The inclusion of a durable goods sector in sticky-price models has strong and unexpected implications. Even if most prices are flexible, a small durable goods sector with sticky prices may be sufficient to make aggregate output react to monetary policy as though most prices were sticky. In contrast, flexibly priced durables with sufficiently long service lives can undo the implications of standard sticky price models. In a limiting case, flexibly priced durables cause monetary policy to have no effect on aggregate output. Our analysis suggests that durable goods prices are the most relevant data for calibrating price rigidity.

Yes. We construct a measure of aggregate technology change, controlling for aggregation effects, varying utilization of capital and labor, nonconstant returns, and imperfect competition. On impact, when technology improves, input use and nonresidential investment fall sharply. Output changes little. With a lag of several years, inputs and investment return to normal and output rises strongly. The standard one-sector real-business-cycle model is not consistent with this evidence. The evidence is consistent, however, with simple sticky-price models, which predict the results we find: when technology improves, inputs and investment generally fall in the short run, and output itself may also fall.

Risk Preferences in the PSID: Individual Imputations and Family Covariation by Miles S. Kimball, Claudia R. Sahm and Matthew D. Shapiro. Published in volume 99, issue 2, pages 363-68 of American Economic Review, May 2009

This paper proposes foundations and a methodology for survey-based tracking of well-being. First, we develop a theory in which utility depends on "fundamental aspects" of well-being, measurable with surveys. Second, drawing from psychologists, philosophers, and economists, we compile a comprehensive list of such aspects. Third, we demonstrate our proposed method for estimating the aspects' relative marginal utilities-a necessary input for constructing an individual-level well-being index-by asking ~4,600 U.S. survey respondents to state their preference between pairs of aspect bundles. We estimate high relative marginal utilities for aspects related to family, health, security, values, freedom, happiness, and life satisfaction.

We survey 561 students from US medical schools shortly after they submit choice rankings over residencies to the National Resident Matching Program. We elicit (i) these choice rankings, (ii) anticipated subjective well-being (SWB) rankings, and (iii) expected features of the residencies (such as prestige). We find substantial differences between choice and anticipated-SWB rankings in the implied trade-offs between residency features. In our data, evaluative SWB measures (life satisfaction and Cantril's Ladder) imply trade-offs closer to choice than does affective happiness (even time-integrated), and as close as do multimeasure SWB indices. We discuss implications for using SWB data in applied work.

We propose a social choice rule for aggregating preferences elicited from surveys into a marginal adjustment of policy from the status quo. The mechanism is: (i) symmetric in its treatment of survey respondents; (ii) ordinal, using only the orientation of respondents' indifference surfaces; (iii) local, using only preferences in the neighborhood of current policy; and (iv) what we call “first-order strategy-proof,” making the gains from misreporting preferences second order. The mechanism could be applied to guide policy based on how policy affects responses to subjective well-being surveys.

Would people choose what they think would maximize their subjective well-being (SWB)? We present survey respondents with hypothetical scenarios and elicit both choice and predicted SWB rankings of two alternatives. While choice and predicted SWB rankings usually coincide in our data, we find systematic reversals. We identify factors-such as predicted sense of purpose, control over one's life, family happiness, and social status-that help explain hypothetical choice controlling for predicted SWB. We explore how our findings vary by SWB measure and by scenario. Our results have implications regarding the use of SWB survey questions as a proxy for utility.