Supermarket Expansion and Changing Food Consumption Patterns in Mexico, 1996-2006

A Thesis Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Master of Science

by Veronica Mazariegos-Anastassiou August 2020

ABSTRACT

Supermarkets have emerged to be the symbol of modern and increasingly complex food value chains globally. Beginning in the 1980s through the mid 2000s, Mexico experienced rapid growth in supermarkets. Concurrently, there began to be signs of worrisome dietary and health changes. Using detailed municipal-level supermarket entry data and a pseudo-panel built from repeated cross-sectional household income and expenditure survey data from Mexico between 1996-2006, I assess how the emergence of supermarkets in Mexico relates to the demand for food in five nutritional quality groups (healthy, unhealthy, contentious, food away from home (fafh), and other) . I estimate a quadratic almost ideal demand system (QUAIDS) model. I find that the number of supermarkets, specifically the number of Walmart stores, in a municipality is associated with the average household shifting away from healthier foods. However, the number of supermarkets is not significantly linked to a household’s demand for unhealthy foods. Despite the convenience afforded by supermarkets to purchase ingredients to prepare foods at home, I find that with more supermarkets, there is a significant increase in the amount of food that is consumed away from home. I conduct robustness checks to rule out that supermarkets are inducing significant changes in key demand drivers: retail food prices and household income. Results indicate that the emergence of supermarkets, especially the entry of a global retail chain, is related to changes in consumer expenditure patterns across product categories with distinct health attributes and with potential public health implications. BIOGRAPHICAL SKETCH

Veronica Mazariegos-Anastassiou was born in Miami, Florida. After obtaining her high school diploma from G. Holmes Braddock Senior High School in Miami, FL in 2006, Veronica entered New York University in New York City, New York. She completed a semester at the American University in Paris, France and interned for a summer with an AISEC chapter in Ilorin, Nigeria. She received a Bachelor of Arts with double majors in International Relations (honors) and Economics in May 2010. During the following year, she worked for the Americas Society/Council of the Americas in Miami, Florida, before completing more than two years of service as a U.S. Peace Corps volunteer in Togo. It was while working in West Africa that Veronica began to focus on agriculture and food access. Upon her return to the U.S., she worked for two year at the international development consulting firm, Social Impact in Arlington, Virginia and Washington, D.C., focusing on the design and implementation of impact evaluations. In 2016, Veronica left Washington, D.C. to farm in Pescadero, California on an organic, diversified vegetable operation, Pie Ranch. After managing a 75-acre farm in 2017, Veronica along with two business and farming partners started their own farm, Brisa de Año Ranch. In 2018, she entered the Dyson School at Cornell University to purse a graduate degree in Applied Economics and Management. While at Cornell, she worked as a Teaching Assistant (2018-2019), a Graduate Research Assistant (2019-2020), co-chaired Cornell’s Latinx Graduate Student Coalition, and was a member of Cornell’s Graduate School Diversity Committee. She continues to co-manage and farm at Brisa de Año Ranch and lives in Pescadero, California.

A cada gota de sudor que derramó mi padre para proveernos todo lo que necesitábamos para vivir y soñar, A la memoria y el legado de mi madre, que inculcó en lo más profundo de mi ser su curiosidad insaciable y su gran amor por el aprendizaje, To Ana and Oscar Luis for inspiring me with your magnificence and creativity, To my farm mentors for sharing your knowledge of the earth and her gifts, for using your bodies to care for land and feed communities, and for daring to imagine a different and just world, And to Cole, for your love, our dreams, and for stewarding land and growing food with me.

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ACKNOWLEDGMENTS

I would like to thank Dr. Mauricio Varela at the University of Arizona for generously sharing data that he compiled and collected. Having access to such data on supermarkets in Mexico greatly expanded the scope of my research.

I would like to thank the handful of peers, professors, and mentors from the Cornell community, that took the time to listen to my research ideas and questions, and in turn provided me with valuable and useful feedback.

Finally, I would like to thank Dr. Christopher B. Barrett and Dr. Miguel I. Gómez for co-chairing my committee. You provided me with essential insight, guidance, enthusiasm, and encouragement. It was a pleasure and a true honor to work with both of you. With deep and sincere gratitude, thank you.

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ...... 1 Supermarkets’ rise in low- and medium-income countries ...... 2 Mexico’s Supermarket Expansion & Health and Dietary Changes ...... 4 CHAPTER 2: DATA ...... 8 Supermarket and Household Expenditure Data ...... 8 Food category classifications ...... 14 Food Product Unit Values and Food Group Price Indices ...... 17 CHAPTER 3: PSEUDO PANEL APPROACH ...... 20 Constructing Cohorts ...... 21 Cohort characteristics ...... 24 CHAPTER 4: ECONOMETRIC MODEL ...... 26 Quadratic Almost Ideal Demand System ...... 26 Pseudo-Panel QUAIDS ...... 27 CHAPTER 5: RESULTS ...... 31 CHAPTER 6: ROBUSTNESS CHECKS ...... 35 Price and Supermarkets ...... 35 Household Income and Supermarket ...... 39 CHAPTER 7: DISCUSSION ...... 43 APPENDIX ...... 46 REFERENCES ...... 56

LIST OF FIGURES

Figure 1: Supermarket Expansion in Mexico: Stores by municipality (1996-2006) ...... 10 Figure 2: Supermarket Expansion in Mexico: Walmart stores by municipality (1996-2006) ...... 11 Figure 3: Municipality participation across survey rounds ...... 23

LIST OF TABLES

Table 1: Supermarkets in Mexico ...... 9 Table 2: Nutrition Food groups and Mean municipal nutrition quality group expenditure shares 1996- 2006...... 16 Table 3: Mean nutrition quality group price across all surveyed municipalities . 19 Table 4: Households and municipalities per survey round ...... 22 Table 5: Representative household and municipality descriptive statistics (1996- 2006) ...... 24 Table 6: Supermarket Parameters for every 100 stores ...... 31 Table 7: Mean Expenditure Elasticities ...... 33 Table 8: Seemingly Unrelated Regressions Results – Price of Top 15 purchased food products and Supermarket variables Top 5, Walmart, and Markets (Other) ...... 37 Table 9: Log Annual Income and Supermarket Variables ...... 40

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CHAPTER 1: INTRODUCTION

Supermarkets have been the interest of research (Reardon and Timmer, 2012) for being the symbol of modern market transactions and a fundamental part of urban planning and government policy to improve and diversify diets. Although interesting in their own right, supermarkets have not merely changed how people purchase foods and other goods, supermarkets themselves are a symptom of a larger systemic shift and ever more complex global food value chains and changing food consumer behavior.

Using detailed municipal-level supermarket entry data between 1996-2006 and household income and expenditure data from Mexico, I construct a pseudo-panel and estimate a quadratic almost ideal demand system model (QUAIDS). I assess how the emergence of supermarkets in Mexico may have influenced, if not shaped consumer behavior and therefore demand for food upon its well-documented expansion throughout the territory. Moreover, to assess how the nutritional quality of the food demanded may have changed, I move away from the convention that categorizes foods by food group (i.e., grains, meat, fruits and vegetables) and attempt to group foods by the food product’s nutritional quality attributes: good foods (healthy), bad foods

(unhealthy), contentious foods, food away from home (FAFH), and other foods.

I hypothesize that supermarket entry is accompanied with a significant shift in household consumption patterns between healthy and unhealthy food. Specifically, I suggest that households increase the share of expenditure of low-nutritional quality

1 foods (unhealthy foods) in lieu of higher quality (healthy) foods as supermarket penetration increases in a municipality.

Results suggest that with the entry of supermarkets in a municipality, particularly the number of Walmart stores, households shift away from healthier foods. Results also indicate that despite the convenience afforded by supermarkets to purchase ingredients to prepare foods at home, with an increase in supermarkets, there is also an increase in the amount of food that is consumed away from home.

The period of supermarket expansion covered by this study coincides with a comparably rapid shift in health and dietary changes that have, in recent years, alarmed public health officials in Mexico and abroad. Although studies have looked at the correlation between shifts in diet and the consumption of certain foods, this study is the first to focus on the role that supermarkets plays using a utility-based structural model based on economic theory. These findings could potentially inform policy and private decision makers, that play a role in configuring food retail spaces, make shifts to build healthier food environments and incentivize the consumption of foods with positive health attributes.

Supermarkets’ rise in low- and medium-income countries

Starting in the late nineteenth century and well into the twentieth century, Western

Europe and the United States underwent a gradual food system revolution, which saw the consolidation of food production and processing, and the rise of large-scale wholesale logistics and large retail stores (such as supermarkets, hypermarkets,

2 convenience stores, and fast food restaurants). Starting in the 1980s, developing countries in Latin America, Asia and Eastern Europe, and some of Africa, experienced their own, more rapid food systems revolution (Barrett et al. 2019, Reardon and

Timmer, 2012). As a result, in the 1990s and 2000s, private-sector modern retail surged in these regions, moving away from fragmented traditional food retail (i.e., mom-and-pop independent stores, village-level markets and wholesale outlets) and embracing more centralized wholesale and retail sectors, in what has been referred to as the supermarket revolution (Reardon et al. 2003).

Supermarkets are both a symptom and a driver of a fundamental transformation of food value chains. The roll out of supermarkets in developing regions was consistent with their emergence in higher income countries, following two sets of paths. Geographically, supermarkets entered first in large cities, then small cities, and finally into rural towns, and therefore, from upper to middle to poorer classes. When it comes to offering, the trend is standard across regions, supermarkets first entered with processed foods, then semi-processed foods, and finally incorporating fresh produce. Processed and semi-processed foods form approximately

85% of supermarkets’ sales (Reardon and Timmer, 2012, Reardon, 2003).

Latin America was at the forefront of this transformation. Prior to the 1980s, the region already counted with a small number of domestically financed supermarkets in larger cities and wealthier neighborhoods reaching at most 10-20% of national food retail. By 2000, supermarkets had reached 50-60% of the national food retail in Latin

American countries, nearing the US’s 70-80% of national food retail (Reardon et al.

2003).

Studies that have looked at this phenomenon have mainly focused on the determinants and mechanisms underlying the spread of supermarkets (i.e., urbanization, income growth, increasingly sophisticated supply chains, and a rise in global retail FDI) and on the impacts the rise in food industry consolidation has on producers, primarily small farmers (Reardon et al. 2003; Reardon et al. 2004; Reardon and Timmer 2012; Rao and Qaim, 2013; Neven et al. 2009; Schewentesius and

Gómez, 2002). Fewer studies have focused on the impacts the food system transformations, culminating in and enabled by supermarkets, and a transformed food environment have on consumers, particularly with respect to their diets. (Popkin and

Reardon, 2018; Khonje and Qaim, 2018; Chenge et al., 2015; Kimenju et al., 2015;

Mergenthaler et al., 2009; Gómez and Ricketts, 2013). It is evident that the emergence of the centralized and “modern” food value chain that gave rise to the supermarket led to an unquestionable transformation of the overall food environment. Supermarkets provide consistent, year-round supply of a wide range of food product categories.

However, this dietary diversity is more likely to be accessible to urban, high income households, while lower income households buy processed and packaged foods in supermarkets, but not fresh produce, dairy, and meats (Gómez and Ricketts, 2013).

Mexico’s Supermarket Expansion & Health and Dietary Changes

Mexico was part of the second wave of modern food retail that rolled out in lower-income regions, which took place in the mid- to late 1990s along with Central

America, and Southeast Asia. The share of modern retail in food in these regions

4 reached between 20 and 50% by the late 1990s (Reardon and Timmer, 2012). A potent driver of the “supermarket revolution” in Mexico was the arrival of global retail chains (Reardon et al. 2003; Bronnenberg and Ellickson, 2015). In part due to changes which relaxed restrictions imposed on foreign direct investment in the 1980s and intense retail competition in higher income countries, foreign companies like Safeway were able to buy large stakes in Mexican supermarket brands (notably, Safeway bought a 49 percent stake in the regional retailer, Casa Ley in the early 1980s). By

1993, new FDI laws allowed foreign firms to hold full ownership rights and repatriate profits. Another major shift came early in the 1990s with Walmart’s entry into Mexico during the NAFTA negotiations via a joint venture with Mexican retailer, Cifra (Atkin et al., 2018). Walmart has since bought out Cifra in 2000 and grown to become

Mexico’s largest employer, as of January 2014.

Concurrent to the expansion of supermarkets, Mexico was also undergoing other notable health and diet-related changes. Mexico leads the world in the rates of overweight individuals (ages 15-74), a trend that had been steadily increasing since the year 2000 (OECD 2017). As of 2015, Mexico also has the second highest rate of obesity, 32.4% in the world, after the United States. Data from nationally representative surveys suggests that 35% of Mexican women ages 20-49 years old were overweight or obese in 1988 compared to 72% in 2006 (Olaiz-Fernández et al.

2006; Langellier, 2015).

Mexico, like many developing regions that began integrating themselves

deeply into a global food supply chain, began to experience what Popkin (2001)

referred to as a “nutrition transition” from basic grains towards a more Western diet.

This westernized diet is associated with increased consumption of vegetable oils,

animal source foods, sweeteners (both natural and artificial) and packaged/processed

foods and decline in the consumption of fruits, and some vegetables (Bermudez and

Tucker, 2003; Popkin et al. 2012).

Mexico experienced a doubling of caloric beverage intake by more than 21% of kilocalories per day for all age groups from 1996 to 2002. By 2008, the Ministry of

Health had created a set of guidelines to inform feeding and welfare programs to help curtail the consumption of sugar-sweetened beverages (Rivera et al. 2008; Popkin et al. 2012). A study by the Pan American Health Organization found that in 2009,

Mexico had the second-highest sales of ultra-processed products in the Latin

American region and that the Mexican population consumed up to 522 kcal per person per day of ultra-processed products (Pan American Health Organization, 2019).

However, the change has not been limited to sugar-sweetened beverages and ultra- processed foods.

Studies that evaluate the health outcomes of the Mexican Conditional Cash

Transfer program for children, Oportunidades 1, have found that although the program has been associated with positive outcomes for child growth and development it is also associated with higher BMI, higher diastolic blood pressure, and higher prevalence of overweight and obesity among individuals in beneficiary households (Fernald et al.

2008 ; Popkin et al. 2012). Moreover, there was increase in consumption of animal source foods, refined carbohydrates, and processed sugar, which when combined are

1 The program was originally called Progresa from 1997-2002. It is more popularly known as Oportunidades, and was running as of 2019 under the name Prospera.

6 linked to the negative health outcomes observed in earlier studies (Kronebusch and

Damon, 2019).

Other factors that have played a role in the overweight and obesity trend, which has been linked to increased mortality rates for diabetes and heart disease, include urbanization, and sedentary lifestyles (Rivera et al. 2002). Langellier (2015) showed that Mexican adults’ consumption of food away from home (including fast food or comida corrida , restaurant food, and street food) is significantly and substantially greater in urban areas compared to rural areas and increases with socioeconomic status and educational attainment.

Faced with these growing diet-related health concerns, in 2019, the Mexican government voted to implement front-of-pack warning labels on food and beverage products that are considered high in calories, sugar, saturated fat, trans fat, and sodium, and those containing non-caloric sweeteners. The new policy is the result of longstanding efforts by researchers and civil society demanding greater transparency of nutritional content and the benefits and costs of foods. However, these public health efforts have been in direct conflict with the interests of the transnational food industry and were, until recently, undermined (White and Barquera, 2020).

CHAPTER 2: DATA

Supermarket and Household Expenditure Data

This study uses three data sources. The first source comes from the National Retailers’

Association ( Asociación National de Tiendas de Autoservicio y Departamentales,

ANTAD ) annual directories, from 1996 to 2006, in addition to data gathered from

Walmex and annual reports corresponding to missing Walmart entry data. This data source was generously made available by Mauricio Varela (2018). The data capture total store count, including foreign and domestic stores, by firm, brand, and municipality for the entire Mexican territory. ANTAD stopped collecting supermarket census data at the municipality level after 2006. Municipalities are a particularly interesting unit of observation since it is the smallest administrative unit in Mexico 2.

This study focuses on food outlets including supermarkets, hypermarkets, and bodegas, described by the ANTAD as a store that sells a wide range of groceries and general merchandise. In addition to creating an annual store count variable by municipality, I use this data to create three supermarket categories. Top 5 supermarkets belong to one of top five supermarket brands in Mexico, with a national or large regional presence and significant expansion during the time period: Casa Ley,

Chedraui, Gigante, Soriana , and , Comercial Mexicana (Varela, 2018). Another increasingly important player in the supermarket space in Mexico at the time was

Walmart. Walmart corresponds to all stores that belong Walmart de México which is

2 Mexico currently has 2,458 municipalities.

8 part of the U.S.-owned Walmart brand. Together, Walmart and the other top five brands represented the top six brands in Mexico and more than 75% of all supermarkets. Finally, the Markets variable captures all other markets that are neither

Top 5 nor Walmart . Table 1 summarizes the number stores by year (every two years beginning in 1996, coinciding with survey data) in each of the three supermarket categories.

Table 1: Supermarkets in Mexico

Supermarket Variables 1996 1998 2000 2002 2004 2005 2006

Top 5 387 474 543 605 699 770 790

Walmart 159 190 211 277 352 410 477

Markets (other) 354 378 388 478 589 591 678

Total Supermarkets 900 1042 1142 1360 1640 1771 1945

Source: Supermarket data was compiled by Varela (2018) using ANTAD directory data and additional obtained from Walmart de México. Top 5 stores correspond to the top supermarket chains (not including Walmart) Casa Ley, Chedraui, Gigante, Soriana, and Comercial Mexicana; Walmart correspond to all stores belonging to the Walmart brand; and Markets (other), are all other supermarket stores.

Figure 1 provides a longitudinal geospatial depiction of the expansion of all supermarkets during the period of interest. A similar overview of Walmart stores throughout the territory is provided in Figure 2. (Refer to A1 for an overview of the top six supermarket brands, including Walmart).

Figure 1: Supermarket Expansion in Mexico: Stores by municipality (1996-2006)

Source: Supermarket data (Varela, 2018). Geospatial municipal data (INEGI). The maps depict the expansion of all supermarkets between 1996-2006 across all municipalities in Mexico. The map is divided into the over 2,000 municipalities in Mexico. A circle overlays a municipality that has a supermarket. The size of the circle increases with every 10 supermarkets in the municipality. Municipalities without a circle do not have a supermarket.

Figure 2: Supermarket Expansion in Mexico: Walmart stores by municipality (1996-2006)

Source: Supermarket data (Varela, 2018). Geospatial municipal data (INEGI). The maps depict the expansion of the U.S. based Walmart between 1996-2006 across all municipalities in Mexico. The map is divided into the over 2,000 municipalities in Mexico. A circle overlays a municipality that has a Walmart brand store. The size of the circle increases with every 10 Walmart stores in the municipality. Municipalities without a circle do not have a Walmart store.

I use the Mexican Household Income and Expenditure survey ( La Encuesta

Nacional de Ingreso y Gasto de los Hogares, ENIGH) conducted biennially 3 by

Mexico’s statistical institute (Instituto Nacional de Estadísticas y Geografía, INEGI) between the months of August and November. The data are cross-sectional and representative at the national level.

Sampling follows a multi-stage, stratified and cluster design. The sample frame is determined by first identifying the number of Primary Sampling Units (PSUs), followed by the households to be surveyed. PSUs correspond to basic geostatistical areas (AGAB) 4 . Following sample size calculations, the number of urban/suburban and rural PSUs is determined based on the PSU’s locality’s size 5 and state. To be consistent with national demographics, more PSUs located in urban/suburban localities are sampled than PSUs in rural localities. Roughly the same number of households are sampled across PSUs. Next, 32 independent samples (corresponding to each state) are taken and distributed among five strata 6 corresponding to the size of the locality of the PSU.

Of interest for this study are the surveys conducted between 1996-2006, including an additional survey round conducted in 2005. The size of the survey with complete expenditure data varies from year to year: 13,810 households in 1996,

3 An additional survey was conducted in 2005. 4 Basic Geostatistical Areas or Area Geostadística Básica (AGEB) poses three attributes: 1) It is recognizable in the terrain and it is delimited by identifiable and persistent features, 2) it is homogenous in its social, economic, and geographic characteristics, 3) Its size is such that it can be surveyed by one persona in one work day. 5 Localities are a subdivision of a municipality. Localities with 2,500 or more inhabitants are considered urban or suburban and localities with less than 2,500 inhabitants are rural. 6 Strata: 1) Metropolitan Areas, 2) Localities with 100,000 or more inhabitants, 3) Localities with 15,000 to 99,999 inhabitants, 4) Localities with 2,500 to 14,999 inhabitants, and 5) Localities with 1 to 2,499 inhabitants (Rural).

10,688 in 1998, 9,931 in 2000, 16,922 in 2002, 22,484 in 2004, 22,866 in 2005, and

20,623 in 2006. The survey contains annual household income and weekly expenditure data, including detailed food and beverage expenditure in 242 product categories. The number of municipalities captured by the ENIGH survey also varied from survey to survey: 416 in 1996, 396 in 1998, 380 in 2000, 495 in 2002, 503 in

2004, 572 in 2005, and 529 in 2006.

In addition to providing household food expenditure data, the survey also provides detailed annual income measures, household composition (including household members’ age and gender), and household head gender and education attainment data. The ENIGH constructs various income measurements: total current income is defined as both monetary income (i.e., wages or salaries, own-business income, cooperative production, rent, and transfers) and non-monetary income (i.e., auto-consumption of goods produced in the household, barter, and gifts); total income is defined as total current income in addition to other income streams (i.e., sale of assets, returns on investments, lottery winnings, etc.) I use total income as the household measure of income since it is the most comprehensive measure of a household’s income disposition and therefore its food purchasing power.

Finally, since the analysis will be at the municipal level, I capture municipality level time-variant characteristics, such as population and infrastructure, from national census data for the years 2000 and 2010 and intercensal data for 1995 and 2005 collected by the INEGI 7 to capture municipality population and percentage of

7 INEGI Census and intercensal historical series (1990-2010) en.www.inegi.org.mx/programas/ccpv/cpvsh/

13 households with electricity (INEGI). I use straight-line interpolation to impute values for the years 1996, 1998, 2002, 2004, and 2006.

Food category classifications

The ENIGH survey collected consumption data on up to 242 food products in the seven rounds captured by this study. I group these food products into 90 food product categories (i.e., corn tortilla, beef, oils, beans, tomatoes, etc.). I exclude tobacco products in order to focus strictly on food and beverages. I then classify these food product categories food into five categories following the dietary recommendations of the EAT-Lancet Commission (Willett et al., 2009): good , bad , contentious , and other . The EAT-Lancet Commission set out to identify the components of a healthy diet from sustainable food systems, based on extensive expert assessment of the published literature. The suggested diet is rich in plant-based foods and has fewer animal-sourced foods. Accordingly, the following food categories I use reflect these guidelines. 8 “Good” foods include foods that are generally beneficial to health and the environment and should be consumed in greater quantities, including whole grains (rice, wheat, corn or other unrefined grains), tubers or starchy vegetables, all vegetables, all fruits, legumes and nuts, eggs, and fish. “Bad” foods refers to foods that should be avoided as they may be harmful to human health and are not necessarily produced sustainably, including ultra-processed (packaged foods), refined grains, fruit juices with added sugar, soda, and saturated fats. “Contentious” foods are foods that are part of a healthy diet, however, should be restricted due to adverse health effects

8 The classification of food products is primarily based on nutrition/health considerations. The sustainability of the EAT-Lancet diet is based in part to the volumes consumed of each type of food. Since I do not have data on the volume consumed by individuals, I cannot speak explicitly to the sustainability of the diets at the household level

14 when consumed in excess, and to negative environmental impacts when produced in large scale. Contentious foods include red meat (beef, lamb, pork, etc.), poultry

(chicken, hens, etc.), and dairy products (milk, cheese, etc.). “Other” foods refer to other food or beverage products that do not easily fit in any of the aforementioned categories (i.e., coffee & tea, alcoholic beverages, pet food, baby food, etc.).

I also include a “food away from home” (FAFH) category which does not definitively conform to any of the nutritional food groups above but does represent a significant portion of spending on food in surveyed homes. FAFH are foods that are consumed away from home at a sit-down restaurant, fast-food restaurant, or prepared food purchased to consume at home. Although I am unable to identify the nutritional quality of food consumed in this category, the increased consumption of food away has been linked with a decline in the nutritional quality of an eater’s diet (Altman et al., 2015; Todd, 2017) and with elevated incidence of obesity (Kim and Ahn, 2020).

Foods prepared away from home tend to come in larger portions sizes, be more energy-dense, be higher in total fat, saturated fat, sodium, and cholesterol on a per- calorie basis and lower on essential nutrients (Lin, 1998). Table 2 summarizes the definition of the food nutrition groups and provides an overview of the average expenditure share for each food group for a representative household at the municipal level during the period covered by this study.

Table 2: Nutrition Food groups and Mean municipal nutrition quality group expenditure shares 1996- 2006.

Nutrition Food Group Description Food group expenditure shares

Group Name Foods 1996 1998 2000 2002 2004 2005 2006

Good foods Whole grains, tubers, vegetables, (Healthy) fruits, legumes, nuts, eggs, olive 0.36 0.36 0.33 0.34 0.31 0.30 0.31 oil, and fish

Bad foods Ultra-processed foods, refined (Unhealthy) grains, beverages and 0.15 0.16 0.16 0.16 0.15 0.15 0.16 vegetables/fruits with added sugar, soda, and saturated fats. Contentious foods Red meat, poultry, dairy products, saturated fats 0.27 0.26 0.26 0.25 0.24 0.25 0.24 FAFH Food consumed at restaurants or (Food away from prepared foods ready to be 0.17 0.17 0.19 0.21 0.24 0.26 0.25 home) consumed at home Other Coffee, tea, alcoholic beverages, 0.05 0.05 0.05 0.05 0.05 0.04 0.04 pet food, baby food, etc. Observations 416 396 380 495 503 572 529 Source: Author constructed food groups using the Eat-Lancet report as a reference (Willett et al., 2009). Mean expenditure shares were calculated using ENIGH survey data: 1996-2006 from INEGI.

Food Product Unit Values and Food Group Price Indices

The ENIGH survey collected data on the household expenditure on food products, as described above. Specifically, respondents were asked for the total amount spent on a given food item and the quantity purchased of the item during a seven-day period. Quantity is measured in either kilograms or liters depending on the nature of the product. For packaged items, quantity is measured by the number of units. In order to analyze a demand system, we need a price vector for the food products and nutrition quality categories. But there is no information on the actual price of the food items in the ENIGH data. So, I use unit value, instead of price. Unit value is defined as the average cost per unit of given product. The unit value is calculated by aggregating items within a food product category at the household level in a given period, and then divide the total amount spent on each category and divide by the total quantity purchased. To minimize measurement error at the household level

I use the municipal level median unit value.

Unit values are an imperfect measurement since they may capture more than just variation in prices. Unit values may also be subject to measurement error in terms of quantity purchased, nonlinear price schedules and differences in quality. The quality and quantity decision made by consumers is particularly relevant when evaluating the use of unit values. Variations in unit values may in fact understate the variation in prices since the consumer could potentially adjust the quality of their purchase to remain at a certain quantity level (Deaton, 1988). Although it is clear that unit values may not perfectly reflect prices, it is the best measurement available to analyze this food demand systems.

Having identified the unit value for all food product categories as a proxy for price, I turn to expressing the unit value in real terms by constructing price indexes. I calculate a “flexible” basket Fisher ideal chained-linked index which is based on items purchased on adjacent periods (i.e., 1996 and 1998; 1998 and 2000, etc.) therefore updating the basket with new or dropped items over time. For food group price indices specification, refer to Appendix A2. Based on adjusted unit values, I then calculate the average unit value of the food products in each nutrition food group by municipality and year.

Table 3 summarizes the average food group prices, at the municipal level, across the seven survey rounds. Price changes tend to correspond to overall national price fluctuations. Good foods are consistently the most inexpensive food group while

Other foods are higher priced. The price of FAFH, however, does appear to have fallen substantially during this period, from a high of over 23 pesos per unit in 2002, to under 10 pesos per unit in 2006. This dramatic change could be due to other complementary changes taking place in the restaurant side of the food system, making low-cost fast food more widespread.

Table 3: Mean nutrition quality group price across all surveyed municipalities

Nutrition Group 1996 1998 2000 2002 2004 2005 2006

Good 5.10 6.25 5.32 5.90 5.23 4.34 5.18

Bad 10.24 10.38 9.92 11.03 10.00 8.59 9.78

Contentious 14.39 16.10 15.25 16.00 13.65 12.23 12.93

FAFH 16.43 20.29 17.42 23.36 11.79 11.28 9.79

Other 18.83 21.00 25.00 22.44 21.30 16.67 20.20

Observations 416 396 380 495 503 572 529 Source: INEGI. ENIGH survey data: 1996-2006.

CHAPTER 3: PSEUDO PANEL APPROACH

In order to evaluate changes in household expenditure across different food groups in

Mexico with the emergence of supermarkets, ideally, I would have household-level observations for the same households across several periods. Unfortunately, panel data of this nature are not available. An alternative to panel data is the pseudo-panel, which consist of a series of independent cross-sections aggregated into cohorts and where the cohort means are analyzed (Deaton, 1985). Cohorts are composed of individuals, or in this case households, that share some common characteristic(s) which do not change.

Examples of cohort defining characteristics include age, place of residence, region, education of a select household member, income level, household composition, etc.

It is important to note that sample-based cohort averages are error-ridden measures of true cohort averages and therefore may not be consistent. There are have been several attempts to correct for this measurement error. It seems that although it is not clear what is considered a sufficiently large cohort size, based on simulations

Verbeek and Nijman (1992) argue that a cohort size of 100 units, in most cases, is large enough to ignore measurement error. They suggest that cohorts may be smaller if units grouped in the unit are sufficiently homogenous. Large cohorts however, present another problem, reduced precision of the estimates, leading to a problem of efficiency in estimation.

In its empirical application, the loss of efficiency has led to authors largely ignoring measurement error (Bernard et al., 2011) and not adhering strictly to the 100- unit cohort minimum (Burguillo et al., 2019; Meng et al., 2014). The goal in

20 constructing cohorts that lead to consistent and efficient estimators thus becomes, to minimize heterogeneity within cohorts and maximize heterogeneity between groups

(Gardes et al., 1995).

Constructing Cohorts

Municipalities are the basic unit of Mexican government. Households in the same municipality share common characteristics and are generally subject to similar public infrastructure and services (i.e., water services, electricity, waste management) are controlled at the municipal level. Although municipalities have decision making power with regards to these services, they have limited fiscal and administrative capacity and therefore remain heavily dependent on financial resources from the federal government (González Rivas, 2012). Furthermore, the municipality level is of interest since ANTAD provides data on the number of markets by municipality for the period of interest.

I create cohorts based on municipality given the characteristics of municipalities and the municipal level supermarket entry data that are available. Since this study is focused on identifying changes, if any, in food expenditure patterns during a period of supermarket expansion, using municipality as the main cohort criteria is sensible. Additional criteria, such as household income and size, were considered, however, increasing homogeneity within cohorts to this degree greatly reduced the number of households in a cohort, thereby compromising the consistency of the cohort estimators. Table 4 summarizes the number of households and municipalities captured per survey round, the average cohort size, and the median and maximum number of supermarkets in municipalities in each survey round.

Table 4: Households and municipalities per survey round

Year 1996 1998 2000 2002 2004 2005 2006 Total Households per 13808 10683 9929 16920 22487 22859 20618 Survey Total number of municipalities/cohorts per 416 396 380 495 503 572 529 Survey Average Cohort Size 33 27 26 34 44 40 39

% of municipalities 29% 33% 36% 30% 35% 35% 42% surveyed with supermarkets

Median number of supermarkets in each 4 5 5 5 5 4 4 municipality with supermarkets

Largest number of 45 54 58 71 85 83 89 supermarkets in surveyed municipalities Source: INEGI. ENIGH survey data: 1996-2006.

It is worth noting that the sample of municipalities captured by the ENIGH changed from year to year, and therefore, not all municipalities are necessarily captured in each of the survey rounds. Given that the goal is to assess changes in food expenditure from period to period in the same municipality it is important to check if there are observations for the same municipality for more than one survey round.

Figure 3 shows the frequency distribution of the municipalities captured for one or more survey rounds, 339 municipalities were only captured by one survey round, while 718 municipalities were captured in two or more rounds, of which 105 municipalities were surveyed in all seven rounds. This implies we will estimate an

22 unbalanced pseudo-panel, as different municipalities will have different numbers of observations over time in the estimation sample.

Figure 3: Municipality participation across survey rounds

Source: INEGI. ENIGH survey data. Histogram of municipality participation in survey. This study covers seven survey rounds. 718 municipalities are surveyed in at least two survey rounds. 105 of these municipalities are represented in all seven survey rounds.

Cohort characteristics

Table 5: Representative household and municipality descriptive statistics (1996- 2006)

Std. Variable Observations Mean Min. Max Dev. Weekly Food Expenditure 3,291 193.42 72.67 37.95 819.97 (Mexican Pesos, MXN) * Annual Income (1996 Real 3,291 8,991 5,953 1,286 174,682 MXN) * Household Members Age 0- 3,291 1.751 .648 0 6.043 15 yrs. old * Household Members Age 16- 3,291 2.59 .422 0 6.457 64 yrs. old * Household Members Age 3,291 0.23 0.16 0 1.109 65 years old * ≧ Population (municipality) + 3,291 285,846 496,108 624 3,641,776

% of households with 3,289 88.9 12.5 18.5 99.8 electricity (municipality) +

*Source: INEGI ENIGH (1996-2006) +Source: INEGI Census and Intercensal data (1995, 2000, 2005, 2010)

Table 5 summarizes the cohort level demographic characteristics across the

seven survey rounds. Over the seven rounds of survey data, the average representative

household’s weekly food expenditure, estimated using the average expenditure of each

food group in a municipality, is about 193 Mexican Pesos (MXN). Annual income is

calculated by using the median income as the representative household’s income for

each municipality. The median tends to me lower than the average since income is

highly skewed to the right. Household composition is estimated for each cohort using

the average of members in three age categories, ages 0-15, ages 16-64, and ages 65

24 and older. Overall municipal population and percent of households with electricity by municipality was obtained from census and intercensal data.

CHAPTER 4: ECONOMETRIC MODEL

In order to understand how household food expenditure pattern has changed, particularly against the growing number of supermarkets in the Mexico, I analyze the consumption of a representative household at the municipal level using a utility-based structural model.

Quadratic Almost Ideal Demand System

The almost ideal demand system (AIDS) introduced by Deaton and

Muellbauer (1980) is a model with many desirable properties when analyzing consumer spending decisions. An AIDS is simple to estimate, satisfies consumer utility maximization theory, aggregates over consumers, and is consistent with known household-budget data. When applied to consumer expenditure data, this model aggregates over households.

Although useful, the AIDS model, falls short in some of its key assumptions.

As pointed out by Banks et al. (1997), the AIDS model implicitly assumes that Engel curves are monotonic in utility and in total expenditure, however the Engel curvatures found empirically do not agree with this assumption. In order to preserve the observable flexibility of the Engel (income-expenditure) curves and remain consistent with consumer choice theory, Banks et al. (1997) propose the Quadratic Almost Ideal

Demand System (QUAIDS). I use the quadratic extension of the AIDS model. See

Appendix A3 for a detailed specification of the QUAIDS model.

Pseudo-Panel QUAIDS

Following Deaton (1985), I adapt the demand system that aggregates observation across household level to fit the pseudo panel approach. The demand system no longer aggregate across households, instead a household is a member of a well-defined cohort group, here defined as the municipality to which the household belongs to, across several survey rounds.

I incorporate the three supermarket variables ( top 5, Walmart, and markets)

, demographic characteristics ( , and municipality characteristics ( ) into the () ) demand system using the scaling technique introduced by Ray (1983) and extended to the QUAIDS model by Poi (2012).

The primary demand-driving demographic variable of interest is the vector

, which includes the three variables that decompose the total number of all supermarkets (top 5 supermarket, Walmart stores, and other supermarkets) in municipality in year . In addition to supermarket count, household demographic variables are captured in vector . These variables corresponds to the municipal-level representative household income and composition in municipality in year . Household income is defined as the municipal-level median (due to skewed distribution) of household total income. Household composition is defined as the municipal average number of household members in three age categories: (1) ages 0-

15 years old, (2) ages 16-64 years old, and (3) 65 years old or older. Household composition has been identified as a determinant of a household’s food consumption due to the varying food needs of different age and gender groups. Brown (1954) notes

27 the different caloric needs of working age adults and children and between men and women, suggesting that a families’ Engel curves vary depending on the household’s composition. Note this model does not disaggregate by gender. Although this was attempted, a model that further disaggregated household members by gender, yielded similar results.

Since our unit of observation is the municipality level, I include municipality level time-variant characteristics in municipality in year as controls in vector . Municipality characteristics include population since it captures market size, and electrification rate, defined as the percent of households in a municipality connected to the electrical grid and with access to electricity. Electrification captures households’ access to refrigeration and freezers, which may affect food shopping patterns.

Assuming a utility maximizing household with demographic characteristics represented by , , , the scaled expenditure function has the form

Where the expenditure is a function of , a price vector, utility , and the demographic characteristics outlined above. The expenditure function is parametrized as

Where is a vector of parameters describing the relationship between the demographic variables , , , and expenditures. The expenditure share equation takes the form

The values used for the expenditure share, and food group prices (captured in the price index price aggregator and , and ) in , ( ) (, , , ) Equation 3 correspond to cohort (municipality) means by year. Total food expenditure, is the total food expenditure of the municipal-level representative , household. Finally, I include state fixed effects, , for the 32 states to control for state level characteristics 9 and year fixed effects.

The parameters are estimated through an iterative , , , , , feasible generalized non-linear least squares regression. The main parameter of interest is the parameter vector of corresponding to the supermarket vector, This . vector will provide the coefficient estimates corresponding to each food group and provide us with insight into how the number of supermarkets in each of the supermarket categories, relate to the expenditure share of the five food groups.

9 Ideally, I would control for municipality, however, I am not able to due to so due the large number of municipalities viz a viz total number of observations.

After estimating the demand system, the mean expenditure and price elasticities are estimated to assess if the demand system estimations are consistent with theory and observed consumer behavior. See Appendix A4 for detailed specifications of the pseudo-panel QUAIDS with demographic characteristics and elasticities calculation.

CHAPTER 5: RESULTS

Table 6 summarizes the parameter estimates for and , corresponding to the three supermarket variables used to estimate the demand system.

Table 6: Supermarket Parameters for every 100 stores

Top 5 Supermarkets Other Nutrition Group Parameters * Walmart (excludes Walmart) Supermarkets

0.001 -0.205** -0.013

(0.039) (0.081) (0.038) 0.010 -0.027 -0.013

(0.014) (0.030) (0.011) -0.012 -0.106** -0.009

(0.021) (0.042) (0.016) 0.006 0.324** 0.034

(0.063) (0.139) (0.058) -0.004 0.014 0.001

(0.010) (0.020) (0.008) 0.084 5.710 0.054 (1.232) (3.530) (0.995) Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01 *The parameter describes the relationship between the supermarket variables: Top 5 supermarket brands (excluding Walmart), Walmart stores, and all other supermarkets and the expenditure share of the five food nutrition groups. The coefficient describes the relationship between the supermarket variable and overall expenditure.

The results indicate that for every 100 Walmart stores in a municipality, the expenditure share of healthy (good) and contentious foods each significantly decreases by more than 20 and 10 percentage points, respectively. The expenditure share of unhealthy (bad) foods does not significantly change with the number of supermarkets.

The expenditure share of FAFH increases significantly with the number of Walmart stores by 32 percentage points. The other category does not significantly change although the direction of the relationship appears to be positive. The estimated

31 magnitudes of these effects are uniformly smaller for the top 5 supermarket chains and other supermarkets and do not appear to be significant, implying that Walmart is the supermarket category driving the changes in expenditure share in the model. Refer to

A5 for the full estimation table.

Expenditure elasticities, and uncompensated and compensated price elasticities were also calculated to make sure that the estimation of the demand model were consistent with theory and observations.

The expenditure elasticities are all positive and mostly inelastic. FAFH is the only food group for which demand is elastic with respect to total expenditures, indicating food away from home is a luxury while the other food groups are necessities. This too is consistent with patterns in consumer behavior.

Since the number of Walmart stores appears to be significantly associated with changes in expenditure shares of various groups, I estimate the mean expenditure elasticities for municipalities with and without Walmart stores to assess if there is any significant difference. Mean expenditure share of healthy foods is significantly lower in municipalities with at least one Walmart store than in those municipalities without

Walmart stores. A lower expenditure elasticity indicates that a decrease in expenditure will not lead to a great decrease in the purchase of healthy foods, however, as expenditure increases, there also will not be a proportional increase in the consumption of healthy foods. This is consistent with the observed decrease in the expenditure share of healthy foods associated with increase in number of Walmart stores. The expenditure elasticity of unhealthy food in municipalities with Walmart

32 stores is the only food group that is not significantly different from municipalities without Walmart stores. Table 7 summarizes these findings.

Table 7: Mean Expenditure Elasticities

(1) (2) (3) (4)

All No Walmart Walmart Difference

Good 0.790*** 0.815*** 0.689*** -0.126*** (0.003) (0.003) (0.005) (0.007)

Bad 0.825*** 0.820*** 0.843*** 0.023 (0.009) (0.004) (0.004) (0.005) Contentious 0.840*** 0.832*** 0.872*** 0.040*** (0,006) (0.002) (0.003) (0.003) FAFH 1.719*** 1.757*** 1.571*** -0.187*** (0.028) (0.011) (0.014) (0.009) Other 0.627*** 0.652*** 0.526*** -0.126*** (0.006) (0.006) (0.010) (0.014)

Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01 Column (1) corresponds to the mean expenditure elasticity across all observations. Columns 2-3 summarizes the mean expenditure elasticity of municipalities that do not have a Walmart store (2) and municipalities that have at least one Walmart (3), in a given observation. Column (4) are the results from a t-test done to assess if the differences between the mean expenditure elasticity of observations without and with Walmart are significantly different.

Uncompensated and compensated elasticities were calculated as a check on the demand system estimation. Price elasticities estimations are consistent with consumer theory and therefore suggest the demand system estimation is reliable (Appendix A6 for uncompensated and compensated price elasticity tables). Compensated price elasticities indicate that the all food groups are substitutes and that the demand for all food groups is price inelastic, to varying degrees. Notably, demand is most price

33 inelastic with respect to healthy foods suggesting that in this system demand for healthy is not sensitive to changes in price.

CHAPTER 6: ROBUSTNESS CHECKS

Although the demand system model parameter estimates indicate that there is a relationship between the number of supermarkets and expenditure share of certain food groups, there might be an underlying relationship between supermarkets and price and income that might be driving the changes in demand. In order to explore these two relationships and test the robustness of the results linking demand to the number of supermarkets, I conduct two sets of auxiliary regressions. The first looks at the relationship between price and the number of supermarkets in each supermarket category and the second looks at the relationship between income and the number of supermarkets in each supermarket category.

Price and Supermarkets

To determine if supermarkets alone may be inducing a change in price, I identify the top 15 items purchased by households across the seven survey rounds. I then use the unit value prices I calculated for these products and I use Zellner’s (1962)

Seemingly Unrelated Regression (SUR) where price is the outcome variable and number of supermarkets the explanatory variable. To control for unobservable year and municipality variation that may have an effect on both the price of good and the number of top 5 stores, Walmart stores, and other supermarkets, I incorporate year and municipality fixed effects into the model.

Where is the log price (or unit value) for each of the top 15 food products, in municipality in year , is the number of top 5 supermarket chain 5 stores, is the number of Walmart stores, and is the number of other supermarkets in municipality in year , is the municipality fixed effect, and the year fixed effect.

Table 8: Seemingly Unrelated Regressions Results – Price of Top 15 purchased food products and Supermarket variables Top 5, Walmart, and Markets (Other)

Top 5 Other Product (Ln Price) Walmart Supermarkets Supermarkets

Corn tortillas 0.008* -0.015*** 0.019*** (0.005) (0.005) (0.005) Soda/sugar sweeten beverages 0.010 0.001 -0.020** (0.008) (0.009) (0.008) FAFH 0.035** -0.046** 0.013 (0.017) (0.019) (0.017) Milk 0.016 -0.044*** 0.032*** (0.010) (0.012) (0.010) Tomatoes 0.003 -0.000 -0.010 (0.013) (0.014) (0.013) Eggs -0.006 0.010* 0.003 (0.005) (0.006) (0.005) Sweet bread -0.013 0.011 -0.035*** (0.009) (0.010) (0.009) Beef -0.000 0.001 -0.001 (0.006) (0.007) (0.006) Processed meats 0.027*** -0.027*** 0.014 (0.010) (0.011) (0.010) Poultry 0.002 -0.004 -0.004 (0.010) (0.011) (0.010) Beans -0.015* 0.013 0.005 (0.008) (0.009) (0.008) White bread -0.006 0.010 -0.006 (0.010) (0.011) (0.010) Onions 0.026** 0.024* -0.008 (0.013) (0.014) (0.013) Peppers 0.005 0.026 0.022 (0.016) (0.018) (0.016) Cheese 0.012 -0.018* 0.016* (0.010) (0.011) (0.010) Observations 310 Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01

Table 8 summarizes the results from the SUR of the top 15 food items purchased by Mexican households in the 1996-2006 period. Results suggest that with

37 few exceptions (namely corn tortillas, sweeten beverages, sweet bread, onions, processed meats, and cheese), the number of supermarkets in the three categories are not associated with a significant difference in prices. Even in the case of foods where a significant change is observed, the items with lower prices tend to be in the unhealthy or contentious category (sweeten beverages, processed meats, milk, and cheese).

Supermarkets are associated with an increase in the price of healthy foods including tortillas (top5 and other markets only), eggs (Walmart), and onions (Walmart and top

5).

This finding stands in contrast with the common belief that supermarkets are associated with lower consumer prices (Atkin et al., 2018; Freire and Rudkin, 2019).

The reason for this is not clear. This inconsistency might point to a uniform versus customized pricing strategy embraced by supermarket brands, which lower prices in some markets but do not necessarily tailor pricing to do the same in all markets

(Dobson and Waterson, 2008). This inconsistency, however, could also be a symptom of the weakness of the unit value measure. Moreover, FAFH is also included on the list of frequently purchased items which are mostly not purchased at supermarkets.

Since FAFH is a composite of food consumed away from home and prepared foods purchased to consume at home, it is difficult to link any change in prices to presence of supermarkets.

Household Income and Supermarkets

I now turn to examining the relationship between supermarkets and household income. Using real income, adjusted using a state-specific deflator 10 , I also look at the relationship between the three supermarket variables and income in order to address the possibility that supermarkets can potentially be related to higher income via an

OLS regression. I include household composition, specifically the number of household members in one of six gender-age categories, male or female, age 0-15 years, 16-64 years, 65 years and older, and a dummy variable for when the household head is female. I control for other factors that may be influencing both household income and number of supermarkets using educational attainment of the household head, municipality, and year fixed effects.

where refers to households, refers to the year, is educational attainment of the household head fixed effect, and and are municipal and year fixed effects, respectively.

10 INEGI Consumer Price Index, monthly data by cities: https://www.inegi.org.mx/temas/inpc/default.html#Tabulados

Table 9: Log Annual Income and Supermarket Variables

Ln Income Top 5 Supermarkets 0.009*** (0.002) Walmart 0.021*** (0.003) Other Supermarkets -0.009*** (0.001) Household Member, Age 0-15 -0.003** (0.001) Household Members, Age 16-64 0.191*** (0.002) Household Members, Age 65 + 0.085*** (0.004) Household Head Gender (Female) -0.052*** (0.005) Formal Education (base = No education) Pre-primary -0.010 (0.021) Some primary 0.087*** (0.010) Primary complete 0.219*** (0.011) Some secondary 0.311*** (0.014) Secondary complete 0.365*** (0.012) Prep vocational incomplete 0.584*** (0.012) Prep vocational complete 0.668*** (0.013) Undergraduate incomplete 0.879*** (0.017) Undergraduate complete 1.138*** (0.014) Graduate studies 1.506*** (0.022) Constant 8.173*** (0.025)

R-squared 0.437 Observations 117,101 Year FE Yes Municipality FE Yes Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01

Table 9 summarizes the results of the OLS model looking at how income relates to supermarkets. Results show that although there is a significant relationship between supermarkets and income, it is relatively small (0.9 percentage points) and not strictly positive. Top 5 stores are associated with higher incomes while other supermarkets are associated with lower income.

Both price and supermarkets and income and supermarkets robustness checks confirm that the number of supermarkets in a municipality have a minimally significant relationship with the prices of the most commonly purchased items or household income. In the cases in which there is a significant relationship between the price of goods and the number of supermarkets, the price of healthy food, with the exception of corn tortillas (Walmart) and beans (top 5 stores), increases while the price of unhealthy and contentious foods decrease. This suggests that supermarkets may be associated with changes in price, however not always in the same direction across food groups. Similarly, the relationship between the number of supermarkets and income is significant but relatively small in magnitude and in opposing directions in the case of other markets and top 5 stores. This suggests that we are able to rule out that the relationship between the number of supermarkets and expenditure share of the food groups of interests is not driven by changes in price or household income. The case of Walmart is more nuanced. Results indicate that incomes are approximately 2

41 percentage points higher in households in municipalities with every additional

Walmart store. This finding could be mirroring an entry strategy focusing on higher income areas.

CHAPTER 7: DISCUSSION

The central question of this study asks whether supermarkets in some way influence food expenditure patterns. Using supermarket and household expenditure data from

Mexico to estimate a QUAIDS model of consumer demand for different food groups, I show that the number of supermarkets in a municipality is significantly associated to demand. Specifically, results point to Walmart, the recent foreign entrant into the food retail market in Mexico during the period covered by this study, as being the main driver of this association.

Although food group expenditure shares are not significantly associated to the number of top 5 supermarket brand stores, and other supermarkets, the number of

Walmart stores is associated with a significant and sizeable decrease in the share of healthy and contentious foods in consumer food baskets. However, estimates do not show a significant relationship between the number of supermarkets and the purchase of unhealthy foods. Price elasticities indicate that consumers’ price sensitivity and income sensitivity across food categories is not very different. In the case of Walmart, mean expenditure elasticities are significantly different across all food groups with the exception of unhealthy foods. Notably, the expenditure elasticity of healthy foods is significantly lower in municipalities with Walmart’s, suggesting that as income, and thereby expenditure, increases, the share of healthy foods is not likely to increase proportionally in these localities.

Results also point to a clear relationship between the number of supermarkets and the expenditure share of FAFH. This could be due to the fact that both

43 supermarkets and FAFH, especially dine-in and fast food restaurants, are both byproducts of more complex value chains and are more luxury goods, as reflected in the expenditure elasticity estimates. It would be interesting to further explore the role

FAFH plays in the food purchasing landscape in Mexico, especially relating to the composition of the foods included in this category (i.e., differences between food consumed away from home and prepared foods that are purchased and consumed at home) and the healthfulness of these foods. The shift towards FAFH is also likely an indication of broader changes in labor markets and education attainment (i.e., women’s increasing presence in the labor force and higher educational attainment requiring working away from home, both leading to less cooking at home). Still, the significance of the shift towards FAFH with an increase in the number of total supermarkets points to fundamental changes in the food environment that results from and drives the expansion of supermarkets, which lead to consumers altering their food purchasing behavior.

Findings suggest that all supermarkets are not the same and that a supermarket’s product selection, size, and location may be useful in understanding different consumption patterns and behaviors. Notably, Walmart surfaces in this study since it is related to significant changes in purchasing patterns, particularly a decrease in the expenditure share of healthy foods.

Walmart’s entry into the Mexican market coincided with an overall increase in the number of supermarkets and other food outlets in the territory. As Mexico’s food supply grew connected to longer and global food value chains, supermarkets and other food retail outlets, emerged thereby consolidating food marketing and dramatically

44 changing food environments. The findings of this study suggest that the conditions of these emerging food environments is related to a significant change in food purchasing patterns, especially a sizeable decrease in the share of healthy foods purchased.

Given the ongoing public health concerns in Mexico (and in the neighboring

U.S. and parts of Latin America) related to diet-related diseases, the findings of this study raise questions about the appropriateness of some supermarkets as the leading urban and regional planning solution for addressing food access concerns. Speaking to growing concerns over the increase of unhealthy foods and efforts to curtail their purchase, results suggest that perhaps it is equally, if not more important to promote healthy foods as well. Moreover, the findings indicate that food purchasing patterns are driven by more than price and income, and that perhaps the nature of a locality’s food environment (i.e., type of supermarkets and/or groceries, restaurants, food production) may play a significant role in shaping food purchasing behavior and should be considered by both the public and private sector when creating health promoting policies and claims.

APPENDIX

A1. Supermarket Expansion in Mexico: Top 6 Supermarket Brand stores by municipality (1996-2006)

Source: Supermarket data (Varela, 2018). Geospatial municipal data (INEGI). The maps depict the expansion of the top six supermarket brands ( Casa Ley, Chedraui, Soriana, Gigante, Comercial Mexicana, and Walmart), between 1996- 2006. ) between 1996-2006 across all municipalities in Mexico. The map is divided into the over 2,000 municipalities in Mexico. A circle overlays a municipality that has a store corresponding to one of these top chains. The size of the circle increases with every 10 “top six” supermarkets in the municipality. Municipalities without a circle do not have a top six supermarket store.

A2. Food Group Price Indices Specification

There are two approaches to choose from: fixed-based indexes or chain-linked indexes. An advantage of the fixed-base indexes is that it is free of “chain drift”, that is, fixed-base indexes will return to unity when prices in the current period return to the base period levels. Nevertheless, chain-weighted indexes have a major advantage over fixed-based indexes: they offer a flexibility in terms of the basket of goods from period to period which takes into account consumer substitution of products purchased across periods. Fixed base indexes, therefore, are not ideal over a long period of time. The Fisher ideal index is considered a “superlative index” (Diewert, 1976), satisfying over 20 “reasonable” tests (Diewert, 1992), and appearing to be relatively less affected by time aggregation than the Laspeyres and Paasche Indexes (Ivancic et al, 2011).

The Laspeyres price index estimator for any two adjacent periods can be expressed as follows:

, = where is the “base period” price of the item , is the price of item in the period following period , and is the share of good 's total expenditure in period . The Paasche price index estimator for any two adjacent periods can be expressed as follows:

, = where is the share of good 's total expenditure in period . The Fisher price index estimator between two subsequent periods is defined as the + geometric 1 mean of the Laspeyres and the Paasche indexes (Fisher, 1923)

, = ∑ ∙ ∑

The Fisher ideal chained linked price index is then calculated by “multiplicatively chaining” the Fisher ideal price estimator. The Fisher ideal chained linked price index can be expressed as follows:

, = ( )

I calculate the Fisher ideal chained price index using municipal level median unit values. I use median unit values instead of mean unit values since the mean is distorted by households that did not consume a certain item. Figure 3 compares the calculated price index with the price index for food, beverages, and tobacco published by INEGI and it suggests that the calculated price index seems to be aligned with the trend officially reported.

Price Index Comparison (base year 1996)

Consumer price index for September, corresponding to the time in which the ENIGH was conducted, Source: INEGI Indice Nacional de Precios al Consumidor (INPC), Mensual por clasifcacion objeto del gasto. (Consumer Price Index, monthly by object classification). https://www.inegi.org.mx/temas/inpc/default.html#Tabulados

A3. QUAIDS model specification

The QUAIDS model introduced by Banks et al.(1997) is based on the following indirect utility function:

ln −ln () ln (, ) = + () () where () is the indirect utility function of a PIGLOG demand system which has budget() shares linear in log total expenditure and

() = ln is a differentiable and homogenous function of degree zero in prices. The Marshallian demand function is obtained by applying Roy’s identity to the indirect utility function. The QUAIDS model in its budget share form is defined as follows:

= + + ln() + ln + + () () () where and are price indices defined by the following equations, () () 1 ln () = + ln () + ln () ln( ) 2 ln () = is the budget share of the nutrition group , is the price of the food items in nutrition group is defined as total food expenditure per household, and is a set of sociodemographic, variables introduced to allow for household heterogeneity. and are parameters to be estimated and the residuals are assumed, , to, be, multivariate, normal and distributed with zero mean.

The QUAIDS model is consistent with utility maximization and therefore the parameters need to be constrained to allow for homogeneity of degree zero on prices and income and symmetry. The following summarizes these constraints:

Adding up:

= 1 ; = 0 ; = 0 Homogeneity:

= 0 ∀ In addition, for homogeneity to hold, the price index must be homogenous of degree 1 in prices and expenditure, and homogenous() of degree 0. () Slutsky symmetry:

A4. QUAIDS with demographics and elasticities post estimation

QUAIDS estimation with demographics

Extending Ray’s Method (1983) to QUAIDS model by Poi (2012).

Let denote the expenditure function, where is price, and , is utility. (, ) Ray’s method uses the following expenditure functional form

(, , ) = (, , ) × (, ) The function scales the expenditure function to account for the demographic characteristics.(, , ) Note that , is further disaggregated in this case, in order to distinguish between the three sets of demographic characteristics: , supermarkets, , household characteristics, and , municipal level characteristics. Ray further decomposes the scaling function as

(, , ) = () × (, , )

The quaids command used to estimate the model in Stata 16, parameterizes as ()

() = 1 + ′ where is a vector of parameters to be estimated which describes how demographic variables (covariates) relate to expenditure and is used to calculate expenditure elasticities. The quaids command also parametrizes as (, , )

ln ∏ (∏ ) (, , ) = ∑ The expenditure share equations take the form

= + + ( + ) ()() + where () (, ) ()()

(, ) =

Here, the adding up condition requires that for . ∑ = 0 = 1, … ,

QUAIDS with demographics elasticities post-estimation

Uncompensated price elasticity of good with respect to changes in the price of good is

1 2 = − + − + + () (, ) () () + × + − ln () (, ) ()() The expenditure (income) elasticity for good is

1 2 = 1 + + + ln () (, ) ()() Compensated price elasticities are obtained from the Slutsky equating:

= +

A5. QUAIDS model estimation– All Supermarket variables ( Top 5, Walmart, and other supermarkets

A6. Price Elasticities Uncompensated (Marshallian) Price Elasticities

price price price good price bad contentious FAFH price other b/se b/se b/se b/se b/se - Good -0.777*** 0.035*** -0.036*** 0.029*** 0.017*** (0.002) (0.001) (0.001) (0.000) (0.000) - Bad 0.060*** -0.938*** 0.039*** 0.017*** 0.032*** (0.001) (0.001) (0.001) (0.000) (0.001) - Contentious -0.064*** 0.021*** -0.778*** 0.015*** -0.003*** (0.001) (0.000) (0.002) (0.000) (0.000) - FAFH -0.365*** -0.147*** -0.242*** 0.916*** -0.048*** (0.006) (0.002) (0.004) (0.002) (0.001) - Other 0.184*** 0.145*** 0.025*** 0.000*** -0.980*** (0.003) (0.002) (0.001) (0.000) (0.000) Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01

Compensated (Hicksian) Cross-price elasticities

price price price price good price bad contentious FAFH other b/se b/se b/se b/se b/se Good -0.506 *** 0.155 *** 0.163 *** 0.133 *** 0.054 *** (0.002) (0.001) (0.001) (0.002) (0.000) Bad 0.328 *** -0.804 *** 0.247 *** 0.159 *** 0.071 *** (0.002) (0.001) (0.001) (0.003) (0.001) Contentious 0.209 *** 0.150 *** -0.561 *** 0.165 *** 0.036 *** (0.002) (0.001) (0.002) (0.002) (0.000) FAFH 0.215 *** 0.121 *** 0.199 *** -0.567 *** 0.033 *** (0.003) (0.002) (0.002) (0.002) (0.001) Other 0.392 *** 0.245 *** 0.179 *** 0.128 *** -0.945 *** (0.002) (0.001) (0.001) (0.002) (0.000) Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01

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