research paper about price elasticity

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A Refresher on Price Elasticity

You can’t form a pricing strategy without it.

Setting the right price for your product or service is hard. In fact, determining price is one of the toughest things a marketer has to do, in large part because it has such a big impact on the company’s bottom line. One of the critical elements of pricing is understanding what economists call price elasticity .

• Amy Gallo is a contributing editor at Harvard Business Review, cohost of the Women at Work podcast , and the author of two books: Getting Along: How to Work with Anyone (Even Difficult People) and the HBR Guide to Dealing with Conflict . She writes and speaks about workplace dynamics. Watch her TEDx talk on conflict and follow her on LinkedIn . amyegallo

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• The effect of rising...

The effect of rising food prices on food consumption: systematic review with meta-regression

• Related content
• Peer review
• Rosemary Green , research fellow 1 2 ,
• Laura Cornelsen , research fellow 2 3 ,
• Alan D Dangour , senior lecturer 1 2 ,
• Rachel Turner , honorary research fellow 1 ,
• Bhavani Shankar , professor of international agriculture, food and health 2 4 ,
• Mario Mazzocchi , associate professor 5 ,
• Richard D Smith , professor of health system economics 2 , dean 3
• 1 Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
• 2 Leverhulme Centre for Integrative Research on Agriculture and Health, London, UK
• 3 Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
• 4 Centre for Development, Environment and Policy, School of Oriental and African Studies, London, UK
• 5 Department of Statistical Sciences, University of Bologna, Bologna, Italy
• Correspondence to: R Green Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK rosemary.green{at}lshtm.ac.uk
• Accepted 3 June 2013

Objective To quantify the relation between food prices and the demand for food with specific reference to national and household income levels.

Design Systematic review with meta-regression.

Data sources Online databases of peer reviewed and grey literature (ISI Web of Science, EconLit, PubMed, Medline, AgEcon, Agricola, Google, Google Scholar, IdeasREPEC, Eldis, USAID, United Nations Food and Agriculture Organization, World Bank, International Food Policy Research Institute), hand searched reference lists, and contact with authors.

Study selection We included cross sectional, cohort, experimental, and quasi-experimental studies with English abstracts. Eligible studies used nationally representative data from 1990 onwards derived from national aggregate data sources, household surveys, or supermarket and home scanners.

Data analysis The primary outcome extracted from relevant papers was the quantification of the demand for foods in response to changes in food price (own price food elasticities). Descriptive and study design variables were extracted for use as covariates in analysis. We conducted meta-regressions to assess the effect of income levels between and within countries on the strength of the relation between food price and demand, and predicted price elasticities adjusted for differences across studies.

Results 136 studies reporting 3495 own price food elasticities from 162 different countries were identified. Our models predict that increases in the price of all foods result in greater reductions in food consumption in poor countries: in low and high income countries, respectively, a 1% increase in the price of cereals results in reductions in consumption of 0.61% (95% confidence interval 0.56% to 0.66%) and 0.43% (0.36% to 0.48%), and a 1% increase in the price of meat results in reductions in consumption of 0.78% (0.73% to 0.83%) and 0.60% (0.54% to 0.66%). Within all countries, our models predict that poorer households will be the most adversely affected by increases in food prices.

Conclusions Changes in global food prices will have a greater effect on food consumption in lower income countries and in poorer households within countries. This has important implications for national responses to increases in food prices and for the definition of policies designed to reduce the global burden of undernutrition.

Introduction

Food prices are a primary determinant of consumption patterns, and high food prices may have important negative effects on nutritional status and health, especially among poor people. 1 The global food price crisis of 2007-08 focused international attention on the effect of changes in food price on nutrition and health. Estimates from the United Nations Food and Agriculture Organization suggest that in 2008 an additional 40 million people were pushed into hunger by the global rise in cereal prices, 2 3 4 and evidence is accumulating that dietary diversity and quality have been negatively affected by food price rises, particularly among the poorest. 5 In contrast, the governments of wealthy countries are increasingly adopting fiscal measures that change the relative price of foods to promote healthy diets. 6 7 Simulation studies have suggested that imposing taxes on foods such as sugar sweetened beverages 8 9 or foods high in saturated fats and salt 10 11 could result in reductions in obesity and cardiovascular mortality, although because of a lack of relevant data the actual impact of such taxes on different population subsections is largely unknown.

Fiscal approaches to control tobacco use have identified that responsiveness to raised tobacco prices is higher in low income countries and among poorer households who spend a greater relative share of their income on tobacco. 12 Similar information on the differing response to food price changes by national and household income level is needed to help with the identification of food price policies to protect population health. A recent report by the Food and Agriculture Organization identified the absence of a robust evidence base with which to guide policies on food price, 13 and important questions remain concerning the impact of changes in food prices on food consumption, especially in poor populations. 14

Several studies of the relation between the price of a given food and demand for that food, known as “price elasticities” (see box) have been conducted, but as yet few attempts have been made to synthesise this literature. 15 16 17 Currently no systematic review of the empirical evidence on the relations between food prices and demand at a global level has been done, and no study has explored whether these relations differ between income groups within the same country.

Food price elasticity

The relation between demand for a given food and its own price among consumers is known as the “own price elasticity of demand.” These elasticities are coefficients that describe the percentage by which the demanded quantity of a food changes in response to a 1% increase in the price of the food. The coefficients are calculated by dividing the percentage change in the quantity demanded by the percentage change in the price and are usually derived as part of econometric models known as “demand systems.” The most common form of these models is the AIDS (Almost Ideal Demand System), 26 of which there are many variations (see supplementary table S1). This type of model is a system of equations that considers the allocation of total available budget into the expenditure for different foods (or other goods) as a function of total expenditure and prices.

Own price elasticity of demand is usually negative, because demand almost always decreases as prices increase. However, the magnitude of the elasticity may be larger or smaller depending on the availability and closeness of substitute foods, necessity of the food, the proportion of budget spent on it, and the time period. All of these factors can be included in the demand system models.

For example, confectionery tends to have larger elasticities, as for most people it is not a necessity and also has a relatively high price, thus requiring a larger proportion of the available budget. Dietary staples, such as cereals, tend to have smaller elasticities, because these foods are necessities in the diet, are usually cheaper, and people conserve their income for spending on such essentials when prices increase. In a similar way, low income countries tend to have higher price elasticities for all foods than high income countries, because food represents a large share of total income in these countries, hence price changes have a larger impact on budget allocation.

A study protocol was prespecified and made available online ( www.lshtm.ac.uk/eph/dph/research/nutrition/research/agriculture/systematic_review_protocol_.pdf ).

Study selection and search strategy

Using a prespecified list of search terms (see supplementary file) we conducted a systematic search with an end date of 15 August 2011 of six relevant databases: ISI Web of Science, EconLit, PubMed, Medline, AgEcon, and Agricola. We also searched other online resources, including Google, Google Scholar, Ideas REPEC, Eldis, and the websites of the US Department of Agriculture, Food and Agriculture Organization, World Bank, and International Food Policy Research Institute. We included papers in the peer reviewed or grey literature with English abstracts using data from 1990 onwards. Two authors (RG and LC) independently conducted the literature search and identified relevant papers. RG and LC then checked all included abstracts and disagreements were resolved after discussion. Abstracts and full texts were screened for inclusion according to prespecified criteria:

Inclusion criteria

We considered studies to be eligible for inclusion if they were nationally representative cross sectional, cohort, experimental, or quasi-experimental studies presenting food price elasticities using data from household level surveys (for example, household expenditure surveys or national food surveys), national aggregate data (for example, food price and food availability data collated by national governments), or supermarket/home scan data (for example, consumer purchasing data generally collected by market research companies), collected after 1990 and disaggregated by food group. We only included studies examining retail prices of food items (not, for example, live animal or nutrient prices), those where price elasticities were calculated using multiple equation methods (for example, Almost Ideal Demand System or similar, see supplementary table S1), and those using uncompensated price elasticities (which also incorporate the indirect effect on consumption induced by the change in available budget generated by the price change).

Data extraction

We compiled a database of all the included studies using Microsoft Access and included information on own price elasticities, that is, the elasticity of demand for foods with respect to the food’s own price (including standard errors and statistical significance where these were available); study type; data source; years of data available; country of study; number of observations (where available); statistical methods used; and whether sociodemographic variables were included in the models. We assessed the quality of the included studies using a prespecified eight item checklist of information provided in the paper: data source, data representativeness, number of observations (where appropriate), statistical methods used, food groupings, statistical significance of the estimates, how price data were obtained, and how demand data were obtained. Papers meeting all eight criteria were considered high quality.

Over 40 different food groupings were used in the included studies, and we subsequently produced our own groupings of foods according to those most commonly presented in the included studies and in line with US Department of Agriculture guidelines. 18 The nine food groups used in our analyses were fruit and vegetables; meat; fish; dairy; eggs; cereals; fats and oils; sweets, confectionery, and sweetened beverages; and other food. Three authors (RG, LC, and RT) extracted the data, and a different coauthor (RG, LC, and RT) independently checked a random sample of 10% of all the extracted studies for errors.

Statistical analysis

We tabulated descriptive statistics for the studies included in the review. To investigate whether study characteristics affected the size of the food price-demand relation we constructed meta-regression models in MLwiN (Version 2.25: Centre for Multilevel Modelling, University of Bristol). The models used random effects to account for multiple estimates coming from the same study (and also to account for multiple studies coming from the same country), and also used 50 bootstrap repetitions to obtain more robust standard errors for the resulting coefficients. We used these meta-regression models to calculate predicted price-demand relations for each food group, and for countries with different income levels. Outputs of these models take the form of the predicted percentage change in demand associated with a 1% increase in the price of each food. We performed sensitivity analysis excluding those studies not graded as high quality. Finally, we performed a prespecified separate analysis on those studies that had reported relations for different income groups. In this analysis we constructed another meta-regression model including all the previously used variables, but also comparing people in the highest income category with those in the lowest income category to determine whether the price-demand relation was different for different income groups within the same country.

All regression analyses included study methods (function and estimation type used in the models), whether the study was published in a peer reviewed journal, the type of data (whether aggregate, cross sectional, panel, or scanner data), and the mean year of data collection as covariates. The covariates were identified through the use of a directed acyclic graph 19 (see supplementary figure S1).

We report our findings in accordance with the PRISMA statement (see supplementary file). 20

Our original search identified 1482 studies, of which 888 met our inclusion criteria based on screening of abstracts (figure ⇓ ). When we screened the full texts of these 888 studies, 158 studies met the inclusion criteria, and we included 136 studies that reported uncompensated price elasticities in our review.

Flow diagram for selection of included studies

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Characteristics of included studies

The included studies reported a total of 3495 estimates of uncompensated food price elasticities from 162 countries (table 1 ⇓ ). The largest number of estimates came from Europe and Asia, and almost half were from low income countries. More than two thirds of estimates came from the grey literature, and over half came from national aggregate data.

 Descriptive statistics for selected variables (n=3495 estimates)

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Differences between food groups and country income levels

Predicted price elasticities from the meta-regression models identify clear and robust trends by country income level: demand for all food groups was more responsive to changes in price in lower income than higher income countries (table 2 ⇓ ). The highest predicted price elasticities (represented by the largest negative coefficients) were found for meat (−0.78, 95% confidence interval −0.83 to −0.73), fish (−0.80, −0.85 to −0.74), dairy (−0.78, −0.84 to −0.73), and other food (−0.95, −1.01 to −0.90) in low income countries, whereas the lowest were found for cereals (−0.43, −0.48 to −0.36) and fats and oils (−0.42, −0.48 to −0.35) in high income countries. The low predicted price elasticity for eggs was based on a relatively small number of observations (see table 1). Sensitivity analysis including only high quality studies (n=40) did not substantially alter these findings.

 Mean percentage change (95% confidence interval) in food demand for 1% increase in food price by country wealth category, taken from predictions of meta-regression models*

Differences between household income groups

We repeated our meta-regression models for the subset of 21 studies (with 355 estimates) that reported relations between food prices and demand for different income groups within the same countries and compared the highest income group with the lowest reported in each study. The 21 included studies were more likely than those in our country level analysis to report data from high income countries and to have used data from supermarket scanner surveys, from which price elasticity estimates are generally larger, because of the higher level of disaggregation of this type of data.

Our analysis identified that demand for food was more responsive to price changes among households with lower incomes (table 3 ⇓ ). The highest elasticities were found for meat (−0.95, 95% confidence interval −1.07 to −0.82), fish (−1.01, −1.17 to −0.84), and other food (−1.06, −1.21 to −0.92) among low income households, and the lowest were found for cereals (−0.72, −0.85 to −0.59), sweets (−0.73, −0.91 to −0.55), and fruit and vegetables (−0.73, −0.84 to −0.62) among high income households. The differences in elasticities between income groups were largest in high income countries, but were also substantial in low income countries (data shown in supplementary table S3).

 Mean percentage change (95% confidence interval) in food demand for 1% increase in food price by household wealth category, taken from predictions of meta-regression models

The relation between food prices and demand is stronger for all food groups in low income countries than in high income countries, indicating that increases in food prices are likely to have a disproportionately greater impact on food consumption in low income countries. Food prices also had a stronger impact on demand for food in lower income households within countries—a relation that has not been explored in previous reviews. Irrespective of national wealth category, the elasticities of dietary staples such as cereals and fats and oils were lower than those of animal source foods (meat, fish, and dairy), suggesting that in all settings, animal source foods represent luxury foods in the human diet. These estimates allow us for the first time to quantify the likely impact of global rises in food prices on demand for food in households and countries with different wealth profiles.

Applications of findings

This is the first review to quantify systematically the relation between food prices and demand for food worldwide, and the first to explore differences in this relation between household income groups. To demonstrate the value of the elasticities presented, we estimated the effect of price changes on presumed consumption (as estimated from Food and Agriculture Organization data on food availability). Food and Agriculture Organization food availability data are a proxy for national level food consumption that have been shown to correlate with other measures of food intake and health outcomes. 21 22 Based on our predicted price elasticities, a 10% increase in the global price of cereals would reduce demand for cereals by 6.1% in low income countries and 4.3% in high income countries, equivalent to 301 kJ (72 kcal) and 167 kJ (40 kcal) reductions on average in cereal availability per person per day in low and high income countries, respectively. The estimated 75% greater reduction in low income countries in demand for cereals that often form the predominant part of the diet shows the unequal impacts of global changes in food prices. Our analysis also suggests that poorer people in low income countries will suffer the most and highlights that higher food prices may substantially increase their risks of undernutrition. For wealthy countries aiming to use taxes and subsidies beneficially to influence dietary patterns, the analyses suggest that compared with low income countries the influence of food prices on demand is attenuated and that household income will largely determine the effectiveness of such strategies at a population level.

Strengths and weaknesses of this study

This review has many strengths, including its systematic and exhaustive nature and the inclusion of peer reviewed and grey literature. Given the diverse nature of studies included we went to significant efforts to allow for the heterogeneity of the data and methods included in our analysis. We also conducted a sensitivity analysis to determine whether differences in study quality might have affected our results. This showed that restricting the analysis to high quality studies only (which were overwhelmingly peer reviewed studies) made little difference to the relations found. Previous studies have attempted such a review for US studies alone 15 and for studies of meat and fish, 23 24 but none have attempted this for all food groups worldwide. In addition, although worldwide data from single sources summarising relations between food prices and demand are available, these tend to be based on aggregate data only that do not allow for differences by income level. 21

Limitations of the study relate largely to the study inclusion criteria and data availability. We limited our review to studies analysing data collected from 1990 onwards, as the relation between food prices and demand may have changed over time (although the “year of data” variable was found to have little impact on the size of the elasticities in our analysis). We also limited our review to studies using multiple equation models to estimate elasticities; simpler models are available but do not provide such robust estimates and are not consistent with the economic theory. We reviewed only studies that had an English abstract. Data were sparse for a few world regions, especially Australasia and South America, and few studies included information on the standard errors of the elasticity estimates, which prevented us from undertaking more sophisticated meta-analysis. We also had to aggregate foods into fairly broad groups to make the data comparable, and this is likely to have diluted some of the relations found. For example, sugary drinks were included within the sugar and sweets category, but sugary drinks typically show higher own price elasticities than other sugary foods, and consequently a stronger relation may have been found if sugary drinks had been examined separately, whereas the overall elasticity found for sweets may have been smaller. Finally, price elasticities assume that the relation between food prices and demand is linear, but this may not always be the case, particularly for large changes in price. Consequently, our estimates may underestimate the changes in demand that might occur in response to large increases in food prices, such as have been observed recently, particularly in developing countries.

Our elasticity estimates for food groups in high income countries are similar to those found in the United States, 15 and for meat are similar to those in a recent review of global meat prices. 23 Previous smaller studies have suggested that the relation between food prices and demand tends to be stronger in lower income countries and among lower income groups within countries, although none has quantified this in a systematic manner. None the less, this existing literature is consistent with our findings, adding weight to their validity.

This study has synthesised the worldwide evidence base to investigate the impact of changing food prices on nutrition and identified potential important negative impacts of food price rises especially among poor people in low income countries. Future work must also systematically evaluate the evidence on the price-demand relation between different foods, or between food and non-food items (cross price elasticities). A better understanding of these relations will help identify the foods that consumers select when their preferred foods can no longer be afforded (whether they reduce spending on all foods or switch to cheaper—healthier or less healthy—alternatives, etc). Further work is also required to understand how and why people choose the foods they eat in different contexts globally. The consequences for human health, as well as global economies, of major shifts in food consumption patterns resulting from changes in food prices are likely to be far reaching and will require much further investigation. 25

What is already known on this topic

The relations between food prices and demand (own price elasticities) vary according to the type of food and income level of a country

Worldwide food prices are volatile, and no systematic review has been done of global food price elasticities to determine how changes in food price will affect demand for food in countries with different income levels

What this study adds

Combined worldwide evidence shows that the impact of food price on demand for food is greatest in low income countries, and within countries among the poorest people

Rises in food prices are most likely to reduce demand for animal source foods such as meat, fish, and dairy, and will have less impact on demand for staple foods such as cereals

Cite this as: BMJ 2013;346:f3703

Contributors: RG designed the study protocol, collected and entered the data, conducted the meta-regression analysis, and drafted and revised the paper. She is guarantor. LC revised the study protocol, collected and entered the data, and revised the draft paper. AD and RS initiated the project, assisted with study design, revised the study protocol, and revised the draft paper. RT entered the data and checked the data, and revised the draft paper. BS and MM assisted with study design and revised the draft paper. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Funding: This study was partly supported by the Leverhulme Centre for Integrative Research on Agriculture and Health. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organization for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: Statistical code and datasets are available from the corresponding author at rosemary.green{at}lshtm.ac.uk .

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/ .

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• Research article
• Open access
• Published: 10 February 2017

Price elasticity of the demand for soft drinks, other sugar-sweetened beverages and energy dense food in Chile

• Carlos M. Guerrero-López 1 ,
• Mishel Unar-Munguía 2 &
• M. Arantxa Colchero 1  

BMC Public Health volume  17 , Article number:  180 ( 2017 ) Cite this article

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Chile is the second world’s largest per capita consumer of caloric beverages. Caloric beverages are associated with overweight, obesity and other chronic diseases. The objective of this study is to estimate the price elasticity of demand for soft drinks, other sugar-sweetened beverages and high-energy dense foods in urban areas in Chile in order to evaluate the potential response of households’ consumption to changes in prices.

We used microdata from the VII Family Budget Survey 2012–2013, which collects information on expenditures made by Chilean urban households on items such as beverages and foods. We estimated a Linear Approximation of an Almost Ideal Demand System Model to derive own and cross price elasticities of milk, coffee, tea and other infusions, plain water, soft drinks, other flavored beverages, sweet snacks, sugar and honey, and desserts. We considered the censored nature of the data and included the Inverse Mills Ratio in each equation of the demand system. We estimated a Quadratic Almost Ideal Demand System and a two-part model as sensitivity analysis.

We found an own price-elasticity of −1.37 for soft drinks. This implies that a price increase of 10% is associated with a reduction in consumption of 13.7%. We found that the rest of food and beverages included in the demand system behave as substitutes for soft drinks. For instance, plain water showed a cross-price elasticity of 0.63: a 10% increase in price of soft drinks could lead to an increase of 6.3% of plain water. Own and cross price elasticities were similar between models.

Conclusions

The demand of soft drinks is price sensitive among Chilean households. An incentive system such as subsidies to non-sweetened beverages and tax to soft drinks could lead to increases in the substitutions for other healthier beverages.

Peer Review reports

Overweight and obesity have increased in less developed countries at a noticeable pace [ 1 ]. It is estimated that overweight and obesity caused 3.4 million deaths and 93.6 million DALYs in 2010 [ 2 ]. Latin American countries have been particularly affected by the nutritional transition, overweight and obesity have become a challenge. In particular, Chile ranked seventh in adult obesity among the organization for economic cooperation and development members [ 3 ]. According to the Chilean National Health Survey 2009–2010, overweight and obesity reached 64.4% in men and 64.3% in women [ 4 ]. In the same period, the prevalence of diabetes mellitus was estimated at 8.4% in men and 10.4% in women [ 4 ]. Among children aged 6–18 years, overweight and obesity prevalence is estimated at 45.5% [ 5 ].

Although there are multiple risk factors associated to overweight and obesity, the literature shows that sugar sweetened beverages (SSB) are a significant risk factor for chronic diseases [ 6 – 9 ]. There is also evidence that intake of high energy dense and poorly nutritious processed and ultra-processed food is associated to obesity and metabolic syndrome [ 10 , 11 ].

Chile is the second world’s largest consumer of caloric beverages, behind Mexico, with an estimated 180 l per capita consumption in 2014 [ 12 ]. In one decade, soda sales doubled and only 19.1% corresponded to calories-free soda [ 13 ]. Between 1987 and 2007, SSB consumption raised from 116 to 289 ml per capita per day and this increase was larger among poor households [ 14 ]. In the same period, in households from the metropolitan area of Santiago, household expenditures on soda, juices with added sugar and sparkling water -as a percent of total spending on food and beverages- increased from 4 to 8.4% [ 15 ].

Since the eighties, Chilean households have increased the proportion of expenditures in processed food with high energy density and added sugars and have reduced the consumption of traditional food. Percentage spending in processed food increased from 42 to 57%, and in the poorest households it went from 53 to 68% [ 14 ]. Consequently, processed and high-processed food represents 55.4% of all energy purchased by Chilean households in 2006–2007 [ 16 ]. Expenditures in food prepared outside the household increased from 12.1 to 20.1% between 1987 and 2007. Poor households show the largest increase [ 15 ].

Several fiscal policies to reduce the consumption of SSB have been recommended and implemented in different countries [ 17 ]. The World Health Organization (WHO) and the Pan-American Health Organization (PAHO) suggest implementing fiscal measures to discourage the consumption of foods and beverages that can harm health. For instance, the Plan of Action for the Prevention of Obesity in Children and Adolescents in the Americas, presented by the PAHO in 2014, advises taxing SSB and high-energy dense products in order to stop the increase in the prevalence of obesity in children and adolescents [ 18 ]. France, Hungary, Egypt, Finland, Mexico and the City of Berkeley in the USA have implemented taxes on SSB or high-energy dense foods. In Chile, there was a tax on sales and imports of beverages of 13% [ 13 ]. Since October 2014, the tax rate increased to 18% for non-alcoholic beverages, naturally or artificially flavored, that have content greater than 15 g of sugar per 240 ml or with an equivalent portion [ 19 ].

Despite the implementation of taxes in several countries, there is limited published scientific evidence on the effectiveness of fiscal measures in reducing the consumption except for Denmark [ 20 ] and Mexico [ 21 ]. In the absence of such evidence, price-elasticity estimates are a useful tool in achieving the objective of measuring the effect of a fiscal policy on consumption and simultaneously to forecast potential substitution effects. Some factors can modify the expected or estimated response to a tax such as the tax incidence, that is how much the tax is passed on to consumers, or increase in advertising campaigns or promotion of taxed products in stores. However, price elasticities provide useful estimates of the potential response to changes in prices.

There is a large variability in the SSB price elasticity estimates in the world. A systematic review on price-elasticity studies of non-alcoholic beverages and foods indicate that the price-elasticity ranges from −0.27 to −1.0 [ 22 ]. In general, SSB show larger absolute values of elasticities, that is, their demand is more sensitive to changes in price. A systematic review of studies performed in United States, France, Brazil and Mexico showed a mean price-elasticity of demand of SSB of −1.2 [ 23 ]. A recent study in Mexico estimated that the price elasticity of SSB was −1.16% and between −1.06 and −1.29 for soft drinks [ 24 , 25 ] In Ecuador, the price-elasticity of SSB ranges between −1.17 and −1.33 depending on the socioeconomic group [ 26 ]. In contrast, studies on price-elasticity of high-energy dense foods are scarce. A study in Mexico found that the demand of this type of food is elastic [ 25 ]. The wide variability in the estimation of price elasticities is partially explained by proportion spent on SSB, the availability of substitutes for SSB in each country, the data used and the methods to derive elasticities.

The objective of this study is to estimate the price elasticity of demand for soft drinks, other SSB and high-energy dense foods in urban areas in Chile. Our paper adds to the existing literature the estimation of a linear approximate almost ideal demand system (LA/AIDS) and a sensitivity analysis using two additional models: a quadratic almost ideal demand system (QUAIDS) and one-equation two-part model used in the literature to derive price elasticities to test of the robustness of our findings. Results from the study could be used in the re-design and evaluation of current fiscal policies related to food and beverages consumption, and the potential reduction in the prevalence of overweight and obesity in Chile.

We used the VII Family Budget Survey (FBS) collected between November 2011 and October 2012 by the National Institute of Statistics in Chile [ 27 ]. The FBS provides information on income and expenditures in urban households and is an important input to calculate the Consumer Price Index. The FBS has a probabilistic, bi-etapic and stratified design. It distinguishes between two zones: Gran Santiago (the Federal Capital City) and the rest of the regional capital cities. The FBS includes information on beverage and food and other household expenditures and socio-demographic variables. The sample size is of 10,527 households. We used the expansion factors in order to take into account the survey design in our estimates.

Empirical model

We estimated a Linear Approximation of the Almost Ideal Demand System (LA/AIDS) by Deaton and Muellbauer [ 28 ] for beverages and foods. The LA/AIDS model is specified as follows:

Where w hzi is the food or beverage expenditure share for food or beverage group i for household h living in zone z ; P zi is the unit value for food or beverage j at zone level estimated as the ratio between purchases in kilograms and expenditure on category j , where the j-th good is the composite numéraire that includes the unit value of other foods and beverages not considered in the demand system [ 29 ]; E is total household expenditure on beverages and food included in the system, η are variables at household level (education, sex and age of the head of the household, and equivalent adults), and log P is the Laspeyres price index, defined as follows [ 30 ]:

Where P is the unit value of the j-th beverage or food category, \( \overline{w} \) is the mean expenditure share in the category and m is the number of zones. Own and cross non-compensated price elasticities of the demand for the categories included in the system were calculated as follows:

Where ε j is the price-elasticity of the food or beverage category, δ equals 1 if it is own price-elasticity and 0 if cross price-elasticity, \( {\overline{w}}_j \) is the mean expenditure share of food or beverage, \( \widehat{\gamma} \) is the estimated parameter of the log expenditure, \( \widehat{\beta} \) is the estimated parameter associated to the unit value of the food or beverage category. In order to treat the censored nature of our response variable, we first modeled the probability of participation, that is the probability of positive consumption of each category by using a probit model and then calculated the Inverse Mills Ratio (IMR). Afterwards we included the resulting IMR of each category into the respective equation of the demand system [ 31 ]. The LA/AIDS model was estimated by Ordinary Least Squares equation by equation. We also estimated price-elasticities for soft drinks by income quintile.

In sensitivity analysis, we additionally estimated a quadratic almost ideal demand system (QUAIDS) that adds a quadratic expenditure term to model a non-linear association with expenditure share [ 32 ]. The IMR was not included in this model since this model does not allow incorporating different variables in each food and beverage equation. We also estimated a one-equation two-part model, in which we first modeled the probability of a positive consumption of soft drinks using a probit model and afterwards an Ordinary Least Square (OLS) regression with the number of equivalent adults, zone, education of the head of the household, age of the head of the household, age squared, income and number of children under 5 years as covariates and calculated the price elasticity of participation (EP) using this formula [ 33 ]:

where X is the vector of independent variables, β is the vector of corresponding coefficients, E(Y|X) is the average value of the estimated probability and β i is the coefficient related to unit values.

We then estimated the intensity price-elasticity for households with positive purchases, using an OLS Regression for the logarithm of the quantity consumed as a function of the logarithm of the unit value and a set of co-variables that included income and price indices for soft drinks and all other food and beverages categories. In this model, the intensity price-elasticity is the estimated coefficient of the logarithm of the unit value. Total price-elasticity was calculated by adding the participation and intensity price-elasticities [ 34 ].

We defined eight beverages and food categories, that can be complement or substitutes for each other: (1) milk (milk and powdered milk); (2) coffee, including teas, infusions and mate tea; (3) plain water; (4) soft drinks; (5) other SSB that include powdered soda, sport drinks, isotonic water, juices, fruit pulp, and flavored water; (6) sweet snacks, containing cookies and other snacks; (7) sugar and honey (sugar, sweeteners and honey); and (8) desserts, including candies, desserts, chocolate, and chewing gum. We estimated beverage and food expenditure share summing expenditures in each category and dividing by total expenditure in the eight categories.

Unit values were calculated dividing expenditures in each category by total amount in kilograms. When the item was powdered, we rehydrated it in order to covert all amounts in kilograms, as indicated by Crovetto [ 14 ]. We averaged unit values by zone (Gran Santiago and the other regional capital cities) and input this average value when the household lacked to report it, that is when expenditure is zero in the category. Unit values were replaced by the mean plus 2.5 standard deviation when they exceeded the mean +−2.5 standard deviations. We calculated the adult equivalents as followed: a 5 years old or younger individual equals 0.77 equivalent adults (EA); 6 thru 12 years equals 0.8 EA; 13 thru 18 equals 0.74 EA and an individual of 19 years and older equals 1 EA [ 35 ].

We adjusted the models for EA of the household, education (last grade completed), sex and age of the head of the household. All models were estimated using STATA v. 13. To estimate the QUAIDS, we employed the Stata program provided by Poi [ 36 ].

Table  1 presents the proportion of households with positive purchases among the included categories in the demand system. Plain water shows the lowest percentage of households with positive expenditure. In contrast, 77.2% of Chilean households report spending in soft drinks. There is also a high prevalence of households with positive expenditures in sweet snacks (Table  1 ).

Table  2 shows the average unit values of beverages, snacks and desserts reported by the households with positive purchases, in 2011–2012 Chilean pesos. Beverages are cheaper by kilogram than snacks and desserts. Among beverages, soft drinks are more expensive than milk, coffee, tea and infusions, and plain water. Socio-demographic characteristics of the households show that in Gran Santiago 58% are headed by men and 62% in regional capitals. There is no difference in the age of the head of the household, or in household size across zones. However, in Gran Santiago the percentage of heads of the households with graduate level of education seems slightly higher than in the rest of regional capital cities (Table  3 ).

Table  4 shows the results of the own and cross price elasticities with respect to soft drinks using LA/AIDS. The left half of the table shows the results of the models that include the IMR. The price-elasticity of all the eight categories is elastic. The price-elasticity of soft drinks is −1.37 implying that a 10% increase in price would be followed by a decrease of 13.7% in the amount consumed, which shows an elastic demand. The most extreme case is plain water, with a price-elasticity of −3.20. This implies that the demand of plain water is very sensitive to changes in price. The estimations of cross-price elasticities show that the degree of substitution of soft drinks with plain water is higher compared to other beverages and high-energy dense foods. A price increase in soft drinks is also associated with a higher quantity consumed of milk, coffee, tea and infusions, other sweetened beverages, sugar and desserts. On the right side of the table, the results of the model estimated without including the IMR. The estimates are robust, with no great differences with respect to the models that include the IMR.

Figure  1 shows the price-elasticity of soft drinks by income quintile. As we can see, the population is fragmented into two major groups. Although all quintiles present an elastic demand to soft drinks, the first and second quintiles show a greater price-elasticity, whilst the third, fourth and fifth income quintiles are less sensitive to changes in prices.

Price elasticity of the demand of soft drinks by income quintile. VII Family Budget Survey, 2011–2012

Table  5 displays the results of the QUAIDS model. Unfortunately, it is not possible to get standard errors or p -values of price-elasticities using the QUAIDS Stata command. However, the results using this model are quite similar than those of the LA/AIDS model. The estimated price-elasticity for soft drinks is also −1.37 and the cross-price elasticities show the same sign and magnitude when compared to results from LA/AIDS model.

Lastly, we derived from the two-part model the price-elasticity of participation (−0.19) and the price-elasticity of intensity calculated on households with a positive consumption of soft drinks (−1.05). Hence, the total price-elasticity using this two-part model is of −1.24, slightly lower that the estimated using the LA/AIDS and QUAIDS.

We estimated a demand system for beverages and high-energy dense foods using a cross-sectional survey in Chile. We found that the demand for SSB in Chile is elastic (−1.37 for soft drinks and −1.67 for other SSB). Likewise, we provided evidence that an increase in soft drinks’ prices could lead to increases in the demand of other goods, such as plain water, milk, coffee, teas or other SSB. The sensitivity analysis shows very similar results, price-elasticity of soft drinks ranges from −1.24 to −1.37.

To our knowledge, this is the first paper that estimates price elasticities of the demand for soft drinks in Chile. Our findings are similar to other studies. Cabrera found an average price-elasticity of SSB of −1.29 in several countries [ 23 ]. Our estimate for soft drinks is also in the range of other Latin American countries, such as Mexico or Ecuador (−1.06 and −1.20, respectively) [ 24 , 26 ]. The variability could be due to different definitions of SSB, different statistical models and also by the particular nature of demand in each country. Regarding income level, the studies by Colchero [ 24 ] and by Paraje [ 26 ] agree that population in lower income groups are more responsive to changes in price, as we found in the Chilean case.

Cross price-elasticities allow us to classify the rest of categories as substitutes and complements. Since their cross price-elasticity is positive, our study suggests that milk, coffee, tea, mate and other infusions, plain water, other SSB, sugar and honey and desserts show a substitute behavior towards soft drinks. For instance, regarding water, our preferred model shows that a 10% increase in price would be associated to an increase of 6.3% of plain water. However, own price-elasticity of water is high, perhaps because of the low proportion of households that report positive expenditures on this item and the high quality of private water supply systems in Chile. Cross price-elasticities of milk, tea, coffee, other infusions and other SSB are smaller, which means that substitution for these products is less clear than water’s.

We acknowledge several limitations in our study, which come primarily from the data. First, we were not able to split the category of soft drinks into non-diet or diet soft drinks, which would have been valuable in order to isolate the possible effect of a tax on soft drinks with added sugar from drinks without calories. Nevertheless, only around one fifth of soft drink consumption in Chile is on calories-free soda [ 13 ]. Since our data is cross-sectional, it is also likely that purchases of food and beverages included in our analysis are under-reported, because it excludes expenditures for consumption outside the household and does not consider purchases of all the households members.

Information on geographic location size, or a cluster variable to a more disaggregated level than zone was not available in the data set. Since the Family Budget Survey did not include information on prices that households face, we rather computed the unit values by dividing the amount of reported expenditure on the analyzed food and beverage category by the quantity purchased in kilograms. As noted by Deaton, this introduces two types of biases. First, there are measurement errors that come from both the numerator and denominator of the unit value calculation [ 37 , 38 ]. Second, a quality effect is also present: households’ characteristics such as size or income affect unit values, because the products are also chosen by their quality. Deaton proposes a system of two equations for each category, which based on certain conditions, allows estimating price and budget elasticities while controlling for differences in the quality of the goods and measurement errors. In spite of its theoretical advantages, we were not able to estimate this Deaton’s model because the analyzed households must belong to small enough geographic units (such as villages) to support the assumption that the prices they face are the same. Deaton’s method assumes that variations in unit values in the same geographic unit are due to differences in the quality of goods. Therefore, to implement his method, information on small geographic location is crucial. We lacked this, and then tried to create artificial clusters based on income and the two zones available in the data set. Nonetheless, we got implausible price-elasticities estimates that we think were caused by the artificial nature of the clusters that we created and the small number of them. Furthermore, the FBS was collected along almost 1 year. Prices could have varied during this period. Regrettably, from the public microdata, it is not possible to know each household’s day of interview, so monetary variables are inflation adjusted. However, Chile presented a low inflation rate between 2011 and 2012 of 3.3 and 3.0%, respectively. Likewise, seasonality of purchases was not considered which could bias our price-elasticity estimations if the pattern of beverages consumption varies according to weather changes.

We also recognize the potential endogeneity due to omitted variables if the unit values are correlated with unobservable variables that influence demand. Ideally, we would have desired to at least aggregate unit values at a geographic level but the data set has only two broad zones. We could have also used predicted unit values from a first stage by calculating the expected unit value using ordinary least squares or generalized linear models adjusting for socio-demographic variables. Unfortunately, this imposed us a trade-off, since doing so introduced collinearity with the variables included in the demand system. Instead, in households without reported unit value we imputed using the averaged unit values at zone level and preferred to use a set of demographic variables to model the decision to purchase the items and so include the IMR into the equations of the demand system.

Despite these data limitations, the LA/AIDS with IMR is our preferred model for several reasons. First, it allows to test the conditions of homogeneity and symmetry through linear restrictions on fixed parameters [ 28 ]. The system also produces low standard errors when the number of commodities is greater than six [ 39 ]. It also allowed us to estimate cross price-elasticities with standard errors. In addition, the LA/AIDS results are somewhat similar than those produced by the QUAIDS and the two-part model. This gives us confidence that the price-elasticity that we estimated is robust.

Our price-elasticity estimates provide essential information to policy design and evaluation. The evidence that we present here suggests that the demand for soft drinks is elastic in Chile. If the recent increase of 5% in the rate of taxes on naturally or artificially non-alcoholic beverages fully passes through prices, we would expect a decrease ~ 6.85% in the consumption of soft drinks and 8.15% in the consumption of other SSB, ceteris paribus . Simultaneously, an increase of 5% in the price of soft drinks would cause an increase about 3% in the consumption for plain water and to a lesser degree in the demand of other beverages, such as milk, coffee, tea and mate. These results however would depend on the type of tax and the pass-through prices.

The type of tax on sweetened beverages produces different outcomes. A specific tax (a fixed amount of money per physical unit of product) has several advantages. It is easier to administer and provides more stable fiscal revenue than ad valorem tax (a percentage of the value of the product) [ 40 ]. In addition, it reduces the gap between expensive and cheap brands [ 41 ]. The specific tax should at least be indexed to inflation to avoid that it dilutes over time. It is also feasible to implement a mixed tax system, where specific and ad valorem taxes coexist. Ad valorem taxes have the advantage that they are automatically adjusted for inflation. In any case, since a tax and its subsequent price increase implies a money transfer from consumers to government, careful assessment of health and economic impacts should be done. In one hand, it is possible that reduction in consumption produces health gains, through weight loss, reduced risk of metabolic syndrome and other desirable effects on health. These consequences are to be seen in the medium and long term and only after a significant increase in prices. Low taxes on sweetened beverages have little or no impact on body weight for instance [ 23 , 42 ]. Substitution to other caloric beverages is also likely and the reduction of calories from soft drinks could be offset by increases of calories in the other products. Additional fiscal revenue should be returned to consumers to reduce potential regressivity effects of the tax, such as providing public drinking fountains, and education programs to reduce the information asymmetry.

Abbreviations

Equivalent adult

Elasticity of participation

Family Budget Survey

Inverse Mills Ratio

Linear Approximation of al Almost Ideal Demand System

Non-Communicable Diseases

Ordinary Least Squares

Pan American Health Organization

Quadratic almost ideal demand system

• Sugar sweetened beverages

United States of America

World Health Organization

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Acknowledgments

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The publication is based on findings of a research financed by the Pan American Health Organization (letter of agreement SCON2016-00287). The Pan American Health Organization had no role in the study design or the analysis and interpretation of the data and did not participate in the elaboration of this publication. The authors alone are responsible for the views expressed in this publication.

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The VII Family Budget Survey data that support the findings of this study are available from the National Institute of Statistics of Chile repository: http://www.ine.cl/epf/VII/base-de-datos.php .

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CMGL conducted data analysis and drafted the manuscript. MUM participated in data analysis and drafted the manuscript. MAC participated in study design, guidance and review of data analysis and drafted the manuscript. All authors read and approved the final manuscript.

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The project was approved by the IRB (Ethics Committee) at the National Institute of Public Health in Mexico (IPF Code 3627801). For this research we are using data publicly available collected by the National Institute of Statistics of Chile: http://www.ine.cl/epf/VII/base-de-datos.php .

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Guerrero-López, C.M., Unar-Munguía, M. & Colchero, M.A. Price elasticity of the demand for soft drinks, other sugar-sweetened beverages and energy dense food in Chile. BMC Public Health 17 , 180 (2017). https://doi.org/10.1186/s12889-017-4098-x

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The Impact of Food Prices on Consumption: A Systematic Review of Research on the Price Elasticity of Demand for Food

T. Andreyeva and M. W. Long conducted the literature review and extracted, synthesized, and analyzed data. T. Andreyeva and K. D. Brownell originated the study. T. Andreyeva led the data interpretation and the writing of the article. All of the authors helped to conceptualize ideas and interpret findings and contributed to the writing and revision process.

In light of proposals to improve diets by shifting food prices, it is important to understand how price changes affect demand for various foods.

We reviewed 160 studies on the price elasticity of demand for major food categories to assess mean elasticities by food category and variations in estimates by study design. Price elasticities for foods and nonalcoholic beverages ranged from 0.27 to 0.81 (absolute values), with food away from home, soft drinks, juice, and meats being most responsive to price changes (0.7–0.8). As an example, a 10% increase in soft drink prices should reduce consumption by 8% to 10%.

Studies estimating price effects on substitutions from unhealthy to healthy food and price responsiveness among at-risk populations are particularly needed.

THE INCREASING BURDEN OF diet-related chronic diseases has prompted policymakers and researchers to explore broad-based approaches to improving diets. 1 , 2 One way to address the issue is to change the relative prices of selected foods through carefully designed tax or subsidy policies. The potential of price changes to improve food choices is evident from growing research on how relative food prices affect dietary quality and obesity, particularly among young people, lower income populations, and those most at risk for obesity. 3 Experience from tobacco tax regulation further underscores the power of price changes to influence purchasing behavior and, ultimately, public health. 4

Experimental research in both laboratory and intervention settings shows that lowering the price of healthier foods and raising the price of less healthy alternatives shift purchases toward healthier food options. 5 – 8 Although these studies demonstrate price effects in specific, isolated settings or on 1 or 2 individual product changes, to our knowledge, the expected effects of broader food price changes have not been systematically reviewed. Such information would be helpful in designing policies that change the relative food and beverage prices paid by all or many consumers.

Relatively small-scale, cost-neutral approaches to improving nutrition in vulnerable populations include the 2009 changes in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages; whole grains, fruits and vegetables, and soy-based milk alternatives were added to these packages, indirectly subsidizing healthy foods for WIC participants. 9 Another larger scale approach is to change prices directly through taxing products such as sugar-sweetened beverages 1 , 10 or subsidizing healthier foods (e.g., a refund on the costs of fruits and vegetables to Supplemental Nutrition Assistance Program participants). 11 Some states already tax soft drinks and snacks at higher rates than other foods, but thus far taxes have been small and designed to generate revenue rather than influence consumption. 12

We sought to estimate the effects of price changes on consumer demand for major commodity foods included in the Dietary Guidelines for Americans food categories. 13 We identified all published US studies of food price elasticity of demand (the expected proportional change in product demand for a given percentage change in price) and combined their estimates into average estimated price elasticities for 16 major food and beverage groups. Our goal was to provide a comprehensive summary of research on food demand and consumption behavior in the United States over the past 7 decades, with particular attention to differences in price effects across income levels.

One timely estimate that can be gained from our review is how altering the prices of soft drinks can alter their consumption, information that is of critical need for policymakers considering soft drink taxes. We compared the sensitivity of estimates across different analytic approaches to modeling food demand. We identify important gaps in the food demand analysis literature and suggest avenues for future research.

We reviewed all US-based studies on the price elasticity of demand for major food categories to determine mean price elasticities by category and assess variations in estimates by study design.

Definition of Terms

The price elasticity of demand is a dimensionless construct referring to the percentage change in purchased quantity or demand with a 1% change in price. It is determined by a multitude of factors: availability of substitutes, household income, consumer preferences, expected duration of price change, and the product's share of a household's income. 14 When the relative change in purchased quantity is below the relative change in price, demand is inelastic (numerically, the absolute value of price elasticity is below 1.0). In contrast, changes in demand that exceed the relative price change reflect elastic demand (the absolute value of price elasticity is above 1.0). For example, when a commodity's purchased quantity falls by 5% owing to a 10% increase in price, the price elasticity of demand is −0.5, reflecting inelastic demand. If the same price increase reduces the commodity's purchased quantity by 15%, demand for the product is elastic (−1.5).

Our review of food price elasticities focused on the effects of price changes on primary demand (also called commodity or category demand), which is consumer demand for a category or group of products measured by quantity purchased. By contrast, brand demand reflects purchases of an individual brand or products. In the case of policy decisions such as those involving taxation or subsidies, parameters of primary demand for a category of products (e.g., soft drinks) are necessary to predict the magnitude of policy-induced changes in consumer demand.

We distinguish between uncompensated and income-compensated price elasticity of demand, with the latter assuming that consumers are compensated for price changes through income changes (i.e., compensated models estimate only substitution between products without including any effects on a consumer's overall budget resulting from price changes). We consider both price demand elasticity and cross-price elasticity of demand for a product. Whereas price elasticity reflects changes in the purchased quantity of a commodity with changes in that commodity's price, cross-price elasticity reflects changes in demand for a particular commodity when prices of other products change. The construct of cross-price elasticities is important from a policy perspective in that relative shifts in prices through taxation or subsidies can affect demand for other products not regulated by policies.

Selection of Studies

Our review included US-based studies estimating the price elasticity of demand for food and nonalcoholic beverages. We reviewed original research articles published in English between 1938 and September 2007. Two independent searches were conducted with the search terms “food and price elasticity,” “price elasticity,” “demand elasticity,” “food demand,” and “price elasticities,” as well as combinations of these terms with “food,” “meat,” “beverages,” and “dairy.” We used a number of databases and search engines to retrieve articles for review, including PubMed, EconLit, JSTOR, and Google Scholar. The reference lists of all retrieved articles were reviewed to identify relevant papers.

In addition to studies published in peer-reviewed journals, our search included working papers, dissertations, and US Department of Agriculture (USDA) technical reports. We retrieved these documents to capture all expert work, particularly USDA studies that appear only in government reports. Tests confirmed the sensitivity of our results to the exclusion of studies from non-peer-reviewed sources. Commentaries, editorials, essays, and consensus statements were excluded. We limited our review to US data because of the possibility of cross-country variations in market, product, and consumer characteristics introducing bias into our interpretations of food price effects in US studies. We included studies focusing on specific population groups or geographic regions to capture all variance in the US data.

Data Extraction and Analysis Variables

Data were independently extracted by one reviewer (T. A. or M. L.) and checked for consistency by the other reviewer. Variables assessed were food product, demand estimation model, data characteristics (study design, time, and source), estimates of price elasticity for all foods and nonalcoholic beverages, estimates of cross-price elasticity for major substitutes or complementary foods, demand elasticity for average and low-income households (if available), statistical significance of elasticity estimates, and publication source and year. Synthesizing data on income elasticity of food demand (food demand responsiveness to income changes) was beyond the scope of our review. We did not use price elasticities for specific types of fruits or vegetables in estimating average fruit and vegetable elasticities because, as a result of the availability of substitutes, demand for specific foods such as apples is more elastic than that for an aggregate group that includes all fruits.

We used the following procedure to extract elasticity estimates. When estimates from multiple periods were reported, we selected the most recent data. In studies providing estimates of both compensated and uncompensated demand elasticity, we used uncompensated elasticity because most of the reviewed studies included only uncompensated demand estimates. We rounded final estimates to the second digit and calculated these estimates as absolute values. In studies with estimates from multiple models, we took mean values. We were interested in estimating the elasticity of fruit prices separately from that of vegetable prices. However, many studies included only one estimate for fruits and vegetables combined, and in these instances we had to assume the same elasticity of demand for fruits and vegetables. If a study estimated demand parameters for both low-income consumers and all consumers, we included estimates for the 2 groups.

Methodological Variation of Studies

Our goal was not to review methodological details of food demand system estimation, which are available in other reviews, 15 – 17 but rather to distill from the existing literature food demand parameters that can be useful to the public health community. In doing so, we accounted for variations in methods and data, which affect individual parameters and may have implications for synthesized average estimates. We segmented studies into 3 mutually exclusive categories based on type of data in estimation: time series, household surveys, and retail scanner data.

Time series data were represented by monthly, quarterly, or annual data on food prices, consumption, and expenditures over time (derived from the USDA and the US Department of Commerce). Survey data were taken from cross-sectional national household surveys (e.g., Nationwide Food Consumption Survey, National Food Stamp Program Survey). More recent studies have often involved retail scanner data from commercial providers (e.g., ACNielsen) that track supermarket transactions. We excluded estimates from laboratory experiments, which could change real-world price sensitivity among customers. We also considered the type of demand system estimation model used.

Consumer demand is a function of multiple factors in addition to prices, including product quality, advertising, preferences, and other demand shift variables. Several studies included advertising in their model or provided quality-adjusted and unadjusted elasticity estimates, which we combined because we had insufficient power to consider them separately. We included a decade of data collection, using the median time point for data over multiple decades.

We pooled estimates of price elasticities across studies by food category (if at least 10 studies were available) and computed ranges and means (along with their 95% confidence intervals) for 16 food and beverage categories: beef, cereal, cheese, dairy products, eggs, fats and oils, fish, food away from home (including fast food and restaurant meals), fruit, juice, milk, pork, poultry, soft drinks, sugars and sweets, and vegetables. We had limited statistical power to synthesize estimates for other foods of interest, including fresh fruits and vegetables, fast food, snacks, and candy.

In the sections to follow, we describe the existing US-based studies involving food demand analyses, provide summary estimates of price elasticities for major food categories, and consider variation in estimates across studies.

Description of Available Literature

We identified 464 relevant citations in our literature search. After all selected articles had been retrieved and reviewed, 184 studies with data on food price elasticity remained. We excluded 5 international studies, 4 review articles, 3 studies involving experimental data, and 12 studies with brand-level food price elasticities, leaving 160 studies in our review (a list of these 160 studies is available on request).

Time series data were used in most studies (99 studies, or 62%), followed by household survey data (34 studies, or 21%) and scanner data (27 studies, or 17%). Only 38 studies were published before 1970. Despite increasing interest in the topic, only 9 studies estimated food price elasticities specifically for low-income groups, with 3 studies examining a broad range of foods. 18 – 20 Consumer demand for meat, particularly beef and pork, has received substantially greater attention than demand for any other food. Of the 160 studies, 31% provided price elasticity estimates for beef; 29% for pork; 14% for poultry; and 10% for fish. Fewer studies provided estimates for milk (15%), cereal (12%), cheese (12%), and fruits or vegetables (11%). For example, we identified only 6 estimates for fresh fruits and vegetables as a combined category (not including studies focusing on individual vegetables or fruits). Other foods were considered in less than 10% of all reviewed studies.

Price Elasticity Estimates

Mean price elasticity estimates for the 16 food and beverage groups considered, along with their 95% confidence intervals and ranges, are presented in Table 1 . Overall, our results are consistent with customary characterizations of the demand response to food prices as inelastic; all mean price elasticity estimates were below 1.0 and ranged from 0.27 to 0.81 (all elasticity estimates here and throughout the text are absolute values). Estimates were relatively less inelastic for soft drinks (0.79), juice (0.76), meats (0.68–0.75), fruit (0.70), and cereals (0.60) and most inelastic for eggs (0.27), sugars and sweets (0.34), cheese (0.44), and fats and oils (0.48). Food away from home was most responsive to changes in prices among other categories (0.81) and more elastic than demand for food at home (0.59; however, the latter value is based on 7 studies).

US Price Elasticity Estimates, by Food and Beverage Category, from 1938–2007

Note. Values were calculated based on the 160 studies reviewed. Absolute values of elasticity estimates are reported. The price elasticity of demand measures the percentage change in purchased quantity or demand with a 1% change in price.

Milk was the most studied category aside from meat (26 estimates). Thirteen studies provided elasticity estimates for specific milk fat levels. Mean elasticities for skim, 1%, and whole milk ranged from 0.75 to 0.79, whereas the mean elasticity for 2% milk was 1.22. 21 – 33 Understanding differences in price elasticity for different types of milk and cross-price elasticity for milk with varying fat content is important in food policy analyses that examine approaches to reducing saturated fat consumption (as recommended in the Dietary Guidelines for Americans).

Because milk is among the 3 leading sources of saturated fat in the American diet, substitution away from whole milk toward milk with lower fat content is one promising avenue for dietary change. 13 We identified 5 studies that evaluated cross-price elasticities for milk with varying fat content. 22 , 26 , 27 , 30 , 32 For a 10% increase in the price of whole milk, increases in purchased quantities ranged between 0.6% and 5% for low-fat or reduced-fat milk and between 0.1% and 2.9% for skim milk. Thus, consumers are more likely to switch to reduced or low-fat milk than skim milk when the price of whole milk increases.

Only a small number of studies evaluated the effects of income level on demand elasticity, and thus we were not able to identify consistent differences in estimated price elasticities between low-income consumers and consumers as a whole. Of the 9 studies reporting price elasticity estimates for low-income populations, 7 presented data for both low-income and all consumers. One study focusing on milk demand showed that demand was more price elastic in low-income populations (1.2 versus 0.66), and a study on fast food depicted a large difference as well (2.09 versus 0.51). 34 , 35 However, 3 studies including estimates for a broader group of foods reported essentially no difference, with average elasticities of 0.62 for low-income populations and 0.64 for consumers as a whole. 18 , 20 , 36

Of particular importance to policymakers, the available estimates of food price elasticity offer little guidance on a number of key food categories included in the Dietary Guidelines for Americans. Many of the studies reviewed focused on aggregate food categories, with little (if any) consideration for disentangling healthier and less healthy options within categories. Specifically, in the case of many key foods in the Dietary Guidelines for Americans, we did not identify any studies that estimated price elasticities, including cross-price elasticities, to predict within-category shifts between healthier and less healthy alternatives. These foods included whole grain products as well as substitutions between brown and white rice, baked and regular chips, lean and regular types of meat, and reduced-fat and regular cheese.

Although the public health community is attempting to increase people's intake of whole grains, existing research offers no data to predict price-induced shifts in purchases of whole grain products. We found no estimates of how quantities of whole wheat bread purchased would react to changes in the price of refined flour bread. Only 1 study estimated price elasticities for diet and regular soft drinks, 28 and the authors did not offer cross-price elasticities (although a number of brand-level studies have examined substitutions between specific brands of diet and regular soft drinks). One study estimated price elasticities for snack food and candy, and 2 studies offered estimates for fast food. 28 , 37 , 38 Despite an increasing focus on nutrient density, we did not identify any studies with elasticity estimates for specific nutrients such as saturated fat.

Sensitivity of Estimates Across Studies

For virtually all estimated demand functions, there is evidence of persistence in food purchasing behavior. For beef, the most commonly analyzed food in our review, we found little variation in elasticity estimates across study designs. Type of demand model, data, peer review status (i.e., peer review versus no peer review), study size (multiple versus single categories of foods), and time of data analysis were not significantly related to the estimates in beef analyses (either jointly in F tests or individually in t -test comparisons). Similarly, the estimated parameters for pork, cheese, and vegetables did not vary significantly according to study methodology. There was some variation in how type of demand system model and data affected estimates in studies on milk, fruit, and fish. However, because of the smaller number of data points (e.g., 18 for fish and 26 for milk versus 51 for beef), these findings must be interpreted with caution.

Given the heightened interest of legislators in the soft drink category and the importance of estimating price elasticity of demand for soft drinks to forecast tax effects, we calculated alternate elasticity estimates based on different assumptions or definitions of soft drinks as a product. The mean price elasticity for the soft drink category (0.79, absolute value) was based on 14 estimates in which definitions of the category varied; category definitions included soft drinks, carbonated soft drinks, juice and soft drinks, soda, soda and fruit ades, nonalcoholic beverages, other beverages (all nonalcoholic beverages excluding milk and juices), and, in 1 study, beverages (the exclusion of this final study had essentially no effect on the mean estimate, increasing it from 0.79 to 0.82).

In a more conservative approach to defining the category of soft drinks, we included 7 studies with estimates for soft drinks, carbonated soft drinks, soda, and soda or fruit ades, with a mean price elasticity of 1.00. Further restricting the definition of soft drinks limited the number of available studies for review. Only 2 estimates were available for carbonated soft drinks (1.08) 39 and soda (0.58), 40 along with 1 study with a combined estimate for soda and fruit ades (1.10) 41 and 1 study with separate regular soft drink (1.05) and low-calorie soft drinks (1.26) estimates. 28 Excluding working papers and the single dissertation resulted in a mean price elasticity of demand for soft drinks of 0.93.

Considerable data are available on price elasticities of demand for certain foods. We found mean price elasticity estimates ranging from 0.27 to 0.81 (absolute values), with the highest price elasticities for food away from home, soft drinks, juice, meats, and fruit and the most inelastic demand for eggs. Higher elasticity estimates suggest greater changes in population purchases as prices shift. From a public health perspective, more elastic demand for food is encouraging if change in demand is a priority (e.g., decreased intake of sugar-sweetened beverages and increased consumption of fruits and vegetables). Such data help bridge the public health and economics communities and begin to establish a vision of where price changes might have the greatest impact on consumer food choices, nutrition, and health.

Although economists have published extensively on the effects of price changes on commodity- and brand-level demand for foods and beverages, substantial gaps in the research base exist. These gaps must be filled to gain a more complete understanding of the public health impact of policies that realign food prices. The studies we reviewed did not assess the effects of price changes on substitutions from unhealthy to healthy food choices for many of the key categories (e.g., whole grains) in the Dietary Guidelines for Americans, which are targets in public health campaigns. There is some evidence to suggest that low-income populations may be more sensitive to price changes than the overall population. 3 Still, current data on the role of income are rather limited, and assessments of differences in responsiveness to food prices according to age, education, culture, or ethnicity are not available.

The effects of cigarette taxes on smoking prevalence demonstrate the significant potential of tax policies to modify purchasing behavior. 4 The public health benefit of even moderate price increases for unhealthy foods can be compared with the demand effect of moderate changes in the price of cigarettes. For example, a negligible change in the price of cigarettes (0.03% of weekly earnings) reduced smoking prevalence by 0.3% among Australian adults. 42 In contrast, the World Health Organization concluded that large tax increases have been the most effective policy for reducing tobacco use. 43 In addition, studies of cigarette taxation suggest that young people may be more responsive to price changes and taxes than the adult population. 44 This is an important consideration in evaluating the potential effects of food tax or subsidy policies on children's food purchases and childhood obesity.

Food Policy Implications

As a result of their negative effects on nutrition and their current taxation status, soft drinks offer a possible target for public health tax policies. 1 , 45 On average, sugar-sweetened beverages contribute 301 kcal (1260 kJ) per day per capita (13% of total daily energy values) to the diets of American adolescents. 46 Assuming no substitution of soft drinks with other caloric beverages and no change in other factors affecting purchasing behavior, our estimates of the price elasticity of soft drinks suggest that a 10% tax on soft drinks could lead to an 8% to 10% reduction in purchases of these beverages.

Small changes add up. One USDA study that estimated potential weight loss from various tax rates on salty snacks under a range of price elasticities predicted that a 10% price increase from a national sales tax could reduce body weight between 0.2 and 0.99 lb (0.1–0.5 kg) per year while generating approximately $1 billion in tax revenue. 47 State governments already target sales taxes at soft drinks and selected snack foods. As of January 2009, 33 states taxed the sale of soft drinks at an average rate of 5.2%. 48 Of importance to policymakers, recent surveys show that the public is willing to pay increased taxes if the funds generated are used to address childhood obesity. 49 , 50

Although the potential public health benefits of price changes in specific food categories can be estimated, it is essential to assess changes in consumer behavior as price changes occur. For example, in the event of higher prices resulting from increased taxes, consumers could increase their caloric consumption from fruit juice to compensate for their reduction in soft drink intake, or, more positively, they might generalize the healthy changes they make to other categories of foods. It is also important to consider how governments use revenues generated by changes in economic policies such as taxes. For instance, regressive food taxes could be offset by using revenues to lower the costs of healthy foods, particularly for low-income population groups.

Such policies are under consideration. The Food, Conservation and Energy Act of 2008 (known as the “Farm Bill”) authorized a $20 million pilot study examining the use of price incentives to promote consumption of fruits, vegetables, and other healthy foods among food stamp recipients. 11 On the basis of our mean price elasticities of 0.70 for fruits and 0.58 for vegetables, a 10% reduction in the price of these foods would increase purchases on average by 7.0% and 5.8%, respectively.

As such, changes in prices alone would probably not increase consumption of fruits and vegetables to the levels recommended in the Dietary Guidelines for Americans. However, price changes combined with public education campaigns and other regulations affecting the food environment in institutional and home settings may have a multiplicative effect that could significantly improve diets, particularly among at-risk population groups. Although demand for food is relatively inelastic, the power of small price changes, especially applied to foods most responsive to such changes, should not be underestimated given that their effects accumulate across a population.

Our review had limitations. For example, we used combined estimates of price elasticity for fruits and vegetables (which were the only available estimates in many studies), and thus we may have underestimated the separate price elasticities of demand for fruits and vegetables. In addition, none of the studies included in our review were published after September 2007 (when we completed the review). Finally, our synthesis of estimates was a simplified calculation of means rather than a meta-analysis, which could not be conducted given the lack of elasticity estimate standard errors in the literature.

Conclusions

Economic shocks such as falling income in a recession or dramatic increases in energy or food prices can lead to changes in purchasing behavior that are not necessarily predicted by elasticity estimates calculated with data collected under normal market conditions. It is important to understand the effects of such economic circumstances on diet quality, particularly in low-income groups. The fear is that increasing food prices or falling incomes in a recession create pressure to purchase the foods lowest in cost, which makes processed, calorie-dense foods more attractive. Given the relative consensus in the economic community about the magnitude of food price elasticities and the observed gaps in research related to substitutions between healthy and unhealthy foods, future research should focus on predicting the impact of specific public health policies aimed at improving diets and reducing the burden of chronic disease.

Acknowledgments

We acknowledge funding support from the Rudd Foundation.

We thank three anonymous reviewers for their helpful comments.

Human Participant Protection

No protocol approval was needed for this study.

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9 facts about americans and marijuana.

The use and possession of marijuana is illegal under U.S. federal law, but about three-quarters of states have legalized the drug for medical or recreational purposes. The changing legal landscape has coincided with a decades-long rise in public support for legalization, which a majority of Americans now favor.

Here are nine facts about Americans’ views of and experiences with marijuana, based on Pew Research Center surveys and other sources.

As more states legalize marijuana, Pew Research Center looked at Americans’ opinions on legalization and how these views have changed over time.

Data comes from surveys by the Center,  Gallup , and the  2022 National Survey on Drug Use and Health  from the U.S. Substance Abuse and Mental Health Services Administration. Information about the jurisdictions where marijuana is legal at the state level comes from the  National Organization for the Reform of Marijuana Laws .

More information about the Center surveys cited in the analysis, including the questions asked and their methodologies, can be found at the links in the text.

Around nine-in-ten Americans say marijuana should be legal for medical or recreational use,  according to a January 2024 Pew Research Center survey . An overwhelming majority of U.S. adults (88%) say either that marijuana should be legal for medical use only (32%) or that it should be legal for medical  and  recreational use (57%). Just 11% say the drug should not be legal in any form. These views have held relatively steady over the past five years.

Views on marijuana legalization differ widely by age, political party, and race and ethnicity, the January survey shows.

While small shares across demographic groups say marijuana should not be legal at all, those least likely to favor it for both medical and recreational use include:

• Older adults: 31% of adults ages 75 and older support marijuana legalization for medical and recreational purposes, compared with half of those ages 65 to 74, the next youngest age category. By contrast, 71% of adults under 30 support legalization for both uses.
• Republicans and GOP-leaning independents: 42% of Republicans favor legalizing marijuana for both uses, compared with 72% of Democrats and Democratic leaners. Ideological differences exist as well: Within both parties, those who are more conservative are less likely to support legalization.
• Hispanic and Asian Americans: 45% in each group support legalizing the drug for medical and recreational use. Larger shares of Black (65%) and White (59%) adults hold this view.

Support for marijuana legalization has increased dramatically over the last two decades. In addition to asking specifically about medical and recreational use of the drug, both the Center and Gallup have asked Americans about legalizing marijuana use in a general way. Gallup asked this question most recently, in 2023. That year, 70% of adults expressed support for legalization, more than double the share who said they favored it in 2000.

Half of U.S. adults (50.3%) say they have ever used marijuana, according to the 2022 National Survey on Drug Use and Health . That is a smaller share than the 84.1% who say they have ever consumed alcohol and the 64.8% who have ever used tobacco products or vaped nicotine.

While many Americans say they have used marijuana in their lifetime, far fewer are current users, according to the same survey. In 2022, 23.0% of adults said they had used the drug in the past year, while 15.9% said they had used it in the past month.

While many Americans say legalizing recreational marijuana has economic and criminal justice benefits, views on these and other impacts vary, the Center’s January survey shows.

• Economic benefits: About half of adults (52%) say that legalizing recreational marijuana is good for local economies, while 17% say it is bad. Another 29% say it has no impact.

• Criminal justice system fairness: 42% of Americans say legalizing marijuana for recreational use makes the criminal justice system fairer, compared with 18% who say it makes the system less fair. About four-in-ten (38%) say it has no impact.
• Use of other drugs: 27% say this policy decreases the use of other drugs like heroin, fentanyl and cocaine, and 29% say it increases it. But the largest share (42%) say it has no effect on other drug use.
• Community safety: 21% say recreational legalization makes communities safer and 34% say it makes them less safe. Another 44% say it doesn’t impact safety.

Democrats and adults under 50 are more likely than Republicans and those in older age groups to say legalizing marijuana has positive impacts in each of these areas.

Most Americans support easing penalties for people with marijuana convictions, an October 2021 Center survey found . Two-thirds of adults say they favor releasing people from prison who are being held for marijuana-related offenses only, including 41% who strongly favor this. And 61% support removing or expunging marijuana-related offenses from people’s criminal records.

Younger adults, Democrats and Black Americans are especially likely to support these changes. For instance, 74% of Black adults  favor releasing people from prison  who are being held only for marijuana-related offenses, and just as many favor removing or expunging marijuana-related offenses from criminal records.

Twenty-four states and the District of Columbia have legalized small amounts of marijuana for both medical and recreational use as of March 2024,  according to the  National Organization for the Reform of Marijuana Laws  (NORML), an advocacy group that tracks state-level legislation on the issue. Another 14 states have legalized the drug for medical use only.

Of the remaining 12 states, all allow limited access to products such as CBD oil that contain little to no THC – the main psychoactive substance in cannabis. And 26 states overall have at least partially  decriminalized recreational marijuana use , as has the District of Columbia.

In addition to 24 states and D.C.,  the U.S. Virgin Islands ,  Guam  and  the Northern Mariana Islands  have legalized marijuana for medical and recreational use.

More than half of Americans (54%) live in a state where both recreational and medical marijuana are legal, and 74% live in a state where it’s legal either for both purposes or medical use only, according to a February Center analysis of data from the Census Bureau and other outside sources. This analysis looked at state-level legislation in all 50 states and the District of Columbia.

In 2012, Colorado and Washington became the first states to pass legislation legalizing recreational marijuana.

About eight-in-ten Americans (79%) live in a county with at least one cannabis dispensary, according to the February analysis. There are nearly 15,000 marijuana dispensaries nationwide, and 76% are in states (including D.C.) where recreational use is legal. Another 23% are in medical marijuana-only states, and 1% are in states that have made legal allowances for low-percentage THC or CBD-only products.

The states with the largest number of dispensaries include California, Oklahoma, Florida, Colorado and Michigan.

Note: This is an update of a post originally published April 26, 2021, and updated April 13, 2023.  

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Americans overwhelmingly say marijuana should be legal for medical or recreational use

Religious americans are less likely to endorse legal marijuana for recreational use, four-in-ten u.s. drug arrests in 2018 were for marijuana offenses – mostly possession, two-thirds of americans support marijuana legalization, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .