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date: 08 December 2019

The Economics of Diet and Obesity: Understanding the Global Trends

Summary and Keywords

In the last several decades obesity rates have risen significantly. In 2014, 10.8% and 14.9% of the world’s men and women, respectively, were obese as compared with 3.2% and 6.4% in 1975. The obesity “epidemic” has spread from high-income countries to emerging and developing ones in every region of the world. The rising obesity rates are essentially explained by a rise in total calorie intake associated with long-term global changes in the food supply. Food has become more abundant, available, and cheaper, but food affluence is associated with profound changes in the nutritional quality of supply. While calories have become richer in fats, sugar, and sodium, they are now lower in fiber. The nutrition transition from starvation to abundance and high-fat/sugar/salt food is thus accompanied by an epidemiological transition from infectious diseases and premature death to chronic diseases and longer lives. Food-related chronic diseases have important economic consequences in terms of human capital and medical care costs borne by public and private insurances and health systems.

Technological innovations, trade globalization, and retailing expansion are associated with these substantial changes in the quantity and quality of food supply and diet in developed as well as in emerging and rapidly growing economies. Food variety has significantly increased due to innovations in the food production process. Raw food is broken down to obtain elementary substances that are subsequently assembled for producing final food products. This new approach, as well as improvements in cold chain and packaging, has contributed to a globalization of food chains and spurred an increase of trade in food products, which, jointly with foreign direct investments, alters the domestic food supply. Finally, technological advancements have also favored the emergence of large supermarkets and retailers, which have transformed the industrial organization of consumer markets.

How do these developments affect population diets and diet-related diseases? Identifying the contribution of supply factors to long-term changes in diet and obesity is important because it can help to design innovative, effective, and evidence-based policies, such as regulations on trade, retailing, and quality or incentives for product reformulation. Yet this requires a correct evaluation of the importance and causal effects of supply-side factors on the obesity pandemic. Among others, the economic literature analyzes the effect of changes in food prices, food availability, trade, and marketing on the nutrition and epidemiological transitions. There is a lack of causal robust evidence on their long-term effects. The empirical identification of causal effects is de facto challenging because the dynamics of food supply is partly driven by demand-side factors and dynamics, like a growing female labor force, habit formation, and the social dynamics of preferences.

There are several important limitations to the literature from the early 21st century. Existing studies cover mostly well-developed countries, use static economic and econometric specifications, and employ data that cover short periods of time unmarked by profound shifts in food supply. In contrast, empirical research on the long-term dynamics of consumer behavior is much more limited, and comparative studies across diverse cultural and institutional backgrounds are almost nonexistent. Studies on consumers in emerging countries could exploit the rapid time changes and large spatial heterogeneity, both to identify the causal impacts of shocks on supply factors and to document how local culture and institutions shape diet and nutritional outcomes.

Keywords: health economics, food, obesity, technology, trade, retailing, food prices, food marketing, food quality, consumer preferences

Introduction

The number of people who are overweight and obese has increased across the globe over the last four decades. Worldwide in 2014, about 266 million men and 375 million women were obese, which corresponds respectively to 10.8% and 14.9% of the world’s men and women. Since 1975, the prevalence of obesity has nearly tripled.1 While high-income countries were the first to experience rising obesity levels, the epidemic has spread to low- and middle-income countries. Obesity prevalence is higher than 30% in men and women of high-income English-speaking countries, but also in South African and Middle Eastern women. “Emerging” countries like Russia, Brazil, India, China, and Mexico contribute to more than 30% of global obesity (NCD Risk Factor Collaboration, 2016).2 This “obesity epidemic” is fueled by the rising prevalence of overweight among children aged 5 to 19, which increased from 4% in 1975 to about 18% in 2016 (World Health Organization, 2017). The world has transitioned from a state in which underweight prevalence was more than double that of obesity, to one in which more people are obese than underweight.

The energy balance equation is the central paradigm used to understand these obesity trends. It simply states that calorie intakes are either expended in physical activity, excreted, or stored as body fat. Hence, rising obesity is explained by a rise in total calorie intake and/or a decrease in calorie expenditure (Hall et al., 2011). Several metabolic mechanisms ensure the stability of body weight around a set point at which energy intakes naturally adapt to energy requirements for work, leisure, and body maintenance. An upward drift in body weight results when the brain reward system overrides the metabolic signals and sustains “hedonic over-eating” and when a modification to metabolism alters the set point in response to environmental changes (van der Klaauw & Farooqi, 2015; Yu et al., 2015). Therefore, whatever the secular trends in calories expended in work-related or leisure-time physical activity, the rise in obesity is necessarily due to a change in the environment that has altered preferences for food and has influenced the homeostatic metabolic mechanisms.3

This article examines specifically the long-term global changes in the food environment, such as food quality, availability, prices, and marketing. Food has become more abundant, but food affluence is associated with profound changes in the nutritional quality of food supply. Calories have become richer in fats, sugar, and sodium and have shown decreases in the amounts of fiber. All of these are risk factors, not only for obesity but also for hypertension, diabetes, and many cancers, which are declining or stable in high- and even middle-income countries but still remain major causes of death and health loss in poor populations of all world regions (Gakidou et al., 2017). The nutrition transition from starvation to abundance and high-fat/sugar/salt foods is thus accompanied by an epidemiological transition from infectious diseases and premature death to chronic diseases and longer lives (Fogel, 2004; Popkin & Gordon-Larsen, 2004). Food-related chronic diseases have important economic consequences in terms of medical care costs borne by public and private insurances and health systems and human capital (e.g., productivity losses, educational attainments).4 These diseases are particularly challenging for people in low- and middle-income countries, as economic growth depends on a large and healthy workforce, and public health systems are often fragile. For instance, obesity alone accounts for 2.5% of China’s national healthcare expenditure, which lies in the range of most estimates for high-income countries, between 1% and 10% (Etilé, 2011; Qin & Pan, 2016; Tremmel, Gerdtham, Nilsson, & Saha, 2017).

This article examines the long-term economic determinants of changes in food supply and diet. Due to space limitations, the coverage is necessarily selective and limited, but it covers the literature on countries other than the United States as much as possible.5 Special attention is paid to the mechanisms underlying the dynamics of food markets in agri-food production, processing, trade, distribution, and consumer demand. Identifying the contribution of various factors to long-term changes in diet and obesity is important, because it can help to design innovative, effective, and evidence-based policies targeting the food supply, such as regulations on trade, retailing, and quality, or incentives for product reformulation. Economists have mainly analyzed policies based on prices, information, or behavioral interventions with the aim of altering consumer behaviors. As repeatedly emphasized by public health researchers, targeting the supply-side drivers of the obesity pandemic is necessary to curb long-term obesity trends (Hill, Wyatt, Reed, & Peters, 2003; Swinburn et al., 2011), which requires a correct evaluation of the importance and causal effects of supply-side factors on the obesity pandemic.

The article is structured as follows: The first section presents some key facts regarding the relationship between economic development and the nutrition and epidemiological transitions. This is followed by a discussion of the key technological, industrial, and trade drivers of the dynamics of food supply, reviews of the empirical evidence documenting the causal impact of supply factors and an examination of the methodological issues that need to be addressed, and analysis of the increasing demand for processed food in relation to changes in the labor supply, specifically women in the workforce. The next section describes endogenous mechanisms of changes in food preferences. Finally, the normative implications of research and issues that have been under-researched are discussed. Specifically, existing studies cover mostly well-developed countries, use static economic and econometric specifications, and employ data that cover short periods of time unmarked by profound shifts in food supply. In contrast, empirical research on the long-term dynamics of consumer behavior is much more limited. This highlights the need for studies on consumers in emerging countries, where rapid time changes and large spatial heterogeneity can be leveraged, not only to identify the causal impacts of shocks on supply factors but also to document how local culture, legal, and political institutions shape the structure of food systems and modulate their impact on obesity and food-related chronic diseases at an individual level.

Economic Development and the Nutrition Transition

In the 1960s, many countries in the developing world still suffered from calorie deficit. By 2016, however, this prevalence of undernourishment had fallen to about 11% across the globe. The world’s food supply had increased over several decades. The nutrition transition started in high-income countries (HIC) that were already at high levels of calorie availability in the 1960s. Middle-income countries (MIC) then started to catch up to the dietary patterns of those with higher incomes (Schmidhuber & Shetty, 2005). Figures 1 and 2 illustrate the links between economic development and the nutrition and epidemiological transitions for a panel of countries ranging from HIC to upper-MIC (UMIC), lower-MIC (LMIC), and low-income countries (LIC).

Figure 1 presents the trends in overweight and obesity across these income groups. In 1980, the average prevalence of adult overweight and obesity exhibited a significant income gradient. In HICs, the prevalence of adult overweight and obesity was on average just below 40% compared to 32–34% in MICs and 11% in LICs. Within these income groups, substantial variations existed. For example, Japan’s prevalence was less than half of Saudi Arabia’s value in 1980. Between 1980 and 2016 obesity levels increased across the globe with stronger growth rates in MICs than in HICs. Consequently, the mean prevalence in UMICs is now almost on par with the prevalence in HICs (about 50%). Across geographical regions, obesity is particularly prevalent in Latin America, Anglo-Saxon HICs, and the Middle East. In contrast, adults in Asian countries have thus far been less affected by the obesity epidemic (e.g., Japan, China, India). Obesity among children follows the similar upward global trend, and even some Asian countries, such as China, are not spared. In 2016, childhood weight issues and obesity prevalence exceeded 35% in Chile, the United States, Saudi Arabia, Mexico, and Egypt. The epidemiological transition is also manifested in diabetes prevalence. On average diabetes prevalence is higher in MICs (10–12%) than HICs (9%), but this may be explained by better treatment opportunities in HICs.

Figure 2 provides information on changes in the average daily calorie supply per capita and the proportions that come from either fat or sugar. Between 1980 and 2011, all countries saw an increase in total calorie supply. The growth was most pronounced in MICs, so the gap in calorie supply is shrinking between income groups. Food composition is also correlated with income, as HICs experience a higher share of calories stemming from fat and sugar than MICs and LICs. On average, in HICs fats account for about 35% of total calories supplied as compared to 22–26% in MICs and less than 20% in LICs. The comparison of trends presented in Figures 1 and 2 reveals certain heterogeneity in the relationship between nutrition and adult obesity. For example, while total calorie supply and the share derived from fat was substantially higher in Germany in 2011 than in Saudi Arabia, the adult obesity prevalence was remarkably higher in Saudi Arabia. Likewise, Nigeria has a lower share of sugar and a similar share of fat in food supply than India, but the former experiences higher overweight and obesity levels. Beyond this heterogeneity, it is clear that in virtually all HICs and MICs the current intake of fat and/or sugar generally exceed the thresholds of 30% and 10% that are recommended by the World Health Organization, whose guidelines for a healthy diet are significant predictors of mortality reduction (Jankovic et al., 2014).

It might be tempting to explain cross-country nutritional or epidemiological differences to country-specific traditional food cultures. For example, the low adult obesity prevalence in Japan may be due to the consumption of seafood. Like Japan, however, Chile has a long coastline and therefore a rich supply of seafood. Chileans’ traditional diet resembled the Mediterranean diet and included many seafood-based recipes. Yet the pace of the nutrition transition in Chile has been particularly fast. In 2016, obesity prevalence in Chile was on par with the percentage in the United States. Historical evidence suggests that differences in trade policies between Chile and Japan may partly explain their diverging trajectories. The 1973 military coup d’état was followed by an opening of the domestic market to world trade and U.S. foreign direct investment. Agricultural exports were strongly subsidized while import tariffs were drastically reduced. Hence, it is possible that these price effects have translated into a change in the composition of the diet, with a much larger share of calories coming from cheap food imports. This example highlights the potentially large impact of trade dynamics on the nutritional quality of food supply.

The Economics of Diet and Obesity: Understanding the Global TrendsClick to view larger

Figure 1. Evolution of overweight and obesity prevalence in selected countries, 1980–2011.

The Economics of Diet and Obesity: Understanding the Global TrendsClick to view larger

Figure 2. Evolution of calorie supply in selected countries, 1980–2011.

The Long-Term Dynamics of Food Supply

This section considers the technological and economic dynamics of the food supply that underlie the global upward trend in calorie supply, the shift in the nutrient composition of calories, and part of cross-country heterogeneity. It details the complementarity between technological innovations, trade globalization, and retailing expansion, which has been extensively discussed by public health researchers. Some authors have already emphasized the impact of agri-food innovations in the trends in food prices and calories and their role in the U.S. obesity epidemic (Cutler, Glaeser, & Shapiro, 2003; Lakdawalla & Philipson, 2009; Ruhm, 2012).

The key innovation in food processing is the division of the production process into two stages, a practice that began in the 1970s. During a first “cracking” stage, raw agricultural products are broken down to extract elementary substances and produce ingredients and additives (e.g., oils, fats, flours, starches). The costs of producing raw animal and vegetal products has started to decrease after World War II, with innovations in mechanization, fertilizers, pesticides, and genetic selection of a few high-yield varieties. In the second “assembly” stage, elements and ingredients are recombined to produce final consumer products. These combinations are designed to maximize consumer experience under desired constraints of costs, palatability, safety, and preservation. Formulations often include various chemicals such as artificial colorants or stabilizers. The creation of food variety no longer occurs at the agricultural level but is pushed down to “assembly” stage. This shift facilitates product innovation, as formulating new recipes is cheaper than developing new plant varieties. This has contributed to the increase in the number of food varieties supplied to consumer markets (Irz, Mazzocchi, Réquillart, & Soler, 2015).

The development of cold chains made long-distance trade of non-stabilized products possible. Various technological advancements helped to prevent spoilage and preserve food products on shelves. For example, “controlled atmosphere processing” allows manufacturers to control the gaseous environment in which foods is stored. This is particularly useful for fruits and vegetables as it slows down the ripening process. New package materials help to prevent migration of flavor-related chemicals to and from a specific type of food to preserve its flavor. Advancements in polyethylene plastics reduced the risk of moisture within packages of frozen products that altered a food’s texture and led to “freezer burn” (Cutler et al., 2003; Irz et al., 2015).

These technological innovations, as well as a sharp fall in transportation costs, led to the globalization of food chains, which can now involve many different actors specialized in specific steps to benefit from economies of scale. Trade liberalization and the opening of agricultural markets boosted this development. Many low- and middle-income countries liberalized their agricultural markets in the 1990s and WTO rules were extended to include agriculture in 1994. Hawkes (2006) illustrated the nutritional impacts with a case study on Brazil. In the 1990s, Brazil liberalized its soybean market, promoted exporting, and facilitated foreign investment of transnational food companies. Soybean oil production increased by 67% between 1990 and 2001 and world prices declined. For importing countries such as China and India, these price declines spurred a rising use of soybean oil in food processing and consumption.

More generally, in developing countries food imports as a share of GDP increased by more than 100% between 1974 and 2004 and processed food accounted for a large share of imports. Trade not only alters food supply through food imports, but also through foreign direct investment (FDI). In the 1970s, FDIs were generally applied to the production of raw commodities for export (e.g., oil crops or cereals). In the 1980s a shift began toward investment in processed food (such as soft drinks, confectionary, dairy products, and baked goods) for the host market. For example, Mexico liberalized its investment rules in the 1990s and foreign investments in Mexico’s food-processing industry increased from $210 million in the late 1980s to $5 billion ten years later. In the following years, sales of processed foods grew by 5–10% per year in Mexico (Hawkes, 2005, 2006). One explanation for this trend may be that multinational companies seek new market opportunities in low- and middle-income countries. Not surprisingly, in 2012, the same multinational “big food” companies were number one or two in food markets in a wide range of MICs such as Brazil, India, Mexico, Russia, and South Africa (Stuckler, McKee, Ebrahim, & Basu, 2012).

The technological innovations have also altered the industrial organization of consumer markets. While it took supermarkets in the United States about 40 years (until the 1980s) to dominate the retail market, the development in emerging countries started later but at a faster pace. In Latin America, the share of supermarkets in the national food retail sales grew from 10–20% in the 1980s to 50–60% by 2000. Supermarkets also emerged in Southeast Asia in the mid- to late 1990s and in China, Vietnam, India, and Russia in the late 1990s and 2000s. They have also been expanding recently in countries of both eastern and southern Africa. Domestic investments as well as FDIs in retailing fueled the development of large and concentrated retailing chains (Reardon, Timmer, Barrett, & Berdegue, 2003; Reardon, Timmer, & Minten, 2012). In urban areas, large retailers entering new markets first offer processed foods at competitive prices, thereby winning market shares; they later move on to propose fresh foods (Minten & Reardon, 2008; Reardon et al., 2012). Small family-owned merchandise stores remain the main retailers in towns and rural areas of MICs and LICs. Case studies show that multinational food companies propose exclusive placement contracts in exchange for equipment (Hawkes, 2006). Such marketing strategies likely contribute to an increased share of processed foods in individual diets (Gomez & Ricketts, 2013). These technological innovations and shifts in trade and industrial organization of food supply have affected both the price and quality of products offered to consumers.

The real price of unhealthy processed and calorie-dense products has fallen over time, whereas the price of healthy products—and notably, fresh fruits and vegetables—has risen. These trends and structures of relative food prices are observed in all developed and developing countries (Christian & Rashad, 2009; Etilé, 2013; Monteiro, Moubarac, Cannon, Ng, & Popkin, 2013; Popkin, Adair, & Ng, 2012). In addition, the price volatility of processed and tradable food is generally lower than the price volatility of fresh products (e.g., for sub-Saharan Africa; Minot, 2014). But what is the evidence of the causal effects of technologies, trade, and organization on the relative prices of food products? Atkin (2013) is one of the few researchers to study the link between trade liberalization and relative prices of different types of foods. He proposed a general equilibrium framework to examine the impact of agricultural trade liberalization on local food prices in India. As foreign demand for local food products (e.g., rice) increases, local traditional food becomes more expensive, which reduces consumption. However, more research is needed to better understand whether agricultural trade liberalization favors a fall in price of processed food and substitutions toward processed products.

Analysis of the welfare consequences of such price changes constitutes the next step in this strand of research. Apart from gains derived from lower prices, the exploding number of available food product varieties may also generate welfare gains for consumers, at least under standard rationality assumptions. Broda and Weinstein (2006, 2010) formulated exact price indices that captured these benefits. They showed that for the United States, trade has made new consumer goods available and generated a drop in the aggregate consumer price index, independently from the variations in market prices. Another implicit source of welfare gain lies in the reduced time costs of preparing meals by combining processed or ultraprocessed and time-saving kitchen appliances (e.g., the invention of deep freezing combined with microwaves). This is not measured by existing price indices, as they are not based on utility functions defined over both activity times and goods.

Technological developments impact food quality and thereby have health consequences for long-term consumer welfare. Market studies by nutritionists have emphasized that calorie-dense and processed foods tend to be deficient in essential nutrients; rich in sugar, fat, and salt; and cheaper in terms of cost per calorie (Baker & Friel, 2014; Drewnowski & Darmon, 2005; Elliott, 2008). The economic explanation for the poor quality of processed food is that fat and sugar are extracted from large-scale crops produced with capital-intensive techniques and are therefore cheap inputs in processing (Irz et al., 2015). Unsurprisingly then, processed foods are major contributors to deviation of household purchases from dietary and weight recommendation in the most advanced countries (Julia et al., 2018; Luiten, Steenhuis, Eyles, Mhurchu, & Waterlander, 2016; Poti, Mendez, Ng, & Popkin, 2015) as well as in emerging countries (Baker & Friel, 2014; da Costa Louzada et al., 2018). Analyzing food consumption patterns in the United States between 2000 and 2012 reveals that more than 80% of calories purchased came from ready-to-eat or ready-to-heat products. The percentage of products over the recommended levels for saturated fat, sodium, and sugar were, respectively, 84%, 92%, and about 67% (Poti et al., 2015). The economic research needs to identify the welfare gains produced by the dynamics of food markets net of their potential effects on consumer health.

Measuring the Impact of Food Supply on Diet and Obesity

This section reviews the empirical literature that examines the impact of changes in food supply on diet and health. Various methodological challenges are described, such as the availability of appropriate data and a fundamental identification challenge: all dimensions of food supply are likely to be endogenously determined by demand-side factors.

Prices, Diet, and Obesity

Hundreds of empirical studies report estimates of own- and cross-price elasticities for various food groups, periods, and countries. Cornelsen et al. (2015) proposed a meta-analysis of price elasticities between seven broad food groups in low-income, middle-income, and high-income countries. They showed that own-price effects are larger in low-income countries, whereas the cross-price effects are about 10 times smaller than own-price elasticities and also very heterogeneous. These findings may reflect cross-country variations in food preferences as well as lack of sufficient variations in substitution behaviors for empirical identification. Some studies have combined food price elasticities with information on the nutritional content of purchases to derive nutrient elasticities. Furthermore, these nutrient elasticities can be used as input in biological models of the nutrient-body weight relationship to simulate the impact of price changes on obesity (Allais, Bertail, & Nichèle, 2010; Huang, 1996).

Demand for quality has only been documented within a few countries. The dimensions of food quantity and food quality affect consumer choices and matter for nutritional outcomes. Systems of demand for quantities—such as the Quadratic Almost Ideal Demand System (Banks, Blundell, & Lewbel, 1997) and the Exact Affine Stone Index system (Lewbel & Pendakur, 2009)—are appropriate for studying substitutions between broad product categories and provide exact measures of consumer welfare. The food classification can be somewhat refined to account for the clustering of products into food categories of different nutritional quality or processing intensity (see Harding & Lovenheim, 2017). However, increasing the number of product categories implies that the mass of consumers not consuming certain foods will become large and causes inference problems.6 A number of utility-based structural models have been proposed to extend discrete-choice models of demand for variety-to-quantity choices and purchase of several products (for an introduction, see Chintagunta & Nair, 2011).

The empirical literature on food demand now faces important methodological issues with replicability and causal identification. Meta-analyses of the literature show that variations in demand specification and estimation methods, as well as publication biases, can affect the estimated price elasticities (Andreyeva, Long, & Brownell, 2010; Gallet, 2007; Green et al., 2013; Nelson, 2013; Powell, Chriqui, Khan, Wada, & Chaloupka, 2013). Methodologies are indeed not standardized, so it is difficult to compare results between countries or periods. Common standards regarding the construction of price indices, the specification of demand functions, and identification assumptions would help. Prices constructed from observed transactions can be endogenous for two reasons. First, supply prices may be largely driven by shocks on quantity demand because they are often important constraints on production capacities in agri-food markets (Stigler, 1954). Second, retailers are likely to set their prices as a function of unobserved consumer preferences for unobserved product attributes (e.g., promotion, display, advertising; see Ackerberg, Benkard, Berry, & Pakes, 2007, or Nevo, 2011). Finding good instrumental variables is challenging. Studies on emerging countries may be able to exploit the larger time and spatial variations in supply prices that are caused by infrastructure-related logistic costs and exposition to weather and trade shocks. Other arguably exogenous sources of supply price variations include the fluctuations in production costs or retailing costs (Bonnet & Réquillart, 2013) and the prices or characteristics of similar products in other markets or for other consumer segments (Dubois, Griffith, & Nevo, 2014; Hausman, Leonard, & Zona, 1994; Nevo, 2001). The rationale here is that the instruments and the instrumented prices are affected by common shocks on the production or retailing processes, but that market-specific valuations of unobserved product attributes are uncorrelated across markets.7 The credibility of these instruments is the subject of a tense debate between advocates of (quasi-) experimental methods and empirical Industrial Organization practitioners (Angrist & Pischke, 2010; Nevo & Whinston, 2010). In addition, fluctuations in input costs may be only weakly correlated with consumer prices while at the same time being multicollinear (e.g., oil prices driving simultaneous variations in the costs of many inputs). Instrumental Variables estimation can only recover local average treatment effects (i.e., causal effects for the population marginally affected by variations in the instrument; Angrist, Imbens, & Rubin, 1996). The effects of variations in input or retailing costs on consumer prices are thus likely to vary between food categories, markets, and products, depending on local market structure and the vertical relationships and contracts between retailers and producers.8 These problems are likely magnified in estimation of demand systems for quantities that use the same type of instruments for many food prices.9 The criticism that these IV approaches do not provide transparently identified results should be addressed in future studies; for instance, by comparing or combining (quasi-) natural experimental methods with structural approaches (Angrist & Pischke, 2010) or reporting tests of multicollinearity among the instruments and appropriate weak instrument tests (Sanderson & Windmeijer, 2016).

Some studies have exploited quasinatural experiments to analyze the relationship between price variations and body mass index (BMI). For instance, Cotti and Tefft (2013) used variations in the minimum wage across the United States to see if fast-food prices impact BMI and obesity. They did not find any evidence of consistent negative relationships between contemporaneous or lagged (up to six quarters) fast-food prices. However, this type of reduced-form IV approach can only recover local treatment effects, and it remains difficult to correctly specify the relationship between lagged prices and current body weight if the impact of consumption on weight is slowly cumulative. Ideally, one should estimate dynamic models, where current BMI would be specified as a function of prices, lagged BMI, and individual fixed effects. Such a dynamic panel specification poses important identification and estimation challenges, as both lagged BMI and the prices are endogenous and there can be some heterogeneity in the coefficients (for a possible solution, see Chudik & Pesaran, 2015). Overall, there is a lack of direct observational evidence on the long-term impacts of price changes—especially the price of processed food. The current transitions in emerging countries provide opportunities for identifying price effects.

Food Availability and Obesity

Few studies have attempted to estimate the causal effect of supermarkets on the prevalence of overweight and obese individuals. Identification concerns revolve around the nonrandom location of stores. Asfaw (2007, 2008) analyzed the impact of supermarket purchases on the dietary patterns and weight outcomes of Guatemalan households. Supermarket food expenditure, instrumented by the wife’s labor market participation and area-level characteristics, has a positive impact on the calorie share from processed food (+6 percentage points when expenditure is doubled) and a negative effect on the calorie share from raw starches. Doubling the share of ultraprocessed food in total food expenditure increases the probability of being overweight by 1.6 percentage points (or +7% on the baseline prevalence). Umberger, He, Minot, and Toiba (2015) estimated the effect of supermarket expenditure share on adult BMI in a cross-section of Indonesian urban households. They used self-reported measures of the importance of the outlet offering high-quality food product as an instrument, but it is not clear why preference for food quality would not directly affect BMI. Kimenju, Rischke, Klasen, and Qaim (2015) found significant positive impacts of supermarket expenditure share on expenditure share of ultra-processed food and calorie purchases in a small sample of households from three Kenyan towns. They used household distance to supermarkets as an instrument for supermarket expenditure. This is unlikely to overcome the nonrandom selection bias if there is strong residential segregation and supermarkets choose to locate in rich boroughs. Notably, they also found a negative association with child undernourishment. More research is needed to take into account heterogeneous effects along the entire distribution of BMI. Supermarket expansion also yields welfare gains, taking the form of lower prices and better food access. Atkin, Faber, and Gonzalez-Navarro (2018) precisely quantified the positive effect of supermarket entries on Mexican consumer welfare for the 2002–2014 period. These findings highlight the potential ambivalent effect of modern retail stores in emerging countries.

The more robust evidence to date is offered by Courtemanche and Carden (2011), who used data on the locations and openings of Walmart Supercenters in the United States matched with individual-level BMI data. Store location is instrumented by using the distance from Walmart’s headquarters in Bentonville, Arkansas, as the retailer expanded progressively around this point. Courtemanche and Carden (2011) found that one additional Walmart Supercenter per 100,000 residents increased average BMI by 0.24 units and obesity prevalence by 2.3 percentage points. Using the same strategy, Volpe, Okrent, and Leibtag (2013) estimated the effect of supercenter stores on the healthfulness of consumers’ groceries collected in Nielsen Homescan data between 1998 and 2006. They uncovered a robust negative effect of the local market share of supercenters on purchase healthfulness. It remains unclear, however, why consumers choose less healthy products when shopping at supercenters as compared to supermarkets, as prices for fresh products have been found to be lower in supercenters. Future research should identify the respective contribution of the four key marketing channels: the relative prices of healthy versus unhealthy food, promotion, placement (e.g., end-of-aisles effects), and products (more varieties offered).

Some studies have also examined the impact of fast foods on obesity in the U.S. context, with the same empirical issue of nonrandom location. Dunn (2010) employed the number of interstate exits in a U.S. county as an IV for the number of fast-food restaurants, which are more likely to be located at exits in order to serve commuters. He reported a positive and significant cross-sectional impact of the number of fast-food restaurants on BMI, but only for females and nonwhites in the subset of medium-density counties. In a study of rural residents of central Texas, Dunn, Sharkey, and Horel (2012) used the distance to the nearest highway as an IV and uncovered evidence of a positive effect of fast-food availability on fast-food consumption and obesity for nonwhite residents only. Anderson and Matsa (2011) relied on similar instrumentation strategies to show that fast-food availability has no impact on the daily calorie intakes and BMI of rural residents of 11 U.S. states. Consumers would offset fast-food calories by eating less at other times. Finally, Currie, DellaVigna, Moretti, and Pathania (2010) used fine-grained panel data on the supply of fast-food restaurants to schools to examine the impact of variations in school proximity to fast foods on obesity prevalence among their students and variations in near-controlling for school fixed effects. The argument here is that fast-food location is random, conditional on school fixed effects and a wide set of time-varying neighborhood characteristics. Currie et al. (2010) applied the same strategy to a large sample of pregnant women observed over consecutive pregnancies, thus introducing individual fixed effects. A fast-food restaurant within 0.1 miles of a school resulted in a 6% increase in obesity rates among students in that school. This result is significant at the 5% level, whereas fast-food restaurants at a distance of 0.25 to 0.5 miles distance have no effect. The effect on pregnant women is quantitatively smaller and more linear in distance. As noted by Currie et al. (2010), differences with other studies might be explained by sample sizes, precision of data on fast-food locations, and a focus on urban students and mothers from minority groups. These studies therefore suggest that fast-food availability may have heterogeneous effects across sociodemographic groups of the U.S. population.

A general implication of this literature is that the impact of variations in food supply differs greatly across and within countries. For instance, a recent review provides evidence that energy intake from street foods in developing countries varies from 13% to 50% according to the location studied. Street food and other food away from home are important contributors to fat, salt, and sugar intake, which raises health policy issues beyond the question of food safety (Steyn & Labadarios, 2011; Steyn, Labadarios, & Nel, 2011; Steyn et al., 2013).

Trade, Diet, and Obesity

A few articles have examined the impact of trade on nutritional health by exploiting country panel data and the Swiss Economic Institute (KOF) globalization indices, which measure the economic, social, and political dimensions of globalization for a large number of countries from 1970 on (Dreher, Gaston, & Martens, 2008). Goryakin, Lobstein, James, and Suhrcke (2015) found a positive effect of social globalization in country fixed-effect regressions and a small negative effect of economic globalization on female overweight. Using fixed-effect regressions, Miljkovic, Shaik, Miranda, Barabanov, and Liogier (2015) found that social globalization has a positive impact on obesity. However, unlike Goryakin et al. (2015), they found that only one subdimension of economic globalization, trade openness, has a positive impact on country obesity, whereas FDI has no effect. One explanation for these contradictory results is that Miljkovic et al. (2015) did not control for rising incomes, which are likely to increase consumption (via the income effect).

Oberlander, Disdier, and Etilé (2017) expanded this literature by examining country-level food supply data between 1970 and 2011 and controlling for time-varying unobservable factors that may simultaneously drive a country’s nutritional transition and its degree of globalization, such as major sociological changes or political shocks (e.g., democratization). They found that social aspects of globalization, such as exposure to foreign cultures, are more important in explaining changes in supply of animal proteins, fats, and sugar than economic aspects of globalization, such as foreign direct investments and trade volumes. They also found that social globalization had a positive impact on BMI. These studies thus suggest that social aspects of globalization are more important in explaining the change in dietary habits than economic aspects of globalization.

Marketing and Advertising

The nutrition transition and the rise in processed food is unambiguously associated with increasing food marketing efforts by companies that contribute to social globalization (Popkin et al., 2012; Stuckler & Nestle, 2012).

A large literature in experimental economics, marketing, and psychology has demonstrated the impact of food marketing, not only on what individuals eat but also on how much they eat. Food marketing affects choices through a variety of perceptual, affective, and cognitive mechanisms (see Chandon & Wansink, 2011, for an extensive review). For instance, adding an extra-large option in a menu shifts upward the preferences of consumers who did not choose the previously largest option (Sharpe, Staelin, & Huber, 2008). Uncontrolled exposure to palatable food cues undermines the achievement of weight control goals in dieters (Stroebe, Van Koningsbruggen, Papies, & Aarts, 2013). Doubling all sizes of a product package makes it appear only 50% bigger and affects consumer preferences (Chandon & Ordabayeva, 2009). Portion sizes act as a normative cue on which consumers tend to rely for stopping eating and, therefore, larger portions cause individuals to eat more (with an elasticity of about 0.7; cf. Zlatevska, Dubelaar, & Holden, 2014). Consumers estimate downward the calorie content of meals when healthy food is added to unhealthy food, counting calories from healthy food as counted negative (Chernev & Gal, 2010). Marketing a brand as “healthy” is sufficient to affect individuals’ perceptions of its nutritional and caloric content and to change their preferences (Chandon & Wansink, 2007). Perceptions of hunger and satiety also vary with distractors that are not directly related to the eating experience. Individuals eat more when they are undertaking an activity that captures their attention, such as watching TV or listening to music (Bellisle, Dalix, & Slama, 2004; Brunstrom & Mitchell, 2006; Morton, Meek, & Schwartz, 2014; Stroebele & de Castro, 2006). Interestingly, this literature has only slightly explored the likely variations across social groups, as if the typical experimental subjects of Western societies—and especially students—were representative of the human species. As argued by Henrich, Heine, and Norenzayan (2010), a comparative review of results from behavioral sciences across populations strongly suggests that experimental outcomes are strongly contingent on broad cultural and socioeconomic factors.

Mass-media advertising is also frequently mentioned as an important driver of changes in consumer habits, but its effects remain poorly understood (Kearney, 2010). Cross-country evidence shows that unhealthy food is one of the most frequently advertised products on television, in developed as well as in developing countries, with children as the primary target (Kelly et al., 2010). Laboratory experiments suggest that exposure to advertising during childhood has a stronger effect on brand recognition and biased quality evaluation than exposure during adulthood (Connell, Brucks, & Nielsen, 2014). Some quasiexperimental evidence is provided by the ban on advertising targeting children on Quebec TV stations. As English-speaking children living in Quebec continued to be exposed to border TV stations in English, it is possible to compare the consumption trends between French-speaking and English-speaking children. Dhar and Baylis (2011) found that the ban decreased fast-food consumption at the extensive margin (the number of purchase occasions), while Goldberg (1990) reported a decrease in cereals purchased by French-speaking households. Some ecological studies propose evidence based on cross-sectional U.S. data matched with advertisement data. Since food companies are likely to concentrate their efforts on regions where the demand is more responsive or larger, advertising is instrumented, with the price of advertisement and the number of households with a television in the area used as exogenous sources of variation. Small but significant correlations have been between fast-food and soft-drink advertising and consumption (Andreyeva, Kelly, & Harris, 2011; Chou, Rashad, & Grossman, 2008). Identifying the causal impact of food marketing on diet and obesity clearly remains a research priority, especially in emerging and developing countries.

Food Supply and the Dynamics of Demand

Long-term changes in food supply are also driven by demand-side factors. This section focuses on the secular rise in labor-market wage rates. The research has also examined education expansion (Brunello, Fort, Schneeweis, & Winter‐Ebmer, 2016; Davies, Dickson, Smith, van den Berg, & Windmeijer, 2018; Etilé, 2014; Galama, Lleras-Muney, & van Kippersluis, 2018).

Following household production theory (HPT), the trade-off between home cooking and the reliance on processed food and food away from home (FAFH) depends on the money and time costs of food at home (FAH) relative to the market price of FAFH.10 The technological progress in the mass production of food has lowered the time cost of meal preparation and reduced the time and money cost of eating away from home. The rise in labor market productivity has increased the opportunity cost of time spent shopping, cooking, meal chores, and eating. For many individuals, it appears more advantageous to work more and purchase prepared meals than to buy raw food and spend time preparing it.11

In most human societies, home cooking was, and still is, traditionally carried out by women. It is therefore unsurprising that the research has examined the consequences of changing labor market opportunities for women—job offers and wage rates—for diet and health. In the short term, labor-market choices are quite rigid and HPT just implies that working hours should be negatively related to cooking and FAH. In the long term, labor-market decisions depend on wage offers on the labor market. Depending on whether an individual decides to work or not, a wage is observed or it remains implicit. This expected wage is a good proxy measure of the opportunity cost of time spent cooking, the more so when individuals do not enjoy cooking per se. Hence, the higher the expected wage rate, the lower the time spent cooking and the higher the expenditure or quantity of FAFH and the share of processed food in the budget.12 Testing this prediction entails two main empirical challenges. First, there are seldom representative data sets, including both food expenditure data and time use data, and accurate information to reconstruct household members’ wage rates and nonwage income. Here, many studies rely on imputation procedures to get more complete data. Second, the opportunity cost of time variables are endogenous, as unobserved shocks and preference factors can simultaneously affect food decisions and labor market variables. Most studies then apply an Instrumental Variables approach (to instrument the wage rate) and/or sample selection models (for decisions to participate to the labor market), which also helps in constructing the expected wage rate for individuals who do not to work full time. Beyond the heterogeneity of methods and specifications, the core prediction of HPT has been confirmed by most empirical studies on U.S. data (Davis, 2014) and in a few other cultural contexts, such as France or Japan (Etilé & Plessz, 2018; Kohara & Kamiya, 2016). Etilé and Plessz (2018) used Oaxaca-Blinder decomposition to quantify the extent to which labor-market changes explain the decline in the time spent on home cooking by married women in France between 1985 and 2010. They found that rising women’s employment and observed wages together account for about 60% of the decrease in the time that married women spend cooking. However, the expected wage rate, which better reflects the change in labor-market incentives that individuals face, explains only 20% of the decline in their cooking time. Etilé and Plessz (2018) concluded that changing labor-market incentives are far from being the main driver of the decline in home cooking in France, while technological progress and the changing preferences of women may account for the remaining 80%.

Since the price substitution effect from increasing wage rates implies a greater reliance on ready-to-eat processed food of poor nutritional quality, maternal employment may have negative consequences for children’s health. Anderson, Butcher, and Levine (2003) found in U.S. longitudinal data that longer maternal working hours are positively associated with children being overweight. However, after controlling for observed and unobserved individual and family characteristics, the association remains significant for high-income households only.13Von Hinke Kessler Scholder (2008) identified in British longitudinal data a causal effect of maternal employment on children being overweight, but only for full-time maternal employment during mid-childhood. Employment at earlier or later ages has no significant impact. Studies by Fertig, Glomm, and Tchernis (2009) and Cawley and Liu (2012) provided some detailed explanations for these findings. They showed that maternal employment implies less time input in children’s diet and physical activity and less supervision of children’s diet, especially when fathers do not offset the fall in mothers’ time inputs. Once again, there is a lack of studies in other cultural contexts to show the consequences of maternal employment of urban middle-class women in emerging countries like China, where the number of overweight children is exploding.

Endogenous Dynamics of Consumer Preferences

Beyond financial incentives, cross-sectional and time variations in preferences are powerful determinants of diet and obesity outcomes. Dubois et al. (2014) illustrated this by examining the nutritional patterns of French, American, and British households with comparable scanner data. They fit an original structural demand system derived from a utility function defined over food products and nutrients. Their model was tailored to identify consumer price elasticities and preferences over food and nutrients. They found that if low-income U.S. households faced French food prices and nutritional quality but kept their preferences, they would consume 16% fewer calories than faced with the U.S. environment but still 13% more calories than low-income French households. From a dynamic perspective, there are at least three channels through which consumer preferences can be affected by shocks on the food environment: habit formation, changing social norms of body shape, and the intergenerational transmission of preferences.

Habit Formation

In intertemporal choice models, there is habit formation when past and current decisions are complementary in the utility function. In the spirit of rational addiction models (G. S. Becker & Murphy, 1988; Dockner & Feichtinger, 1993), Dragone (2009) proposed a rational eating model, wherein past food consumption increases the current marginal utility of eating (habit formation) but may also deteriorate health and, therefore, welfare if body weight is above some optimal level. Such a model can predict oscillating dynamics of food consumption—with alternating episodes of over- and underconsumption. It also implies that the effect of short-term shocks on food prices will be amplified over time.

Three mechanisms can sustain habit formation in food consumption. First, consumers face short-term costs of adapting their purchase behavior because of time constraints, cognitive constraints, or incomplete information about available alternative options. Consumers routinely put the same products in their supermarket baskets (Adamowicz & Swait, 2012; Seetharaman, 2004; Wood & Neal, 2009). Brand commitment and loyalty is a second important and distinct psychological factor explaining why consumers do not easily switch (Dubé, Hitsch, & Rossi, 2009).14 Physiological addiction to food, and specifically to sugar and fat, is a third factor of habit formation. Neurosciences have evidenced some similarities between the reinforcements produced through dopamine and opioid receptors in response to the administration of drugs and the intake of sugar- or fat-rich foods,. Although the concept of neurophysiological addiction to food is debatable (at least its application to people without eating disorders), there is a consensus about the impact of sugar-rich and fat-rich food on tastes (Benton & Young, 2016; Gearhardt et al., 2011; Hebebrand et al., 2014; Volkow, Wise, & Baler, 2017).

A few economic studies have looked at habit formation in food consumption. For instance, Zhen, Wohlgenant, Karns, and Kaufman (2011) estimate a dynamic model of demand for beverages and show that sugar-sweetened beverages, milk (especially lowfat milk), and fruit juices are habit forming. Richards, Patterson, and Tegene (2007) estimated a discrete choice model of demand for snack products and showed that the protein, fat, and carbohydrate content of past choices has a negative impact on the marginal utility of nutrients in current choice.

Overall, however, the literature is not compelling because robust identification of the habit-formation effect is very challenging. One wants to estimate dynamic demand models in which past consumption is introduced as an additional regressor to explain current consumption. However, past and current choices are correlated through time-variable and fixed unobserved factors. Even after conditioning on fixed effects, past choices are likely to be endogenous if time-varying random shocks have persistent effects. Auld and Grootendorst (2004) provided evidence that mis-specification of the error structure can produce spurious results. Another difficulty is that some volatility in the exogenous determinants of consumption is required to detect habit formation, while such volatility remains weak or nonexistent at the annual frequency (Dynan, 2000; Naik & Moore, 1996). At a quarterly or monthly frequency, it may be difficult to separate habit formation effects from consumer inventory considerations, forecasting of price promotions, and seasonal effects.

Some studies have proposed indirect evidence. For instance, Atkin (2013) proposed a two-step approach in a study of the relationship between regional food tastes, prices, and nutrition in India. He first constructed a proxy measure of regional tastes for several food products by estimating a demand system on cross-sectional household budget data. He then showed that past prices predict current tastes, which provides evidence of habit formation. Another study, by Dragone and Ziebarth (2017), compared the consumption of processed food by East and West Germans after the reunification. They found that East Germans consumed more “novel” foods, which were previously unavailable. They interpreted this as evidence that the marginal utility of these products was lower for West Germans because they were already habituated to consume them.

Social Norms

Social norms are prescriptions shared within social groups that reflect expectations about how group members should ideally behave (Bicchieri, 2005). In most societies, individuals have incentives to stick to a social norm of body shape, which can be proxied by an ideal BMI that is likely to differ from medical norms regarding BMI. Blanchflower, Van Landeghem, and Oswald (2009) and Clark and Etilé (2011) used individual longitudinal data to show that the correlation between subjective well-being and BMI is negative when BMI is above some threshold that differs from the medical thresholds for overweight and obesity. This threshold is higher for men, for low-educated individuals, and for married people when the partner is heavier. In a Catalan health survey, Gil and Mora (2011) found that individuals tend to under-report their body weight when the average weight of their gender-age group is lower. These studies suggest that the social stigma and well-being penalties associated with overweight people decrease with the distance between actual BMI and some ideal BMI that varies across sociodemographic groups, and therefore reflects the role played by social norms.

Yet individual ideal BMI, and therefore social norms, adapt to variations in actual BMI. Etilé (2007) showed that individual perceptions of ideal BMI are significantly elastic to actual BMI, with elasticities close to +0.8 for men and +0.5 for women. Therefore, part of the spread of obesity in the population can be explained by endogenous dynamics whereby changing food supply shifts the distribution of BMI to the right, which modifies perceptions of ideal BMI change and relaxes normative incentives to control one’s food intake (Burke & Heiland, 2007; Offer, 2001).15 This is consistent with evidence that younger generations are less likely to perceive themselves as overweight for a given objective weight status (Burke, Heiland, & Nadler, 2010). This may also imply that private or public policies seeking to reduce the social pressure to be thin can improve welfare over the short term but may exacerbate the obesity epidemic (Dragone & Savorelli, 2012).

Social Dynamics of Preferences

Intergenerational transmission of preferences is likely to play an important role in the dynamics of social norms throughout the nutrition transition. Children of obese parents are more likely to be obese at all ages (Branca, Nikogosian, & Lobstein, 2007; Classen, 2010). This may be due to shared genetic risks, to epigenetic influences (phenotypic plasticity during fetal development in response to maternal nutritional intakes), or to family transmission of food habits and beauty norms in the objective of preserving some cultural identity (Benyshek, 2013; Moisio, Arnould, & Price, 2004; Plessz & Etilé, 2018; Wardle, Sanderson, Guthrie, Rapoport, & Plomin, 2002; Wills, Backett-Milburn, Roberts, & Lawton, 2011).

The intergenerational transmission of preferences can be modeled in an evolutionary setting by assuming that (imperfectly) altruistic parents want to transmit their preferences to their offspring. They may fail, however, because vertical socialization efforts are costly—they have to spend time teaching their children their own food habits—and because vertical socialization is in competition with various forms of horizontal socialization (Bisin & Verdier, 2001, 2011). This is typically the case when parents have healthy food habits but children are exposed to food marketing, neighborhood effects, and peer pressure that develop a taste for unhealthy food. The evolutionary approach to social dynamics allows for various population dynamics of the distribution of cultural traits, with convergence to homogeneous or heterogeneous distributions depending on the cost of vertical socialization relative to the strength of horizontal socialization, and on the psychological cots of interacting with people having distinct preferences. The quest for cultural distinction by minority groups may sustain a steady state where preferences remain heterogeneous, thus explaining the resistance of some national food models to the diffusion of food practices oriented toward convenience and ultraprocessed food (Bisin, Patacchini, Verdier, & Zenou, 2011).16

Dynamic models of cultural identity have also been used to analyze the impact of globalization. Olivier, Thoenig, and Verdier (2008) analyzed the respective impact of economic globalization and social globalization on consumer culture, under the premise that consumers have to choose between a domestic cultural good (e.g., national cuisine) and a foreign cultural good. In addition to a private hedonic value, consumption signals membership in a cultural group (domestic vs. foreign), which creates a cultural externality that increases with the share of individuals affiliated with the cultural group; that is, a larger group reinforces the sense of belonging and facilitates social exchange. Parents have more incentives to transmit their own culture (vertical socialization) when the domestic good has a high cultural externality (e.g., high market share) or when its price is relatively low. If the world integration of the domestic market induces a fall in the market price of the domestic good, then its consumption is associated with larger cultural externalities and becomes more attractive. Hence, economic globalization may preserve (food) cultures across countries.17 However, this effect reverses if returns to scale increase, especially for foreign products, which is likely to happen in food markets dominated by very large multinational firms. With such a market structure, economic integration implies a larger decrease in the prices of the cultural goods produced by the largest firms, which thus have a dynamic advantage in terms of preference transmission in the integrated market (Maystre, Olivier, Thoenig, & Verdier, 2014). Predictions regarding the effect of social globalization on food and nutrition are unambiguous. When countries become socially integrated, the population consuming the domestic good mechanically decreases, which induces a reduction in its cultural externality. Consequently, domestic demand declines, which weakens the dominance of the domestic food culture. As the same process takes place simultaneously in all countries, cultural heterogeneity across countries decreases. Therefore, social globalization causes a convergence of (food) cultures across countries and a convergence of nutrition patterns.

A large empirical literature has examined peer and neighborhood effects in food consumption and obesity, providing solid evidence that horizontal socialization affects food (and exercise) behaviors in adults as well as teenagers (Ali, Amialchuk, & Heiland, 2011; Cairns, Angus, Hastings, & Caraher, 2013; Carrell, Hoekstra, & West, 2011; Dhar & Baylis, 2011; Fortin & Yazbeck, 2015; Kling, Liebman, & Katz, 2007; Ludwig et al., 2013; Ludwig et al., 2011; Sadeghirad, Duhaney, Motaghipisheh, Campbell, & Johnston, 2016). Analyzing processes of vertical socialization and the intergenerational dynamics of food habits is more difficult because it requires data sets that span several generations of individuals. This would clearly help to explain why a certain degree of cultural divergence in food cultures remains across human populations.

Conclusion

In a neoclassical perspective, consumers respond rationally to changes in relative food prices by optimally modifying their diet. Food decisions have two key motivations: the maximization of immediate satisfaction from eating, at the lowest cost in terms of time and money, and eating to preserve health. Immediate pleasures and costs are weighted against future rewards and pain via a discount factor, in such a way that behaviors are time-consistent.18 Some individuals will prefer junk food to fruits and vegetables up to the point where the marginal satisfaction of eating junk food is equal to the discounted marginal disutility of worse future health (Levy, 2002).19

Taking into account the likely effect of food marketing and adding endogenous mechanisms of preference changes such as habit formation, social norm dynamics, and preference transmissions produces a different understanding of the long-term trends in diet and obesity. The increasing availability of convenient, cheap, and unhealthy food combined with the increasing pressure of food marketing, the limited regulation of marketing practices, and the endogenous dynamics of consumer tastes explains why the population has been trapped in high-BMI equilibrium (Cutler et al., 2003; Ruhm, 2012; Smith, 2004).20 The food supply therefore influences preferences over food, eating experiences, and body weight. A related aspect is that variations in the food environment are likely to interact with life and economic shocks in orienting the diet and BMI trajectory of individuals. A small literature has suggested that economic insecurity could increase the propensity of individuals to put on weight, as eating is a means to cope with stress (Offer, Pechey, & Ulijaszek, 2010; Smith, Stoddard, & Barnes, 2009; Wisman & Capehart, 2010).

Finally, future research will have to tackle two important challenges. First, empirical studies to date have seldom investigated the long-term impacts of supply-side variables, especially prices, on consumption behaviors and nutritional health. Beyond the need for cohort data spanning very long periods, empirical models will have to take the interrelated dynamics of consumption and health into account. Consumer heterogeneity also matters. Fast food attracts customers because it proposes lower prices and saves consumers’ time. But fast-food marketing may specifically affect individuals with self-control problems or those addicted to sugar. Renewed empirical methodologies are required to go beyond the means and average effects.

Second, despite the global trends, there is a large amount of cross-country heterogeneity. For example, soft drink consumption is much lower in South Korea, Finland, Sweden, or even France than it is in the United States, Mexico, or the U.K. (Stuckler et al., 2012). Regularity in time patterns and eating commensality are more important for the French than for Anglo-Americans, which may explain why French people still cook even if they cook less (Fjellström, 2009; Grignon & Grignon, 2009; McIntosh et al., 2009; Warde, Cheng, Olsen, & Southerton, 2007).21 Cultural variations in preferences likely modulate the impact of technological trends and labor-market changes on eating patterns. There is a major lack of cross-cultural comparisons and studies on developing and emerging countries, which may help us to identify the impacts of specific aspects of food supply, consumer demand, and, more broadly, food systems.

Further Reading

Anderson, M. L., & Matsa, D. A. (2011). Are restaurants really supersizing America? American Economic Journal: Applied Economics, 3(1), 152–188.Find this resource:

Atkin, D. (2013). Trade, tastes, and nutrition in India. American Economic Review, 103(5), 1629–1663.Find this resource:

Chandon, P., & Wansink, B. (2011). Is food marketing making us fat? A multi-disciplinary review. Foundations and Trends® in Marketing, 5(3), 113–196.Find this resource:

Cornelsen, L., Green, R., Turner, R., Dangour, A. D., Shankar, B., Mazzocchi, M., & Smith, R. D. (2015). What happens to patterns of food consumption when food prices change? Evidence from a systematic review and meta‐analysis of food price elasticities globally. Health Economics, 24(12), 1548–1559.Find this resource:

Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003). Why have Americans become more obese? Journal of Economic Perspectives, 17(3), 93–118.Find this resource:

Dragone, D., & Ziebarth, N. R. (2017). Non-separable time preferences, novelty consumption and body weight: Theory and evidence from the East German transition to capitalism. Journal of Health Economics, 51, 41–65.Find this resource:

Etilé, F. (2007). Social norms, ideal body weight and food attitudes. Health Economics, 16(9), 945–966.Find this resource:

Etilé, F., & Plessz, M. (2018). Women’s employment and the decline of home cooking: Evidence from France, 1985–2010. Review of Economics of the Household, 16(4), 939–970.Find this resource:

Fogel, R. W. (2004). The escape from hunger and premature death, 1700–2100: Europe, America, and the Third World (Vol. 38). Cambridge, U.K.: Cambridge University Press.Find this resource:

Irz, X., Mazzocchi, M., Réquillart, V., & Soler, L.-G. (2015). Research in food economics: Past trends and new challenges. Revue d’Études en Agriculture et Environnement, 96(1), 187–237.Find this resource:

Kearney, J. (2010). Food consumption trends and drivers. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1554), 2793–2807.Find this resource:

Oberlander, L., Disdier, A. C., & Etilé, F. (2017). Globalisation and national trends in nutrition and health: A grouped fixed‐effects approach to intercountry heterogeneity. Health Economics, 26(9), 1146–1161.Find this resource:

Olivier, J., Thoenig, M., & Verdier, T. (2008). Globalization and the dynamics of cultural identity. Journal of International Economics, 76(2), 356–370.Find this resource:

Popkin, B. M., & Gordon-Larsen, P. (2004). The nutrition transition: Worldwide obesity dynamics and their determinants. International Journal of Obesity, 28(3), S2.Find this resource:

Reardon, T., Timmer, C. P., Barrett, C. B., & Berdegue, J. (2003). The rise of supermarkets in Africa, Asia, and Latin America. American Journal of Agricultural Economics, 85(5), 1140–1146.Find this resource:

Smith, T. G. (2004). The McDonald’s equilibrium: Advertising, empty calories, and the endogenous determination of dietary preferences. Social Choice and Welfare, 23(3), 383–413.Find this resource:

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Notes:

(1.) Data from international organizations report country statistics for overweight and obese individuals based on the body mass index (BMI), which is calculated as body weight in kilograms divided by squared height in meters. Although this indicator is cheap to collect, it is prone to downward biases when it is self-reported. It also has heterogeneous relationships with health risks because it does not distinguish between fat mass and fat-free mass. Burkhauser and Cawley (2008) and Cawley, Maclean, and Kessler (2017) extensively discuss the value of having more accurate measures of obesity for research and policy evaluation.

(2.) Low- and middle-income countries face a “double burden” of disease, with undernutrition coexisting with the explosion of diabetes and obesity at a population level and even within households.

(3.) Using various sources of longitudinal country data on calorie availability and expenditure, Bleich, Cutler, Murray, and Adams (2008) found that increased calorie supplies account for 80% of the rise in obesity. Results from U.S. studies suggest that there has been a drop in on-the-job physical expenditure since the shift to a service economy in the 1970s (Lakdawalla & Philipson, 2007; Lakdawalla & Philipson, 2009), which was not offset by a rise in leisure-time activities (Sturm, 2004).

(4.) Cawley (2015) proposed an extensive review of U.S. studies on the economic consequences of obesity. This literature estimates these costs by comparing the medical expenditures of obese versus nonobese individuals after adjusting for a limited set of control variables and confounders (usually age, sex, and smoking). Cawley and Meyerhoefer (2012) overcame this limitation by using child BMI to instrument adult BMI in household data. They found considerably higher costs, which suggests that obesity-related externalities are very large, at least in the population marginally affected by the instruments. See Etilé (2011) for earlier references.

(5.) Cawley (2015) proposed a complete review of U.S. studies. Important issues such as risk and information, specific findings from behavioral economics, and childhood obesity are discussed in other articles in the Oxford Research Encyclopedia of Economics and Finance, such as “The Economics of Diet and Obesity: Public Policy,” “The Economics of Childhood and Adolescent Obesity,” and “Globalization, Trade, and Health Economics.”

(6.) Zeroes in consumption data are problematic when they reflect true taste variations in the population, and not just in frequency of purchase. They can be modeled either as corner solutions of the utility maximization problem, as the result of some first-step selection process, or both (Jones, 1989). Some empirical demand studies estimate first-step selection equations into the consumption of the various food groups, derive analogs of the Mill’s ratio, and introduce it in the demand equations, which are estimated with the structural constraints being enforced. The concern here is that little is known about the ability of this approach to correct for selection biases. It is indeed well known that selection problems can be solved only if there are variables that affect the probability of selection but not the outcome. The statistical characteristics of restriction exclusions needed to identify multiple selection into consumption in a demand system framework is unknown.

(7.) Berry, Levinsohn, and Pakes (1995) proposed alternatively to instrument the price of a product by the attributes of similar competing products under the assumption that product characteristics are predetermined.

(8.) Bakucs, Fałkowski, and Fertő (2014) analyze how market structure affects farm-retail price transmission.

(9.) Consider, for instance, the “prices-in-adjacent-markets” set of IVs. Since variations in prices across markets are likely driven by the same factors (e.g., rental prices, local taxes, or wages), a demand system is likely to be underidentified with these IVs and estimates will reflect the behavior of an empty set of the population. The absence of weak instrument bias cannot be tested with F-statistics specific to each instrumental regressions, but with tests that account for the fact that many variables are instrumented in each equation (Stock, Wright, & Yogo, 2002).

(10.) HPT considers that individuals do not enjoy utility from food purchases per se, but rather from a composite commodity called a “meal,” which is produced by the combination of food products and the time required for cooking, cleaning, etc. See Gary S. Becker (1965) and Pollak and Wachter (1975) for the theoretical foundations. The model has been extended to collective household decision making, which helps to analyze the impact of both spouses’ endowments and labor market opportunities on household production and labor market choices (Browning, Chiappori, & Weiss, 2014). See also Davis (2014) for an extensive review of the literature on food-at-home production and consumption and food-away-from-home consumption.

(11.) Changes in technology and women’s labor-market participation are therefore related. The long-term macroeconomic impact of technological progress in the household sector on the rise in married female labor-force participation is analyzed in Greenwood, Seshadri, and Yorukoglu (2005). They estimated that technological progress in the household sector accounts for more than 50% of the rise in female labor-force participation in the United States over the last century.

(12.) For a discussion on the distinction between the short- and long-term perspective on time use decisions and implications for the empirical analysis, see Frazis and Stewart (2012).

(13.) Liu, Hsiao, Matsumoto, and Chou (2009) proposed a semiparametric analysis of the impact of full-time maternal employment in the same data (the National Longitudinal Survey of Youths 1979: NLSY79), but for a single later wave (2000 vs. six waves before 1996 for Anderson et al., 2003). After correcting for mother’s selection in full-time employment, they find that it increases the child overweight risk by 12.3%.

(14.) Brand loyalty may proceed from the willingness to reduce the uncertainty of consumption experience (Erdem & Keane, 1996). Yet branding and more generally marketing actions intend to create some affective value that goes beyond having a riskless experience with a product (Delgado-Ballester & Luis Munuera-Alemán, 2001). In line with this, marketing-like actions have been shown to affect neuronal correlates of experienced utility from food in addition to consumer expectations (Lee, Frederick, & Ariely, 2006; Plassmann, O’Doherty, Shiv, & Rangel, 2008).

(15.) These norm-based social dynamics can produce a transition from a low-BMI population equilibrium to a high-BMI equilibrium just after a small initial shock, even if the food environment remains stable (Strulik, 2014).

(16.) Specific food practices, such as cooking fresh and varied products, have become an element of social distinction for the French upper-class, while this practice was shared by all social classes 50 years ago (Bourdieu, 1984; Grignon & Grignon, 1980, 1999; Régnier & Masullo, 2009).

(17.) Yet, even though economic globalization may preserve food cultures, it may deteriorate nutritional outcomes through changes in processing techniques.

(18.) In intertemporal decision problems, individuals are time consistent when, in the absence of shocks,

they carry out at time t the action they decided on at time t-1 for period t. When the intertemporal utility function is separable, time consistency requires exponential discounting.

(19.) Rosin (2008) provided an excellent survey of models of weight management, with formal analysis.

(20.) Smith (2004) did not appeal to existence of rationality biases to explain the dynamics toward a high-BMI equilibrium. Rather, he proposed an evolutionary account, whereby the food industry has incentives to exploit signals that were used by humans in the distant past to find palatable food. This evolutionary account is consistent with Kahneman’s dual process theory of mind, whereby the working mind is divided in a fast and automatic “hot” system (System 1) and a reflective “cold” system (System 2). System 1 developed earlier than System 2, which developed later in human evolution in order to help System 1 to cope with more complex, nonnatural, environments (institutions, cooperation, etc.).

(21.) Commensality refers to eating with other individuals, i.e. with colleagues at the workplace, other students at school, or with family members at home. Comparing family meal practices in France and England, Pettinger, Holdsworth, and Gerber (2006) found that French household members eat together more often, cook raw ingredients more often, and are more likely to follow a regular meal pattern. Eating home-cooked food is a key element of the “proper family meal,” with a crucial gender connotation, so that cooking remains a strong moral imperative for women, an element of their social identity (Akerlof & Kranton, 2000). Unsurprisingly then, grazing and the disappearance of the family meal is a real and well-documented source of concern in the U.K. and theUnited States, but is not a major a problem in France and continental Europe (Fjellström, 2009).