British, French, Spanish, or mixed colonial heritage are rarely

statistically significant, but the coefficient pattern is regular: there is a small positive

development boost in states with British colonial heritage and a larger development penalty in

states with French, Spanish, or mixed colonial heritage.50

Finally, an informal test of the power of the empirical framework is provided by using

other cultural behaviors as dependent variables (results not presented). Consider descent,

marriage transactions, postpartum sexual intercourse taboos, local political succession, religion,

and physical separation of adolescent boys from female relatives: the first three of these do not

have a statistically or economically significant economically correlation with development in a

direct OLS estimation of equation (5). 2SLS specifications with actual or expected genetic

heterozygosity as the excluded variable confirm these results, though only descent has a non-

weak first-stage correlation with genetic heterozygosity.

49

See Bockstette, Chanda, and Putterman (2002) for a more detailed discussion of the beneficial effects of state

antiquity on development. 50

94 percent of SCCS societies are located in states that were colonized by European nations (Britain, France,

Spain, Portugal, the Netherlands, Belgium, Italy, or Russia).

49

The latter three behaviors have statistically significant OLS correlations from direct

estimation, but even when genetic heterozygosity satisfies the relevance assumption in the first

stage, as it does for religion and separation of boys, the 2SLS coefficients on these behaviors are

not statistically distinguishable from zero; for separation of boys, the second stage coefficient is

positive while the direct OLS coefficient is negative. So genetic heterozygosity does not predict

variation in all distinct cultural behaviors once other correlates have been held constant. Even

when it does, significant OLS coefficients are not always confirmed in a 2SLS framework.

6. Conclusion

The role of culture in economic development is not difficult to unpack logically. Culture is a set

of norms or standards of behavior that develop within and for social groups of individuals.

Cultural behaviors cannot be isolated from social transactions, and economic exchange is a social

transaction. Cultural rules create incentives by specifying which transactions are prohibited,

which individuals can be party to allowable transactions, the expectations associated with any

transaction (which vary by an individual‘s culturally-determined status), and the short- and long-

term outcomes flowing from such transactions.51

They also directly produce human capital.

While the link from culture to development may be easy to trace logically, empirical

identification that is both robust and economically meaningful is not straightforward. The data

assembled in this paper permit me to make sound empirical advances on these hypotheses. I use

a rich database of cultural norms the primary sources for which are close observation of actual

behavior and which are measured at the community level, with an equal distribution of data

51

In all of these ways cultural norms maintain multiple sets of fixed prices by, for example, making the fixed price

of an activity infinite for some and not others.

50

points from all major world regions, to overcome cultural measurement issues.52

I use

population-level records of genetic information in an instrumental variables strategy designed to

overcome simultaneity in the joint determination of income and culture. This variation-in-

variation could not have been optimized by group members during the generation of cultural

technologies. Instead, the generation of genetic variety proceeded by a regular process that

gradually removed potential varieties of genetic material as populations moved farther away

from an origin in East Africa.

The schematic model developed in this paper relies on a common source for the

generation and persistence of cultural and genetic variety. The migration and arrival of humans

in unfamiliar environments set in motion a series of population-level biological and behavioral

changes. The results of these changes have been perpetuated across generations by the vertical

transmission of both sets of information. Since the transmission of cultural information proceeds

with less fidelity, any correlation between genetic and cultural variation should be biased

towards zero.

Empirical results indicate that cultural technologies have a significant impact on

development even after controlling for a wide range of environmental and geographic variables,

production techniques, and a time trend. Results hold for alternative measures of development

and several alternative samples. Class stratification, inheritance rights, and game complexity all

produce behavioral changes which are beneficial for long-run development. I have argued that

stratification encourages the division of labor, specialization, and the dissemination of job-

specific skills, techniques, and technologies across generations. Inheritance rights are an early

form of property rights. Game complexity measures the sophistication of informal education and

52

The decentralized nature of the cultural data gathering is worth mentioning twice. The absence of a central

authority or overarching goal leaves observers free to record what is culturally and contextually meaningful.

51

encourages the development of cognitive skills, including flexibility in problem-solving.

Development of more sophisticated versions of any of these cultural technologies can lead to

cross-sectional increases of up to 115 percent of a standard deviation of development.

Cultural rules are part of the larger institutional background in which economic

transactions take place. While significant headway has been made in uncovering the response of

income to parts of that background, more is needed on the interaction among and between the

latter. If the supposition that institutional backgrounds are cohesive with their development

following a path set down by the very first institutions is correct, there is always a danger that

omitted institutional variables cloud the empirical picture. Further research on the interaction

between formal institutions and culture is possible within the SCCS. Evidence at the country

level is also necessary to confirm that society-level mechanisms are at work in larger

amalgamations of people.

52

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56

A. Appendix: Regressor Correlations and Alternative Geographic Controls

TABLE A.1 — UNCONDITIONAL CORRELATIONS AMONG REGRESSORS

Class

stratification

Inheritance

rights

Game

complexity Agriculture Pastoralism Foraging Nomadic

Food

variability

Pathogen

stress

Agricultural

potential Rainfall

Inheritance rights 0.45

Game complexity 0.44 0.17

Agriculture 0.35 0.61 0.07

Pastoralism 0.28 0.25 0.14 -0.05

Foraging -0.43 -0.63 -0.13 -0.73 -0.55

Nomadic -0.35 -0.52 -0.06 -0.77 0.13 0.52

Food variability 0.23 0.26 0.33 0.37 -0.07 -0.23 -0.36

Pathogen stress 0.12 0.31 -0.11 0.45 0.11 -0.41 -0.30 -0.02

Agricultural potential 0.13 0.18 -0.02 0.39 -0.14 -0.19 -0.35 0.12 0.36

Rainfall -0.05 0.13 -0.27 0.30 -0.32 -0.02 -0.38 -0.03 0.40 0.40

Equatorial climate -0.18 0.06 -0.32 0.27 -0.21 -0.12 -0.24 -0.20 0.49 0.33 0.57

Arid climate 0.01 -0.06 0.13 -0.18 0.35 -0.08 0.28 0.02 -0.04 -0.20 -0.61

Temperate climate 0.21 0.04 0.23 -0.10 -0.05 0.15 -0.02 0.24 -0.44 -0.12 -0.09

Note: Class stratification, inheritance rights, and game complexity are all ordered categorical variables with 5, 4, and 3 categories, respectively. Higher categories

mean more of the cultural behavior as explained in the text. Production variables agriculture, pastoralism, and foraging are measured as percent of subsistence coming

from that activity. Nomadic, food variability and climate variables are categorical 0/1 indicators; rainfall is an ordered categorical variable. Pathogen stress is a

cumulative index of the presence of 7 separate pathogens. Agricultural potential aggregates indicators for slope, soil, and climate. Rainfall is an ordered categorical

variable measuring average annual rainfall in millimeters.

57

TABLE A.2 — IV REGRESSIONS: ALTERNATIVE REGIONAL GEOGRAPHIC CONTROLS

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Geographic controls: AL SSA SSA, CM SSA, CM,

EA

SSA, CM,

EA, PAC

SSA, CM, EA, PAC,

NA

SSA, EA,

NA, SA

AL, SSA,

CM

AL, EA,

PAC

AL, NA,

SA

AL, SSA,

CM, +

Panel A: IV Second Stage Dependent Variable is Development Proxy

First factor (n=114) 2.774* 6.845*** 10.097** 8.333* 12.649+ 0.471 6.635** 10.934* 2.648* -1.096 10.315**

(1.22) (1.36) (3.10) (3.80) (7.57) (11.45) (2.38) (4.22) (1.02) (3.98) (3.91)

Class stratification (n=152) 1.944* 4.388*** 6.906* 9.523 27.749 -1.170 3.802** 7.294+ 1.739* 0.211 6.023*

(0.86) (1.06) (2.79) (8.14) (73.70) (9.98) (1.40) (4.05) (0.71) (2.06) (2.70)

Inheritance rights (n=119) 3.262+ 9.285** 11.388* 5.517 9.913 7.105 23.245 10.302* 2.969* -13.523 10.263+

(1.93) (2.76) (4.85) (3.86) (7.68) (30.07) (24.99) (4.82) (1.43) (42.79) (5.49)

Game complexity (n=144) 2.390* 8.159*** 9.386* 5.972* 4.943** -1.580 8.252* 14.283 2.183* 0.307 25.361

(1.09) (2.21) (3.67) (2.53) (1.60) (7.72) (3.87) (10.32) (1.02) (1.44) (34.99)

Panel B: IV First Stage Instrument: Genetic Heterozygosity

First factor 0.988*** 1.180*** 0.865* 0.856+ 0.621 -0.595 0.940* 0.689* 1.200*** 0.617* 0.734*

(0.21) (0.26) (0.34) (0.50) (0.49) (0.88) (0.38) (0.32) (0.21) (0.31) (0.32)

R2 0.47 0.40 0.41 0.41 0.41 0.42 0.40 0.48 0.49 0.48 0.51

Class stratification 1.194** 1.852*** 1.164* 0.764 0.301 -0.735 1.951* 0.852 1.399*** 0.827 1.060+

(0.40) (0.46) (0.55) (0.82) (0.85) (1.60) (0.75) (0.54) (0.41) (0.67) (0.55)

R2 0.29 0.27 0.29 0.29 0.29 0.29 0.28 0.31 0.29 0.30 0.35

Inheritance rights 0.708*** 0.878*** 0.752* 1.156* 0.698 -0.259 0.271 0.713+ 0.973*** 0.094 0.737+

(0.20) (0.25) (0.35) (0.46) (0.46) (0.61) (0.28) (0.36) (0.21) (0.24) (0.37)

R2 0.50 0.50 0.50 0.50 0.52 0.53 0.54 0.50 0.54 0.53 0.53

Game complexity 1.124*** 0.999** 0.901* 1.300* 1.800*** -0.918 0.852+ 0.470 1.195*** 1.278*** 0.275

(0.25) (0.31) (0.38) (0.51) (0.51) (1.02) (0.48) (0.36) (0.26) (0.34) (0.39)

R2 0.22 0.11 0.11 0.11 0.14 0.21 0.19 0.26 0.24 0.23 0.28

58

TABLE A.2 — CONTINUED

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Geographic controls: AL SSA SSA, CM SSA, CM,

EA

SSA, CM,

EA, PAC

SSA, CM, EA, PAC,

NA

SSA, EA,

NA, SA

AL, SSA,

CM

AL, EA,

PAC

AL, NA,

SA

AL, SSA,

CM, +

Panel C: Ordinary Least Squares

First factor (n=114) 3.774*** 4.597*** 4.721*** 4.486*** 4.570*** 4.378*** 4.497*** 4.337*** 3.852*** 3.715*** 3.915***

(0.54) (0.49) (0.52) (0.56) (0.56) (0.58) (0.55) (0.57) (0.54) (0.61) (0.62)

R2 0.60 - 0.68

Class stratification (n=152) 1.550*** 1.790*** 1.732*** 1.636*** 1.660*** 1.610*** 1.730*** 1.526*** 1.549*** 1.515*** 1.376***

(0.24) (0.24) (0.26) (0.27) (0.27) (0.26) (0.24) (0.26) (0.23) (0.26) (0.27)

R2 0.54 - 0.62

Inheritance rights (n=119) 2.013** 2.589*** 2.354*** 2.112** 2.261** 1.886* 2.099** 2.025** 1.997** 1.538* 1.911**

(0.61) (0.59) (0.62) (0.64) (0.70) (0.73) (0.71) (0.61) (0.63) (0.70) (0.58)

R2 0.44 - 0.57

Game complexity (n=144) 1.484*** 2.708*** 2.606*** 2.476*** 2.508*** 2.269*** 2.475*** 2.104*** 1.706*** 1.351** 1.804***

(0.43) (0.40) (0.40) (0.40) (0.43) (0.46) (0.45) (0.47) (0.47) (0.44) (0.47)

R2 0.48 - 0.58

Legend: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Note: The dependent variable is the development proxy (see Table 2). Geographic controls included are listed at the top of each column, where AL = absolute

latitude, SSA = Sub-Saharan Africa, CM = Circum-Mediterranean, EA = East Asia, PAC = Insular Pacific, NA = North America, and SA = South America. SSA,

CM, EA, PAC, NA, and SA are all indicator variables. Cultural technology regressors used are listed at the left in rows. First factor is the first latent variable

extracted from a factor analysis of the three cultural technologies Class stratification, Inheritance rights, and Game complexity (see Table 4). Panel A reports the

second stage results from IV regressions with the row cultural variable, the column geographic controls, and subsistence activity and focus year variables from Table

3 (second stage coefficients on geographic, subsistence, and focus year variables have been suppressed to save space). Column 11 includes additional controls:

Wheat/Barley and Bovines (see Table 5), as well as Food variability, Pathogen stress, Agricultural potential, Rainfall, and Climate (see Table 4). The number of

observations, (n=...), applies to columns 1 through 10; Column 11 specifications lose 6, 10, 6, and 8 observations for the First factor, Class stratification, Inheritance

rights, and Game complexity, respectively. Each row's cultural technology is instrumented with expected genetic heterozygosity. Panel B reports corresponding first

stage results (first stage coefficients on other excluded variables have been suppressed to save space); Panel C reports coefficients from an OLS regression of the

dependent variable on the respective cultural technology plus the same controls listed in Panel A (OLS coefficients on those controls have been suppressed to save

space). Robust standard errors are in parentheses. In regressions with focus year dummies, the dummy for focus years 1936-1960 is omitted. Only societies with

focus years greater than 1750 are included.

59

B. Appendix: Cultural Technologies and Development Pathways

B.1. Inheritance Rights

The theoretical and empirical association of property rights and income has been studied

previously. Costly investments will not be made if the embodied investment can be seized by

others without recompense (Demsetz 1967 and Alchian and Demsetz 1973). Inheritance rights

over movable or fixed property are recorded in the SCCS, so the threat of expropriation is not

precisely the correct description of the mechanism creating disincentives to invest. Inheritance

rights give property holders the right to dispose (post-mortem) of possessions according to how

they see fit rather than protect property from being seized.

Besley (1995) identifies three separate channels via which property rights and investment

could be linked. Property rights insecurity may act like a random confiscatory tax on

possessions, property rights might make possessions more easily collateralizeable (leading to

lower interest rate charges), or property rights could lead to expanded trading opportunities and

the ability to exploit gains from trade, resulting in enhanced investment incentives by expanding

the range of profitable investments. Specifically in the case of inheritance, property rights may

also encourage the adoption of longer time horizons (the life cycle of a lineage, for example,

instead the life cycle of an individual) and may lessen the likelihood of a tragedy of the

commons.

Besley (1995) finds property rights predictive of investment in agricultural production in

modern Ghana. Bandiera (2007) finds qualitatively similar conclusions for investment in

agricultural production in modern Nicaragua. Brunt (2007) finds property rights increase

agricultural output through fixed capital investment in South Africa; British administration

finally offered this novel institution near the middle of the 19th

century. Johnson, McMillan, and

60

Woodruff (2002) find that property rights are an important predictor of reinvestment in

manufacturing firms in a sample of formerly communist countries.

B.2. Competitive Contests

Time spent in play has been described by anthropologists and other social scientists as human

capital acquisition. Play can be thought of as an educational program or pure research and

development activity with all the attendant externalities from either (Gosso et al. 2005).

What follows is a partial list of functions or consequences of play in both human and

closely related species: language acquisition, learning to deceive and recognize deception, brain

growth and cognitive development, motor skill development, behavioral flexibility, the

development of social strategies, including the management of conflict and compromise, an

originator and disseminator of novel behavior, which in turn can furnish new food sources,

change social positions, extend geographical range, and open new ecological niches (Fagen

1981); a discoverer of roles or identities, general problem solving, creativity, divergent thinking

(Bock 2005); mental flexibility and ―planfulness‖, a discoverer of the benefits of delayed

gratification, adjudication, control, and leadership (Power 2000); and a preparer for adult

competency in subsistence work (Bock and Johnson 2003).

Equally important as the skill correlates of play is the manner or method in which play

teaches and in particular the incentive and reward structure implicit in play. All the works on

play cited in this appendix mention that play is a behavior that lacks consequences. This

combined with the reward structure from play, in which a winner takes all but losers remain

nearly as well off as they were before (materially), creates incentives for experimentation.

Bateson (2005) suggests play as a nearly cost-free optimization search technique. Since in play

61

there is no cost to moving away from the previously established optimum, play makes

optimization less costly, facilitating innovation and encouraging creativity.

The SCCS does not observe or record time spent in play, but rather the complexity of

competitive contests. This assists in empirical identification of the society-wide developmental

impact of play as the observations will not be affected by a latent labor-leisure tradeoff

dimension that could be heterogeneous across levels of development. In addition, by focusing on

the elements of contests rather than the type of play, the observations do not implicitly penalize

lower income societies for a lack of, e.g., fantasy or imaginative play (Gosso et al. 2005).

Empirical evidence suggests that those children who show the highest level of play

involvement and complexity score highest on cognitive functioning (Power 2000). Empirical

tests in this paper show the same mechanisms may be working at the society-wide level. If play

is a proxy for informal education or research and development, then such results should be

expected. Some of the increased productivity from formal schooling, for example, may be

externalities created by socialization and preference formation not explicitly part of the

curriculum. Also like a formal educational program, complexity in play can lead directly to

increased productivity by encouraging innovation along more than one vector.

B.3. Class Stratification

Within groups of humans living together, inequality or a ―pecking order‖ may be

inevitable, since humans belong to a species of mammal that shows dominance behavior

(Hofstede 2001). Or, maybe the dominance, command, and obedience required to sustain a

social ordering, together with a desire to evaluate, creates inequality everywhere (Béteille 1977).

Perhaps instead inequality arises everywhere because a society needs to motivate people to fill

62

positions in the social structure, and some positions require special talents or training that is

costly (Davis and Moore 1945).

SCCS observations show that inequality and social hierarchies may not exist in every

human society. Where they do occur, however, they occur with variability in complexity and

fixedness. Not observed in the SCCS but also relevant for the relationship between stratification

and development are the values concerning the exercise of power that can be expected to

accompany a system of power differentiation. Since ―history is full of dead and overthrown

stratification systems‖ (Smith 1966), any link between variation in stratification systems and

development is perforce a link between legitimacy or longevity of stratification systems and

development.

Stratification systems vary also by the attributes to which social power is distributed or

from which power flows. French and Raven (1959) classify the bases of social power into five

types: reward power, coercive power, legitimate power (based on rules), referent power (based

on personal charisma), and expert (specialist) power. The SCCS observation of status excludes

purely political or religious statuses as well as individual-level variation in repute achieved

through skill, valor, piety, or wisdom, but there is otherwise no precise indication of which

attributes produce social strata nor why those attributes and not others are evaluated.

The channel from more complex and durable stratification to increased development is

that of division of labor and specialization.53

Some stratification may be sufficient for a stable

social ordering and a stable social ordering is necessary before production and exchange can

occur.54

But further durability and complexity in stratification leads to rewards being distributed

53

Orans (1966) provides an early argument for such a channel. 54

Social stratification may not be necessary for order in large groups: the Chimbu of New Guinea, where exchange

is virtually all reciprocal and interpersonal relations are regulated by an elaborate ritual system, have population

63

and power exercised through a ranking of occupations (Hodge et al. 1966) and pecuniary success

can displace other values such as honor or purity as the basis for evaluation (Béteille 1977). The

highest level of class stratification observed in the SCCS is ―Complex (social classes) –

correlated in large measure with extensive differentiation of occupational classes.‖ A division

of labor by class in turn makes possible all the gains from specialization and exchange.

A division of labor that exists across generations may facilitate the transfer of occupation-

specific skills and promote further productivity within occupations and class.

The argument is not that inequality and dominance must necessarily precede the division

of labor, specialization, and gains from trade. Instead, social stratification serves as a focal point

or a coordination mechanism that eventually produces a division of labor even if it was not

developed consciously for such a project. In other words, a non-contingent class structure can

eventually produce an ordered and durable division of labor and specialization, no matter what

the original basis of differentiation was. Furthermore, neither class stratification nor subsequent

division of labor are irreversible: Carneiro (1967) provides an example of such a system created

and disassembled annually among North American Plains Indians. Each band was relatively

egalitarian during most of the year, but during the annual buffalo hunt, the aggregation of several

different bands into a single hunting group was followed by the creation of a ruling council, a

paramount chief with considerable authority, men‘s societies that assisted the ruling bodies, a

police to enforce the rules that prevailed during the hunt, and a regulatory body to ―[preserve]

order on the march and during the sun dance.‖

The degree of mobility between classes is also important as a moderator of the effects of

class division. The more rigid is the stratification system, the less chance a society will have of

densities reaching 400 people per square mile, yet have no kings, chiefs, social stratification, or ranking. Tellingly,

they also have ―none of the trappings of civilization whatsoever‖ (Flannery 1972).

64

discovering any new facts about the talent of its members (Tumin 1953). The incentives created

by the division of people into categories could be blunted by the maintenance of an unyielding

class structure. There is recently a recognition within economics that non-monetary incentives

like status, role, and identity can enhance performance (Besley and Ghatak 2008; Akerlof and

Kranton 2005). Belonging to a class may not only enhance productivity directly through

specialization but indirectly by providing motivation to ascend to the next higher class or to

cement one‘s status in one‘s own class. Bernard and Killworth (1973) provide early anecdotal

evidence that such incentives are important for motivating labor.

There may be other positive effects of stratification on development. For example,

Béteille (1977) notes that stratification and differential status creates markets for most things

associated with status. Durkheim (2006 [1893]) claims that effective regulation of any

profession comes primarily from members, who are the only actors with the information

necessary to determine costs and benefits. He also claims that when men with common interests

seek each other out, this group will transcend the individual and become the basis for a

generalized morality. Bernard and Killworth (1973) provide evidence that two uniquely

specialized groups will produce a greater joint product if each is held to the standards of their

own profession, even if production takes place under great intergroup friction (when standards

vary across professions). Flannery (1972) stresses the greater information-processing potential

of specialized groups when that information is federated (across ranks) but not centrally

organized. Thus, social stratification may function much like the Hayek (1945) price

mechanism: though each group knows only a fraction of the collective information set, resource

allocations can still be based on local conditions and opportunities. Garicano (2000) translates

the Hayek price mechanism into a hierarchical production-knowledge allocation mechanism that

65

facilitates problem-solving at the firm level, but the efficiency-enhancing aspects of such

hierarchies apply to any group engaged in production.

C. Appendix: Cultural Technologies at Work

Consider the Amhara and the Aymara. Both are agriculturalists, the Amhara cultivating teff (a

cereal) in a temperate climate at approximately 13 degrees north of the equator in East Africa,

the Aymara cultivating barley in an equatorial climate at approximately 16 degrees south of the

equator in South America.55

Both societies face similar levels of pathogen stress, seasonal food

variability, and soil quality. But the Amhara are approximately twice as developed as the

Aymara.56

Though they show similar levels of game complexity and property rights, the Amhara

have the highest level of class stratification and the Aymara the second-lowest.

Hoben (1973) notes that the Amhara are composed of a number of distinct segments

differentiated from one another by ―occupation, power, and honor‖, and the division between

peasants and elite is compounded by a distinction between layman and churchmen, and between

military and non-military. Messing (1957) documents that there is an ethnic division of skilled

labor and endogamy within the ethnic/occupation groups. Though an aura of ―untouchability‖

applies to certain occupational groups (smiths, tanners) and practitioners are often suspected of

witchcraft and black magic, nonetheless their wares are ―much prized‖ not only for their material

utility but also because of their ―supernatural‖ strength, endurance, and workmanship.

55

Amhara secondary crops include barley, wheat, maize, millet, and hops. Aymara secondary crops include

potatoes and quinoa. 56

Though the SCCS places the Amhara in the Circum-Mediterranean region (within which they have mean levels of

development), they would be the most highly developed Sub-Saharan African society. They are near the 85th

percentile in the SCCS-wide distribution of development. In terms of development, the Aymara are a mean South

American society, and near the 45th

percentile in the SCCS-wide distribution.

66

Consequently, ethnic/occupational groups have monopolies on their respective markets as any

manufactures by outsiders are thought to be substandard.57

Within the Aymara, however, social classes have not arisen: though there are leaders,

leadership does not carry with it privilege or special rewards for families. On the social plane all

are considered equal; what classes exist are extremely fluid and mobile, lack formal internal

organization, and fluctuate with the fortunes of families and individuals. Neither is there a

division of labor: everyone is a farmer even if income is supplemented by learning a trade

Furthermore, the means of acquiring wealth are extremely limited and available to few.

(Tschopik 1951).58

Compare both to Haitians, agriculturalists producing the cereal sorghum in an equatorial

climate 19 degrees north of the equator in Central America. They face similar levels of pathogen

stress, rainfall, food variability, and soil quality as the Amhara and the Aymara. The Haitians

were as developed as the Amhara and had an equal level of class stratification (and are similar to

both Amhara and Aymara in inheritance rights and game complexity). Though two separate

episodes of colonization brought physical and human capital, in the roughly 100 years between

the departure of the first colonial power and the arrival of the second, there was variation in the

presence and ubiquity of class stratification. Leyburn (1941) notes that only during that century

when class divisions (primarily between political, military/police, farming, and artisanal groups)

were mandated and enforced from above and through endogamy – in essence, a re-creation of the

57

Importantly, Messing (1957) also notes that wealth enables each of the many social classes to achieve some

upward mobility and Hoben (1970) notes that differences between classes were largely of ―achieved rank, not

‗blood‘, ideals, or basic style of life. Peasant and [elite] shared similar military aspirations, the same ancestors, and

above all the same vertical principles of social organization and ranking.‖ 58

Under the Inca aristocracy, Aymara society was more internally stratified, and there were several levels of class in

between the Inca elite and Aymara commoners. During the same era, Aymara cultural, social, and political

complexity was ―considerably enriched.‖ (Tschopik 1946)

67

French colonial hierarchy without the French colonials – did Haitian economic production reach

colonial levels.59

Koreans, Turks, and the Mapuche of Chile are all agriculturalists growing primarily

cereals (rice or wheat) in temperate climates at approximately 38 degrees absolute latitude. They

face similar levels of pathogen stress, food variability, and soil quality, and have similar levels of

game complexity and property rights. However, the Koreans and the Turks have developed high

levels of class stratification while the Mapuche are relatively egalitarian; Turks are in the 95th

,

Koreans the 90th

, and Mapuche the 80th

percentile of SCCS-wide development.

Within the Mapuche there are ―loose‖ divisions between wealthy and commoners, but no

organization into occupational groups (though many different crafts are practiced) and personal

rank and prestige derive chiefly from ―martial prowess and from wealth; generous hospitality,

and eloquence in speech‖ (Cooper 1946). Furthermore, no great authority attaches to leaders;

they serve mostly consultative or persuasive roles with no authority to sanction or coerce. (Faron

1968). Tasks that require more labor than is available to the household or kin group are

organized communally on a voluntary basis, and the work parties that form for land clearing,

housebuilding, or road repair do not remain once the work is finished. Similarly, much day-to-

day work is organized on a reciprocal basis rather than through market exchange. Though

individual ownership of land is well-established and agricultural partnerships both within the

Mapuche and with the surrounding Chileans are frequent, sophisticated, and economically

productive, still little renting or selling of land occurs because it amounts to a loss of ―cultural

integrity and agrarian status‖ (Faron 1961).

59

Furthermore, when those class divisions were essentially erased by an ―in perpetuity‖ re-distribution of land,

formal recognition of private property, the encouragement of small-scale, personal agricultural production, and the

creation of a rudimentary social safety net (employer-provided medical care, social security for the elderly), output

fell steadily and did not recover (Leyburn 1941).

68

Finally, consider two foraging societies, the Pomo and the Chiricahua, settled at 39 and

32 degrees latitude (respectively) in the Western United States. The Pomo are semisedentary

and face no variability in food supply while the Chiricahua are nomadic and face some

variability in food supply. Neither has high levels of pathogen stress. Among all foragers, the

Pomo are relatively well developed (90th

percentile) while the Chiricahua are just below average

(40th

percentile). Among all societies in the SCCS, the Pomo are still well off (75th

percentile)

and the Chiricahua are not (25th

percentile). Though they have identical levels of game

complexity, the Pomo have a higher level of inheritance rights and class stratification.

Gifford (1923), Barrett (1952), and Loeb (1926) all document that the Pomo had definite

rules about individual ownership of economically productive assets like trees, dams, gathering

grounds, and fishing landings. These and other productive capital like harpoons, bows and

arrows, fish and duck nets, or dwellings were passed to family members on the death of their

original owner (Loeb 1926). Opler (1965) notes that the Chiricahua instead destroy all property

belonging to, any objects regularly used by, and any gifts coming from the deceased. So

powerfully do they wish to completely alter the situation associated with the deceased that camp

life is completely reconstructed in a new locality and the deceased‘s home is usually burned.

The pre-death settlement is rarely visited.

Loeb (1926) also notes that there is hereditary occupational specialization among the

Pomo, especially for chieftainship, membership in the secret society, the office of doctor or

shaman, hunting, and fishing. Importantly for the Pomo, a person can be a member of a

profession only if he is ―initiated into the profession by an elder relative and given the magical

outfit and charms necessary‖; outfits, charms, and work songs are all private property handed

down within families. Among the Chiricahua, however, distinctions of hereditary status are

69

recognized, but not rigidly maintained; ability and personal magnetism are as important as birth

and wealth for determining leadership. Furthermore, there is virtually no specialization in

occupation: every hunter also fishes and makes his own arrows, for example (Opler 1965).

70

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