Recipient Organization
UNIV OF CONNECTICUT
438 WHITNEY RD EXTENSION UNIT 1133
STORRS,CT 06269
Performing Department
Agri & Resource Economics
Non Technical Summary
There are growing concerns about the impact of devastatingly low milk prices on producers in the last five years, due to decreasing demand for conventional milk, increasing supply, and insufficient government safety nets. This has prompted exit particularly of small dairy farms, which have difficulty competing with larger farms in a commoditized milk market unless they can receive higher prices and margins from specialty milk markets. Whether such a move will improve the profitability of small dairy farms will depend on how specialty milk and environmental concerns shift consumer demands and how firms respond. The proposed project will first estimate consumer valuation of local and organic milk in the Northeast, applying a random coefficients logit demand model to a rich dataset of consumer sales transactions and product labeling. Implications for producers will stem from computed marginal costs, price cost margins, and Lerner indexes accruing to local, organic, and conventional milk products. We will also provide estimates on how consumers' environmental attitudes impact their responsiveness to prices and substitution between local and non-local milk. Without this knowledge, we cannot develop effective marketing strategies and policy instruments to enhance the competitiveness of local fluid milk under new, changing market conditions. This project is well aligned with two priorities set by NIFA: Animal Health and Production and Animal Products, and Agricultural Economics and Rural Communities.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
The overarching research goal of this project is to provide new knowledge of the drivers of consumer preferences for local milk and the ensuing consequences for cost and profit margins in order to guide private and public policy strategies aimed at this sector. To this end, this project will pursue three specific objectives:Objective 1: Identify how locality and whether milk is organic affect consumer choices of fluid milk in the Northeast. We will define locality using the geographic distance between the point of purchase and the point of production and then apply a random coefficients model to depict consumer behavior with respect to local and organic milk, using scanner and labeling data.Objective 2: Measure the marginal costs, gross profit margins and Lerner indexes of market power for local, organic and conventional fluid milk in the Northeast. We will assume that retailers maximize profits at the product brand level following Bertrand-Nash price competition and obtain implied marginal cost of various milk products to compute gross profit margins and Lerner indexes.Objective 3: Examine how heterogeneous consumer environmental preferences affect consumption decisions with respect to local milk. By measuring local vs. non-local milk consumption at the state level, we will estimate the effects of consumer environmental attitudes on the share of local milk consumption as well as the effects on consumer's elasticities of substitution.
Project Methods
Methodology for Objective 1: Identify how locality and whether milk is organic affect consumer choices of fluid milk in the Northeast. We will define locality using the geographic distance between the point of purchase and the point of production and then apply a random coefficients model to depict consumer behavior with respect to local and organic milk, using scanner and labeling data. In order to incorporate consumer valuation of milk characteristics, including whether or not it is local and/or organic, we model consumer choices using a Berry, Levinsohn, and Pakes (1995) demand model (hereafter BLP)--a characteristics base approach based on a random coefficients logit demand model that takes into account consumers' and products' heterogeneity, considers the potential endogeneity of prices, overcomes the dimensionality problem of typical product space (traditional) demand approaches, and restrictive substitution patterns of standard logit or nested logit models. Data to operationalize the model comes from two sources. Consumer purchase data is from sales data from the Information Resources Incorporated (IRI) database, provided by the Zwick Center for Food and Resource Policy at the University of Connecticut. The milk data set contains brand-level information in the New England area and will be used to compute prices, quantities, and market shares of local and non-local milk. Labeling to identify local brands and bottling and consumption locations will come from Mintel GNPD data base, to be acquired under this project and merged with the sales data. Based on the zip codes of consumer panelists and milk brands, we will use ArcGIS to calculate the distance between point of consumption and point of production as the measure of locality. Then the two databases (i.e., the one on locality and the one on milk sales) will be combined.Methodology for Objective 2: Measure the marginal costs, gross profit margins and Lerner indexes of market power for local, organic and conventional fluid milk in the Northeast. Following Nevo (2001), from the first-order conditions of profit maximization, we will derive the marginal cost at the product-brand level and then the price-cost margins as represented by the Lerner indexes at the product-brand level. Assuming that firms engage in Nash-Bertrand price competition, i.e., each firm maximizes profits by setting prices that take into account the prices of other products, but not how other firms react (Vincent 2015). From the first-order condition for profit maximization, the implied marginal costs and markups can be obtained for each product brand, including local milk, and from which the Lerner indexes will be calculated from inverted market shares' expressions. For comparison, we will estimate marginal cost and Lerner indices (percent markup) for all milk products in the sample and compare consumer reactions to price changes in local, organic, and conventional milk. This objective will use the same database assembled for objective 1 as well as the demand estimates.Methodology for Objective 3: Examine how heterogeneous consumer environmental preferences affect consumption decisions with respect to local milk. This will be done in two parts: one using a constant elasticity of substitution model at the state level and another one levering information from objectives 1 and 2. By measuring local vs. non-local milk consumption at the state level, we will estimate the effects of consumer environmental attitudes on the share of local milk consumption as well as the effects on consumer's elasticities of substitution. We adopt a constant elasticity of substitution model treating local vs. non-local in the same fashion as done in international trade models such as the Armington model. The model is then augmented by adding a proxy for consumers' environmental concerns. Data to operationalize the model under this objective will come from two sources. Sales data will come from Information Resources Incorporated (IRI) aggregated to city levels, and the sample will be national to obtain enough variation in the data. From sales data, we will measure the ratio of local and non-local milk quantity consumed and the ratio of prices of local to non-local milk. The Mintel GNPD dataset will be used to identify local milk brands and the nutritional characteristics of the milks involved. Because we need to clearly define local milk rather than using a continuum as in objective 1, we define local as within state boundaries and designate as local milk that which is bottled in the same state where it is consumed (Darby et al., 2008). Following Wagner (2016), two proxies are used for environmental concerns. The first is the scores assigned by the League of Conservation Voters to each city's delegation to the U.S House of Representative. The second is the percentage of solid waste recycled and standardized Sierra Club membership per capita. Control variables include socio-demographic characteristics and share of milk in a state's agricultural sales, provided by USDA. To better integrate objectives 3 and 1, the BLP model in objective 1 will be extended to include a proxy for environmental concerns at the individual household level. For instance, Mainieri et al. (1997) linked green buying to consumer's environmental concerns. Although the consumer data does not have direct variables to measure environmental concerns, environmentally-friendly buying can be used as a proxy. Thus, purchases by consumers of products like recyclable or reusable packaging, energy-efficient light bulbs, and biodegradable detergents can be combined into a score, such as using principal components, to elicit a measure that can be incorporated into the BLP model from objective 1.