Online AppendixTaxing to Reduce Obesity

By ARTURO AGUILAR, EMILIO GUTIERREZ, ENRIQUE SEIRA

Draft: July 27, 2017

A1. BMI evolution and measure in our data

The taxes being analyzed in this paper were established as a government response to a growing trendin obesity levels in Mexico. Thus, a variable of interest in this study is the body mass index (BMI),which we use to classify households based on its anthropometric characteristics. Following WHOstandards, we classify households as normal, overweight or obese according to its household’s headbody mass index (BMI) and explore if the effects analyzed in the paper differ for each group. In partof the analysis BMI is also used as a dependent variable itself.

Our BMI data comes from yearly surveys that the Kantar World Panel (KWP) implements to house-holds from our main dataset. As part of the survey, KWP gathers self-reported information of weightand height from the male and female household heads.1 The use of self-reported data might raisesome concerns. We performed some checks with strongly suggest that the BMI measure has highquality. First, it respects the ranking Mexico vis-a-vis the other countries we have in our data. Sec-ond, it is consistent with time trends in BMI as reported to the WHO. Third, and very importantly, it isvery close to data from the ENSANUT survey which is the most important health survey in Mexico,is representative of the country and measures height and weight in situ by trained nurses. Fourth, isdisplays tight relationships with demographics in an intuitive way. Finally, we note that although itwould be better to have measured weight, more than half of the countries reporting to WHO use selfreported statistics like the one in this study. This evidence provides support to the use of this variablein the main text.

First, we show how Mexico compares to other countries in terms of BMI, including a subset ofcountries for which we have KWP data available. Figure A1 confirms that Mexico is one of thecountries with highest average BMI. This map, taken from the WHO website maps, indicates that theaverage BMI in Mexico for individuals aged 18 or older is 27.5.2 Compared with the rest of CentralAmerican countries for which we have KWP data available, Mexico ranks as the highest BMI country.According to KWP data, mean values for Costa Rica, Guatemala, Panama and El Salvador are 26.9,26.3, 27.1 and 26.6, respectively. Mean values according to the WHO are very similar: Costa Rica,26.7; Guatemala, 25.8; Panama, 26.4; and El Salvador, 26.8. The only difference between the KWPand WHO rankings is that KWP reports a larger mean BMI for Panama than for the rest of CentralAmerican countries. Nonetheless, Panama is also the country for which the sample of householdsincluded in KWP is smallest. KWP reports information for 4108, 4468, 3600, and 4008 householdsin Costa Rica, Guatemala, Panama and El Salvador, respectively. Figure A2 gives a first insight ofhow the distribution of BMI compares between Mexico and the Central American countries using the

1More information about KWP and data gathered are available in section A2 of this online appendix.2http://gamapserver.who.int/gho/interactive_charts/ncd/risk_factors/bmi/atlas.html

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2

FIGURE A1. WORLD OVERWEIGHT STATUS

Note: Source: http://gamapserver.who.int/gho/interactive_charts/ncd/risk_factors/bmi/atlas

female head information from KWP. It shows that Mexico whole distribution is to the right of theother four countries for which we have data.

Part of the interest in obesity fighting taxes is not only the level, but the rate of growth of obesity andBMI. Figure A3 shows the striking growth for selected countries. Figure A16 mirrors this increasingtrend with the five countries for which we have data from Kantar. This is important. One concernwith self reported data is that respondents do not update their weight information. We are witnessinga yearly increase in BMI in our data for all the countries, implying that households are updating theirweight.

Finding that our data respects rankings and trends is comforting. But perhaps the most comfortingevidence was to find out that our self-reported BMI measures coincide substantially with those col-lected in-situ by the 2012 Mexican Health and Nutrition Survey (ENSANUT). Figure A4 plots BMIdensities according to both data sources. The distributions have a very similar mean (see Table 1 inthe paper), although ENSANUT reports a slightly larger variance.

One other consistency check one might implement is to explore correlations of BMI against vari-ables which we know should correlate with BMI. Figure A5 shows the that BMI in our data is closelyrelated to age. Finally, we are not very concerned about using the female household head as a measureof obesity (see Figure A6).

Together, these checks give us confidence that BMI measures reported in KWP are good approxi-mations one of our main variables of interest.

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FIGURE A2. BMI DISTRIBUTION, CENTRAL AMERICA AND MEXICO

0.0

2.0

4.0

6.0

8.1

Den

sity

10 20 30 40 50BMI

Mexico Costa RicaGuatemala PanamaSalvador

Note: Kernel density estimates of BMI for our five Latin America countries as reported in KWP.

FIGURE A3. OVERWEIGHT IN CONTEXT

2030

4050

6070

Sha

re O

verw

eigh

t

1980 1990 2000 2010 2020

Australia Canada France Italy Korea

Mexico Spain Switzerland United Kingdom United States

Note: Source: OECD Obesity Update. 2012.

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FIGURE A4. BMI DISTRIBUTIONS KWP AND ENSANUT

0.0

2.0

4.0

6.0

8.1

Den

sity

0 20 40 60BMI of Household's head

KWP ENSANUTkernel = epanechnikov, bandwidth = 0.7540

Note: Kernel density estimates of BMI according to KWP and ENSANUT.

FIGURE A5. BMI-AGE PROFILES

2526

2728

29B

MI F

emal

e H

ouse

head

20 40 60 80Age

95% CI lpoly smoothkernel = epanechnikov, degree = 0, bandwidth = 2.08, pwidth = 3.12

(a) Female Profile

2526

2728

29B

MI M

ale

Hou

sehe

ad

20 40 60 80Age

95% CI lpoly smoothkernel = epanechnikov, degree = 0, bandwidth = 2.88, pwidth = 4.33

(b) Male Profile

Note: Non-parametric estimations (local polynomials). KWP data.

FIGURE A6. RELATIONSHIP WEIGHT AND BMI OF MALE AND FEMALE HOUSEHOLD HEAD

6065

7075

80W

eigh

t Fem

ale

Hou

sehe

ad

40 60 80 100 120Weight Male Househead

95% CI lpoly smoothtrimmed at>45kg and <110

(a) Weight

2628

3032

BM

I Fem

ale

Hou

sehe

ad

20 25 30 35 40BMI Male Househead

95% CI lpoly smoothtrimmed at>20 BMI and <40

(b) BMI

Note: Non-parametric estimations (local polynomials). KWP data.

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A. Cross-sectional relationship between BMI and SD Prices and Purchases

FIGURE A7. CROSS-SECTIONAL RELATIONSHIP BETWEEN BMI AND SD PRICES AND PURCHASES

8.5

99.

510

10.5

Avg

. Pric

e pe

r Lite

r

0 100 200 300Yearly Liters per capita

Confidence Interval 95% Local Polynomial

(a) Price-Liters

2727

.528

28.5

BM

I

6 8 10 12 14Price per Liter

Confidence Interval 95% Local PolynomialYearly Liters per capita

(b) BMI-Price

Note: The Figure uses data from the Mexican chapter of the Kantar World Panel. The figures plot local polynomial regressions betweenaverage price per liter and yearly liters per capita, i.e. a naıve demand function (left) and BMI of female head vs average price per liter(right).

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A2. Kantar World Panel: some examples

Section III in the main paper describes our data sources. Our main source comes from Kantar WorldPanel (KWP). KWP is a company with more than 50 years of experience measuring consumptionof households in dozens of countries. Together with Nielsen they are the most important companiesworldwide.

One remarkable characteristic from the KWP data is its high frequency (weekly) and the the highlevel of disaggregation, which allows us to make a detailed statistical analysis from different perspec-tives. KWP’s product catalogue comprises a total of 58, 721 barcodes (SKUs); 26,101 for foods anddrinks. It classifies barcodes along ten variables. The categories used to classify products include:basic ingredient, flavor, size, brand, product’s characteristics (e.g. light, lactose-free, caffeine-free,etc.), among others. Table A1 below shows an example of how five products are classified along thesedimensions.

TABLE A1—PRODUCT CLASSIFICATION EXAMPLE

id product subproduct brand clas01 clas02 clas03 clas04 clas05 content3510 Cereals No subproduct Kelloggs Corn Flakes . Apple w cinnamon . . 750 gr105944 Cookies Sweet Marinela Principe . Strawberry-filled Chocolate . 44 gr43379 Carbonated drinks Regular Lift Red apple . . . . 1000 ml72523 Milk Pasterurized Alpura . Regular Non lactose-free . . 1000 ml133285 Mayonnaise Regular Kraft Mayo Regular . . Light 443 gr

We used the above information to detect which barcodes were subject to the tax and which wereexempt and to impute nutritional content as we detail in this Appendix. Before proceeding withthis detail however, we would like to exemplify the richness of our data by plotting prices for someselected products. Figure A8 shows the price per liter through time for three Coca-Cola packagesizes: the 355 ml. can, the 600 ml. bottle and the 2 liter bottle. We could potentially plot these graphsby storebrand, by city or by type of household.

Figure A9 gives another example. It plots the price per unit (liter or kilogram, depending on theproduct) for the following products: (i) a 40 gram bag of Lays chips (a taxable food product), (ii) a640 gram of Bimbo bread (a not taxable food), (iii) a 2 liter bottle of Citrus Punch juice (a taxabledrink), and (iv) a 20 liter bottle of Ciel natural water (a non-taxable drink).

Along with the information on purchases, KWP also administers a yearly questionnaire that cap-tures a set of socio-economic characteristics from which SES categories are derived using standardmethodologies. SES categories are derived from different measures of household assets: number ofrooms, type of floor, number of bathrooms, whether the dwelling has a gas stove, number of lightbulbs, number of cars, and household head’s education. Following the procedure, about 21, 52 and27 percent of households in the data are classified in the A/B/C+, C/D+ and D/E SES categories,respectively. Finally, each year KWP gathers also self-reported information on the age, weight andheight of the household’s male and female heads, which allows us to calculate BMI. Using BMI, weproduce obesity and overweight indicators following WHO standards. The SES and BMI categoriesare employed in the analysis to study heterogenous effects.

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FIGURE A8. PRICE PER LITER OF COCA COLA BY PACKAGE SIZE

1920

2122

2324

1314

1516

177

89

01/01/13 25/06/13 01/01/14 25/06/14 01/01/15

Can Coke 600ml Coke 2L Coke

Pric

e of

Fla

g Ite

ms

Sod

as (P

rice

per L

iter,

Nom

inal

Pes

os)

Note: Source: KWP data

FIGURE A9. FOUR PRODUCTS EXAMPLE, PRICES

1401

6018

0200

2202

40Ch

ips (4

0gr L

ays)

01/01/13 25/06/13 01/01/14 25/06/14 01/01/15

Taxed Items

3638

4042

Loaf

Brea

d (64

0gr B

imbo

)

01/01/13 25/06/13 01/01/14 25/06/14 01/01/15

Untaxed Items

66.5

77.5

8Ju

ice (2

L Citru

s Pun

ch)

01/01/13 25/06/13 01/01/14 25/06/14 01/01/15

1.21.2

51.3

1.35

1.4Bo

ttled W

ater (

20L C

iel)

01/01/13 25/06/13 01/01/14 25/06/14 01/01/15

Price

of F

lag Ite

ms(P

rice p

er Li

ter/K

g, No

mina

l Pes

os)

Note: Source: KWP data

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A3. Nutritional Content Data and Imputation

A. Collection of Nutritional Data

We mentioned above that we start with 26,101 barcodes for foods and drinks. However some of theseare exactly the same product in the sense they have exactly the same substance in it, and just varyin the quantity per unit. For example a can of 355ml coke has the same substance as a 500ml coke.Once we remove the size differences we end up with 15, 622 barcodes. Our goal in this section isto describe how we recorded or imputed caloric and nutritional content for each of these given thatKantar does not do it.

The first step consisted of recruiting a team of more than 20 enumerators and train them to search forthe items in supermarkets (physically and through their internet website3), manufacturer’s website andlocal grocery stores. This work involved several months of work. Enumerators took pictures of thenutritional label and then coded such label on a predetermined format. This enabled us to do doublecoding for a sample of barcodes and verify that the enumerators actually went to the supermarket andfound the appropriate product. Each enumerator was given a list of products by the researchers ana format to fill out and enter the nutritional content available in the product’s label, mainly: servingsize, calories, sugar, fat, saturated fat, iron, carbohydrates, cholesterol, and sodium. We prioritizedbarcodes that covered a large percentage of purchase events. Finally we performed quality check ona random sample of 5 percent of the barcodes. We found less than 1 percent of imputing errors.

Using this process we managed to collect direct information for 1,953 barcodes which cover 72percent of purchase events and 68 percent of household’s expenditure of the weekly scanner data for2013 and 2014. Table A2 shows the proportion of events and expenditure that were collected for eachproduct-category that covers all of KWP data.4

We could find information of more than 95 percent of the products we selected, and therefore wethink there is no systematic selection problem in the data. However, we excluded some products thatwere very local and that comprised a very low fraction of observations.

B. Caloric and nutrient imputations

After collecting the nutritional information for a very large fraction of purchase events, we had toimpute the nutritional characteristics of the remaining products that appear in KWP’s dataset. Thenext steps were followed to perform such imputation:

1) Convert sizes to the same units: We begin by using serving sizes along with calories and nutri-ents of the products that we gathered to generate a variable that shows the amount of calories ornutrients per 100 grams (or per 100 ml. for drinks). We will refer to this variable as our densitymeasure.

2) Run an exploratory analysis to measure the variance of caloric density for different aggre-gations, using the variables {product,subproduct,brand,clas01, clas02,clas03, clas04,clas05}exemplified in Table A1. We call these the matching variables. The highest level of disaggre-gation is when we use all these 8 variables to create cells. At these level which we call L8 wefind tiny variances with cells. This just means for instance that that Alpura Milk has the samelevel of calories across its package sizes. If we go up one level to L7, there is some variancebut is tiny. This means for instance that Alpura chocolate milk has similar caloric density thanAlpura strawberry milk.

3Mainly Walmart’s website was used: [www.walmart.com.mx]4Product category is a variable in KWP data.

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TABLE A2—INFORMATION COLLECTED BY TYPE OF PRODUCT

ProductExpenditure

(percent)Purchase events

(percent)Cooking oil 83 81Bottled water 38 38Sparkling water 87 85Baby food 45 55Canned tuna 85 82Cereal bar 90 89Powdered drinks 90 90Energy drink 95 95Carbonated soft drink 98 98Sports drink 89 89Non-carbonated soft drink 74 73Snacks 96 96Coffee 85 87Condensed milk candy 45 30Seasonings and broths 78 81Cereal 94 91Chocolate 94 93Tomato puree 88 88Liquid seasoning 56 53Creamer/Substitutes of cream 63 76Sour cream 60 60Cream Spreads 69 60Breadcrumbs 96 93Cookies 80 84Cornflour drink (atole) 73 66Ice creams and popsicles 57 70Vegetable Juice 94 95Condensed Milk 96 96Powdered Milk 35 35Evaporated Milk 95 95Milk 60 60Flavored Milk 85 83Margarine 70 71Mayonnaise 83 84Jams 73 71Honey 53 56Flavored Milk Powder 90 92Industrialized bread 92 90Pastas 63 62Refrigerated Dessert 99 100Powdered Desserts 66 65Tomato puree 87 87Ketchup 86 89Snack sauce 69 71Homemade bottled sauce 84 85Pasta Sauce 53 35Instant Soups 91 90Iced-tea 37 35Yogurt 80 81

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3) We explored by hand each product-group5 follows a different classification (which is reflectedin the variables shown in Table A1), and within each product-group we imputed the nutri-ent/caloric density to the closest category to which it belonged.

4) In other words, for each article with missing caloric and nutrient information, we search for themost similar product with information available based on the matching variables. We seek toestablish such match with the highest level of disaggregation possible. If an exact match was notpossible for a given level of disaggregation, we used a lower level of disaggregation, that is, weuse a subset of the matching variables. The following table exemplifies this procedure. In thiscase, subproduct and clas01 were chosen as the matching variables. As can be seen, there are4 products with subproduct = REGULAR6 and clas01 = COLA with missing informationfor calories. In this case, these products are matched to those with the same subproduct andclas01 for which information is non missing. The average value of the density measure ofthose products with available information is imputed to those with missing information. Thesame process is followed with the two missing values for the subproduct = REGULAR andclas01 = LEMON LIME products. However, we can see that we have in the first line acherry-flavored drink which cannot be matched to other drink with the same subproduct andclas01 values. In this case, a lower level of disaggregation is used and that product is imputedwith the average density measure value from all drinks that have subproduct = REGULAR.

5) For barcodes for which we were not reasonably sure about the quality of the imputation wesend enumerators to get the information on the supermarket.

To assess the quality of our imputation procedure, we performed the following exercise:

1) We randomly drop the observed values for 10 percent of the products for which we have avail-able caloric and nutrient information.

2) We implement the imputation algorithm described above for the product whose informationwas deleted in the previous step. We compare the imputed versus the observed values.

3) We repeated this exercise 5 times.

4) Figures A10 to A11 plot the density of difference between the observed and imputed values forcalories, sugar and fat. Similar graphs for the rest of the nutrients are available upon request.As the graphs display, there is a big concentration in zero which speaks of the adequacy of theprocess implemented to input the missing information. The imputation errors for calories arerelatively small: for liquids we obtain from the out of sample exercise that the absolute valueof the imputation error is, on average, 8.2 calories per 100 ml, while on average caloric densityis 104.7 calories per 100ml. For solid foods, the corresponding average value of the imputationerror is 61.6 calories per 100 gr, while on average caloric density is 298.4 calories per 100 gr.

5The different types of products-groups available are: cooking oil, bottled water, sparkling water, baby food, canned tuna, cereal bars,powdered drinks, energy drinks, carbonated soft drinks, sport drinks, non-carbonated soft drinks, snacks, coffee, condensed milk candy(cajeta), seasonings and broths, cereal, beer, chocolate, tomato puree, liquid seasoning, creamer, sour cream, sweet spreads, breadcrumbs,cookies, cornflour drink (atole), ice creams and popsicles, vegetable juice, condensed milk, powdered milk, evaporated milk, regular milk,milk-based flavored drinks, margarine, mayonnaise, jams, honey, milk-based flavored powder, industrialized bread, pastas, refrigerateddessert, powdered desserts (e.g. jello), ketchup, salsas, homemade bottled salsas, pasta sauce, instant soups, iced-tea, yogurt

6DIET/LIGHT is the other possible value for subproduct

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TABLE A3—IMPUTATION EXAMPLE

num product subproduct brand clas01 kcal 100ml124 CARB. DRINK REGULAR GARCI CRESPO CHERRY48 CARB. DRINK REGULAR CABALLITOS VANILLA / ROOT BEER 292 CARB. DRINK REGULAR PEPSI COLA 45.37 CARB. DRINK REGULAR BIG COLA COLA 424 CARB. DRINK REGULAR RED COLA COLA90 CARB. DRINK REGULAR CHIVA COLA133 CARB. DRINK REGULAR SMART COLA39 CARB. DRINK REGULAR BIG COLA DOBLE COLA 431 CARB. DRINK REGULAR COCA COLA COLA 4252 CARB. DRINK REGULAR AURRERA COLA3 CARB. DRINK REGULAR BIG COLA MEGA COLA 42115 CARB. DRINK REGULAR COCA COLA LIFE COLA 1811 CARB. DRINK REGULAR 7 UP LEMON-LIME 348 CARB. DRINK REGULAR SPRITE LEMON-LIME 3681 CARB. DRINK REGULAR BIG FRESH LEMON-LIME97 CARB. DRINK REGULAR AURRERA LEMON-LIME

FIGURE A10. OUTSAMPLE IMPUTATION ERRORS FOR CALORIES

(a) Liquids (b) Solids

Note: Gaussian kernel density estimations shown

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FIGURE A11. OUTSAMPLE IMPUTATION ERRORS FAT AND SUGAR

(a) Fat (b) Sugar

Note: Gaussian kernel density estimations shown

A4. Tax variable

As described in the main text, two different tax designs were implemented beginning on January 1st,2014: (i) one peso per liter of sugar added drinks, and (ii) an 8 percent ad-valorem tax for non-basicfood items with a caloric density above 275 kilocalories per 100 grams.

To have an accurate identification of the products in KWP’s dataset that would be subject to thesugary drink tax, we started by identifying the tax exempt products. For drinks these include bottledwater, sparkling water, beer, milk, powdered milk, evaporated milk, atole, etc. We then classified astaxable drinks those that fell into powdered drinks, energy drinks, carbonated and non-carbonated softdrinks, sports drinks, vegetable juice, flavored milk and iced-tea. For the products that are consideredfor the tax, the only exception considered is when they are sub-classified as “diet, light” or “non-sugaradded” in KWP’s categories. Then we went inside these broad clarifications to find cases of barcodesthat had no added sugar and therefore should be exempted. We did this barcode by barcode, and forthose where we were not sure we asked industry experts.

As for the ad-valorem tax, non-basic food products with higher caloric density than 275 kilocalo-ries per 100 grams with the following product classification are classified as tax eligible: cerealbars, snacks, condensed milk candy (cajeta), seasonings and broths, cereal, chocolate, sweet spreads(e.g. nutella, peanut butter), breadcrumbs, cookies, ice creams and popsicles, margarine, mayonnaise,jams, honey, industrialized bread, pastas, refrigerated dessert, powdered desserts (e.g. jello), ketchup,salsas, homemade bottled salsas, pasta sauce, and instant soups, etc.

Tables A4 and A5 show, by product-group, the percentage of items classified as being subject toeither the sugary drink (tax beverage)or the high caloric-dense food tax (tax food).

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TABLE A4—TAX VARIABLE: SUGARY DRINKS

Product typeBarcodes with tax

(percent)Bottled water 0Sparkling water 0Powdered drinks 74Energy drink 94Carbonated soft drink 92Sports drink 100Non-carbonated soft drink 70Coffee 10Beer 0Cornflour drink (atole) 0Vegetable Juice 100Powdered Milk 0Evaporated Milk 0Drinking Milk 0Flavored Milk 100Iced-tea 86

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TABLE A5—TAX VARIABLE: CALORIC DENSE FOOD

Product typeBarcodes with tax

(percent)Cooking oil 0Baby food 0Canned tuna 0Cereal bar 100Snacks 100Condensed milk candy 98Seasonings and broths 5Cereal 98Chocolate 96Tomato puree 0Liquid seasoning 17Creamer/Substitutes of cream 0Sour cream 0Sweet spreads 100Breadcrumbs 100Cookies 100Ice creams and popsicles 0Condensed Milk 0Margarine 99Mayonnaise 99Jams 0Honey 76Flavored Milk Powder 97Industrialized bread 63Pastas 100Refrigerated Dessert 10Powdered Desserts 6Tomato puree 0Ketchup 4Snack sauce 0Homemade bottled sauce 0Pasta Sauce 0Instant Soups 36Yogurt 0

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A5. Price Imputation

Barcode selection: We first look at barcodes that exist (i.e. were sold to someone, in some store, insome city in the Kantar data) in all years (i.e. at least sold in one week of 2012, 2013, 2014). Weonly study those barcodes.

The problem of missing values: we are interested in the prices that would be paid for the same2013 basket of consumption for each household7. We keep the basket fixed at 2013 since we wantto isolate any changes on quantities in 2014 derived from higher taxes. We therefore need to pricethe 2013 basket for each week of 2014 and 2013 (and for some exercises for 2012 as well), but it isvery common that a household does not buy the same barcode every week, and therefore in that weekwe have to impute the price that that household would face for that barcode in the that storebrand. Itturns out that for 2012, 2013 and 2014 we have to impute 52,096,512 values of price.

Creation of “cells” for imputation: Next we create (the most dissagregated) cells as a product ofcombinations of city-storebrand-barcode-time to be able to impute mean cell prices for the case theprice is missing for a barcode b which household i purchased in 2013 in a given storebrand j8 in itscity c, in a week t. We could create a full city-store-barcode-week Cartesian product, but that wouldgenerate 6,462,260,532 cells for SDs and 13,854,472,632 cells for HCFs.9 Many of these cells wouldnot be useful however; for example if barcode A is only sold in one city, it does not help to definecells with that barcode for other cities. Similarly, if only 7 Eleven stores sell barcode X it is not usefulto generate a cell of what this barcode would cost at Wallmart as that cell would always be empty. Forparsimony we first define an event-unit (EU) as a triplet (city, store, barcode). Some cities then mayhave more EUs since they may have more storebands or more barcodes. Then we expand EUs to the156 weeks of 2012-2014. This is our finest level of disaggregation. We do the imputation separatelyfor sugary drinks (see Table A6) and for High Caloric Foods (Table A7).

Lets us explain Table A6 for SDs. We begin with the EUs × 156 which generates 24,326,328cells.10 Many of these are empty of course. In fact, only 4.2 percent are non-empty; i.e. 1,021,706cells. This might seem like a lot of missing information, however these 1 million plus non-emptycells are sufficient to impute 33.1 percent of the all the missing household ibjt prices however. Oneway to measure the imputation “precision” is to have an idea of the within cell price variation, i.e. thevariation not explained by the 1,021,706 cell dummies. It turns out that regressing actual prices (i.e.before imputation) on these dummies returns an R2 of 0.98.

To impute prices for the remaining 76.9 percentof missing observations we are forced to go to ahigher level of aggregation. We could for example aggregate across cities but keep separate cellsfor storebrand-barcode-week, or alternatively we could first aggregate across storebrands and keepseparate cells for city-barcode-week. It turned out that even if the second aggregation generates morecells11, which of these two we used first in the sequential imputation was not very important.12 Usingthe storebrand-barcode-week aggregation meant that we could fill-out an additional 29.6 percent ofthe original 24,326,328 cells.13 Now turning to household level imputation, which is what we careabout, this second step imputes an additional 30.8 percent of the original missing values. This means

7A basket of consumption for household i is a duple D:=(barcode, storebrand), i.e. it not only involves what good the household buysbut also where it buys it. A duple D is in the 2013 basket for household i if in any week of 2013 the household bought that barcode is astorebrand j. For example if I only buy coke 500ml in Wallmart and coke 500ml in Costco my basket only has two elements. Differentiatingacross storebrands eliminates price changes due to pure substitution across stores

8A storebrand is for example Wallmart, 7 eleven, Oxxo, Superama, etc.9There are 91 cities, 2863 SD barcodes, 6138 HCF barcodes, 159 storebrands, and 156 weeks.

10Note that we are note that cells do not directly have household in their ‘cartesian’ product.118,092,188 vs 6,485,700.12In fact they have the same R2 of 0.94 in a regression of unimputed price against their cells.13So at this point we have filled out 4.2 percent + 29.6 percent of the original cells.

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TABLE A6—SEQUENTIAL PRICE IMPUTATION FOR SDS

Imputation of Prices for SDs

Prices Panel Fixed Baskets Panel

Step Aggregation Level R2 CellsFill. Cells(percent)

Imp. Observ.(percent)

1 City, store, week, article 0.98 243263281 4.2 33.12 Store, week, article 0.94 6485700 29.6 30.83 City, week, article 0.94 8092188 16.6 10.34 Week, article 0.85 303264 36.1 17.65 Month, article 0.84 69984 8.0 4.76 Quarter, article 0.83 23328 2.9 1.67 Year, article 0.82 5832 2.5 1.5

Both panels are balanced in the sense that for each price or fixed basket we have 156 weeks ofobservations. 1 Imputed cells of the first aggregation level.

that until this point we have imputed 63.9 percent of the original missing values.We proceed in an analogous fashion to higher and higher levels of aggregation, but note that we

do not need to aggregate much to get almost all of the imputations: by the 3rd step we have 3/4 ofthe imputation and by the 4th step we have more than 9/10. Note actually that even the coarser celldummies have substantial explanatory power: theR2 in step 7 is 82 percent. This means that barcodesare very good predictors of prices and that prices at the barcode level for SDs do not vary too muchby city, time and store.

TABLE A7—SEQUENTIAL PRICE IMPUTATION FOR HCFS

Imputation of Prices for HCFs

Prices Panel Fixed Baskets Panel

Step Aggregation Level R2 CellsFill. Cells(percent)

Imp. Observ.(percent)

1 City, store, week, article 0.97 540012721 4.3 33.32 Store, week, article 0.72 14202864 28.0 29.73 City, week, article 0.89 17483232 15.2 10.34 Week, article 0.41 641472 35.7 17.55 Month, article 0.3 148032 9.2 4.86 Quarter, article 0.27 49344 3.8 1.97 Year, article 0.19 12336 3.8 2.5

Both panels are balanced in the sense that for each price or fixed basket we have 156 weeks ofobservations. 1 Imputed cells of the first aggregation level.

The procedure is analogous for HCFs –but we use HCF barcodes instead of SD barcodes and theTable is interpreted analogously so we wont explain it verbally here. Let us just say that beyond thehigh R2s we have reported, three extra facts make us confident that these imputations are reasonable.First, if we estimate a barcode-level regression with week, household and barcode fixed effects (TableA8), the estimated increase in SD and HCF prices are 14.3 and 5.5 percent, respectively, which arevery close to our estimates presented in the main text. This means that our imputation+aggregationis not distorting the effect of the tax on price. The second is that the distribution of prices before andafter imputing is very similar, as shown in Figure A12. Third, note that we follow the same imputationprotocol for all years. Since our strategy is to compare across years, the imputation should not haveimportant influence on our estimates.

17

FIGURE A12. PRICE DISTRIBUTIONS BEFORE AND AFTER IMPUTATIONS

0.0

2.0

4.0

6.0

8.1

Den

sity

0 20 40 60 80 100Price per Liter

Original Imputedkernel = epanechnikov, bandwidth = 0.6111

(a) SDs

0.0

05.0

1.0

15.0

2D

ensi

ty

0 100 200 300Price per Kg

Original Imputedkernel = epanechnikov, bandwidth = 3.5394

(b) HCFs

Note: Price densities in KWP before and after imputation.

TABLE A8—PRICE EFFECTS AT THE BARCODE LEVEL

Robustness: Impact of the Tax on Prices

Price per Liter Price per Kg Price per 100 Cal(1) (2) (1) (2) (1) (2)

Tax 0.143 0.139 0.055 0.046 0.044 0.046[0.001] [0.002] [0.001] [0.002] [0.001] [0.001]

Barcode×Storebrand×Week FE Yes No Yes No Yes NoBarcode×Storebrand FE No Yes No Yes No Yes

Observations 2,651,936 2,651,936 3,471,284 3,471,284 3,471,110 3,471,110R-squared 0.616 0.657 0.567 0.621 0.795 0.825

All regressions include a quadratic time trend. This table reports RD regression results using an specification analogousto equation (2), except that an observation is a purchase event (i.e. it is dissagregated by barcodes, etc.). We use weightsproportional to liters or proportional to kilograms to have a meaningful comparison with the regressions in the paper. Robuststandard errors clustered at the date level in brackets.

18

A6. Additional Descriptive Statistics

In the main text we show how the main descriptive statistics compare between KWP and other na-tional representative surveys. In this section, we add some descriptive statistics that may be usefulto better understand the composition of households’ consumption bundles in KWP and look at thisstatistics by SES and BMI groups. Table A9 presents additional descriptive statistics regarding thecomposition of households’ consumption bundle according to KWP. Food and drinks comprise 68.9percent of total expenditures in KWP, while 14.2 and 13.5 percent of total expenditures correspondto HCF and SD, respectively. In terms of taxing calories, 42.6 percent of the calories consumed onaverage come from a food or drink product that is now taxed (25.8 percent correspond to HCFs and16.8 percent to SDs). Interestingly, neither the fraction of total expenditures devoted to HCF or SDnor the fraction of total calories purchased from HCF and SD vary considerably across SES and BMIcategories.

TABLE A9—DESCRIPTIVE STATISTICS

Weekly Exp Share of Total Expenditures(2013 mxn pesos) Food HCFs SDs

TotalTotal 392.2 68.9 14.2 13.5

Socio-Economic StatusABC+ 441.2 68.9 14.1 12.8CD+ 402.6 68.7 14.2 13.5DE 350.9 69.3 14.3 14.0

Body Mass IndexSlim/Ideal 364.5 68.4 14.7 12.6Overweight 407.0 69.4 13.9 14.3Obese 391.5 68.6 14.3 13.2

Mean Total Share of Total CaloriesCalories HCFs SDs

TotalTotal 16912.8 25.8 16.8

Socio-Economic StatusABC+ 17285.3 26.8 16.5CD+ 15839.2 25.8 17.0DE 15756.0 25.3 16.8

Body Mass IndexSlim/Ideal 15756.0 26.4 15.5Overweight 17697.7 25.3 17.8Obese 16737.6 26.1 16.4

Table based on our classification of barcodes into taxable and non-taxable, as well as theimputation of calories described in section A2.

19

A7. More on identification

A. Is the Change in Prices Attributable to the Taxes?

Figure A13 presents additional evidence in support of our identifying assumption: that the pricechanges observed in January 2014 are attributable to the taxes introduced. The figures and evidencepresented here are akin to that presented in the paper but including an extra year to the analysis. Thishelps us to corroborate that the changes in prices observed in January 2014 is not common to otherJanuaries.

FIGURE A13. EVOLUTION OF PRICES PER UNIT OF QUANTITY 2012-2014

7.5

88.

59

9.5

Pric

e pe

r Lite

r for

Sug

ary

Drin

ksFo

r Tot

al P

opul

atio

n (F

ixed

Bas

ket,

Nom

inal

Pes

os)

01/01/12 01/01/13 01/01/14 01/01/15

(a) Price per Lt48

5052

5456

Pric

e pe

r Kilo

gram

for T

axed

Foo

dFo

r Tot

al P

opul

atio

n (F

ixed

Bas

ket,

Nom

inal

Pes

os)

01/01/12 01/01/13 01/01/14 01/01/15

(b) Price per Kg

Note: Average consumption bundles are defined from all observed purchases during 2013 for each household, and fixed. For calculatingtheir cost, we multiply the number of units of each barcode in each households’ bundle by their current price. The assignment of currentprices to barcodes is explained in detail in section A5.

20

TABLE A10—JANUARY 2014 PRICE CHANGES IN CENTRAL AMERICA

Log of Price per liter of SDMexico Costa Rica Guatemala Panama El Salvador

Tax 0.146 0.001 -0.007 0.038 -0.019[0.002] [0.005] [0.003] [0.005] [0.004]

Observations 762,706 45,044 48,832 37,785 48,123R-squared 0.983 0.853 0.981 0.958 0.961

Log of Cost of the 2013 Consumption BundleMexico Costa Rica Guatemala Panama El Salvador

Tax 0.051 -0.003 -0.014 0.019 -0.045[0.009] [0.005] [0.003] [0.003] [0.004]

Observations 762,706 45,044 48,832 37,785 48,123R-squared 0.694 0.992 0.988 0.988 0.975

The tax variable indicates if the observation is captured after January 1st 2014,which is the date when the tax began to be implemented in Mexico only. Allestimations include household-week of the year fixed effects and a quadratic timetrend. Data employed for this estimation comes from the weekly KWP Mexicoand Central American chapter for 2013 and 2014. Each column indicates to whichcountry the KWP data corresponds. The unit of observation is the household-weekpair. Standard errors clustered at the week level in brackets.

21

B. RD analysis using calories as a running variable

TABLE A11—RD FOR HCF PRICES

Regression Discontinuity for HCF Prices

NP (bw=5) Linear

Density>275 0.087 0.077[0.037] [0.035]

Observations 8,966 3,322R-squared - 0.008

The first column presents the results of anon parametric (NP) RD estimate. The sec-ond presents a parametric estimate adjust-ing a linear trend on each side of the dis-continuity. Each article is weighted accord-ing to the acts of buying registered in ourdataset. For the linear specification, we re-strict the estimation to the domain of [180,400] kcal per 100 grs.

FIGURE A14. GRAPHICAL ANALYSIS: DIFFERENCE IN BARCODES’ LOG PRICES BY CALORIC DENSITY

-.10

.1D

iffer

ence

of P

rice

per K

g

0 200 400 600Calories per 100 gr

Non Taxed Taxed

Note: We show a non parametric regression at the barcode level, where the vertical axis measures the difference in log price per kilogrambetween 2013 and 2014 and the x-axis orders all barcodes according to their caloric density (kilocalories per 100 grams). The red verticalline indicates the caloric density threshold after which the HCF tax applied (i.e. 275 calories per 100 grams). Shaded areas show 95 percentconfidence intervals.

22

A8. Main Results

The main findings in the paper indicate that the tax caused a 14.6 and 5.3 percent price increase inSDs and HCFs, respectively. Also, the quantities consumed were somewhat affected in the case ofSDs where the consumption decreased 6.7 percent, while HCFs had a non significant 0.3 percentchange in terms of quantities.

Tables A12 and A13 show the results of regressions analogous to those that produce the previousresults, this time using the log of the price per 100 calories and the log of calories purchased fromSD and HCF, respectively. All the estimated coefficients are very similar in magnitude to those wepresent when using liters of SD or kilograms of HCF as our unit measure.14 The results obtained fromthese specifications find a 14.5 and 4 percent price increase for SDs and HCFs, respectively. Mean-while, quantities of SDs deacrease 6.3 percent and those of HCFs have a non-significant increase of0.5 percent. In summary, the changes found are very similar when using calories instead of liters andkilograms.

TABLE A12—RESULTS USING LITERS AND PRICE PER CALORIE FOR SD

Impact of the Tax on the Price per 100 Calories of Sugary Drinks

By Body Mass Index By Socio-Economic StatusTotal BMI Overweight Obese ABC+ CD+ DE

Tax 0.145 0.141 0.145 0.148 0.141 0.146 0.147[0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002]

Observations 762,706 143,255 329,988 289,277 148,408 348,672 265,440R-squared 0.985 0.987 0.985 0.985 0.984 0.986 0.986

Impact of the Tax on the Purchase of Liters of Sugary Drinks

By Body Mass Index By Socio-Economic StatusTotal BMI Overweight Obese ABC+ CD+ DE

Tax -0.067 -0.062 -0.065 -0.072 -0.081 -0.063 -0.064[0.0006] [0.007] [0.007] [0.006] [0.007] [0.006] [0.006]

Observations 762,706 143,255 329,988 289,277 148,408 348,672 265,440R-squared 0.777 0.779 0.772 0.776 0.789 0.773 0.774

The tax variable indicates if the observation is captured after January 1st 2014. All estimationsinclude household-week of the year fixed effects and a quadratic time trend. Each column shows theresult for a different subsample. Data employed for this estimation comes from the weekly KWPMexico chapter for 2013 and 2014. The unit of observation is the household-week pair. Standarderrors clustered at the week level in brackets.

Also, Table A11 shows the result from using a regression discontinuity design as the one illus-trated in Figure A14 in the main text. The estimates shown in the table correspond to estimating thefollowing specification:

(1) ln (PSDb ) = β0 + β1 I(Denb > 275) + f(Denb) + εb

where b denotes barcode and Denb is the caloric density of barcode b. In the estimates presented,we use a non-parametric estimation in the case of column 1 and a linear trend with different slopes

14Given that magnitudes of liters and kilograms are different, the results presented here normalize the caloric content to that of oneaverage liter and kilogram of SD and HCF to make the coefficients easily comparable across the tables.

23

TABLE A13—RESULTS USING KILOGRAMS AND PRICE PER CALORIE FOR HCF

Impact of the Tax on the Price per 100 Calories of HCF

By Body Mass Index By Socio-Economic StatusTotal BMI Overweight Obese ABC+ CD+ DE

Tax 0.040 0.039 0.041 0.038 0.030 0.040 0.045[0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002]

Observations 762,706 143,255 329,988 289,277 148,408 348,672 265,440R-squared 0.866 0.848 0.884 0.855 0.866 0.870 0.862

Impact of the Tax on the Purchase of Kilograms of HCF

By Body Mass Index By Socio-Economic StatusTotal BMI Overweight Obese ABC+ CD+ DE

Tax 0.003 0.007 0.004 0.002 -0.008 0.006 0.007[0.003] [0.003] [0.003] [0.003] [0.004] [0.003] [0.003]

Observations 762,706 143,255 329,988 289,277 148,408 348,672 265,440R-squared 0.677 0.685 0.676 0.675 0.679 0.668 0.688

The tax variable indicates if the observation is captured after January 1st 2014. All estimationsinclude household-week of the year fixed effects and a quadratic time trend. Each column shows theresult for a different subsample. Data employed for this estimation comes from the weekly KWPMexico chapter for 2013 and 2014. The unit of observation is the household-week pair. Standarderrors clustered at the week level in brackets.

before and after the cutoff in column 2. To make the results comparable to those presented with thehousehold-week as unit of observation, each observation used (which corresponds to a barcode) isweighted by the number of transactions done with that barcode.15

The results found with this specification give a 7.7 to 8.7 percent change in prices, which is aroundthe 8 percent tax rate, meaning something close to a 100 percent pass through. The results of usingthis specification are slightly larger than the 5.3 percent estimated in the main regressions. It shouldbe indicated though that as a traditional RD result, the estimate is valid for the value of density at thecutoff. Also, it is important to recall that in the paper, no evidence of strategic decisions around thediscontinuity is found (see Figure A28).

15The purpose of this is to represent that a product with very high demand should capture more importance that a product that is seldomconsumed.

24

A9. Estimates of effects on BMI

The taxes introduced were aimed at combatting obesity. For this reason, individuals’ BMI may be themost relevant outcome to analyze. If the intake of total calories did not change as a result of the taxes,we should not observe significant changes in BMI. Before presenting a differences in differencesanalysis, Figure A15 just plots kernel densities of BMI for several years in Mexico, from two yearsbefore and two years after the tax. The resemblance is striking. There was no change in BMI in anypart of the distribution.

FIGURE A15. OVERWEIGHT IN CONTEXT0

.02

.04

.06

.08

Den

sity

10 20 30 40 50BMI

2015 20142013 2012

Note: Source: Kernel density estimates of BMI as reported in KWP.

Figure KWP A15 of course does not contain a counterfactual comparison. We would need a controlgroup for which there was no tax in 2014. Our empirical strategy next is to compare Mexico vs itsLatin American neighbors. This is reasonable since (a) we find the same pre-trends in BMI, (b) thetend to consume similar baskets of goods, (c) they were not subject to the tax and we were able to getBMI data at the household level from the same source (KWP) using the same methodology.

We use yearly self-reported information on the weight and height of female household heads forparticipating households in Costa Rica, Guatemala, El Salvador and Panama as a control group16 forMexican households. Our identification assumption is that in the absence of the taxes households inMexico would have experienced a similar change in BMI as households in the these four countries.As shown below this assumption is reasonable; these countries have similar pre-trends in BMI thanMexico.

Figure A16 shows that, consistent with WHO’s data 17, (average across households) BMI has in-creased both in Mexico and the rest of countries in Central America during the period of analysis. Thisadds confidence that the BMI data is not noise with the same mean each year. Moreover, althoughBMI is higher in Mexico than in the rest of Central America, the time trends showed by both Mexicoand the rest of Central America are relatively similar before the enactment of the tax, supporting theidentification assumption of parallel pre-treatment trends.

16KWP collects BMI for a subset of 89 percent of male households heads in Mexico, but unfortunately this information is onlyavailable for 2 years in Central America. We therefore focus on BMI of the female head. We have 54,143, 4,442, 4,918, 4,026 and 4,315household-year observations from Mexico, Costa Rica, Guatemala, El Salvador and Panama, respectively on the female heads BMI.

17See section A1 of the online appendix.

25

FIGURE A16. BMI TRENDS IN MEXICO VS CENTRAL AMERICAN COUNTRIES

26.5

2727

.528

Ave

rage

BM

I of H

ouse

wife

2010 2011 2012 2013 2014 2015Year

Mexico Central America

Note: The graph simply plots BMI for our sample of female household heads in Mexico, and for the rest of Central American countries,pooling all samples together.

Graphical evidence suggest that there was not change in the evolution of BMI over time for Mexicoafter the taxes were introduced. In order to provide statistical evidence we estimate the followingdifference-in-difference (DD) specification:

(2) BMIijy = αj + θy +

2015∑t=2011

βtMexicoj ∗ I(y = t) + εijy

Where i, j and y denote individual, country and year. The equation includes dummies for 5 years2011-2015 (θy), dummies for each Central American country (αj) to allow for country-specific BMIlevels, and an interaction of the Mexico (treatment) dummy with the year dummies. We clusterstandard errors at the country level. The βt’s are the coefficients of interest, which measure thedifferential change in BMI (respect 2010) experienced in Mexico versus other Central Americancountries each year.

FIGURE A17. BMI AND OBESITY DIFFERENCES IN DIFFERENCES ANALYSIS

(a) BMI (b) Obesity

Note: This Figure plots the coefficient estimates from equation 2. Results from years 2011, 2012, and 2013 show that the parallel trendsassumption is satisfied. Results from 2014 and 2015 show that there is no effect of the tax.

26

Because the panels are unbalanced, we present regression results with and without female house-hold head fixed effects. Figure A17 plots the estimated βt’s along with their 95 percent confidenceintervals. The joint F-test for βj = 0 ∀ j ∈ {2011, 2012, 2013} is not rejected (p-value=0.17),suggesting that the parallel trends assumption holds. The coefficients for 2014 and 2015 show noeffect of the tax on BMI for Mexico. The effect of the tax on weight may take time, but the fact thatβ2015 is larger than β2014 is discouraging. Panel B of figure A17 uses the fraction of obese popula-tion as a dependent variable and reaches similar conclusions. Given the strong relationships betweenhousehold members BMIs found in the literature, we believe our results may apply more broadly toother household members.18

FIGURE A18. POWER GRAPH

0.2

.4.6

.81

Pow

er

-.9 -.8 -.7 -.6 -.5Treatment Effect on BMI

Note: Figure A18 displays a power simulation for BMI using our exact same specification 2 and our same data base.

Figure A18 displays a power simulation for BMI using our exact same specification 2 and our samedata base. This gives us an exact power calculation that takes into account the whole structure of ourdata and specification. The simulation proceeds as follows: we take 2013 for Mexico as our placebotax year and define the DID dummy = 1 for a household if the country is Mexico and the year is ≥2013. For each obervation for which DID=1, we add a placebo effect on the BMI dependent variable.The placebo effect is drawn randomly from a normal distribution with mean µ and standard deviationσ. The standard deviation is taken from the empirical distrbution of the female head BMI year-to-yearchange, while µ runs from 0 to -1 BMI points, in steps of 0.02.

For each µwe run 400 simulations. In each simulation for each household we draw a placebo effectfrom the normal distribution described above. Then we estimate the regresion in equation 2, givingus 400 estimated effects. Finally we count the fraction of the placebo effects which are statisticallydifferent from zero at 95 percent confidence. This fraction is our measure of power for each µ.

Figure A18 shows that we can detect an effect of about 0.75 BMI points with above 80 percentpower. For a person with 1.60 meters in height, this is equivalent to about 2kg. We think this isa reasonable effect to detect even on average in 2 years, to decrease obesity people would need todecrease oftentimes more than 10kg. Not finding effects of 2kg is informative indeed.

18We actually estimate a coefficient of 0.46 (t-stat=95) in a regression of male and female household heads BMI’s. Section A1 of thisonline appendix reports quality checks on the weight and BMI measures. Height in the data does not change –as expected– but weight doeschange by reasonable magnitudes with mean changes between 1 and 2 kilos. In power simulations that use our exact empirical specificationwe obtain that we are able to detect effect sizes of about 0.75 BMI points and 1.3 kilos with 90 percent power and 95 percent confidence.

27

A10. Mechanisms

A. Changes in Prices and Quantities by Barcode

FIGURE A19. PRICE AND QUANTITY PERCENTAGE CHANGES: HETEROGENEITY ACROSS BARCODES

(a) (b)

(c) (d)

Note: These graphs histograms of changes in price or quantity for each barcode of SD or HCFs from 2013 to 2014.

B. Change in Consumption Decisions

The results presented in section VI of the paper show interesting patterns in the consumers decisionmaking process. In particular, the substitution towards cheaper goods within the high caloric densityfoods calls for further analysis and attention. Given that this substitution happens within the HCFgroup it can mean that consumers are switching towards cheaper and lower quality products within anhomogeneous category (e.g. choosing a cheaper brand of the same product) or changing consumptionamong HCF categories toward a cheaper group (e.g. changing peanuts or pistachios for chips). Tofurther investigate this, in Table A14 we show the change in the 2013 value of the chosen consumptionbasket for different categories of product. This means that the different estimates will only capturewithin product category substitution. The evidence from the table shows that within product categorysubstitution is an important driver of the results presented in section V. The case of snacks and cookiesis notable for its large decrease. The rest of the categories included in this table show lower decreases

28

FIGURE A20. DIFFERENTIATED IMPACT OF THE TAX ON SDS AND HCFS PRICES

1.8

22.

22.

42.

62.

8Lo

g of

Pric

es p

er L

iter

0 1 2 3 4Liters per Unit

2013 2014

(a) SDs

.88

.9.9

2.9

4.9

6.9

8Lo

g of

Pric

es p

er H

undr

ed C

alor

ies

0 500 1000 1500 2000Calories per Unit

2013 2014

(b) HCFs

Note: The left hand side graph plots the average of the log price of SD barcodes in 2013 and 2014 against the content in liters of eachbarcode. The right hand side graph plots the average of the log price per 100 calories of HCF barcodes in 2013 and 2014 against thecontent in calories of each barcode.

FIGURE A21. PRICE CHANGE IN CALORIES PER UNIT: SDS VS HCFS TAX

.03

.035

.04

.045

.05

.055

.06

Diff

eren

ce o

f Log

s of

the

Pric

es p

er H

undr

ed C

alor

ies

for T

axab

le F

ood

.135

.138

.141

Diff

eren

ce o

f Log

s of

the

Pric

es p

er L

iter f

or S

ugar

y D

rinks

0 500 1000 1500 2000 2500Calories per Unit

Sugary Drinks Taxable Food

Note: The figure orders all SD and HCF barcodes in the Kantar World Panel according to the amount of calories per unit. A unit is apackaged good sold individually that is identified by a barcode. The vertical axis measures the difference in log prices between 2013 and2014. The blue solid line illustrates the case of sugary drinks (SDs) as defined by the tax and in our estimations. The red dotted line showsthe result for high caloric dense foods (HCFs).

29

that range from 4.5 to 0.4 percent for categories such as industrialized bread, crackers, cereals andspreadable creams.

TABLE A14—SUBSTITUTION WITHIN A PRODUCT CATEGORY

Substitution Patterns Within Food Categories

Log 2013 PCSnacks Bread Cookies Crackers Cereal Spreadables

Tax -0.115 -0.033 -0.075 -0.045 -0.004 -0.008[0.007] [0.002] [0.002] [0.002] [0.002] [0.001]

Observations 762,706 762,706 762,706 762,706 762,706 762,706R-squared 0.669 0.644 0.626 0.638 0.640 0.647

The tax variable indicates if the observation is captured after January 1st 2014. All esti-mations include household-week of the year fixed effects and a quadratic time trend. Eachcolumn shows the result for baskets of different categories of products. Data employedfor this estimation comes from the weekly KWP Mexico chapter for 2013 and 2014. Theunit of observation is the household-week pair. Standard errors clustered at the week levelin brackets.

30

C. Substitution Towards Smaller Package Sizes

In 2013 the City of New York issued the Sugary Drinks Portion Cap Rule to prohibit the sale ofseveral sweetened drinks of more than 16 ounces (0.5 liters) in volume.19 This rule was based ontwo motivations. The first was the trend in sizes of drinks; between 1977 and 1996 the averageenergy intake and portion size of the average soft drink consumed in the US increased from 144 to193 kcal (Nielsen and Popkin (2003)). The second is psychological evidence showing that largerportion sizes increase consumption (Wansink B., 2005). This later motivation may be compoundedfor large volume units of sodas, since soda cannot be stored once opened without quickly losing somecarbonation.

One may ask if a price disincentive toward larger package sizes in the form of a tax makes sensein terms of both reducing consumption and targeting this reduction towards those who are moreobese. This is a reasonable question. Figure A22 shows that indeed higher BMI households tend toconsumer larger package sizes. In this section we answer affirmatively to both issues. In the previoussubsection we analyzed the effect of taxes from a reduced form perspective, here we turn to a anempirical strategy with more structure that allows us to estimate own and cross prices elasticitiesacross unit sizes and to simulate alternative tax schedules and their effect on consumption. We focuson SDs since they experienced the larger effects and products are relatively homogeneous acrosssizes, which allows us to focus on size.

FIGURE A22. PACKAGE SIZES VS BMI AND PRICES

2728

2930

BM

I

500 1000 1500 2000 2500 3000Unit Volume

Confidence Interval 95% Local PolynomialYearly Liters per capita

(a) BMI-Prices

11.

051.

11.

151.

2

Pric

e pe

r Lite

r for

Sug

ary

Drin

ks (F

ixed

Bas

ket,

Inde

x 20

13=1

00)

01/01/13 25/06/13 01/01/14 25/06/14 01/01/15

(0,355] (355,600] (600, 1500] (1500,NaN)

(b) Price per Package Size

Note: The left hand side graph shows the relationship between self reported BMI in the Mexican chapter of the Kantar World Panel andthe average package size of SD barcodes bought. Package size is measured with unit volume and expressed in mililiters. Higher BMIconsumers consume units with larger package sizes. The right hand side graph illustrates the evolution of the price index for differentpackage sizes of SD.

Products containing more liters of sugary drinks increased prices in a higher proportion with respectto smaller packages within the same category. Conversely, the price of barcodes in the HCF categorythat contain more calories per unit increased relatively less with respect to barcodes with less caloriccontent within the same category. This finding is illustrated in Figure A21. Figure A20 gives moredetail about this contrasting fact by plotting in two graphs the changes in prices, one for SDs andanother for HCFs. Figure A20 shows that both, in the case of SDs and HCFs, products with moreliters (or calories) per unit have a lower price per liter (or calorie). This is shown by the negative slopein the graphs that relate price per liter and liters per unit (or price per calorie and calories per unit).

19The regulation was struck down by the Court of Appeals before it came into effect.

31

The contrasting effect of the tax between SDs and HCFs is evidenced by the fact that this negativerelation shifted to the right in both cases as a result of taxes. However, this shift is more pronouncedat higher values of liters per unit for SDs and contrarily, in lower values of calories per unit for HCFs.Figure A22 shows that prices increased more for SD with larger unit sizes (panel b).

D. An AIDS model of package sizes

One can think of different package sizes as being different products with their own demands.20 Weassume this is the case and estimate an Almost Ideal Demand System (AIDS), where consumersallocate different shares of expenditure to different package sizes of SD. AIDS has the well-knownadvantages of exact aggregation and consistency with utility maximizing behavior while allowing fora flexible specification of own and cross price elasticities. Since there are more than a dozen sizesof soda package sizes, in order to have a parsimonious model we aggregate package sizes into fourgroups: (0,355],(355,600], (600,1500],(1500,NaN).21 We also have a fifth group to which we allocateall other food expenditures, which allows for substitution between SD and other goods.

Following the AIDS literature, we assume weak separability between the consumption of SDs andall other expenditures. The AIDS expenditure function is displayed in equation 322

log (C(u, pi; Θd)) = α0 +∑g

θgd log (pgi) +1

2

∑g

∑g′

γ∗gg′ log (pgi) log (pg′i)+

+ uβ0∏g

pβggi

(3)

where d indexes demographic group, g indexes product size group, pi is a vector of prices in-curred by household i, Θd is a vector of tastes that are identical for households with demographicsd. Shepard’s Lemma is applied to this function to generate equations 4 of budget shares Sgi as afunction of package sizes and demographics and log prices for each package size, as well as the logreal household expenditures mi/Pi.

(4) Sgi = θgd +∑g′

γgg′ log (pg′i) + βg log

(mi

Pi

)

Following Deaton and Muellbauer (1980), we approximate the price index Pi by a Stone index,lnP ∗

i =∑

g Sgd log (pgi) making the system linear and easily estimable. We use monthly-householdlevel information for all months in 2013.23 The main identification assumption is that prices withindemographic groups are uncorrelated with idiosyncratic tastes. This is a strong assumption that is

20Below we provide some evidence that different types of households buy different package sizes.21The respective market share in terms of items purchased are 17.6, 17.4, 14.2, and 50.8 respectively. When we calculate the market

shares in terms of liters they are, of course, even more concentrated in large packages: 80.8 percent of liters in 2013 were sold in the morethan 1,500 ml category. This concentration is striking in itself.

22This can be generated by a two-stage budgeting process in which households first decide on the allocation of income across broadproduct categories, SD being assigned m pesos, and then allocate their budget within SD size z categories and choose an expenditurefunction given in equation 3.

23We tried also several generalizations with similar results: (a) using a QUAIDS model (Banks et al. (1997)), which allows for richerincome effects (we forgo the use of IV in this more complex environment); (b) not imposing a Stone index and instead using a fully non-linear estimation; (c) using data at the week or year level; (d) estimating the equations with and without week dummies to take advantageof different types of variation in prices; (e) estimating the share equations simultaneously.

32

typically imposed in the AIDS literature. Following Hausman et al. (1994) and Atkin (2014), weestimate the equations using prices in neighboring cities as instruments for the prices. This instru-ment satisfies the exclusion restriction if cost shifts, unrelated to aggregate SD demand in the city ofinterest, are causing price changes in other cities. We impose the restrictions on parameters impliedby utility maximization (adding up, symmetry).24

AIDS model fit

In this section of the appendix we provide some evidence of the quality of the estimated AIDS modelto fit the data and to predict the changes in expenditure out of sample. This is important since theAIDS models have several assumptions which may bias their predictions if they turn out to be wrong.The paper provides one measure of fit by predicting the 2013 liters consumed, and a measure ofpredictive accuracy by projecting the liters consumed out of sample for the year 2014. The resultsshow a reasonable fit. Here Figure A23 shows that the actual 2013 and predicted 2014 share ofexpenditures for the four categories of SDs are very similar. Given that the AIDS estimation wasdone with information from 2013 finding a good 2013 fit is not very surprising. Nonetheless, themodel is then employed to forecast the levels of expenditure shares that would result from the taxeffects and they closely match the actual 2014 shares in the data. The evidence from the graph givesa robust support to our estimates since the observed and predicted levels look very similar despite thefact that this is an out of sample estimation.

FIGURE A23. AIDS IN SAMPLE AND OUT OF SAMPLE FIT

0.0

2.0

4.0

6.0

8.1

Sha

re o

f Exp

endi

ture

2013 2014

(0,3

55]

(355

, 600

]

(600

, 150

0]

(150

0,NaN

)

(0,3

55]

(355

, 600

]

(600

, 150

0]

(150

0,NaN

)

by category of Sugary DrinksAverage Shares and Predicted Shares

Original Predicted

Note: This Figure uses the AIDS model estimated on 2013 data and shows the model predicted “in-sample” shares of 2013. It also showshow the model fares in predicting 2014 shares. This later prediction is an “out-of-sample” prediction as the model was not estimated with2014.

24The AIDS model and the instrumental variables we use rely on mildly strong assumptions. Failure of these assumptions will biascoefficient estimates and predictions. One way to assess the accuracy of the assumptions is to see how the model performs out-of-sample,which is what we do. We estimate the model using 2013 data only and then use the model to predict expenditure shares for 2014. Thisis a stringent test, as we know that soda prices changed in 2014 due to the tax and they changed differentially across package sizes. Theappendix shows that out-of-sample predictions are close to the actual 2014 data, providing some validation of the model.

33

AIDS Results

Table A15 presents the matrix of own and cross-price uncompensated elasticities. Confidence inter-vals are calculated by bootstrapping 1,000 times the elasticity estimates and reporting the 5th and 95thpercentiles.25 Note that all own price elasticities are negative and statistically different from zero. Theelasticity differs across package sizes substantially. The coefficients next to the diagonal are all pos-itive and smaller than those in the diagonal, indicating that similar package sizes are substitutes andthat cross responses are smaller than own-price responses.

Table A16 presents a series of exercises using the estimates of the four tax-price incidence pa-rameters Ij , j = 1, .., 4 displayed in column 2 and the 16 own and cross price elasticity parametersεij , i, j = 1, 2, 3, 4 of Table A15. The first column reports the average 2013 price per liter for SDproducts for each package size. Column 2 reports the estimated percent increase in prices that eachcategory experienced with the introduction of the tax on SDs. Column 3 shows liters consumed in2013 per household per week on average. It also measures liters and tax revenue at the week perhousehold level. Column 4 reports the actual tax structure of one peso per liter across all packagesizes, liters consumed, and tax revenue in 2014. Column 5 is a check on model fit and shows the litersand revenue that our AIDS model would predict using the 2014 tax structure. The fit is strikinglyclose, $6.62 pesos in the data vs. $6.51 for our model. This is a not trivial, since we are using 20estimated parameters and the 2014 prices are substantially different from those of 2013.

Columns 6 and 7 perform counterfactual exercises.26 Scenario S1 in Column 6 simulates whatwould happen with a tax of 1 peso per liter only on the largest packages only. It shows that taking intoaccount all own and cross price elasticities, revenue would be similar to the current 2014 revenue.

One might also ask whether there are tax profiles that achieve more reduction in liters consumedwhile keeping revenue constant. There are several trade-offs to take into account when consideringtilting the tax towards larger or smaller packages. Larger packages command the largest market share;they also showed the greatest price pass through. These two reasons suggest that taxing large packagesmore heavily may achieve larger reductions in liters, all else constant. Note however that they do nothave the largest own-price elasticities, and that we have to take into account substitution patternsrepresented by the cross price elasticities as well if we want to calculate the counterfactual correctly.Problem 5 below takes these four considerations into account, and finds which is the optimal taxprofile (τ1, τ2, τ3, τ4), in the sense of minimizing liter consumption subject revenue staying the sameas in 2014.

(5)

Minimizeτ

4∑i=1

Li(τ) ≡4∑j=1

(

4∑i=1

(L0i εijIj))τj

subject to 0 ≤ Li(τ), i = 1, 2, 3, 4.

0 ≤ τi ≤ 1, i = 1, 2, 3, 4.

4∑i=1

Li(τ)τi = R

Column 7 reports the result. A tax of $1.32 pesos per liter only for the largest packages and a

25To mimic the city-cluster structure of the data in each iteration we draw a random sample with replacement of all the householdswithin each city. When using monthly data, this meant sampling the entire history of a household.

26All these counterfactual analysis assume that incidence and elasticities are constant with respect to the tax rate. This seems reasonablefor the range of price variation we use.

34

zero tax on all the other package sizes is optimal. So we do not have to invoke behavioral biasesor problems of self-control to make an argument for taxing larger packages. There is an additionalargument however. There is a strong and tight positive relationship between BMI and package sizeof SDs. The lefthand panel of Figure A22 shows that this relationship in our data. So, a tax onlarger packages targets more overweight population, which may mean that the tax is more focused onover-consumption and is therefore only distorts consumption where it should.

TABLE A15—UNCOMPENSATED PRICE ELASTICITIES

Uncompensated Price Elasticity Matrix Estimates IV

With Respect the Price ofCommodity Group (ml) (0,355] (355,600] (600,1500] (1500,NaN)

(0,355]-0.99 0.3 0.38 -0.38

[-1.13, -0.85] [0.15, 0.46] [0.23, 0.56] [-0.64, -0.14]

(355,600]0.16 -0.92 0.52 0.25

[0.08, 0.25] [-1.09, -0.71] [0.39, 0.65] [0.03, 0.47]

(600,1500]0.16 0.4 -1.47 0.14

[0.09, 0.24] [0.3, 0.5] [-1.6, -1.36] [-0.01, 0.3]

(1500,NaN)-0.03 0.02 0.02 -0.79

[-0.06, -0.01] [-0.01, 0.05] [-0.01, 0.05] [-0.88, -0.7]

This table shows own and cross price elasticities estimated with the AIDS model. Rows in-dicate product package sizes while columns indicate the price. For instance, the bottom leftcoefficient of -0.03 indicates the percent change in the quantity of the largest package (morethan 1.5 liters) when the price of the smallest package (less than 355 mililiters) changes by1 percent. Prices in a city were instrumented with the prices in its nearest city. Confidenceintervals and significance levels were obtained from 1000 bootstraps.

35

TABLE A16—COUNTERFACTUAL TAX ANALYSIS

Estimated Impact of Alternative Tax Scenarios

CounterfactualScenarios

Package sizecategory (ml)

(1) Price2013

Observed

(2) PriceincidenceEstimated

(3) Liters2013

Observed

(4) Tax-Data2014

Observed

(5) ModelFit

2014(6) S1 (7) S2

(0,355] 16.56 5.6 0.18 1 1 0 0(355,600] 12.57 11.30 0.39 1 1 0 0(600,1500] 9.68 9.90 0.74 1 1 0 0(1500,Nan) 6.71 16.30 5.58 1 1 1 1.32

Total Liters a - - 6.91 6.62 6.51 6.58 6.35Revenue b - - - 6.62 6.51 5.25 6.62Price Incr.c - - - 14.6 15.0 16.3 21.5Change in Liters d - - - -6.7 -5.79 -4.78 -8.10

This table presents simulated scenarios for different tax profiles. The first column reports the average 2013 priceper liter for SD products for each package size. Column 2 reports the estimated percent increase in prices thateach category experienced with the introduction of the tax on SD. Column 3 shows liters consumed in 2013 perhouseholds per week on average. Column 4 reports the actual tax structure of one peso per liter across all packagesizes, liters consumed and tax revenue in 2014. Column 5 is a check on model fit and shows the liters and revenuethat our AIDS model would predict by using the 2014 tax structure. Column 6 simulates what would happenif a tax of 1 peso per liter was applied only on the largest packages. Finally, Column 7 shows what tax profileminimizes liters consumed while keeping tax revenue constant. Data employed for this table includes the 2013and 2014 household consumption from the Mexico chapter of the KWP. The units of measure are a total liters perhousehold and week,b pesos spent per household and week,c weighted average percentage, d total percentage.

36

A11. Robustness checks

A. Anticipation

An alternative behavioral response to taxes is to keep large stocks as the tax implementation ap-proaches. With this response, household can substitute consumption just after the tax implementa-tion, particularly with non perishable products. We perform a robustness test in section VII.A of themain text, where we keep out of the sample a window of time around the beginning of the tax imple-mentation and obtain similar results. Following the marketing literature, in this section we performtwo additional exercises to support that dynamic considerations are not biasing our estimates. Weexplore if (a) there were more purchases of SDs and HCFs in December 2013 vis-a-vis December2012 to accumulate inventories; (b) whether the probability of buying was lower in January 2014than in January 2012 or 2013; and (c) if households spend more weeks without making purchasesof SD and HCF in January 2014 versus January 2012 and 2013. The results of this exercise are pre-sented in Table A17. No evidence of stockpiling behavior from the households is found. The onlystatistically significant coefficient is an increase of 0.1 weeks in time to buy after January 1st 2014.This is economically very small.

TABLE A17—EVIDENCE OF INVENTORY BEHAVIOR

Robustness: Storage of SDs and HCFs

SDs HCFsLog Liters Buy Weeks Log Kgs Buy Weeks

Dec 2012 0.066 0.031[0.105] [0.041]

Jan 2012 0.004 -0.105 -0.007 -0.100[0.009] [0.033] [0.012] [0.034]

Jan 2013 -0.000 -0.055 -0.008 -0.036[0.003] [0.021] [0.004] [0.031]

Mean 1.68 0.86 1.31 0.65 0.90 1.18SD [0.96] [0.34] [1.26] [0.43] [0.3] [0.75]

Observations 708,461 1,071,550 1,053,177 708,461 1,071,550 1,057,024R-squared 0.473 0.204 0.282 0.223 0.097 0.090

This table estimates regressions for different measures of SD and HCF purchases: quantities pur-chased, the likelihood of purchase, and the number of weeks with no purchase. Columns 1 and 4examine whether there were more purchases of SDs and HCFs in December 2013 vis-a-vis Decem-ber 2014 and 2012. Columns 2 and 5 estimate if the probability of buying was lower in January2014 vis-a-vis January 2013 and 2012. Columns 3 and 6 estimate if the household spends a largerspell without purchases of SDs and HCFs in January 2014 vis-a-vis January 2013 and 2012.All regressions include household and month of the year fixed effects and a quadratic time trend.Clustered standard errors at the week level in brackets.

B. Falsification tests

Our second exercise is a set of falsification tests following the logic and output format of a Fisherexact test27. Our aim is to show that the effect captured with the tax variable in our main estimationsis indeed a quite an unusual event in terms of size. This test consists in replicating our main specifi-cation (equation 2) several times and using in each replication a different ‘placebo’ date to generate

27We do not call it a Fisher exact test as that test requires sampling from the same distribution and here we are sampling from differentmonths.

37

the tax variable. In total 34 replications were done, and in each replication a different month be-tween February 2012 and November 2014 was employed to create the tax variable. In particular, thereplication that uses January 2014 is exactly our main specification. The other replications establishartificially the tax at dates where it did not happen and as such are placebo estimates.

If say end of quarter months were particularly good at SDs sales, this would show up in theirrespective month effects. So that these falsification tests can potentially detect if our results are dueto seasonality for example.

We proceeded to estimate the 34 regressions and collected the resulting {β1, . . . , β34} from ourestimations. We do this for each dependent variable separately. If our equation is correctly specifiedand if in reality the tax in January 2014 did have an effect, we would expect all betas other than theone for January 2014 to be statistically zero, except perhaps for sampling variance. With 90 percentconfidence we should therefore observe that the coefficient for January 2014 is either in the top 10percent of the distribution when the effect is positive (e.g. price) or the lowest 10 percent when theeffect is negative (e.g. quantities), and to be outside this range when there is no effect of the tax (e.g.calories). This test is more precise when we have more coefficients/years. Unfortunately we just have3 years of data. We still think they are informative though.

Figure A24 uses the set of 34 coefficients and plots an empirical cumulative distribution function(CDF) for each different dependent variable used. We add a vertical red line to indicate the positionof the January 2014 coefficient (the true tax). We find that for the price of SDs our true tax effect is inthe top 90 percent of coefficients as expected from our previous results, whereas for HCF it is abovethe 80th percentile. For kilos of food it is in around the 30th percentile (strongly indicating a zeroeffect), while for SD the reduction is below the bottom 20th percentile. For total calories the increaseis close to the 70th percentile. The results of these tests are consistent wit the results of statisticalsignificance we got in the paper.

38

FIGURE A24. FISHER EXACT PLACEBO TEST

(a) Price per Lt (b) Price per Kg

(c) Liters (d) Kgs

(e) Calories

Note: This figure estimates 34 regressions with placebo months to mimic the tax month. It then ranks the estimated coefficient and plotsthe cumulative density of these, i.e. the fraction that fall below a particular value. The vertical red line shows where the coefficient for theJanuary 2014 lies. We expect true effects to lie at the extremes of the distribution. This is akin to a Fisher exact test.

39

C. Fraction of Total Expenditures in KWP

One potential criticism of our results is that we capture only a subset of food expenses. In orderto explore how the estimates presented vary with respect to the fraction of total purchases capturedin our dataset, we perform two exercises. First, we categorize households into quintiles of the totalconsumption of calories. Second, we classify households by a proxy for the expenditure that KWPcaptures. We then estimate the main regressions of the paper by these quintiles and show that resultsdo not vary by quintile. This lends credence to our estimates not being biased according to the shareof expenditure we capture.

Before we go to the results, let us briefly describe how we calculate the proxy for the fraction oftotal food expenditure captured by Kantar. First we noted that KWP contains a set of socio-economicvariables also captured in the Mexican Income and Expenditure Survey (ENIGH) which has totalexpenditure in food. Second we estimated a regression of total expenditures against these socioeco-nomic variables on the ENIGH sample.28 We then impute total expenditures to each household in ourKWP dataset using the predicted values from the estimated OLS regression, given the coefficients ob-tained in the regression using ENIGH and the socio-economic variables captured in KWP. By doingthis, we then obtain a (noisy) estimate of total expenditures for each household in our dataset, andare then able to compute, for each household, what fraction of total expenditures is captured in theKWP dataset. We then categorize households into quintiles of the fraction of total expenditures thatare captured in KWP and run our main specification for each of these subsamples, using the price perliter of SD, the price per 100 calories of HCF, total liters of SDs, total calories from HCFs, and totalcalories purchased.

Results are presented graphically in Figures A25 and A26, which focus on quintiles of total caloriesand total expenditures captured in KWP, respectively. As can be seen, the estimated increase in theprice of sugary drinks is (slightly) increasing with the fraction of expenditures and calories capturedin our dataset, while the drop in consumption of liters of sugary drinks is decreasing in magnitude. Asfor calories from taxed foods, the log of price is also increasing with the fraction of total expenditurescaptured, while the change in purchases of calories from these products is negative for the lowestquintile, and positive for the highest. In sum, households for which we capture a larger fraction oftheir total food expenditures exhibit a smaller decrease in calories from SDs and even a slightly largerincrease of calories from HCFs. This is reflected on the fact that total calories increase slightly morefor this group compared to our results (i.e. the mean), although this increase is also not statisticallysignificant. A similar result is obtained if quintiles are based on total calories consumed. Taken atface value, this means that if anything the decrease (increase) in calories would be less (more) if wecould capture more of the food expenditures.29

28We also tried with includying some variables of food expenditures that were in KWP and ENIGH and got similar results on theimputations.

29A caveat from this exercise is that the classification used to form the quintiles might not necessarily reflect groups for which a largerportion of the expenditure is captured, but rather households that have a diet more based on packaged processed foods, which is the bulkof the dataset. In this case the positive relation between effect on consumption and proportion of expenditures captured might only reflectthe fact that those with a diet more based on packaged products are less likely to decrease their consumption and thus will see total caloriesless affected. However, we do not feel this alternative explanation is a threat to our results for the following reasons: (i) the positive relationbetween the effect on consumption and proportion of expenditures is non monotonic; (ii) even though we only capture a portion of the totalcalories consumed, given that packaged goods are the ones affected by the tax and, as such, the main source of calories, the effect on thecalories not captured would need to be substantial to overturn the total calories result.

40

FIGURE A25. MAIN REGRESSIONS WITH CALORIC CONSUMPTION INTERACTION

.14

.145

.15

.155

Coe

ficci

ent o

f P

rice

per L

iter

For

Qui

ntile

s of

Dai

ly P

er C

apita

Cal

orie

s

Quintile 1(338 kcal)

Quintile 2(470 kcal)

Quintile 3(581 kcal)

Quintile 4(730 kcal)

Quintile 5(1198 kcal)

Coefficient Confidence Interval 95%

(a) Price per Lt

.048

.05

.052

.054

.056

.058

Coe

ficci

ent o

f Pric

e pe

r Kilo

gram

F

or Q

uint

iles

of D

aily

Per

Cap

ita C

alor

ies

Quintile 1(338 kcal)

Quintile 2(470 kcal)

Quintile 3(581 kcal)

Quintile 4(730 kcal)

Quintile 5(1198 kcal)

Coefficient Confidence Interval 95%

(b) Price per Kg

-.12

-.1-.0

8-.0

6-.0

4

Coe

ficci

ent o

f Li

ters

F

or Q

uint

iles

of D

aily

Per

Cap

ita C

alor

ies

Quintile 1(338 kcal)

Quintile 2(470 kcal)

Quintile 3(581 kcal)

Quintile 4(730 kcal)

Quintile 5(1198 kcal)

Coefficient Confidence Interval 95%

(c) Liters

-.01

0.0

1.0

2

Coe

ficci

ent o

f K

ilogr

ams

For

Qui

ntile

s of

Dai

ly P

er C

apita

Cal

orie

s

Quintile 1(338 kcal)

Quintile 2(470 kcal)

Quintile 3(581 kcal)

Quintile 4(730 kcal)

Quintile 5(1198 kcal)

Coefficient Confidence Interval 95%

(d) Kgs

-.04

-.02

0.0

2.0

4.0

6

Coe

ficci

ent o

f To

tal C

alor

ies

F

or Q

uint

iles

of D

aily

Per

Cap

ita C

alor

ies

Quintile 1(338 kcal)

Quintile 2(470 kcal)

Quintile 3(581 kcal)

Quintile 4(730 kcal)

Quintile 5(1198 kcal)

Coefficient Confidence Interval 95%

(e) Calories

Note: These graphs plot the coefficient β in equation 2, for different subsamples. In this case, we classify households by quintiles oftheir consumption of total calories. All regressions include household-week of the year dummies and a quadratic time trend as controls.Each point in the graph shows the results of a different regression for the subsample specified. 95 percent confidence intervals from robuststandard errors clustered at the week level are also shown.

41

FIGURE A26. MAIN REGRESSIONS WITH EXPENDITURE PERCENTAGE INTERACTION

.13

.135

.14

.145

.15

.155

Coe

ficci

ent o

f P

rice

per L

iter

For

Qui

ntile

s of

Cap

ture

d E

xpen

ditu

re

Quintile 1(10 %)

Quintile 2(16 %)

Quintile 3(23 %)

Quintile 4(31 %)

Quintile 5(50 %)

Coefficient Confidence Interval 95%

(a) Price per Lt

.046

.048

.05

.052

.054

.056

Coe

ficci

ent o

f Pric

e pe

r Kilo

gram

F

or Q

uint

iles

of C

aptu

red

Exp

endi

ture

Quintile 1(10 %)

Quintile 2(16 %)

Quintile 3(23 %)

Quintile 4(31 %)

Quintile 5(50 %)

Coefficient Confidence Interval 95%

(b) Price per Kg

-.15

-.1-.0

50

.05

Coe

ficci

ent o

f Li

ters

F

or Q

uint

iles

of C

aptu

red

Exp

endi

ture

Quintile 1(10 %)

Quintile 2(16 %)

Quintile 3(23 %)

Quintile 4(31 %)

Quintile 5(50 %)

Coefficient Confidence Interval 95%

(c) Liters

-.04

-.03

-.02

-.01

0.0

1

Coe

ficci

ent o

f K

ilogr

ams

For

Qui

ntile

s of

Cap

ture

d E

xpen

ditu

re

Quintile 1(10 %)

Quintile 2(16 %)

Quintile 3(23 %)

Quintile 4(31 %)

Quintile 5(50 %)

Coefficient Confidence Interval 95%

(d) Kg

-.10

.1.2

Coe

ficci

ent o

f To

tal C

alor

ies

F

or Q

uint

iles

of C

aptu

red

Exp

endi

ture

Quintile 1(10 %)

Quintile 2(16 %)

Quintile 3(23 %)

Quintile 4(31 %)

Quintile 5(50 %)

Coefficient Confidence Interval 95%

(e) Calories

Note: These graphs plot the the coefficients β in equation 2, for different subsamples. In this case, we classify households by quintilesof the predicted fraction of expenditures in ENIGH that KWP captures, according to their socio-economic characteristics. All regressionsinclude household-week of the year dummies and a quadratic time trend as controls. Each point in the graph shows the results of a differentregression for the subsample specified. 95 percent confidence intervals from robust standard errors clustered at the week level are alsoshown.

42

D. Industry level production

To strengthen the evidence from section VII.C, here we present two additional plots. Figure A27 givesevidence that soda exports and imports are not a concern in terms of validity of our estimates giventhat the proportion of sodas imported and exports is negligible and did not exhibited an importantchange after the tax was introduced. It also gives graphical evidence of the evolution of prices andquantities of sodas nationally with the data used in Table IX. As the figure shows, prices suddenlyincreased their level at the date in which the tax began to be implemented and consumption exhibiteda slight decrease.

FIGURE A27. COMMERCE AND NATIONAL PRODUCTION OF SODA

0.2

5.5

.75

SD

Impo

rts a

s P

erce

ntag

e o

f Tot

al P

rodu

ctio

n

11.

52

2.5

SD

Exp

orts

as

Per

cent

age

of T

otal

Pro

duct

ion

2009 2010 2011 2012 2013 2014Year

Exports Imports

(a) International Commerce

-.2-.1

0.1

.2Lo

g vo

lum

e (1

000'

s lt)

, s.a

.

4.4

4.5

4.6

4.7

4.8

4.9

Log

soda

pric

e in

dex

01/01/6 01/01/8 01/01/10 01/01/12 01/01/14 01/01/16Date

Log Soda Price log Liters

(b) National Production

Note: Source: INEGI

E. Changes in nutritional content

An interesting question that is widely unexplored in the literature is whether taxes of this kind haveeffects, not through changes in the kilograms of food or liters of sugary drinks purchased by con-sumers, but in changes in products’ nutritional content. This supply side effect could potentially bean important channel to reducing caloric intake and obesity above and beyond price-induced changesin demand. Since the tax on SD is per liter, there are few incentives for producers to reformulate anddecrease caloric content unless they drive added sugar to zero; but the tax on HCFs implies incentivesto manipulate products’ nutritional content, as the tax applies only to foods with caloric densitiesabove the 275cal/100gr threshold.

A priori, it is hard to predict if a tax of this magnitude is enough to generate changes in products’caloric content, since this depends on the cost for producers to change product contents and howthese changes may affect demand. We can test if producers changed the caloric content of barcodes.The main challenge is that we only have a one-time cross-section of barcode nutritional informationfrom about 1.5 years after the tax came into effect, so we cannot conduct a before/after comparisonat the barcode level. We therefore rely on an excess mass test. Under the assumption that the cost ofdecreasing calories is smoothly increasing in the size of the caloric decrease, because the tax incentiveis a threshold incentive we should expect excess mass on the left side of the 275 calories per 100 gramsthreshold.

Figure A28 plots the histogram of caloric densities for all food barcodes in our dataset. No excessmass is observed to the left of the tax threshold. This is confirmed by a McCrary test. No significantreformulations seem to have taken place 1.5 years after the tax on HCFs came into effect.

43

FIGURE A28. SUPPLY SIDE RESPONSE ON CALORIC DENSITY

0.0

01.0

02.0

03.0

04D

ensi

ty

0 200 400 600 800 1000Calories per 100 grams, all food items

Note: This figure plots the frequency of caloric densities of all barcodes in the Kantar World Panel. Caloric densities are measured inkilocalories per 100 grams since this is the unit used in the tax definition for HCFs. The red vertical line indicates the caloric densitythreshold (275 kilocalories per 100 grams) that was used for the HCF tax definition.

A12. REFERENCES

Atkin, David, “Trade, Tastes, and Nutrition in India.,” American Economic Review, 2014, 103 (5),1629–1663.

Banks, James, Richard Blundell, and Arthur Lewbel, “Quadratic Engel Curves And ConsumerDemand,” The Review of Economics and Statistics, 1997, 79 (4), 527–539.

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