A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, e.g., time spent searching for tasks, working on tasks that are rejected, and working on tasks that are ultimately not submitted. We further explore the characteristics of tasks and working patterns that yield higher hourly wages. Our analysis informs platform design and worker tools to create a more positive future for crowd work. Figure 12. (a) Hourly wage distributions of seven HIT categories provided by Gadiraju et al. [25] (with an additional category Research). (b) Strip plots showing median hourly wages of HITs associated with the topical keywords in Table 4.

In evaluating the efficacy of physician-delivered counseling interventions for health behavior changes such as smoking cessation, a major challenge is determining the degree to which interventions are implemented by physicians. The Patient Exit Interview (PEI; J. Ockene et al., 1991) is a brief measure of a patient's perception of the content and quantity of smoking cessation intervention received from his or her physician. One hundred eight current smokers seen in a primary care clinic completed a PEI following their physician visit. Participants were 45% male, 95% Caucasian, with a mean age of 42 years and an average of 22 years of smoking. The PEI correlated well with a criterion measure of an audiotape assessment of the physician-patient interaction (r = .67, p < .001). When discrepancy occurred, in general it was due to patients' over-reporting of intervention as compared with the criterion measure. Implications and limitations of these findings are discussed.

This paper develops a revealed preference methodology for exploring whether time inconsistencies in household choice are the product of nonstationarities at the individual level or the result of individual heterogeneity and renegotiation within the collective unit. An empirical application to household-level microdata highlights that an explicit recognition of the collective nature of choice allows the vast majority of household behaviour to be rationalised by theory that assumes preference stationarity at the individual level. For our particular short panel data set, simply permitting limited intrahousehold heterogeneity in time preferences allows the choices of 98.4% of the sample to be rationalised by a model that assumes exponential discounting at the individual level. We also …nd that couples characterized by lower divergence in spousal discount rates are older, more likely to have children and wealthier, which we take as indications of experiencing higher match quality. JEL Classi…cation: D11, D12, D13, C14.

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