Ethnic Classification in Global Perspective: A Cross-National Survey of the 2000 Census Round

Academic interest in official systems of racial and ethnic classification has grown in recent years, but most research on such census categories has been limited to small case studies or regional surveys. In contrast, this article analyzes a uniquely global data set compiled by the United Nations Statistical Division to survey the approaches to ethnic enumeration taken in 141 countries. The motives for this analysis combine theoretical, applied, and policy objectives. I find that 63% of the national censuses studied incorporate some form of ethnic enumeration, but their question and answer formats vary along several dimensions that betray diverse conceptualizations of ethnicity (for example, as “race” or “nationality”). Moreover, these formats follow notably regional patterns. Nonetheless, the variety of approaches can be grouped into a basic taxonomy of ethnic classification approaches, suggesting greater commonality in worldwide manifestations of the ethnicity concept than some have recognized.

Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality

Spatially explicit data pose a series of opportunities and challenges for all the actors involved in providing data for long-term preservation and secondary analysis—the data producer, the data archive, and the data user. We report on opportunities and challenges for each of the three players, and then turn to a summary of current thinking about how best to prepare, archive, disseminate, and make use of social science data that have spatially explicit identification. The core issue that runs through the paper is the risk of the disclosure of the identity of respondents. If we know where they live, where they work, or where they own property, it is possible to find out who they are. Those involved in collecting, archiving, and using data need to be aware of the risks of disclosure and become familiar with best practices to avoid disclosures that will be harmful to respondents.

为中国人民服务

我来自加拿大安大略省的多伦多市,相信大家对这个地方并不陌生,它是大山的家乡,白求恩医生也在此出生并接受医疗培训。我所在的约克大学就有一个本科生学院是以白求恩医生的名字命名的。白求恩医生的事迹感召着我们．我们每一位获奖外国专家都将永远为我们有机会“为人民服务”,为中国人民服务而心存感激,这是我们无上的荣耀。     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

Coalitional stability and efficiency of partitions in matching problems

?zkal-Sanver (Theory Decis 59:193–205, 2005) studies stability and efficiency of partitions of agents in two-sided matching markets in which agents can form partitions by individual moves only, and a matching rule determines the matching in each coalition in a partition. In this study, we present the relationship between stability and efficiency of partitions that is analyzed for several matching rules and under various membership property rights codes, now allowing coalitional moves.     

Dynamic reasoning and time pressure: Transition from analytical operations to experiential responses

Based upon the Decision Field Theory (Busemeyer and Townsend 1993), we tested a model of dynamic reasoning to predict the effect of time pressure on analytical and experiential processing during decision-making. Forty-six participants were required to make investment decisions under four levels of time pressure. In each decision, participants were presented with experiential cues which were either congruent or incongruent with the analytical information. The congruent/incongruent conditions allowed us to examine how many decisions were based upon the experiential versus the analytical information, and to see if this was affected by the varying degrees of time pressure. As expected, the overall accuracy was reduced with greater time pressure and accuracy was higher when the experiential and analytical cues were congruent than when they were incongruent. Of great interest was the data showing that under high time pressure participants used more experiential cues than at other time pressures. We suggest that the dynamic reasoning paradigm has some future potential for predicting the effects of experiential biases in general, and specifically under time pressure.     

Beliefs about overconfidence

This experiment elicits beliefs about other people’s overconfidence and abilities. We find that most people believe that others are unbiased, and only few think that others are overconfident. There is a remarkable heterogeneity between these groups. Those people who think others are underconfident or unbiased are overconfident themselves. Those who think others are overconfident are underconfident themselves. Despite this heterogeneity, people overestimate on average the abilities of others as they do their own ability. One driving force behind this result is the refusal to process information about oneself: not only does this lead to overestimation of one’s own ability, but by means of social projection also to overestimation of others’ abilities.     

Beneficial safety decreases

We construct a model of rational choice under risk with biased risk judgement. On its basis, we argue that sometimes, a regulator aiming at maximising social welfare should affect the environment in such a way that it becomes ‘less safe’ in common perception. More specifically, we introduce a bias into each agent’s choice of optimal risk levels: consequently, in certain environments, agents choose a behaviour that realises higher risks than intended. Individuals incur a welfare loss through this bias. We show that by deteriorating the environment, the regulator can motivate individuals to choose behaviour that is less biased, and hence realises risk levels closer to what individuals intended. We formally investigate the conditions under which such a Beneficial Safety Decrease—i.e. a deteriorating intervention that has a positive welfare effect—exists. Finally, we discuss three applications of our model.     

Does product complexity matter for competition in experimental retail markets?

We describe a first experiment on whether product complexity affects competition and consumers in retail markets. We are unable to detect a significant effect of product complexity on prices, except insofar as the demand elasticity for complex products is higher. However, there is qualified evidence that complex products have the potential to induce consumers to buy more than they would otherwise. In this sense, consumer exploitability in quantities cannot be ruled out. We also find evidence for shaping effects: consumers’ preferences are shaped by past experience with prices, and firms may in principle exploit this to sell more.     

Digit ratios (2D:4D) as predictors of risky decision making for both sexes

Many important decisions involve financial risk, and substantial evidence suggests that women tend to be more risk averse than men. We explore a potential biological basis of risk-taking variation within and between the sexes by studying how the ratio between the length of the second and fourth fingers (2D:4D) predicts risk-taking. A smaller 2D:4D ratio has been linked to higher exposure to prenatal testosterone relative to estradiol, with men having lower ratios than women. In financially motivated decision-making tasks, we find that men and women with smaller 2D:4D ratios chose significantly riskier options. We further find that the ratio partially explains the variation in risk-taking between the sexes. Moreover, for men and women at the extremes of the digit-ratio distribution the difference in risk-taking disappears. Thus, the 2D:4D ratio partially explains variation in financial risk-taking behavior within and between sexes and offers evidence of a biological basis for risk-taking behavior.