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AbstractThe delineation of constituency boundaries and variations in vote distribution across districts often favor certain parties at the expense of others. Applying a hitherto under-utilized formula (Brookes, 1959; Johnston et al., 1999), this study investigates whether the mechanism translating votes into seats in Japan's single-member districts results in systematic partisan advantage that may influence election outcomes. Simulations are conducted for the 2003 and 2005 general elections under two scenarios: where the governing coalition and the main opposition party receive equal vote shares, and where their vote shares are reversed from the actual results. Components of electoral bias are then disaggregated into size and distribution effects, and the impact of malapportionment, electorate size, turnout, and the role of third party/independent candidates on overall electoral bias is examined. Results show that while partisan bias exists, disadvantages toward one party in some components are likely to cancel out benefits derived from others, producing a relatively small net effect. Furthermore, electoral bias in Japan is found to award sectoral rather than partisan seat bonuses.

This investigation explored hindsight bias in college students' perceptions of sexual assault. Participants read a vignette about a man and woman who met at a party with the conditions varied across the vignettes: the alcohol use of the characters, the outcome of the vignette, and in the rape outcome, the victim's actions after the assault. Hindsight bias was assessed using both the posttest-only method and the pretest/posttest method and for both the outcome of the vignette and post-outcome events. Results revealed significant evidence for the existence of hindsight bias with the pretest/posttest method. Additionally, results revealed some evidence for hindsight bias contamination in perceptions of vignette characters, but in ways contrary to those predicted by the Defensive Attribution Hypothesis (Shaver, 1970).

I present a rationale for a government to discriminate between debt and equity financing when taxing corporate income. For risk-averse entrepreneurs, equity generates more surplus than debt, because it provides financing and insurance. A government seeking to extract surplus from entrepreneurs would naturally tax equity-generated income more than debt-generated income. I also establish a less obvious reason why the government might want to extract surplus from entrepreneurs. It is well understood that when the quality of projects is unobservable to investors, risk-averse entrepreneurs with higherreturn projects might retain a larger share of equity to signal their type (Leland and Pyle (1977)). I show that in such an adverse selection setting, while competitive investors are constrained to offer actuarially fair terms, the government can use taxes to discriminate between types. This degree of freedom allows manipulation of the relevant incentive constraints so that a lower level of debt suffices for separation, and an increase in overall efficiency can be obtained. Since entrepreneurs separate along their debt-to-equity ratios, the optimal non-linear tax schedule to achieve the desired discrimination is isomorphic to one that taxes debt-generated income at a lower rate than equitygenerated income.

ABSTRACTRecent research has ignited a debate in social science history over whether and how to draw conclusions for whole populations from sources that describe only select subsets of these populations. The idiosyncratic availability and survival of historical sources create a threat of sample-selection bias—an error that arises when there are systematic differences between the observed sample and the population of interest. This danger is common in studying trends in health as measured by average stature—scholars can often observe these trends only for soldiers and other similar groups; but whether these patterns are representative of those of the broader population is unclear. This article illustrates what simple patterns in a potentially selected sample can be used to recognize the presence of sample-selection bias in a source, and to understand how such bias might affect conclusions drawn from this source. Applying this intuition to the use of military data to describe stature in the antebellum United States, I present several simple empirical exercises based on these patterns. Finally, I use the results of these exercises to describe how sample-selection bias might affect the use of these data in testing for differences in average stature between the Northeast and the Midwest.

AbstractMuch research examining gender bias in politics analyzes responses to explicit survey questions asking individuals whether they prefer male over female leaders or agree that male political leaders are superior. Drawing insights from the measurement of other types of prejudice, this article explores the methodological shortcomings of a widely used question of this type. Analyzing the results of two surveys—one national and one state-level—I compare response patterns to a standard, highly explicit question that is frequently administered by the Pew Research Center with those for a modestly altered item that employs multiple strategies to reduce social desirability bias. Compared with the alternative measure, the conventional item seriously underreports prejudice against women leaders. Moreover, the underreporting of bias is especially prevalent among individuals belonging to groups that are strong advocates of gender equality.

Machine generated contents note: 1. Credit Rating Agencies as Gatekeepers -- 2. Establishing the Determinants of Sovereign Debt Ratings: Is There Really Room for Bias? -- 3. Resilience in Spite of Controversy: Conditions for Bias in the Credit Rating Industry -- 4. Trends in Sovereign Debt Ratings: Are There any Preliminary Signs of Bias? -- 5. Introducing Greater Rigor -- Methodological Approach -- 6. Are Poorer Countries Disadvantaged by the CRAs? Empirically Establishing a Bias -- 7. Now That We Have Found Bias, What Are We Going to Do with It?