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We study the causes of “nutritional inequality”: why the wealthy eat more healthfully than the poor in the United States. Exploiting supermarket entry and household moves to healthier neighborhoods, we reject that neighborhood environments contribute meaningfully to nutritional inequality.We then estimate a structural model of grocery demand, using a new instrument exploiting the combination of grocery retail chains’ differing presence across geographic markets with their differing comparative advantages across product groups. Counterfactual simulations show that exposing low-income households to the same products and prices available to high-income households reduces nutritional inequality by only about 10%, while the remaining 90% is driven by differences in demand. These findings counter the argument that policies to increase the supply of healthy groceries could play an important role in reducing nutritional inequality.

With the cooperation of a large mobile service provider, we conduct a novel field experiment that simultaneously randomizes the prices of two competing movie theaters using mobile coupons. Unlike studies that vary only one firm’s prices, our experiment allows us to account for competitor response. We test mobile targeting based on consumers’ real-time and historic locations, allowing us to evaluate popular mobile coupon strategies in a competitive market. The experiment reveals substantial profit gains from mobile discounts during an off-peak period. Both firms could create incremental profits by targeting their competitor’s location. However, the returns to such “geoconquesting” are reduced when the competitor also launches its own targeting campaign. We combine our experimentally generated data with a demand model to analyze optimal pricing in a static Bertrand–Nash equilibrium. Interestingly, competitive responses raise the profitability of behavioral targeting where symmetric pricing incentives soften price competition. By contrast, competitive responses lower the profitability of geographic targeting, where asymmetric pricing incentives toughen price competition. If we endogenize targeting choice, both firms would choose behavioral targeting in equilibrium, even though more granular geobehavioral targeting combining both real-time and historic locations is possible. These findings demonstrate the importance of considering competitor response when piloting novel price-targeting mechanisms. Data are available at https://doi.org/10.1287/mksc.2017.1042 .

The widely used estimator of Berry, Levinsohn, and Pakes (1995) produces estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks, and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking demand models where the Bellman equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization. For static BLP, the constrained optimization approach can be as much as ten to forty times faster for large-dimensional problems with many markets.

Mycobacteria are responsible for several human and animal diseases. NOD2 is a pattern recognition receptor that has an important role in mycobacterial recognition. However, the mechanisms by which mutations in NOD2 alter the course of mycobacterial infection remain unclear. Herein, we aimed to review the totality of studies directly addressing the relationship between NOD2 and mycobacteria as a foundation for moving the field forward. NOD2 was linked to mycobacterial infection at 3 levels: (1) genetic, through association with mycobacterial diseases of humans; (2) chemical, through the distinct NOD2 ligand in the mycobacterial cell wall; and (3) immunologic, through heightened NOD2 signaling caused by the unique modification of the NOD2 ligand. The immune response to mycobacteria is shaped by NOD2 signaling, responsible for NF-κB and MAPK activation, and the production of various immune effectors like cytokines and nitric oxide, with some evidence linking this to bacteriologic control. Absence of NOD2 during mycobacterial infection of mice can be detrimental, but the mechanism remains unknown. Conversely, the success of immunization with mycobacteria has been linked to NOD2 signaling and NOD2 has been targeted as an avenue of immunotherapy for diseases even beyond mycobacteria. The mycobacteria–NOD2 interaction remains an important area of study, which may shed light on immune mechanisms in disease.

We estimate the effect of information and expertise on consumers’ willingness to pay for national brands in physically homogeneous product categories. In a detailed case study of headache remedies, we find that more informed or expert consumers are less likely to pay extra to buy national brands, with pharmacists choosing them over store brands only 9 percent of the time, compared to 26 percent of the time for the average consumer. In a similar case study of pantry staples such as salt and sugar, we show that chefs devote 12 percentage points less of their purchases to national brands than demographically similar nonchefs. We extend our analysis to cover 50 retail health categories and 241 food and drink categories. The results suggest that misinformation and related consumer mistakes explain a sizable share of the brand premium for health products, and a much smaller share for most food and drink products. We tie our estimates together using a stylized model of demand and pricing.

Complete Freund’s adjuvant (CFA) has historically been one of the most useful tools of immunologists. Essentially comprised of dead mycobacteria and mineral oil, we asked ourselves what is special about the mycobacterial part of this adjuvant, and could it be recapitulated synthetically? Here, we demonstrate the essentiality of N -glycolylated peptidoglycan plus trehalose dimycolate (both unique in mycobacteria) for the complete adjuvant effect using knockouts and chemical complementation. A combination of synthetic N -glycolyl muramyl dipeptide and minimal trehalose dimycolate motif GlcC14C18 was able to upregulate dendritic cell effectors, plus induce experimental autoimmunity qualitatively similar but quantitatively milder compared to CFA. This research outlines how to substitute CFA with a consistent, molecularly-defined adjuvant which may inform the design of immunotherapeutic agents and vaccines benefitting from cell-mediated immunity. We also anticipate using synthetic microbe-associated molecular patterns (MAMPs) to study mycobacterial immunity and immunopathogenesis.

We test an information theory of prosocial behavior whereby ego utility and self-signaling crowd out the effect of consumption utility on choice. The data come from two field experiments involving purchases of a consumer good bundled with a charitable donation. Across experimental cells, we randomize the price level and the donation level. A model-free analysis of the data reveals nonmonotonic regions of demand when the good is bundled with relatively large charitable donations. Subjects also self-report lower ratings of “feeling good about themselves” when offered bundles with large donations and price discounts. The evidence suggests that price discounts crowd out consumer self-inference of altruism. Alternative motivation-crowding theories are rejected due to their inability to explain the nonmonotonic data moments. The standard use of interaction effects and other falsification checks to explore the underlying choice mechanism in an experimental setting is complicated in our self-signaling context. Instead, a novel feature of our analysis consists of using the experimental data to estimate the structural form of a model of consumer demand with self-signaling. We specify a model in which consumers obtain both consumption and ego utility from their choices. Ego utility derives from a consumer’s posterior self-beliefs after making her choice. An estimator is proposed that handles the potential multiplicity of equilibria that can arise in the self-signaling model. The model estimates allow us to quantify the economic role of ego utility and to explore the underlying signaling mechanism. Nested tests reject the hypothesis of no self-signaling. Alternative model specifications that potentially allow for nonmontonic demand without the self-signaling structure exhibit an inferior fit to the data. The model estimates imply that consumer response to the donations are mainly driven by ego utility and not by consumption utility (i.e., not by altruistic motives). The findings from the combination of a field experiment and a structural model contribute to a growing literature on self-signaling and consumer behavior by quantifying the magnitude of self-signaling on preferences and choices. The results also have implications for the design of a cause marketing campaign and the potential negative synergies between price and nonprice promotions. Data are available at https://doi.org/10.1287/mksc.2016.1012 .

US companies invested over $500 billion in 2021 in intangible brand capital, over 2% of GDP. During the past decade, US companies have also been growing their internal marketing capabilities, an often overlooked source of human capital. We discuss the private and social benefits of these intangible brand capital stocks. While the private returns to companies are fairly clear, the academic literature has been divided over the social benefits and costs of advertising and promotion, the two key investment vehicles. We also discuss the implications of brand capital for measured productivity.

The authors analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, they show that popular, simple solutions that misspecify the initial conditions are likely to lead to bias even in relatively long panel data sets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2–2.5. The authors propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in a Markov chain Monte Carlo procedure. The approach assumes that the joint distribution of prices and consumer choices is in equilibrium, which is plausible for the mature consumer packaged goods products commonly used in empirical applications. In Monte Carlo simulations, the approach recovers the true parameter values even in relatively short panels. Finally, the authors propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.

Background Colorectal cancer (CRC) is one of the prevailing causes of cancer mortality in the world. A common screening test for CRC is based on the human hemoglobin immunochemical based fecal occult blood test (iFOBT), which consists in the detection of blood in the patient’s stool. In addition to iFOBT, recent studies support the use of the gut microbiome as a biomarker for CRC prediction. However, these studies did not take into account the effect of blood itself on the microbiome composition, independently of CRC. Therefore, we investigated the microbiome of patients undergoing the iFOBT screening in order to determine the effect of blood alone. Our cohort consisted of patients who had no blood in their stools ( n  = 265) or did have blood but no underlying precancerous or cancerous lesions ( n  = 235). We also identified bacterial taxa specifically associated with the presence of blood in stools. Results We observed significant differences in the intestinal bacterial composition that could be solely caused by the presence of blood in stools. More precisely, we identified 12 bacterial species showing significant differences in abundance between both our study groups. These species, Bacteroides uniformis, Collinsella aerofaciens , Eggerthella lenta and Clostridium symbiosum demonstrated increased abundance in the presence of blood. In contrast, the species Prevotella copri , Coprococcus eutactus and catus , Faecalibacterium prausnitzii , Roseburia faecis , Blautia obeum , Gemmiger formicilis and Clostridium celatum showed decreased abundance in patients with blood in their stools. Notably, we found multiple taxa that were reported in previous studies linking microbiome composition and diseases. Conclusions We show that, in the absence of disease, blood in the stools has a major influence on the composition of the microbiome. Our data suggest that blood itself should be taken into consideration when investigating the microbiome signatures of intestinal diseases.