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Cultural Differences

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ResearchBlogging.org A new paper in Science, Differences Between Tight and Loose Cultures: A 33-Nation Study, is making the media rounds. Here’s NPR:

…The idea for this study really dates to the 1960s. Back then, an anthropologist decided to evaluate a few dozen obscure cultures and see if he could rank them on a scale from “tight” to “loose.” He defined tight cultures as having a lot of rules, which people violate at their peril. Loose cultures are more relaxed in their expectations, and more forgiving of people who deviate.

The Tightness Scale

“So for example, you might have been asked, how appropriate is it to curse in the bank or kiss in a public park, or eat or read a newspaper in a classroom? And we were able to derive scores of how constrained, in general situations, they are, versus how much they have latitude in different countries.”

“Some of the cultures that are quite tight in our sample include places like Singapore, Japan, Pakistan,” Gelfand says. “Whereas many loose societies include countries like New Zealand, the Netherlands, the United States.”

The abstract from the paper is a little harder to parse:

With data from 33 nations, we illustrate the differences between cultures that are tight (have many strong norms and a low tolerance of deviant behavior) versus loose (have weak social norms and a high tolerance of deviant behavior). Tightness-looseness is part of a complex, loosely integrated multilevel system that comprises distal ecological and historical threats (e.g., high population density, resource scarcity, a history of territorial conflict, and disease and environmental threats), broad versus narrow socialization in societal institutions (e.g., autocracy, media regulations), the strength of everyday recurring situations, and micro-level psychological affordances (e.g., prevention self-guides, high regulatory strength, need for structure). This research advances knowledge that can foster cross-cultural understanding in a world of increasing global interdependence and has implications for modeling cultural change.

This schematic from the paper illustrates the general model of how differences in “tightness” emerge:

Like many social science studies the authors relied a lot on survey data and conversion of rank ordered categorical responses into dependent variables. That’s a problem insofar as you need to take the quantities that are generated out of their statistical analyses with a grain of salt. They aren’t measuring someone’s height or temperature. Rather, they’re generating an aggregate measure from a range of concrete subcomponents. Granted, this measure has been shown to correlate well with individual questions in terms of how they vary cross-culturally. This is the “tightness,” the higher the score, the more tight the society. I do have some issues with this usage of a summary for a range of characters, but first let’s hit the raw results.

They are displayed in tabular format in the paper. That’s fine, but I decided to change it up a little for the purposes of presentation here. I took their table and focused on the “tightness” score, and added my own column which placed each national sample into a subjective broader region-cultural category.


Language Group Nation Tightness
Urdu South Asian Pakistan 12.3
Malay East Asian Malaysia 11.8
Hindi South Asian India 11
English East Asian Singapore 10.4
Korean East Asian South Korea 10
Norwegian West European Norway 9.5
Turkish Mediterranean Turkey 9.2
Japanese East Asian Japan 8.6
Chinese East Asian China 7.9
Portuguese Mediterranean Portugal 7.8
West European West European Germany (East) 7.5
Spanish Latin American Mexico 7.2
English Anglosphere United Kingdom 6.9
West European West European Austria 6.8
Italian Mediterranean Italy 6.8
West European West European Germany (West) 6.5
Icelandic West European Iceland 6.4
English West European France 6.3
Chinese East Asian Hong Kong 6.3
Polish Eastern Bloc Poland 6
Dutch West European Belgium 5.6
Spanish Mediterranean Spain 5.4
English Anglosphere United States 5.1
English Anglosphere Australia 4.4
Greek Mediterranean Greece 3.9
English Anglosphere New Zealand 3.9
Spanish Latin American Venezuela 3.7
Portuguese Latin American Brazil 3.5
Dutch West European Netherlands 3.3
Hebrew Mediterranean Israel 3.1
Hungarian Eastern Bloc Hungary 2.9
Estonian Eastern Bloc Estonia 2.6
Ukrainian Eastern Bloc Ukraine 1.6

Tables leave something to be desired in gaining a gestalt understanding of relationships, so here’s a bar plot rank ordered by tightness score, with colors corresponding to region-culture. A lot of this is presumably not too surprising. Pakistan is the “tightest” nation which they sampled. But is Norway much tighter than Estonia? I picked this pair because Estonia is the most Nordic of the ex-Soviet Baltic nations, it traditionally being a Lutheran society due to German and Scandinavian influence and hegemony until absorption into the Russian Empire. Those who have visited both nations are probably the best to ask how this comports with their own experiences.

Backing up a bit, in the introduction to the paper they take a very broad historical view. They seem to imply that there is a gap between “small scale” hunter-gatherer societies and more dense agricultural ones in terms of the importance of social norms and conformity. There’s a plausible ecological rational for this: there are many more opportunities for “free riding” in dense and large scale societies. In contrast, inter-personal relationships are probably sufficient for cultures which exist mostly at the band level. The Code of Hammurabi is only necessary in cultures where personal relationships have diffused to the point where impersonal rules and heuristics need to be interposed between parties which are literally or de facto strangers. This is probably the difference between survival and extinction in a world which was predominantly at subsistence.

In the supplements there is a table of correlations between “tightness” and predictor variables, controlling for per capita GNP. I’ve selected out the most interesting (to me):


Variable N Correlation P-value Effect size
Population density in 1500 (Log) 11 0.77 0.01 0.59
Population density (Log) 32 0.31 0.10 0.10
Rural Population density (Log) 30 0.59 0.01 0.35
Food deprivation 30 0.52 0.01 0.27
Fat supply 30 -0.46 0.01 0.21
Natural disaster vulnerability 30 0.47 0.01 0.22
Historical prevalence of pathogens 32 0.36 0.05 0.13
Death due to communicable diseases (Log) 31 0.59 0.01 0.35
Prevalence of tuberculosis (Log) 31 0.61 0.01 0.37
Infant mortality rate (Log) 32 0.42 0.02 0.18
Openness of media 29 -0.53 0.01 0.28
Murder rate 31 -0.45 0.01 0.20
% attending religious services 31 0.54 0.01 0.29

Notice the difference between population density in 1500 vs. population density today in terms of prediction! This may point us to the possibility that the long arm of cultural memory still reigns supreme to some extent. The effect size is the square of the correlation, and gives us a sense of how much of the variation in the dependent variable is predicted by the independent variable when you hold GNP per capita content. Of course it is important to observe that the N has dropped when you go back to 1500, probably because the individual data points are nations, and nations can’t always be projected back in time. All that being said I like predictor variables like population density and death due to communicable diseases best, because they’re a lot less clear and distinct than something like openness of media. Openness of media is a valid measure in my opinion, but since the statistic we’re predicting only comes out via a process of human directed calculation, having both ends of the line be open to disputation is not optimal.

As for the tightness measure itself, there’s some strangeness here. On the one hand, some if it makes sense. But scores for other nations surprising, as noted by the authors. For example, Israel. But that just leads to ad hoc explanations:

…Gelfand was surprised to find that Israel — which is under threat from its neighbors and its desert environment — is still culturally loose. Gelfand suspects that’s in part because lots of Israelis came from relatively loose cultures in Eastern Europe.

“It’s also a culture of argumentation, debate, dissent, that really is very much consistent with Judaism. And these things all promote looseness,” she says.

There are two points here. I’ll address the second first: the time depth of the culture of disputation in Judaism is something I’ll actually dispute. One can make the case that as a generality this is very much a feature of modern Ashkenazi Jewish culture, with the opening of the public debate to all sectors of society. Of course I grant that disputation between eminent rabbis occurred in the past, but pre-modern Jewry was run like most pre-modern societies, there were authorities on on high who dictated what was, and wasn’t, permissible. European Jewish communities were run as corporate subnational entities before their liberation in the wake of the Enlightenment. The expulsion of Baruch Spinoza from the Sephardic Jewish community of the Netherlands illustrates the nature of pre-modern Jewish, and gentile, society on the cusp (by this, I mean that the religiously plural Netherlands of the period exhibited a cohabitation between pre-modern exclusiveness and parochialism, and post-modern pluralism). Modern day stereotypes and generalizations are often very much the result of modern day conditions.

But the first point is of more concern to me: the aggregation of genuinely different societies into one sample. The idea that the European Jewry shared something with its Eastern European milieu is a questionable assertion. European Jews for much of the pre-modern era were in the West, but not of it. More accurately, Jews in the world of Islam and Christianity were suffered to exist, but lived in a parallel world unless they converted to the majority religion and left the Jewish community. The Yiddish (and later standard German) speaking Eastern European Jew had a strained and complex relationship with the nation-states of Eastern Europe which arose in the wake of the collapse of the old empires (Austria-Hungary, the Second Reich, the Ottoman Empire, and the Russian Empire). Can we speak of Hungarian or Romanian Jews who were distinctive from each other because of their association with the Hungarian or Romanian majority? As an illustration, Paul Erdos’ family had changed their name from Englander, as part of the process of de-Germanization and indigenization of Hungarian Jews.

This issue of the “nations” which were evaluated crops up elsewhere. The Indian sample was from west-central India, on the margins of the Hindi-Punjabi-Gujarati “cow belt.” It was very similar in “tightness” to Pakistan. But what would the “tightness” be in southern India? It may be very different. Additionally, comparing Iceland to China, as if they are comparable units, is obviously ridiculous (something the authors acknowledge). Despite my qualms with the “tightness ” statistic I would be very interested to see how this varies on a subnational scale. If it is measuring something informative and useful the correlations should start going up as you proceed down to a finer grain (“tightness” may be representative of only one region, while GNP per capita and the independent variables are drawn from the whole nation).

Though the top line of the research is focused on inter-cultural differences, the authors argue for the importance of cultural context to individual response and expectation. This is actually pretty obvious on the internet, and even among Americans. There are lots of cryptic subcultures and cultures which bubble up out of the woodwork when something of dispute comes to the fore. Prior to the issue which highlights the differences, one may not have been aware of implicit or background variation in norms.

The future direction of this sort of research will be in the direction of gene-culture coevolution and pathogen-culture coevolution, and their combinations. Pathogens are critical covariates of any shift toward dense living, and in the modern world tend to hit those from historic low density backgrounds much worse. The difference between high conformity and low conformity to me is well illustrated by the varied paths toward Christianization of the peoples of Oceania. In Polynesia the missionaries generally converted the chiefs, who then brought their people to the new faith en masse. Apparently this was just not feasible among Australian Aborigines, who were only predominantly Christianized by the 1970s. This development had to occur one individual at a time, because the “big men” in these societies simply had no ability or will to enforce conformity of religious belief.

Citation: Gelfand MJ, Raver JL, Nishii L, Leslie LM, Lun J, Lim BC, Duan L, Almaliach A, Ang S, Arnadottir J, Aycan Z, Boehnke K, Boski P, Cabecinhas R, Chan D, Chhokar J, D’Amato A, Ferrer M, Fischlmayr IC, Fischer R, Fülöp M, Georgas J, Kashima ES, Kashima Y, Kim K, Lempereur A, Marquez P, Othman R, Overlaet B, Panagiotopoulou P, Peltzer K, Perez-Florizno LR, Ponomarenko L, Realo A, Schei V, Schmitt M, Smith PB, Soomro N, Szabo E, Taveesin N, Toyama M, Van de Vliert E, Vohra N, Ward C, & Yamaguchi S (2011). Differences between tight and loose cultures: a 33-nation study. Science (New York, N.Y.), 332 (6033), 1100-4 PMID: 21617077

(Republished from Discover/GNXP by permission of author or representative)
 
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One of the major parameters which shape individual success, and macroeconomic growth in the aggregate, is time preference. Time preference basically measures an individual’s future-time orientation. Would you for example take $1,000 in the present, or wait 30 days and accept $1,500 dollars? It doesn’t need to be money, children can exhibit time preference as well. Would you like one candy bar now? Or two candy bars in an hour? I also think time preference permeates our lives more concretely. Would you like to eat some greasy food now, or would you forgo epicurean pleasures in the present for a sleeker frame in the future?

Here’s an illustration of the correlates of time preference:

In one of the most amazing developmental studies ever conducted, Walter Michel of Stanford created a simple test of the ability of four year old children to control impulses and delay gratification. Children were taken one at a time into a room with a one-way mirror. They were shown a marshmallow. The experimenter told them he had to leave and that they could have the marshmallow right then, but if they waited for the experimenter to return from an errand, they could have two marshmallows. One marshmallow was left on a table in front of them. Some children grabbed the available marshmallow within seconds of the experimenter leaving. Others waited up to twenty minutes for the experimenter to return. In a follow-up study (Shoda, Mischel, & Peake, 1990), children were tested at 18 years of age and comparisons were made between the third of the children who grabbed the marshmallow (the “impulsive”) and the third who delayed gratification in order to receive the enhanced reward (“impulse controlled”).

The third of the children who were most impulsive at four years of age scored an average of 524 verbal and 528 math. The impulse controlled students who scored 610 verbal and 652 math! This astounding 210 point total score difference on the SAT was predicted on the basis of a single observation at four years of age! The 210 point difference is as large as the average differences between that of economically advantaged versus disadvantaged children and is larger than the difference between children from families with graduate degrees versus children whose parents did not finish high school! At four years of age gobbling a marshmallow now v. waiting for two later is twice as good a predictor of later SAT scores than is IQ.

The issue of causality is probably one which you will immediately bring up. There is a correlation between higher IQ and low time preference (consuming less in the present to have a potential for more consumption in the future), but who knows how the feedback loops here work? For example, unlike many males my age I gave up playing video games around the age of 16. I calculated that I was substituting video games for reading, and that that would have long term consequences which I was not pleased with. Video games were very pleasurable in the short term, addictive even. But I decided that there simply were not enough hours in the day that I could do everything I needed to do, so I stopped playing them (I am aware that many, many, very smart people are avid video game enthusiasts. I’m just using it to illustrate the trade offs one might make). How much less erudite, as Dr. Dan MacArthur might say, would I be if I did continue to expend many hours per week on video games?

A new working paper on the SSRN website has some interesting data on time preference cross-culturally. How Time Preferences Differ: Evidence from 45 Countries:

We present results from the first large-scale international survey on time discounting, conducted in 45 countries. Cross-country variation cannot simply be explained by economic variables such as interest or inflation rates. In particular, we find strong evidence for cultural differences, as measured by the Hofstede cultural dimensions. For example, large levels of Uncertainty Avoidance are associated with strong hyperbolic discounting. We also find relations between time preferences and risk preferences, like loss aversion. For instance, subjects with high loss aversion tend to show larger time discounting. Moreover, our analysis shows an impact of time preferences on the capability of technological innovations in a country and on environmental protection.

To get published in orthodox economics you need to do a lot of mathematical modeling, but I’m not too interested in that. Rather, let’s look at some of the descriptive results. The first two figures shows the percentage of participants who chose the $3800 option when they were asked to choose between $3400 this month or $3800 next month. The last figure has on the x-axis “time pace.” This is an overall-pace measure is calculated out of three measures: walking speed, postal speed, and clock accuracy.
timep1timep2timep3

Some of the text is very illuminating as to cross-cultural differences:

Even for the students from Princeton University, the percentage choosing the patient option is lower than the percentage of German students (80% vs. 89%). Actually some students from our Norway survey even complained that the question was ridiculous because everybody would choose to wait for one month given the high implicit interest rate.

Other results were not surprising:

This result suggests that although the wealth level (and hence a general level of a country’s economy) is crucial to stimulate innovation, the attitude towards future also plays an important role. For example, while 69% of Taiwanese participants prefer to wait in the one-month question, only 44% of our Italian students prefer to wait. The two countries have the same GDP per capita in 2007, but Taiwan scored much higher in the innovation factor than Italy (5.26 vs. 4.19). It is worthwhile to investigate further to what extent and under what mechanism a general attitude towards future is related to the innovation activity.

And yet some were (at least to me):

After controlling the macro-economic variables (GDP per capita, growth rate, inflation rate), participants from countries with higher degree of Individualism and Long Term Orientation are more likely to wait. In contrast, for the present bias and long-term discount factor, the country with higher Uncertainty Avoidance score tend to discount the next year more.

In other words, societies and individuals who were more individualistic tended to have low time preference (more future-time orientation). It would be interesting to further decouple confounding variables. I assume that more intelligent people are more individualistic as well, so that might be the source of the correlation.

I didn’t focus on the formal model too much here because this seems highly exploratory, and there were many non-significant results. But I think this paragraph is of some interest:

In summary, it seems that we need different models for waiting tendency and medium/long-term discount factor. The waiting tendency depends more on the fundamental economic variables such as the country’s wealth level, and on general attitudes in a society such as individualism and the mentality towards past and future. In comparison, the medium/long-term discount factor depends more on the dynamic factors such as growth rate, and the attitudes toward uncertainty.

(Republished from Discover/GNXP by permission of author or representative)
 
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Razib Khan
About Razib Khan

"I have degrees in biology and biochemistry, a passion for genetics, history, and philosophy, and shrimp is my favorite food. If you want to know more, see the links at http://www.razib.com"