“Food or Fuel”: Calculating Elasticities to Understand “Heat or Eat” Behavior
Anthony G. Murray, USDA-ERS and Bradford F. Mills, Virginia Tech
Background and Methodology
From 1999 through 2009 U.S. household energy costs increased rapidly. The sharp increase in
costs to heat or cool a residence forces some consumers to make difficult choices. The core
finding from previous research suggests that many low-income households face an extremely
difficult decision between heating a residence or eating. Rising prices and limited budgets of
low-income households prevent them from choosing sufficient quantities of both fuel and food,
leading them to a choice of “heat or eat”.
Studies to date support the existence of a food-fuel trade-off for low-income households within
the United States, but none have attempted to calculate the cross-price elasticities associated with
this trade-off. Elasticities are important because they provide a very simple, intuitive measure
for how consumers react when prices change. Quantifying energy-food trade-offs in low-income
households provides policy makers with information needed to understand the impacts that
energy price shocks have on household food security and to design assistance programs and
other safeguards to protect vulnerable households.
This paper generates own-price, cross-price, and expenditure elasticities using a Quadratic
Almost Ideal Demand System (QUAIDS), a refinement that better estimates non-linear Engel
curves compared to the original Almost Ideal Demand System. These elasticity estimates are
then used to examine how energy price shocks impact household food consumption. Poor
households often face the most severe “heat or eat” decision and Southern households have
unique demographic and climatic considerations. Therefore, elasticities are also estimated
separately for these sub-samples.
The paper relies on several data sources. Household expenditure data from 1999 until 2009 is
obtained from the Bureau of Labor Statistics' Consumer Expenditure Survey (CES). Price data is
collected from the ACCRA Cost of Living Index, a proprietary price index, and the Energy
Information Administration (EIA). Finally, state level climate data are compiled from the
National Oceanic and Atmospheric Administration (NOAA). Due to data limitations, the final
sample for analysis includes 121,435 households from 35 states.
Findings
Elasticities are calculated from the parameter estimates using mean prices, expenditures, and
demographics. All elasticity estimates are significant at conventional levels. Own-price
elasticities are consistent with expectations. Food at home and food away from home show
inelastic own-price elasticities. Own-price elasticities for natural gas and electricity, however,
are more elastic. Cross-price elasticities are of particular importance because they describe the
heat or eat trade-offs made by households. Cross-price elasticity estimates reveal that
expenditures on food fall when energy prices increase and expenditures on energy fall when food
prices increase.
Poor households are the most likely to face the “heat or eat” dilemma. Elasticity estimates for
poor households seem to confirm a more pronounced reaction to price changes. Own-price
elasticity estimates for poor households are higher for food at home, natural gas, and electricity
compared to non-poor households. Thus, price increases reduce demand for basic commodity
groups more in poor households than in non-poor households.
Many cross-price elasticities also suggest poor households react differently to price changes than
non-poor households. Cross-price elasticity estimates for poor households are lower for both
natural gas-food at home and electricity-food at home. Lower cross-price elasticity estimates
might imply extremely constrained budgets for poor households who operate at a minimum
necessary expenditure level for survival. Reductions in expenditures below these levels might
endanger the well-being of family members, making elasticity estimates smaller and masking a
more dangerous underlying problem.
Southern households are often categorized as regionally different based on climate and regional
preferences compared to the rest of the United States. Most elasticity estimates calculated for the
Southern sub-sample, however, do not support this characterization. Both own-price and crossprice elasticity estimates for all commodity groups except natural gas are relatively similar to
those estimated for the nation as a whole. The small differences in fuel-food cross-price
elasticity estimates support the notion that Southern households react comparably when facing
energy price shocks.
Poor households, those most susceptible to food and energy insecurity, exhibit heat or eat tradeoffs in consumption behavior. When an energy shock causes energy prices to rise, poor
households reduce consumption of food expenditures as well as the directly impacted energy
commodity. Specifically, estimates show that an energy price shock of 10 percent can lead to
reductions in food at home expenditures of up to five percent. Policy makers must therefore
realize that energy shocks have a significant impact on household food expenditures, especially
food at home. On the other hand, policy makers do not need to make any special concessions or
regulations for Southern households. Southern households adjust consumption similarly to the
rest of the United States, even though they live in a unique climate with more cooling and less
heating needs and have substantially more poor and rural households.
Policy makers could expand and improve upon federal energy assistance programs. Future
energy assistance programs can help safeguard poor households in ways not directly tied to
utility bills. Energy assistance benefits that help poor households maintain food expenditure
levels equal to pre-shock status would reduce the food or fuel trade-offs that low-income
households make and preserve household food security in the face of energy price shocks.
Contact:
Anthony G. Murray
USDA-Economic Research Service
355 E Street SW
Washington DC 20024
(202)-694-5256
agmurray@ers.usda.gov