Nielsen Scanner Data Research Articles

Cross-State Border Nicotine Vaping Products Purchase-Early Evidence From State Emergency Sales Restrictions in 2019.

While retail sales and retailer inspection studies generally indicate high compliance with state sales restrictions on Nicotine Vaping Products (NVPs) within the restricted area, studies using survey data generally indicate that most users could readily continue gaining access to restricted NVPs. Our study bridges a gap in the current literature and investigates the potential role of cross-state border purchases to evade state emergency NVP sales restrictions in 2019. The study sample was restricted to NVP sales from the states neighboring Massachusetts, Rhode Island, and Washington, three states that implemented all NVP or flavored NVP sales restrictions in 2019. Among these neighboring states, the 2019 weekly county-level NVP sales by flavors (tobacco, mint/menthol, and other flavors) were compiled using Nielsen Scanner data. A quasi-experimental, comparison group pre-post study design was used to study the impacts of NVP sales restrictions on cross-state border NVP purchases. Weekly NVP sales for border counties significantly increased in response to the MA, RI, and WA bans for tobacco flavored (56%, 45%, 14%, respectively), menthol/mint flavored (51%, 2%, 41%, respectively), and other flavored (79%, 3%, 4%, respectively) products, compared to sales for non-border counties (all p-values < .01). Our study identified significant cross-state border NVP purchases in all studied states to circumvent NVP emergency sales restrictions in response to the EVALI outbreak. Policymakers should factor in these purchasing behaviors to evade sales restrictions when evaluating any future potential policies at the state or local levels. While retail sales data and retailer inspections indicate high compliance with Nicotine Vaping Product (NVP) flavor sales restrictions from major retail outlets, survey data obtain mixed findings on the effects of sales restrictions on vaping behaviors. Our study identified a significant increase in cross-state border NVP purchases to circumvent NVP sales restrictions in 2019, consistent across all three settings of Massachusetts, Rhode Island, and Washington. Policymakers should factor in these cross-state border NVP purchases in response to sales restrictions when evaluating any future potential NVP sales restrictions.

The Grocery Trolley Race in Times of Covid-19: Evidence from Italy

We study the effects of the containment measures imposed by the Italian Government during the first wave of the Covid-19 pandemic on sales of the retail trade sector, focusing on different types of grocery chain stores. We document a sustained growth in revenues for storable products, beginning right before restrictions on mobility were introduced, and lasting throughout the whole lockdown period. The surge has been driven by the dynamics of smaller outlets, located in urban areas and closer to the city centre, while hypermarkets experienced a drop, probably relating to their more peripheral position. Thanks to the remarkable granularity of the Nielsen scanner data and the staggered implementation of restrictions across regions, we causally identify the short-term effects of mobility constraints on outlets’ sales. According to our estimates, large grocery stores in areas subject to lockdown measures earned revenues around 10% lower than their control group did. In view of the protraction of the pandemic and the need for the Government to continue managing the containment of infections, our study helps quantifying the costs for the economy of mobility restrictions, also highlighting possible distortions arising among different categories of outlets.

Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis.

BackgroundElectronic nicotine delivery system (ENDS) brands, such as JUUL, used social media as a key component of their marketing strategy, which led to massive sales growth from 2015 to 2018. During this time, ENDS use rapidly increased among youths and young adults, with flavored products being particularly popular among these groups.ObjectiveThe aim of our study is to develop a named entity recognition (NER) model to identify potential emerging vaping brands and flavors from Instagram post text. NER is a natural language processing task for identifying specific types of words (entities) in text based on the characteristics of the entity and surrounding words.MethodsNER models were trained on a labeled data set of 2272 Instagram posts coded for ENDS brands and flavors. We compared three types of NER models—conditional random fields, a residual convolutional neural network, and a fine-tuned distilled bidirectional encoder representations from transformers (FTDB) network—to identify brands and flavors in Instagram posts with key model outcomes of precision, recall, and F1 scores. We used data from Nielsen scanner sales and Wikipedia to create benchmark dictionaries to determine whether brands from established ENDS brand and flavor lists were mentioned in the Instagram posts in our sample. To prevent overfitting, we performed 5-fold cross-validation and reported the mean and SD of the model validation metrics across the folds.ResultsFor brands, the residual convolutional neural network exhibited the highest mean precision (0.797, SD 0.084), and the FTDB exhibited the highest mean recall (0.869, SD 0.103). For flavors, the FTDB exhibited both the highest mean precision (0.860, SD 0.055) and recall (0.801, SD 0.091). All NER models outperformed the benchmark brand and flavor dictionary look-ups on mean precision, recall, and F1. Comparing between the benchmark brand lists, the larger Wikipedia list outperformed the Nielsen list in both precision and recall.ConclusionsOur findings suggest that NER models correctly identified ENDS brands and flavors in Instagram posts at rates competitive with, or better than, others in the published literature. Brands identified during manual annotation showed little overlap with those in Nielsen scanner data, suggesting that NER models may capture emerging brands with limited sales and distribution. NER models address the challenges of manual brand identification and can be used to support future infodemiology and infoveillance studies. Brands identified on social media should be cross-validated with Nielsen and other data sources to differentiate emerging brands that have become established from those with limited sales and distribution.

Automatic Debiased Machine Learning of Causal and Structural Effects

Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high‐dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.

Evaluation of Changes in Grams of Sugar Sold After the Implementation of the Seattle Sweetened Beverage Tax

Adults and children routinely exceed recommended intake amounts of added sugars established by dietary guidelines. Taxes are used as a policy tool to reduce demand for sugar-sweetened beverages (SSBs) given consumption-related adverse health outcomes but may induce substitution to other sources of added sugars. To examine the extent to which changes in grams of sugar sold from taxed beverages may be offset by changes in grams of sugar sold from untaxed beverages, sweets, and stand-alone sugar after the implementation of the Seattle, Washington, Sweetened Beverage Tax (SBT) on January 1, 2018. This study used difference-in-differences analyses to examine changes in grams of sugar sold from taxed and untaxed products in Seattle compared with Portland, Oregon, at year 1 and year 2 post tax. This study used Nielsen scanner data from supermarkets and mass merchandise as well as grocery, drug, convenience, and dollar stores on unit sales and measurements for beverage and food product universal product codes (UPCs) for each site for the pretax period (January 8-December 30, 2017) and the corresponding weeks in year 1 post tax (2018) and in year 2 post tax (2019). Nutritional analyses assessed grams of sugar for each UPC. The analytical balanced sample included 1326 taxed beverage UPCs, 239 untaxed beverage UPCs, 2054 sweets UPCs, and 81 stand-alone sugar UPCs. Statistical analysis was performed from January to August 2021. Implementation of the Seattle SBT. Changes in grams of sugar sold from taxed beverages, untaxed beverages, sweets, and stand-alone sugar. At both year 1 and year 2 post tax in Seattle compared with Portland, grams of sugar sold from taxed beverages decreased 23% (year 2 posttax ratio of incidence rate ratios [RIRR] = 0.77; 95% CI, 0.73-0.80). Sugar sold from untaxed beverages increased at year 1 post tax by 4% (RIRR = 1.04; 95% CI, 1.00-1.07) with no change at year 2 post tax. Sugar sold from sweets increased by 4% at both year 1 and year 2 post tax (year 2 posttax RIRR = 1.04; 95% CI, 1.03-1.06). There were no changes in stand-alone sugar sold. This study using difference-in-differences analysis found a net 19% reduction in grams of sugar sold from taxed SSBs at year 2 post tax after accounting for changes in sugar sold from untaxed beverages, sweets, and stand-alone sugar. These results suggest that SSB taxes may effectively yield permanent reductions in added sugars sold from SSBs in food stores.

Consumer Preferences for E-cigarette Flavor, Nicotine Strength, and Type: Evidence from Nielsen Scanner Data.

Many countries regulate e-cigarette attributes such as flavors and nicotine strength. Despite a large literature on consumer preferences for e-cigarette attributes, none used consumer-level longitudinal purchase data. We therefore use a unique large dataset on U.S. adults' e-cigarette purchases as to gain insight into real-world consumer choices and purchasing patterns. A total of 7098 purchase transactions, made by 1239 households on e-cigarette from various retail outlets for 2013 through 2017, were collected from Nielsen scanner data. We then apply an innovative multinomial logit model to examine which factors affected adult choices over 23 e-cigarettes products broken down by eight brands, three flavors, three nicotine strength levels, and two types. We found that tobacco flavor, medium nicotine strength (12< mg/ml ≤18), or disposables lead to higher adult purchases and such preference can vary over cigarette smoking status, purchase frequency, gender, race, and age. Specifically, smokers tend to purchase tobacco flavor, non-smokers or female vapers tend to purchase medium strength, and infrequent vapers tend to purchase disposables. Vapers are quite responsive to e-cigarette prices and also display loyalty (inertia) to e-cigarette brands, flavor, and nicotine strength. When modeled together, e-cigarette flavors, strength, type, price, and previous choice were all found to influence vapers' current purchase decision in the actual retail environment. The evidence presented here indicates that the impacts of certain restrictions on e-cigarette attributes, such as a flavor ban or maximum level of nicotine content, might have heterogeneous impacts on adults.

Consumer proximity and brand loyalty in craft soda marketing: A case study of Ale‐8‐One

AbstractCraft soda has become increasingly popular among consumers. Local manufacturers have opportunities to use a variety of strategies to differentiate their products from nationally recognized brands, including the use of real or natural ingredients, premium ingredients, and locally sourced ingredients, handmade and small batches, and creative taste. Loyal consumers of craft sodas often live within the proximity of the production location, and the manufacturers are normally small with a limited distribution area. From the local manufacturer's standpoint, it is important to segment the market based on the closeness to their distribution territory when studying the market demand, or at least to recognize if such locational preferences exist with a view toward identifying market expansion strategies. In this paper, we focus on the case for a Kentucky local craft soda producer, Ale‐8‐One. We examine the demand for craft and noncraft soft drinks using Nielsen scanner data to test whether consumer behavior is different in the core markets. The results show that, in this case, craft soda consumers are less price responsive and more brand loyal in the core market, compared with the outside market. [EconLit Citations: C55, D12, Q13].

Who Leads the Price in Honolulu’s Food Market? An Evaluation of the Competitiveness of Local Foods

We investigate the market dynamics of local and imported foods in Honolulu’s retail market. Our study employs weekly Nielsen scanner data covering 3 years for four selected foods, namely lettuce, tomatoes, milk, and eggs. The results from vector-autoregressive models and impulse response functions indicate that the prices of imported foods are exogenously determined while prices of local foods are responsive to variations in the prices of their imported counterparts. Moreover, imported food sales seem to recover faster after a price shock than local food sales, perhaps because of consumers’ relatively higher dependence on cheaper imported foods. One important policy implication from the findings is that Hawaii’s food marketers need to further differentiate their products, even in the low-price categories, in order to face competition from imports.

The Benefits of Transaction-Level Data: The Case of Nielsen Scanner Data

This study uses Nielsen scanner data to illustrate the benefits of transaction-level data. Specifically, we explore whether granular consumer purchases contain incremental value-relevant information about the corresponding manufacturers. Using weekly consumer purchases data generated by point-of-sale systems from 2006 to 2018, which capture around $2 trillion of U.S. retail sales, we construct a measure of aggregated consumer purchases at the firm-quarter level, and find that it strongly predicts manufacturer revenues. In addition, analyst forecasts of revenues have predictable errors and revisions, which implies that analysts do not fully incorporate the information in consumer purchases in a timely manner. Exploring investment implications, we find that hedge portfolios that buy (sell) stocks of firms with high (low) abnormal consumer purchases generate annualized returns on the magnitude of 14% to 19% depending on specification. This return predictability holds after controlling for risk factors and firm characteristics, and is robust across time. Finally, about 36% of the quarterly hedge returns are concentrated over the three-day window around earnings announcements, which suggests that the returns result from the correction of biased expectations rather than risk. These findings suggest that consumer purchases convey useful insights into firm fundamentals, shedding light on the benefits of using transactional data for market participants.

Do Local Soda Taxes Affect Prices and Consumption? A Tale of Two Cities

Many US cities have voted on or are considering soda taxes due in part to the growing literature about soda's negative health effects. However less is known about supplier and consumer responses to such taxes, particularly when they are implemented at a local rather than national level. Responses to local taxes could differ due to general equilibrium effects and institutional factors. In addition, more opportunities for avoidance may generate unintended consequences that have important policy implications. Rather than aggregating to the city level, this study leverages the high-frequency and high dimensional aspects of the Nielsen scanner data to investigate how prices and consumption of various goods change in response to local soda taxes. Comparing the cases of Berkeley and Philadelphia highlights which findings are constant across these very different cities and are likely to generalize to other local soda taxes. The results indicate incomplete pass-through rates that decline with beverage container size and moderate decreases in consumption of both diet and regular soda. Supply and demand elasticities can be separately identified using the tax as an instrument.

Assessing key factors influencing orange juice demand in the current US market

AbstractThe US orange juice (OJ) industry has experienced dramatic changes and pressure from both the supply and demand side in the past decade. It is critical for the industry to understand and enhance consumer's demand to sustain the higher prices associated with decreased production and maintain demand for the point in time where production can recover. This study utilized both Nielsen scanner data and a unique consumer tracker database to explore the impact of key factors on OJ consumption to estimate the return on investment. The usage of two datasets and multiple methods increases the reliability of results. Our results identified several demand drivers including demographics, health conditions, promotion awareness, and consumer's positive perceptions. Moreover, this study implies that a larger positive impact can be obtained by focusing on the positive aspects over responding to negative news. Therefore, we suggest the industry enhance generic promotion and advertising awareness and develop marketing programs that emphasize the positive attributes and benefits of OJ.

Regional Consumption Responses and the Aggregate Fiscal Multiplier

We use regional variation in the American Recovery and Reinvestment Act (2009-2012) to analyze the effect of government spending on consumer spending. Our consumption data come from household-level retail purchases in the Nielsen scanner data and auto purchases from Equifax credit balances. We estimate that a $1 increase in county-level government spending increases local non-durable consumer spending by $0.29 and local auto spending by $0.09. We translate the regional consumption responses to an aggregate fiscal multiplier using a multiregional, New Keynesian model with heterogeneous agents, incomplete markets, and trade linkages. Our model is consistent with the estimated positive local multiplier, a result that distinguishes our incomplete markets model from models with complete markets. At the zero lower bound, the aggregate consumption multiplier is twice as large as the local multiplier because trade linkages propagate the effect of government spending across regions.

The price relationship between organic and non-organic vegetables in the U.S.: evidence from Nielsen scanner data

ABSTRACTThis study investigates price relationships between organic and conventional carrots, tomatoes, and lettuce in the U.S. utilizing Nielsen scanner data from 2006–2015. We employ a threshold vector error correction model (TVECM), threshold vector autoregressive model (TVAR), and threshold cointegration test to test whether market integration exists between organic and conventional vegetables as well as the existence of asymmetric price transmission. The results find positive long-run relationships between organic and conventional prices of carrots and tomatoes and show the existence of asymmetric price transmission in price pairs of lettuce and tomatoes. Our findings suggest that the price relationship between organic and conventional vegetables varies by characteristics, such as shelf life, volatility in the price premium, and substitutability.

The Price Relationship between Organic and Non-Organic Vegetables in the U.S.: Evidence from Nielsen Scanner Data

This study investigates price relationships between organic and conventional carrots, tomatoes, and lettuce in the U.S. utilizing Nielsen scanner data from 2006 to 2015. We employ a threshold vector error correction model (TVECM), threshold vector autoregressive model (TVAR), and threshold cointegration test to test whether market integration exists between organic and conventional vegetables as well as the existence of asymmetric price transmission. The results find positive long-run relationships between organic and conventional prices of carrots and tomatoes and show the existence of asymmetric price transmission in price pairs of lettuce and tomatoes. Our findings suggest that the price relationship between organic and conventional vegetables varies by characteristics, such as shelf life and volatility in the price premium. Furthermore, the results indicate that consumers in the U.S. recognize organic and conventional carrots and tomatoes as partially substitutable goods, while U.S. consumers consider organic and conventional lettuces as different goods (no cross-price relationship).

US Household Demand for Organic Fruit

We estimate US household monthly elasticities of demand for some of the more popular organic fruits. To our knowledge, this is the first US-wide, multi-year analysis of price and income elasticities for various organic fruits. We calculate elasticities of demand for low-income, middle class, and rich income bracket households using three estimation techniques: two econometric methods and one machine learning method (least absolute shrinkage and selection operator (LASSO)). Demand estimates are based on Nielsen scanner data from approximately 60,000 households collected from 2011 to 2013. Generally, we find that own-price conditional and unconditional elasticities of demand for organic fruits are negative. Unconditional elasticity magnitudes tend to be largest in the representative middle-class household. Income elasticities of demand measurements are inconsistent and often statistically insignificant. This finding is consistent with the survey literature finding that many consumers buy organic food for mostly moral or ethical reasons. We run two policy experiments: a 10% subsidy of organic fruits, and a 10% tax on conventional fruits. Our hypothetical policies engender a stronger reaction among the general public than habitual buyers of organic fruit; unconditional purchase and expenditure elasticities are generally larger than conditional purchase and expenditure elasticities. Finally, we find that elasticities measured with the LASSO technique are not radically different than those measured with econometric methods. The most noticeable difference between the two analytical techniques is that LASSO is more likely to find price and income elasticities of demand that indistinguishable from zero, both substantively and statistically.

Internet cigarette vendors make tax-free claims and sell cigarettes cheaper than retail outlets

ObjectiveThis paper aims to (1) assess whether promotion of tax-free sales among Internet cigarette vendors (ICVs) changed between 2009 and 2011, (2) determine which types of ICVs are most likely...

LOCAL PREMIUM OR LOCAL DISCOUNT: THE CASE OF PACKAGED FRESH TOMATOES IN HAWAII

AbstractThis article utilizes the 2011 Nielsen scanner data for the Honolulu fresh tomato market to explore the existence of price premium for local food. Hedonic analysis is conducted to delineate the price impact of the local attribute. Contrary to the widely perceived local price premium in the consumer preference literature, mixed results of price premiums and discounts are discovered for local tomato products. Additional investigation suggests that the prices of local tomatoes are likely influenced by seasonal output fluctuation. The limited market distribution capacity facing local producers may have contributed further to the retail discounting.

National Demand for Fresh Organic and Conventional Vegetables: Scanner Data Evidence

Using AC Nielsen scanner data on U.S. household consumption of selected fresh vegetables from 1999 to 2003, this study provides an overview of the organic fresh vegetable market by investigating market shares and price premiums of selected organic fresh vegetables and estimating the interrelationship between consumer demand for organic and conventional fresh vegetables. The linear Almost Ideal Demand System was found to fit the data best among alternative demand models. Expenditure, own, and cross price elasticities were computed for both organic and conventional vegetables based on the best fitting model.

Modeling Heterogeneity and State Dependence in Consumer Choice Behavior

In frequently-purchased-consumer-goods markets, consumer brand choices exhibit substantial persistence across purchase occasions. In this article, I estimate a choice model that admits of both heterogeneity in preferences and true state dependence as sources of this persistence, using Nielsen scanner data on ketchup. I find evidence for true state dependence in the choice process, even after controlling for a rich heterogeneity structure. Simulation of the model indicates that the long-term effect of a promotion-induced purchase on future purchase probabilities is positive but small.

A Multiplicative Fixed-Effects Model of Consumer Choice

The issue of consumer heterogeneity in discrete choice analysis has been attracting much attention recently. Research has suggested that heterogeneity can result in biased parameter estimates which, in turn, can lead to incorrect conclusions. Among the many methods proposed in the literature to handle heterogeneity, fixed effects models seem to be the most attractive from a substantive point of view. However, in order to provide consistent estimates, these models typically require long purchase histories. This difficult constraint has prevented their widespread use. In this paper, we propose a new model which offers the benefits of fixed effects models without requiring long purchase histories. Our approach differs from the classic formulation in two ways. First, we calibrate the common and fixed-effects sequentially rather than simultaneously. This two-stage estimation permits us to obtain unbiased estimates of the common parameters even when the sample has households with very few observations. Second, we incorporate the fixed effects in a multiplicative rather than in an additive form. Our assumptions regarding the error term result in a probit model with heteroskedastic and temporally correlated random utilities. We develop a method of moments based estimator to calibrate parameters of models with this type of error structure. This procedure exploits the property that the method of moments yields estimates which are asymptotically as efficient as the maximum likelihood estimates given an appropriate starting point and a matrix of instruments. This is a very general procedure in that its mechanics are not tied to the proposed model. It can be readily applied to other problems where probit models are used to analyze data with serial correlation. This is a methodological contribution of the paper. The model and estimation procedure are illustrated on the A. C. Nielsen scanner data base for the liquid detergent category. Our results indicate that the proposed model can provide a better fit than random effects and loyalty based models and better predictive performance than random effects models. From a substantive perspective, our analysis provides the following findings. (a) the distribution of price sensitivities has a low variance—thus, most households have very similar price sensitivity in this category; (b) the promotion response distribution has a high variance—suggesting that there are some households that are highly responsive to promotions while there are quite a few households that are less influenced by promotions; (c) preferences and price sensitivity have a negative covariance—which is an indication that a reduction in preference for a brand is associated with an increase in price sensitivity and vice versa; (d) preferences and promotion response have a positive covariance—thus, as the preference for a brand increases, response to promotions by the brand also increases; and (e) price sensitivity and promotion response have a negative covariance—implying that, as households become more price sensitive, purchases may be driven more by price than by promotions; in other words, rather than responding to promotions such as displays and features as signals of a reduced price, price sensitive households actively evaluate prices in arriving at a choice. The proposed approach can be used by theoretical as well as applied researchers of brand choice behavior. Specifically, estimates of fixed-effects provided by the model can be used to cluster households into groups with similar parameters. Profiling the resulting groups in terms of demographics would provide interesting insights—from a theoretical perspective—regarding the household characteristics that are associated with different types of response behavior. It would also help managers to better target their brands. Using the proposed approach for cross-category analyses of fixed-effects would be an interesting area of future research. For instance, given a sample of households and their purchase histories in multiple categories, the proposed model can be used to explore the relationship between household behavior and category characteristics. In particular, issues such as the following can be investigated by comparing household-level estimates across categories: (a) are households that are highly price sensitive in one category also price sensitive in other categories? (b) what household characteristics are associated with high (low) price sensitivity or high (low) promotion response across categories? and (c) in what types of categories are households likely to exhibit high (low) price sensitivity or high (low) promotion response?