managed and what goals they should serve. Random mutation in social ...... omitted institutional variables cloud the emp
Culture and Development Jon R. Jellema* Department of Economics, U.C. Berkeley December 2008 Job Market Paper
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:
[email protected].
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 practices promoting division of labor and specialization, informal education and 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 lead 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 Thailand and Ireland, the Czech Republic and Belgium, Colombia and Japan, or Bhutan and Chile, for example. These relationships remain visible within geographic regions and production technologies and each cultural practice remains individually predictive when others are held constant. To measure culture convincingly, I use observations from detailed ethnographies covering more than 1200 populations worldwide. This dataset 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 practices is generated by problems like the management of group resources and the labor of individual 1
members. The fixed environment, choice of subsistence activity, and risk stemming from the interaction of these two variables force social decisions regarding how endowments will be managed and what goals they should serve. Random mutation in social behavior is equally important, so similar endowments do not necessarily generate similar cultures.1 All of these variables (endowments, risk, production) in addition to culture determine economic development simultaneously. The event generating variation in these constraints under which social practices develop 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 population frequency of potential genomic possibilities, is inversely proportional to distance from East Africa, the original start line for this era of migration that eventually brought humans to the extreme south 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. It records only neutral genetic diversity, or that portion of overall genetic diversity that is not under natural. Because it is not a 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 this candidate instrument and specific cultural technologies. Since both genetic and cultural information are transmitted with modification from parent to offspring and persist across generations, this result is expected: populations with similar ancestors are more alike in all vertically-transmitted information. I exploit the 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.
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.
2
More generally, I provide empirical support for the hypothesis that unpredictable cultural norms create long-lived preferences and incentives.2 I affirm that luck in these historical endowments can alter the course of economic development.3 From a 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 historical background and description of the mechanism linking migration, cultural behaviors, genetic variety, and development. Section 4 describes data sources. Section 5 presents empirical specifications and results, brief discussions of the pathways between cultural behaviors and development, and examples of the cultural behaviors at work. Section 5 also includes tests of the validity of the instrument and robustness testing. Section 6 offers concluding remarks. Extended robustness testing, detailed discussion of the pathways from cultural technologies to development, and detailed examples of cultural behavior at work are found in the appendices at the end. 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 institutions are complements in income generation and that culture remains a significant predictor of income when other institutions are held constant. Using historical eras or chronologically distant events 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 2 3
See Akerlof and Kranton (2005), Logan and Rhode (2008), or Roland (2004). See Diamond (1997) for physical technologies and Tabellini (2007) for cultural technologies.
3
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) suggest 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 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. Versions of the genetic data underlying this paper‘s proposed instrument have 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 With 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 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. 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.
4
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. This paper, with an emphasis on the local generation of observable behaviors and their effects on 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 output. 3.
Instrument, Hypothesis and Historical Background
Economists have long suspected that culture and development covary - see Smith (1759), Weber (1905), or Bowles (1998) for a modern summary - 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 the mechanism generating both cultural and instrumental variation. 3.1.
Candidate Instrument
The instrument 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,
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. 9 Effectively, this means I use variation-in-variation to identify variation in culture.
5
or a single nucleotide10 can potentially occur. An individual carries only one variant at any site where multiple variants are possible. If an individual is carrying a particular variant in the 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 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: two randomly selected Ainu individuals will more often carry different blood types. Genetic information that is not advantaged selectively exhibits varying population frequencies due to what is essentially sampling error: populations carry only those varieties their ancestors carried.11 More precisely, they carry only those varieties transmitted by reproductive individuals within the ancestral group. Endogamous populations with different ancestors carry different sets of gene variants and not every ancestor has contributed genetic information. This compound sampling error leads to variation at the population level even in the absence of clearly advantageous varieties. Blood type is not selectively advantageous, but sampling error and endogamy have created divergent gene frequencies. I use variety in genetic information that does not confer a selective advantage averaged over up to 1000 multi-variant sites. Each site is not known to specify the production of any protein nor is it associated with any observable behaviors or physical characteristics.12 Imagine each population is associated with 20 pages, drawn randomly, one for each population member,
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. 11 Population variation may also arise from natural selection, or the differential reproductive success of genomes with or without certain varieties. Sickling of red blood cells, for example, occurs more frequently in populations living in environments where resistance to malaria confers a reproductive advantage. Sexual selection may also produce variation if observable characteristics or behaviors increase the likelihood of sexual reproduction by increasing attractiveness to mates. 12 Though a large portion of the human genome is ―non-coding‖ in this way, these sequences are not necessarily all non-functional.
6
from a phonebook containing all possible 10-digit phone numbers.13 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 reach 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 and from that count a summary measure that describes overall heterogeneity in phone numbers (how many different numbers are observed in the fourth digit, for example, and how often?) would be calculated and averaged over all digit locations. Neutral genetic variety from sampling error is distributed around the world in a predictable manner (see Sections 3.5 and 4.2). I calculate this distribution for a sample of populations and use it as the instrument.14 It is unobservable, does not confer a selective advantage, and was not a choice variable. Taking an average over several hundred sites effectively dampens the signal from any one site, so even if some sites are discovered to be under selection, the average information remains neutral.15 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 a sample of populations, it is possible to identify the cultural codes that have persisted by vertical transmission across generations from their correlations with persistent neutral genetic codes (see Section 3.5). 3.2.
Historical Diffusion of Human Populations
Figure 1 describes routes taken by modern humans to all currently inhabited areas of the globe. The dates shown are estimated arrival times in years before present; 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 13
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. 14 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. 15 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.
7
150,000 years ago; expansion out of Africa between 100,000 and 65,000 years ago; and the first arrival of humans in North America between 40,000 and 10,000 years ago (Mellars 2006). Intracontinental migration dates and arrival times are similarly imprecise (Lahr and Foley 1994). 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. Adapted from Cavalli-Sforza and Feldman (2003).
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 8
selection of genetically non-representative migrating groups produces population variation in the human genome.16 3.3.
Cultural Innovation and Maintenance
Each migrating group confronted a distinct and unfamiliar environment requiring new strategies and technologies for subsistence and reproduction.17 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 of these may be explicitly optimal solutions to unambiguous problems,18 some may be the result of extensive trial and error, and some may have occurred serendipitously.19 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.20 This ability to reoptimize 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.21 Once developed, local solutions to social problems will be transmitted from parent to child and generation to generation (Bisin and Verdier 2001; Cavalli-Sforza and Feldman 1981). The vertical transmission of local cultural solutions results in a stable set of cultural values which are slow to change (Roland 2004). Some of the cultural technology may prove advantageous as 16
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. 17 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. 18 Also likely is toleration of ideas that are not welfare-improving but that are maintained by collective action or coordination failures (Olson 1965). 19 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. 20 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. 21 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).
9
populations grow and begin to produce above subsistence levels.22 Larger populations and production above subsistence in turn make possible specialization, intra-group trade, and groupwide 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 = 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 represents time elapsed since settlement and captures the gradual improvements in technology that come from extensive trial and error or familiarity with the local environment, including knowledge of other groups 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 group, the discovery of a new food source, a re-interpretation of a sacred myth, or any event that 22
As discussed below, culture 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..
10
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. Shocks 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.23 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. Persistence leads to stability in technology H. Cultural strategies are re-generated each period t so depreciation α of the stock of accumulated knowledge is counterbalanced by generational renewal. More distant technology depreciates faster: α’(t) is assumed to be less than zero. Figure 2 shows an example of these processes: the distribution of evil-eye belief for populations 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 material harm like sickness, loss of vitality or even death.24 In Sub-Saharan Africa, it is distributed 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 occurs with the least frequency, it is seen at least as far east as Fiji. In North America, evil eye belief is confined to the west coastal corridor and in South America it has not spread east or south. Figure 2 shows discontinuities in both the frequency and spatial distribution of behavior which suggest that migration and endogamy generate unpredictable variation in cultural strategies. Significant physical boundaries or long distances can produce clusters of groups with similar technologies, but groups that have crossed such boundaries do not predictably choose those technologies their ancestors had. 23
Durham (1991) provides several examples of the transmission with modification of cultural behaviors adopted after environmental shocks to subsistence production. Grimm and Klasen (2008) find that local migration induces change in formal institutions. 24 Evil-eye belief is not predictive of development – see section 5.5.1.
11
FIGURE 2 – EVIL EYE BELIEF WORLDWIDE
Circum-Mediterranean
Sub-Saharan Africa
East Asia
12
FIGURE 2 CONTINUED– EVIL EYE BELIEF WORLDWIDE
The Pacific
South America
North America
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.
13
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. 3.5.
Human Diffusion and the Generation of Cultural and Genetic Diversity
Neutral genetic variation is correlated with variation in social behaviors via like processes of innovation, diffusion, and permanence. 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.25 Populations with less divergence in neutral genetic information have shared common ancestors more recently. When migrants m leave a sending population s, founder effects and genetic drift eliminate some portion γm of existing genetic variants. A subsequent migration of
25
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).
14
m′ from s′ (= m) again takes away γm′ of genetic variants26 at s′, leaving m′ and s′ closer genetically than m′ and s. As neutral genetic information evolves among distant but related populations, those more closely related ancestrally will be closer genetically. As with genetic information, isolated populations that more recently shared a cultural information set should be more culturally similar today. Each migratory move may generate cultural mutation because moves are accompanied by new environments and the passage of generational time.27 But cultural behaviors are renewed generationally via vertical transmission from parent to offspring, so on average they should be most alike in populations with more recent common ancestors.28 More distantly related populations with more shocks, generations, or migratory stops between them should be less alike culturally.29 The instrumental variable strategy relies on a correlation between variation in genetic information and variation in cultural behavior: two ancestrally close populations will be more similar culturally and genetically than two ancestrally distant populations. Figures 3a and 3b demonstrate this correlation for a group of populations in Asia and the Pacific. Figure 3a shows the distribution of postpartum sexual taboos in this region and Figure 3b the relationship between pairwise cultural and genetic differences for the populations in 3a. Pairwise genetic distance is based on differences in neutral variety and summarizes the length of time a pair has been isolated from each other: the greater the genetic distance, the greater distance to a common ancestor (Cavalli-Sforza, Menozzi, and Piazza 1994). The greatest differences in cultural behaviors are found in those pairs who have been isolated from each other the longest, and the most similarity in cultural behavior among those pairs with close common ancestors.
Migrants m′ receive genetic information available from s′; some genetic information from s is not available to m′. Cultural information is not always transmitted or maintained with great fidelity. 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. 28 This is borne out empirically both in this study (see Figure 3a & 3b and section 5) and indirectly in other studies. Fernandez and Fogli (2007), Alesina and Fuchs-Schündeln (2005), and others document pervasive cultural similarities between migrants and ancestors. 29 This remains true if C evolves wholly randomly. Consider a random walk with each migrating population receiving ancestral culture plus a zero-expected value random shock (Cm = Cs + η, P(η>0) = ½, P(-η) = ½): in expectation, m′ and s′ (=m) will be more culturally similar than m′ and s. Spolaore and Wacziarg (2009) note this relationship in forming expectations regarding the empirical correlation between distances in genetic information and distances in other ―vertically-transmitted characteristics‖. 26 27
15
FIGURES 3a and 3b – GENETIC and CULTURAL DIFFERENCES in EAST ASIA and PACIFIC 3a
3b 0.4
mean genetic distance; bar endpoints at +/− 1 st. dev.
Genetic Distance
0.3
0.2
0.1
0 0
1
2
3
4
5
Absolute Difference in Postpartum taboos Note: Figure 3a shows the distribution of postpartum sexual taboos in East Asia and the Pacific. Postpartum taboos take 6 categorical values, from no taboo to prohibitions longer than 2 years. Larger/darker dots are longer taboos. Figure 3b shows mean genetic distance for those pairs of populations with postpartum taboo differences (in absolute value) along the x-axis.
4.
Data
The data described here permit an instrumental variables estimation of equation (1), which describes the effect of culture on levels of development, for a world cross-section of populations. 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 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
16
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.30 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.31 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.32 This suggests that population-based measures are not appropriate proxies for income everywhere. Instead I construct an income proxy33 that directly
30
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. 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. 31 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. 32 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
17
TABLE 1 — DESCRIPTIVE STATISTICS World
Sub-Saharan Africa
CircumMediterranean
East Asia
Insular Pacific
North America
South 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 (median range, per square mile)
5 to 25
5 to 25
26 to 100
26 to 100
26 to 100
1 to 5
1 to 5
Total population (median range, thousands)
10 to 100
100 to 1000
100 to 1000
100 to 1000
1 to 10
1 to 10
1 to 10
Urbanization (mean size range, persons)
200 to 399
200 to 399
1000 to 5000
200 to 399
100 to 199
100 to 199
50 to 99
Climate (median Köppen Geiger type)
equatorial
equatorial
arid/ temperate
temperate/ equatorial
equatorial
temperate
equatorial
Pathogen stress (world percentile)
50th
85th
50th
50th
40th
10th
60th
Observation year (median)
1915
1920
1920
1930
1930
1870
1928
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 from two Neo-European countries (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.
measures output by aggregating physical capital, administrative capacity, and financial market depth. Table 2 presents the index components and their range while Figure 4 presents a map marking the location of all SCCS societies with development proxy levels indicated by marker size.34 There are nearly 2000 variables recorded in the SCCS, from weaning practices to sources
0. Empirical results (discussed below) hold when either urbanization or total population are substituted for the development proxy described below. 33 The SCCS does not observe prices or consumption. 34 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 development index including boat building is nearly perfectly correlated (ρ = 0.98) with the index excluding boat building.
18
TABLE 2 — DEVELOPMENT PROXY COMPONENTS World
World
Median
Mean
motorized vehicles
human carriers
pack animals
unimproved trails
paved roads
unimproved trails
improved trails
none
public, ceremonial, military, or industrial
none
personal residence
potters, weavers,
potters, weavers,
potters, weavers,
Minimum
Maximum
Land transport: method
human carriers
Land transport: routes
Large or impressive structures
Craft specialization
none
and
or
or
metalwork
metalwork
metalwork
Writing and records
none
true writing and records
mnemonic devices
mnemonic devices
Money
none
true money
alien currency
domestic articles
friends, relatives
banks
friends, relatives
internal money
Credit source
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.
of political authority. Though certain variables record beliefs (in 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 behavior on development. A more detailed discussion of specific cultural behaviors appears in Section 5. 4.2.
Genetic Data
For approximately 60 percent of SCCS societies I match genetic information from CavalliSforza, Menozzi, and Piazza (1994), which observes allele frequencies for nearly 2000 populations on every inhabited continent and several Pacific islands. From variant frequencies 19
FIGURE 4 – SCCS SOCIETIES and 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 5 and accompanying text in Section 4.2.
20
population-wide genetic heterozygosity (variety) 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 alleles occur with equal frequency, heterozygosity increases. Information from only a small number of loci for a limited number of SCCS societies is available. Using a result from population genetics, I also calculate for all SCCS societies expected heterozygosity from alleles at over 780 loci. The measure, which is based on the previously described linear decay of genetic heterozygosity as migratory distance from East Africa increases, summarizes the evolution of these migrations: they occurred serially, with genetically nonrepresentative population subsets, from a single origin. Figure 5 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.35 The previous Figure 4 exhibits expected heterozygosities obtained from applying Ramachandran et al. (2005) slope coefficients (dark dashed line in Figure 5) to SCCS societies - lighter marker shading means less genetic heterozygosity.36 Expected neutral heterozygosity has attractive properties. 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 (―isolation by distance‖) 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. By averaging over
35
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. 36 The expected SCCS heterozygosity shown in Figure 4 is positively correlated with actual SCCS heterozygosity based on 3 loci (shown as light diamonds in Figure 5) with a ρ approximately equal to 0.75.
21
FIGURE 5 — EXPECTED HETEROZYGOSITY FOR TWO SAMPLES
slope = -6.5 x 10-
6
slope = -9.9 x 10-6
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 4, where lighter shading indicates less heterozygosity.
22
many loci that are non-coding,37 expected heterozygosity ensures that the signal from any one locus, including those possibly under selection, is muted. 5.
Empirical Results
5.1.
Specification
Table A.1 in the appendix lists unconditional correlations among regressors38 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 (see Table 3), A and E are vectors describing subsistence activity and environmental conditions, respectively, X is a vector of additional controls, and λ is a constant. Empirical estimation of β should explicitly incorporate the unobservable partial effect of
Yj on Cj (see equations (1) through (3)); without it, direct estimation may produce inconsistent coefficients. But 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 . I show that Gj satisfies the relevance assumption using standard statistical tests on Ψ; overidentification tests and regional empirical patterns suggest Gj is uncorrelated with εy and correctly excluded from equation (5). 5.2.
What Might Class, Inheritance Rights, and Game Complexity be Good For?
Table 3 lists cultural behaviors in C. More detail is given in Appendix A, but a convenient shorthand for the pathways between Table 3 behaviors and development is the following: class stratification coordinates the division of labor, games are education and inheritance rights are an
37
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 used does not substantively change instrument relevance or two-stage least squares coefficients in Tables 4 through 9 upcoming. 38 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.
23
TABLE 3 — CULTURAL TECHNOLOGIES Categories
Minimum
Maximum
World Median
World Mean elite - control over scarce resources distinguished from proletariat
5
absence among freemen
complex (social classes)
incipient wealth distinctions not crystallized
Inheritance rights
3
absence of rights in real or moveable property
presence of rights in real and moveable property
presence of rights in real and moveable property
presence of rights in real or moveable property
Game complexity
4
none of skill, chance, strategy
all of skill, chance, and strategy
skill & chance or skill & strategy
skill & chance or skill & strategy
Class stratification
Note: Each cultural behavior is recorded as an ordered categorical variable. For class stratification, higher categories represent more crystallized, complex, and widespread stratification; purely political and religious statuses and individual-level variation in "repute achieved through skill, valor, piety, or wisdom" are excluded. For inheritance rights, higher categories represent the presence of rights over more forms of property. Though SCCS records include property recipients, I code the variable to represent only the presence of rights. For game complexity, higher categories represent more complex contests; only contests with an outcome, i.e. a winner and a loser, are objects of observation.
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, the 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 24
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 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 B. 5.3.
Instrumental Variables Results
5.3.1. Cultural Behaviors and Development Table 4 presents results from two-stage least squares (2SLS) estimation of equations (5) and (6) with both actual and expected genetic heterozygosity as instrumental variable candidates. With the first latent variable extracted from factor analysis of all Table 3 cultural behaviors39 as the dependent variable, columns (1) and (2) employ actual heterozygosity calculated over 1 locus as the instrument. Columns (3) and (4) use actual heterozygosity over two loci, and (5) and (6) use expected heterozygosity from 783 loci applied to SCCS populations (see section 4.2). Panel A presents second-stage results first for the unconditional correlation of development with cultural 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 heterozygosity and confirms that it satisfies the relevance assumption – only when sample size is severely reduced (column (4)) is the first-stage relationship weak. Panel C presents OLS coefficients from a direct estimation of equation (5) with controls the same as those in Panel A. In Table 4 and all subsequent specifications, sample standard errors are calculated using the Huber-White estimator which is robust to heteroskedasticity in residuals εy and εc.40 The developmental benefits of these cultural behaviors are substantial: from second-stage results in Panel A, a standard-deviation increase in the first factor leads to increases of between 39
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. 40 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.
25
TABLE 4 — IV REGRESSIONS OF DEVELOPMENT PROXY (1)
(2)
(3)
(4)
(5)
(6)
6.416*** (1.20)
5.721* (2.80)
5.529** (1.62)
10.979 (6.91)
3.324*** (0.97)
3.335* (1.41)
Panel A: IV Second stage First factor Agriculture
0.683 (0.41)
0.721* (0.32)
1.128 (0.89)
Foraging
0.128 (0.39)
-0.031 (0.27)
0.839 (1.18)
Nomadic
1.801 (2.40)
0.756 (1.41)
1.197 (5.62)
Food variability
-0.219 (1.54)
-0.864 (0.93)
-6.440 (5.56)
Pathogen stress
-0.375+ (0.21)
-0.256 (0.16)
-0.684 (0.44)
Agricultural potential
0.084 (0.16)
0.108 (0.14)
0.183 (0.42)
Rainfall
0.253 (0.31)
-0.192 (0.31)
-0.457 (1.66)
Climate?
no
yes
no
yes
no
yes
Focus year?
no
yes
no
yes
no
yes
Panel B: IV First stage
Instrument heterozygosity is based on: 1 locus, 3 alleles 2 loci, 7 alleles expected (783 loci)
Heterozygosity R2
2.277*** (0.51)
1.334* (0.51)
3.404** (1.07)
1.823 (1.29)
1.256*** (0.23)
1.056*** (0.24)
0.16
0.45
0.18
0.43
0.19
0.48
5.577*** (0.60)
3.954*** (0.72)
4.736*** (0.98)
3.828* (1.43)
4.728*** (0.54)
3.880*** (0.61)
0.36 39
0.62 36
0.42 115
0.61 108
Panel C: Ordinary Least Squares First factor
R2 0.53 0.70 Number of observations 68 63 Legend: + p
