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People seem to have pure time preferences about trade-offs concerning their own pleasures and pains, and such preferences contribute to estimates of people's individual time discount rate. Do pure time preferences also matter to interpersonal welfare trade-offs, including those concerning the welfare of future generations? Most importantly, should the intergenerational time discount rate include a pure time preference? Descriptivists claim that the intergenerational discount rate should reflect actual people's revealed preferences, and thus it should include a pure time preference. Prescriptivists claim that the intergenerational discount rate should be based on moral analysis, and thus they (often) claim that the rate of pure time preference should be zero. I argue that regardless of which view is correct, a focus on pure time preference is misplaced. First, the most plausible interpretation of descriptive preferences for intergenerational trade-offs is that people are socially biased and not time biased. Second, social bias is superior to time bias as a prescriptive reason to discount the welfare of future people. Third, recent advances in measuring social bias as a social discount rate make social bias a viable replacement for time bias in economic analyses of intergenerational welfare trade-offs.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

© The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due toexcludingobserved covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of biasunmaskingand shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

In recent decades, legal scholars have advanced sophisticated models for understanding prejudice and discrimination, drawing on disciplines such as psychology, sociology, and economics. These models explain how inequality is implicit in cognition and seamlessly woven into social structures. And yet, obvious, explicit, and overt forms of bias have not gone away. The law does not need empirical methods to identify bias when it is marching down the street in Nazi regalia, hurling misogynist invective, or trading in anti-Muslim stereotypes. Official acceptance of such prejudices may be uniquely harmful in normalizing discrimination. But surprisingly, many discrimination cases ignore explicit bias. Courts have refused to consider evidence of biased statements by government officials in cases alleging, for example, that facially neutral laws were enacted for the express purpose of singling out Muslims. Courts outright ignore explicit bias when they consider intentional discrimination to be justified by goals such as law enforcement. And courts have developed a "stray remarks doctrine" in employment discrimination cases to prevent juries from hearing evidence of explicit bias. This Article identifies and criticizes legal arguments against consideration of explicit bias, including concerns about the feasibility of inquiries into intent, worry about undermining otherwise legitimate policies, the desire to avoid chilling effects on free speech, and the fear that confronting explicit bias will result in backlash. It argues that discrimination law should dispense with doctrines that shield explicit bias from consideration.

In recent decades, legal scholars have advanced sophisticated models for understanding prejudice and discrimination, drawing on disciplines such as psychology, sociology, and economics. These models explain how inequality is implicit in cognition and seamlessly woven into social structures. And yet, obvious, explicit, and overt forms of bias have not gone away. The law does not need empirical methods to identify bias when it is marching down the street in Nazi regalia, hurling misogynist invective, or trading in anti-Muslim stereotypes. Official acceptance of such prejudices may be uniquely harmful in normalizing discrimination. But surprisingly, many discrimination cases ignore explicit bias. Courts have refused to consider evidence of biased statements by government officials in cases alleging, for example, that facially neutral laws were enacted for the express purpose of singling out Muslims. Courts outright ignore explicit bias when they consider intentional discrimination to be justified by goals such as law enforcement. And courts have developed a "stray remarks doctrine" in employment discrimination cases to prevent juries from hearing evidence of explicit bias. This Article identifies and criticizes legal arguments against consideration of explicit bias, including concern about the feasibility of inquiries into intent, worry about undermining otherwise legitimate policies, the desire to avoid chilling effects on free speech, and fear that confronting explicit bias will result in backlash. It argues that discrimination law should dispense with doctrines that shield explicit bias from consideration.

An evaluation of the relevance of Ur bias & Ru bias to the postrevolutionary Chinese case. Statistical data are presented on the nature & extent of differentiation in Ru-Ur living standards, the political basis of relevant economic policies is examined, & the relationship between these policies is investigated relative to changes in labor productivity in each sector & to intersectoral savings transfers. Both Ur bias & Ru bias hypotheses illuminate certain dimensions of Chinese development strategy. But the realities of Ru-Ur relations have been complex, & analysis must be supplemented by a focus on the division between state & society, & the question of state bias. 10 Tables, 73 References. HA.