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This article, titled "A Comment on: 'Presidential Address: Economics and Measurement: New Measures to Model Decision Making' by Ingvild Almås, Orazio Attanasio, and Pamela Jervis," discusses the use of economic theory to develop new measurement tools and how the resulting data can be used to further develop economic theory. The author agrees with the address and focuses on the study of how economic agents perceive the economic environment. The article provides a concrete example of learning about a household's coefficient of relative risk aversion and discusses different approaches to measuring this parameter. It concludes by highlighting the importance of understanding how economic agents perceive their environment in order to make the best use of measurement tools. [Extracted from the article]

We measure trends in affective polarization in twelve OECD countries over the past four decades. According to our baseline estimates, the United States experienced the largest increase in polarization over this period. Five countries experienced a smaller increase in polarization. Six countries experienced a decrease in polarization. We relate trends in polarization to trends in potential explanatory factors. [ABSTRACT FROM AUTHOR]

Existing theories of media competition imply that advertisers will pay a lower price in equilibrium to reach consumers who multi-home across competing outlets. We generalize and extend this theoretical result and test it using data from television and social media advertising. We find that the model is a good match, qualitatively and quantitatively, to variation in advertising prices across demographic groups, outlets, platforms, and over time. We use the model to quantify the effects of competition within and across platforms. (JEL G34, K21, L13, L82, M37) [ABSTRACT FROM AUTHOR]

A large literature in cognitive science studies the puzzling "Flynn effect" of rising fluid intelligence (reasoning skill) in rich countries. We develop an economic model in which a cohort's mix of skills is determined by different skills' relative returns in the labor market and by the technology for producing skills. We estimate the model using administrative data from Sweden. Combining data from exams taken at military enlistment with earnings records from the tax register, we document an increase in the relative labor market return to logical reasoning skill as compared to vocabulary knowledge. The estimated model implies that changes in labor market returns explain 37% of the measured increase in reasoning skill, and can also explain the decline in knowledge. An original survey of parents, an analysis of trends in school curricula, and an analysis of occupational characteristics show evidence of increasing emphasis on reasoning as compared to knowledge. [ABSTRACT FROM AUTHOR]

Linear panel models featuring unit and time fixed effects appear in many areas of empirical economics. An active literature studies the interpretation of the ordinary least squares estimator of the model, commonly called the two-way fixed effects (TWFE) estimator, in the presence of unmodeled coefficient heterogeneity. We illustrate some implications for the case where the research design takes advantage of variation across units (say, US states) in exposure to some treatment (say, a policy change). In this case, the TWFE can fail to estimate the average (or even a weighted average) of the units' coefficients. Under some conditions, there exists no estimator that is guaranteed to estimate even a weighted average. Building on the literature, we note that when there is a unit totally unaffected by treatment, it is possible to estimate an average effect by replacing the TWFE with an average of difference-in-differences estimators. [ABSTRACT FROM AUTHOR]

Isaiah Andrews is an exceptionally warm and caring person, a remarkable teacher, a collaborator and mentor, an exemplary contributor to his department and profession, and a brilliant econometrician. In this article, we review Isaiah's contributions to econometric theory in the context of Isaiah's receipt of the 2021 John Bates Clark Medal. [ABSTRACT FROM AUTHOR]

We propose a positive model of empirical science in which an analyst makes a report to an audience after observing some data. Agents in the audience may differ in their beliefs or objectives, and may therefore update or act differently following a given report. We contrast the proposed model with a classical model of statistics in which the report directly determines the payoff. We identify settings in which the predictions of the proposed model differ from those of the classical model, and seem to better match practice. [ABSTRACT FROM AUTHOR]

We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate ĉ of a structural quantity of interest c that is exactly or asymptotically unbiased under some base model. The researcher also reports descriptive statistics γˆ that estimate features γ of the distribution of the data that are related to c under the base model. A reader entertains a less restrictive model that is local to the base model, under which the estimate ĉ may be biased. We study the reduction in worst‐case bias from a restriction that requires the reader's model to respect the relationship between c and γ specified by the base model. Our main result shows that the proportional reduction in worst‐case bias depends only on a quantity we call the informativeness of γˆ for ĉ. Informativeness can be easily estimated even for complex models. We recommend that researchers report estimated informativeness alongside their descriptive analyses, and we illustrate with applications to three recent papers. [ABSTRACT FROM AUTHOR]

We propose a formal definition of transparency in empirical research and apply it to structural estimation in economics. We discuss how some existing practices can be understood as attempts to improve transparency, and we suggest ways to improve current practice, emphasizing approaches that impose a minimal computational burden on the researcher. We illustrate with examples. [ABSTRACT FROM AUTHOR]

We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends ("pre-trends") in the outcome. Alternative approaches perform poorly in our simulations. (JEL C23, C26) [ABSTRACT FROM AUTHOR]

We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite‐sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century. [ABSTRACT FROM AUTHOR]

Community rescue occurs when a community that experiences lethal stress persists only through the spread of rare types, either genotypes or species, resistant to the stress. Rescue interacts with trophic structure because physical stress experienced by a focal assemblage within the community may also be experienced by its predators and prey. In general, trophic structure will facilitate rescue only when a stress has a less severe effect on a focal assemblage than on its predators. In other circumstances, when stress affects prey or has only a weak effect on predators, trophic structure is likely to hamper rescue. We exposed a community of phytoplankton and zooplankton derived from a natural lake to acidification in outdoor mesocosms large enough to support trophically complex communities. Rescue of the phytoplankton from severe acidification was facilitated by prior exposure to sublethal stress, confirming previous results from microcosm experiments. Even communities that have previously been less highly stressed were eventually rescued, however, because their zooplankton predators were more sensitive to acidification and became extinct. Our experiment shows how community rescue following severe stress is modulated by the differential effect of the stress relative to trophic level. [ABSTRACT FROM AUTHOR]

We use a novel retail panel with detailed transaction records to study the effect of the Supplemental Nutrition Assistance Program (SNAP) on household spending. We use administrative data to motivate three approaches to causal inference. The marginal propensity to consume SNAP-eligible food (MPCF) out of SNAP benefits is 0.5 to 0.6. The MPCF out of cash is much smaller. These patterns obtain even for households for whom SNAP benefits are economically equivalent to cash because their benefits are below their food spending. Using a semiparametric framework, we reject the hypothesis that households respect the fungibility of money. A model with mental accounting can match the facts. (JEL D12, H75, I12, I18, I38) [ABSTRACT FROM AUTHOR]

Correspondingly, a given distribution I F i may now be consistent with a set HT ht of possible values of I c i .[4] Importantly, though not in general a singleton, for bounded HT ht the set HT ht will typically be limited in size. This alternative relaxation is not nested by the one we define based on bounded neighborhoods HT ht . The set HT ht tells us which values of I c i are consistent with the complete distribution of the observed data. [Extracted from the article]

We use data from the American National Election Studies from 1996 to 2016 to study the role of the internet in the 2016 U.S. presidential election outcome. We compare trends in the Republican share of the vote between likely and unlikely internet users, and between actual internet users and non-users. Relative to prior years, the Republican share of the vote in 2016 was as high or higher among the groups least active online. [ABSTRACT FROM AUTHOR]

We propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way violations of identifying assumptions would affect the results. When the key assumptions are orthogonality between error terms and excluded instruments, we show that our measure provides a natural extension of the omitted variables bias formula for nonlinear models. We illustrate with applications to published articles in several fields of economics. JEL Codes: C10, C52. [ABSTRACT FROM AUTHOR]

We combine eight previously proposed measures to construct an index of political polarization among US adults. We find that polarization has increased the most among the demographic groups least likely to use the Internet and social media. Our overall index and all but one of the individual measures show greater increases for those older than 65 than for those aged 18-39. A linear model estimated at the age-group level implies that the Internet explains a small share of the recent growth in polarization. [ABSTRACT FROM AUTHOR]