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BACKGROUND Locally grown foods, through farm‐to‐school (FTS) activities, may be a key component to balancing foodservice budgets and alleviating financial constraints in school districts. Therefore, the purpose of this study is to examine the impact of local food expenditures on school foodservice revenues and earnings. We anticipated a positive impact of local food expenditures on foodservice revenues and earnings. METHODS Ordinary Least Squares (OLS) regression analysis was conducted using data from the 2013 US Department of Agriculture Farm to School Census. The questionnaire primarily asked all US public school districts about their FTS operations during 2011‐2012 school year. RESULTS Although our results initially showed a negative impact of local milk and nonmilk expenditures on foodservice revenues from food sales, when combined with revenues from the federal government, the impact is positive. The positive effect seems to hold when adding foodservice revenues from both food sales and federal funds. Our study found a similar pattern for foodservice earnings. CONCLUSIONS This may indicate that competitive foods are still widely preferred in school districts. Revenue from the federal government is critical to maintain FTS activities viable to students and community members although federal funds and food sales may not cover total foodservice expenditures.

Purpose The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems. Design/methodology/approach The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE). Findings The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models. Originality/value The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.