The economic impact of craft breweries is based on government licensing data, Infogroup, industry sources, and survey data from New York State Brewers Association (NYSBA). Craft breweries include those that produce their own craft beers, craft production facilities contracted to produce craft beers for other companies, and companies marketing their own craft beer brand. Since the term “craft” is not necessarily defined, the NYSBA determined which of New York’s licensed breweries should be considered as craft breweries.
Based on these combined datasets, it is estimated that there are about 434 active craft breweries in the State of New York. Employment data from the NYSBA survey was used when available. If no survey data for the company was available, Infogroup employment figures are used for estimate the jobs in each facility. Median job figures were used where these employment figures were not available.
Craft Brewery Retailing
Most craft brewing facilities sell at least some of their beer at either a taproom or a restaurant located either at the brewery, or contiguous to it. The jobs at these facilities (chefs, servers, bartenders, dishwashers) are generally similar to those in a restaurant or a bar, rather than a brewing operation. This means that they will have significantly different wage scales and productivity than the manufacturing and sales jobs associated with the brewery itself. Data on the number of these brewery retailing jobs were gathered from the NYSBA survey where available. If survey data are not available, the number of retail jobs would be the difference between total jobs at the brewery as reported by Infogroup, and the brewing jobs that JDA ran through the NYSBA model. For those facilities that JDA staff identified as those that serve food and where no job figures are available, a median of 12 retail jobs is used. Craft brewing retailers were removed from the on-premise retail and the tourism restaurant job counts based on the inverse of the share of revenues in these facilities resulting from craft beer sales.
The wholesaling tier is responsible for the transportation of New York craft beer from craft breweries, importation of craft beers from other states or countries, and for the storage of these products for a limited amount of time. Data to identify these facilities include Alcohol Wholesaler Permit Lists from the US Department of the Treasury Alcohol and Tobacco Tax and Trade Bureau (TTB), Infogroup, the National Beer Wholesalers Association, and the Wine and Spirits Wholesalers of America.
Not all of the craft beer produced in New York goes through the wholesaling system. Based on data from the New York State Liquor Authority, it is possible to identify those breweries that self-distribute. In addition to the self-distribution of beer, a significant amount is sold via breweries that sell food. Of the 434 craft breweries in New York, 149 of them have a full menu of food that they sell along with their beers. In addition, this model assumes that each brewery sells or samples beer at their facilities. Using brewing jobs as a proxy, all of these on-premise and self-distribution activities, account for about 48 percent of the craft beer produced in New York. The remainder is sold via beer distributors.
Beer distribution jobs are allocated across the whole sector by taking a percentage of total craft beer wholesaling output in New York, multiplying by the New York craft production share going into distribution, and multiplied by the distribution margin from Beer Serves America.
The retailing tier is comprised of both on-premise retailing and off-premise retailing. On-premise retailers allow for the consumption of craft beer at a specific facility. Examples of on-premise retailers include businesses such as restaurants, bars, and sporting venues. Off-premise retailers sell craft beer to take away and consume elsewhere. Examples of these types of retailers include businesses such as grocery stores, convenience stores, and warehouse clubs. Retail sales that are occurring at craft brewery owned facilities such as tasting rooms or breweries that sell food are not included in this impact. These impacts are captured in the craft brewery sector.
Employment data were gathered at the zip code level from Infogroup, The Economic Census of Retail Trade by Product Line, and Bureau of Economic Analysis, Personal Consumption Expenditures by Type of Product, is used determine the type of off-premise stores that sell beer as well as the percent of sales at each store type that is due to the sale of beer. IMPLAN Use data and expenditure by product line data are used to determine the type of on-premise stores that sell beer as well as the percent of sales at each store type that is due to the sale of craft beer. The craft beer share of these sales is based on the percent of self-distributed and wholesale sales of craft beer in the state marked up on– and off-premise margins from Beer Serves America.
Craft Beer Tourism
One of the important elements of the impact of craft breweries on the New York economy is their attractiveness to tourists. Every year, hundreds of thousands of people visit craft brewery regions across the state in part to visit (or even stay at) a craft brewery, learn about brewing and sample different craft beers. In order to estimate the economic impact of these visits it was first necessary to calculate the number of customers visiting to the state’s 434 craft breweries. This was done at the county level based on an econometric model that used detailed tourism data for beverage alcohol producers from those counties across the country where studies have been conducted. A function was developed that estimated the number of visits per craft brewery based on the number of craft brewery in each of the 58 counties in New York that produce craft beer. This relies on the idea of economic clustering, which suggests that a larger grouping of craft breweries would attract more visitors than a smaller grouping. The tendency of locational clustering of similar types of firms has been documented by economists since at least the beginning of the twentieth century. British academic Stephen Brown described the rule of ‘retail compatibility,’ which explains how retail businesses, such as restaurants, know that two compatible firms in close proximity will show an increase in business volume directly proportionate to the incidence of consumer interchange between them. This concept was confirmed by a study by Andrei Rogers who found that the clustered spatial pattern exhibited by consumer goods retailers appears to contradict a common hypothesis that these stores tend to repel one another.
While Rogers suggests that population densities have a lot to do with the clustering, there is significant economic theory that suggests that the tendency of activities to cluster is related more to competitive characteristics than to generalized demographic characteristics.
Using this model JDA calculates that a craft brewery existing alone in a county would receive just under 40 visitors per employee per week, and that the number of annual visitors would rise linearly at a rate of about 1 additional visit per week for each additional craft brewery in the county. As such, a county with 10 craft breweries would see 50 visits per week per worker, while one with just 1 craft brewery would report 40. Visits do not reflect tourists. This number was derived using both survey information from 96 different New York craft breweries, and an adjustment to the model based on the share of tourist days in New York relative to the state’s adult population.
Based on this model and survey data results, it is estimated that a total of 48.6 million unique visits to New York craft breweries occur each year. The bulk of these are local visitors attending an event, having dinner or just stopping by to purchase a bottle of craft beer. The number of visits conducted by tourists is much lower, about 9.7 million for 2018. No state specific data are available to estimate the number of craft breweries each individual visitor goes to on a trip, however, an extensive study of wineries in Napa California, suggests each person visits on average 3.29 wineries. Using this as a proxy and dividing by the number of tourist visits provides an estimate of just over 3.0 million actual visitors going to craft breweries across the state.
Once the number of visitors was calculated, spending propensities was created using data as broken into 25 industries based on percentages derived from the US Department of Commerce, Bureau of Economic Analysis. These were in turn, combined into aggregate categories for processing with the IMPLAN model and adjusted to reflect the number of leisure travelers. This was adjusted again to account for local spending. As such, rather than basing the direct tourism impact on jobs (as with the rest of the study), it is based on estimated visitor spending on key tourism categories.
Economic Impact Model
The IMPLAN model is designed to run based on the input of specific direct economic factors. It uses a detailed methodology (see IMPLAN Methodology section) to generate estimates of the other direct impacts, tax impacts and indirect and induced impacts based on these entries.
Once the initial direct employment figures have been established, they are entered into a model linked to the IMPLAN database. The IMPLAN data are used to generate estimates of direct wages and output. Wages are derived from data from the U.S. Department of Labor’s ES-202 reports that are used by IMPLAN to provide annual average wage and salary establishment counts, employment counts, and payrolls at the county level. Since this data only covers payroll employees (those eligible for unemployment insurance), they are modified to add information on those who are not, such as: independent workers, agricultural employees, and construction workers. Data are then adjusted to account for counties where non-disclosure rules apply. Wage data include not only cash wages, but health and life insurance payments, retirement payments, and other non-cash compensation as well. They include all income paid to workers by employers.
Total output is the value of production by industry in a given state. It is estimated by IMPLAN from sources similar to those used by the BEA in its RIMS II series. Where no Census or government surveys are available, IMPLAN uses models such as the Bureau of Labor Statistics’ growth model to estimate the missing output.
The model also includes information on income received by the federal, state, and local governments, and produces estimates for the following taxes at the federal level: corporate income, payroll, personal income, estate and gift, excise taxes, customs duties, and fines, fees, etc. State and local tax revenues include estimates of corporate profits, property, sales, severance, estate and gift and personal income taxes as well as licenses, fees, and certain payroll taxes.
State sales and excise taxes were calculated based on total craft beer sales data from multiplied by the state sales tax rate of 4 percent, and the estimated barrelage multiplied by the state excise tax rate of $4.34.
Input-output analysis, for which Wassily Leontief received the 1973 Nobel Prize in Economics for, is an econometric technique used to examine the relationships within an economy. It captures all monetary market transactions for consumption in a given period and for a specific geography. The IMPLAN model uses data from many different sources – as published government data series, unpublished data, sets of relationships, ratios, or as estimates. The Minnesota IMPLAN group gathers this data, converts them into a consistent format, and estimates the missing components.
There are three different levels of data generally available in the United States: federal, state, and county. Most of the detailed data are available at the county level, but there are many issues with disclosure, especially in the case of smaller industries. IMPLAN overcomes these disclosure problems by combining a large number of datasets and estimating variables that are not found in the merged data. The data are then converted into national input-output matrices (Use, Make, By-products, Absorption, and Market Shares) as well as national tables for deflators, regional purchase coefficients, and margins.
The IMPLAN Make matrix represents the production of commodities by industry. The Bureau of Economic Analysis (BEA) Benchmark I/O Study of the US Make Table forms the bases of the IMPLAN model. The Benchmark Make Table is updated to current year prices, and rearranged into the IMPLAN sector format. The IMPLAN Use matrix is based on estimates of final demand, value-added by sector, and total industry and commodity output data as provided by government statistics or estimated by IMPLAN. The BEA Benchmark Use table is then bridged to the IMPLAN sectors. Once the re-sectoring is complete, the Use tables can be updated based on the other data and model calculations of interstate and international trade.
In the IMPLAN model, as with any input-output framework, all expenditures are in terms of producer prices. This allocates all expenditures to the industries that produce goods and services. As a result, all data not received in producer prices are converted using margins derived from the BEA Input-Output model. Margins represent the difference between producer and consumer prices. As such, the margins for any good add up to one.
Deflators, which account for relative price changes during different time periods, are derived from the Bureau of Labor Statistics (BLS) Growth Model. The 224 sector BLS model is mapped to the 536 sectors of the IMPLAN model. Where data are missing, deflators from BEA’s Survey of Current Businesses are used.
Finally, the Regional Purchase Coefficients (RPCs) – essential to the IMPLAN model – must be derived. IMPLAN is derived from a national model, which represents the “average” condition for a particular industry. Since national production functions do not necessarily represent particular regional differences, adjustments need to be made. Regional trade flows are estimated based on the Multi-Regional Input-Output Accounts, a cross-sectional database with consistent cross interstate trade flows developed in 1977. These data are updated and bridged to the 536 sector IMPLAN model.
Once the databases and matrices are created, they go through an extensive validation process. IMPLAN builds separate state and county models and evaluates them, checking to ensure that no ratios are outside of recognized bounds. The final datasets and matrices are not released until extensive testing takes place.