CB, BMP, and LST have declared that no competing interests exist. JAR reported that he has received personal fees from a private company (Tres Montes Lucchetti) for a role as an advisor on the design, implementation and evaluation of an obesity prevention program in primary schools conducted by two Universities in Guadalajara (ITESO and UDG) in the State of Jalisco. JAR also received a research grant from Danone Mexico to study the intake of milk and dairy in Mexico, but received no honorarium.
Conceived and designed the experiments: CB JAR BMP LST. Performed the experiments: CB JAR BMP LST. Analyzed the data: CB LST. Wrote the first draft of the manuscript: CB LST. Contributed to the writing of the manuscript: CB JAR BMP LST. Agree with the manuscript’s results and conclusions: CB JAR BMP LST. All authors have read, and confirm that they meet, ICMJE criteria for authorship.
In an effort to prevent continued increases in obesity and diabetes, in January 2014, the Mexican government implemented an 8% tax on nonessential foods with energy density ≥275 kcal/100 g and a peso-per-liter tax on sugar-sweetened beverages (SSBs). Limited rigorous evaluations of food taxes exist worldwide. The objective of this study was to examine changes in volume of taxed and untaxed packaged food purchases in response to these taxes in the entire sample and stratified by socioeconomic status (SES).
This study uses data on household packaged food purchases representative of the Mexican urban population from The Nielsen Company’s Mexico Consumer Panel Services (CPS). We included 6,248 households that participated in the Nielsen CPS in at least 2 mo during 2012–2014; average household follow-up was 32.7 mo. We analyzed the volume of purchases of taxed and untaxed foods from January 2012 to December 2014, using a longitudinal, fixed-effects model that adjusted for preexisting trends to test whether the observed post-tax trend was significantly different from the one expected based on the pre-tax trend. We controlled for household characteristics and contextual factors like minimum salary and unemployment rate. The mean volume of purchases of taxed foods in 2014 changed by -25 g (95% confidence interval = -46, -11) per capita per month, or a 5.1% change beyond what would have been expected based on pre-tax (2012–2013) trends, with no corresponding change in purchases of untaxed foods. Low SES households purchased on average 10.2% less taxed foods than expected (-44 [–72, –16] g per capita per month); medium SES households purchased 5.8% less taxed foods than expected (-28 [–46, –11] g per capita per month), whereas high SES households’ purchases did not change. The main limitations of our findings are the inability to infer causality because the taxes were implemented at the national level (lack of control group), our sample is only representative of urban areas, we only have 2 y of data prior to the tax, and, as with any consumer panel survey, we did not capture all foods purchased by the household.
Household purchases of nonessential energy-dense foods declined in the first year after the implementation of Mexico’s SSB and nonessential foods taxes. Future studies should evaluate the impact of the taxes on overall energy intake, dietary quality, and food purchase patterns (see
An 8% tax on high-energy, non-essential foods in Mexico reduces the amount of these foods consumed, an effect that is more pronounced in lower income families, Taillie and colleagues reveal.
In January 2014, Mexico passed an 8% tax on nonessential foods with energy density ≥275 kcal/100 g, including salty snacks, chips, cakes, pastries, and frozen desserts; and a 1 peso/liter (~10%) tax on sugar-sweetened beverages. To date, there has been very limited research as to how larger health-related food/beverage taxes change household food purchases, or whether low socioeconomic status (SES) households are more responsive to such taxes. Using a dataset that follows household food purchases over time, we examined whether the volume of taxed foods showed greater declines in the post-tax period than we would have expected based on trends in the volume of taxed food purchases prior to the tax. We also examined whether post-tax changes in the volume of taxed food purchases was greater among low SES households. We found that the mean volume of purchases of taxed foods in 2014 declined by 25 g per capita per month, or a 5.1% change beyond what would have been expected based on pre-tax (2012–2013) trends. There were no changes in the purchase of untaxed foods in the post-tax period. Low SES households’ purchases of taxed foods declined by 10.2% and medium SES households by 5.8%, whereas high SES did not change. These findings show that in the post-tax period, purchases of taxed foods declined more than we would expect if pre-tax trends had simply continued, particularly among low and medium SES households. Future research should explore how these shifts are linked to changes in the nutritional quality of the overall diet.
Currently, the prevalence of overweight and obesity in Mexico is over 33% for children and about 70% for adults [
Worldwide, there is very limited empirical evidence on the effect of food/nutrient taxes [
Because both the nonessential energy-dense food and the SSB taxes were implemented concurrently, we cannot evaluate the independent effect of each. Therefore, the objective of the current work was to longitudinally examine changes in the volume of taxed and untaxed food purchases after both taxes were implemented, relative to the counterfactual (i.e., expected volume of taxed and untaxed food purchases if the taxes had not been implemented), overall and by SES subgroups.
This study uses data on volume of household food purchases from January 2012 to December 2014 from The Nielsen Company’s Mexico Consumer Panel Services (CPS). The analysis used de-identified data and was granted an exemption from the University of Chapel Hill and National Institute of Public Health (INSP) institutional review boards. Enumerators visit participating households every 2 wk to collect diaries of purchases and receipts and register purchases by checking the pantry and a designated bin where the household members keep empty product packages. All items available with a barcode are scanned by the enumerator. The data for each purchase includes number of units, volume, price paid, and date of purchase.
Nielsen CPS samples households from 53 cities with >50,000 inhabitants and estimates weights for each household so that the sample is representative of the urban Mexican population.
From all households that participated in the Nielsen CPS in at least 2 mo during January 2012–December 2014, we excluded three households because of incomplete data on covariates. Our analytical sample includes 204,584 household-months, across 6,248 unique households. Average household follow-up was 32.7 mo; 78% participated in all 36 mo.
SES categories were based on those provided by The Nielsen Company, which are defined with a score system that classifies households in seven categories as proposed by the Mexican Association of Market Intelligence and Opinion. This measure of SES was validated and is the standard one used in market research in Mexico. The score considers the education level of the member with the largest household income contribution and seven household assets: number of rooms, type of floor, number of bathrooms, shower, gas range, number of light bulbs, and number of cars. The cutoff points for the seven categories are defined a priori to capture specific household characteristics and are not based on a population distribution; therefore, the sample in each category is not equal (e.g., the extreme categories combined have <10% of the sample). We classified SES as low (lower two categories), medium (middle three categories), and high (higher two categories). Additional variables include household composition (nine variables, each with the number of household members that were within each gender/age group [as presented in
In this paper, we focus on volumes of overall taxed and untaxed foods and on subcategories of each. Classification of foods into untaxed and taxed categories was conducted by a team of registered dietitians from Mexico. In the case of law ambiguities for food classification, we consulted with the Ministry of Finances for clarification. For further description of each subcategory and the food classification process, see
Our analysis does not cover all food categories that households purchase; we did not include categories for which Nielsen CPS does not collect data or did not collect data consistently throughout the 36 months of the analysis. Examples of food categories not analyzed are chocolates, candies, and sweet bread from bakeries (taxed if energy density ≥275 kcal/100 g, though small bakeries were exempt from the tax in 2014), and unpackaged produce, tortillas, and unsweetened bread from bakeries (mainly untaxed).
All analyses were conducted in Stata, version 13 (College Station, TX). We first describe unadjusted, mean per capita volume purchases of taxed and untaxed foods (g/capita/month) from January 2012 to December 2014. Because the tax was implemented at one point in time across the entire country and, hence, we did not have a control population, we compared the purchases before and after the tax. Our pre-specified analytical strategy was based on the approach used by Colchero et al. in evaluating Mexico’s SSB tax [
The model specification was as follows:
The unit of analysis (g/capita/month) was the per capita volume of
Using this model, we predicted the mean adjusted volume purchased in each month pre-tax (2012–2013), post-tax observed (2014), and post-tax counterfactual (2014 but as if
Because the food purchase data had a skewed distribution, we tested a generalized linear model with log-link, which gives unbiased estimates [
We then performed analyses stratified by SES (low, medium, high) using the same specification as
Nearly all households purchased some food from untaxed (99.7% of households) and taxed (96%) foods each month. However, for subcategories, there was a large proportion of non-consumers. As a result, we used a two-part model [
The two-part model was as follows: Total amount of food subcategory purchase (g/capita/month) = [Probability of food subcategory purchase (probability/month)] * [Amount of food subcategory if purchased (g/capita/month)]
In all analyses, we used the household weights provided by Nielsen and estimated standard errors via bootstrapping by drawing 1,000 random samples with replacement with selection at the household level.
Monthly trends in unadjusted volume purchased (g/capita/month) of (A) taxed and (B) untaxed foods. Source: Authors’ own analyses and calculations based on data from Nielsen through its Mexico Consumer Panel Service (CPS) for the food and beverage categories for January 2012–December 2014.
Taxed | Untaxed | |||||||
---|---|---|---|---|---|---|---|---|
Post-Tax Counterfactual (g/capita/month) | Post-Tax Observed (g/capita/month) | Observed vs. Counterfactual | Post-Tax Counterfactual (g/capita/month) | Post-Tax Observed (g/capita/month) | Observed vs. Counterfactual | |||
Absolute Difference (g/capita/month) | % Difference | Absolute Difference (g/capita/month) | % Difference | |||||
All | ||||||||
Jan–Jun 2014 | 484 (467, 501) | 467 (453, 482) | -3.4% | 1553 (1508, 1598) | 1559 (1520, 1599) | 6 (-27, 40) | 0.4% | |
Jul–Dec 2014 | 500 (482, 517) | 466 (452, 480) | -6.7% | 1585 (1539, 1631) | 1568 (1527, 1609) | -17 (-53, 20) | -1% | |
All 2014 | 492 (475, 509) | 467 (453, 480) | -5.1% | 1569 (1524, 1614) | 1564 (1525, 1602) | -5 (-38, 27) | -0.3% | |
Low SES | ||||||||
Jan–Jun 2014 | 430 (394, 466) | 392 (367, 416) | -8.9% | 1248 (1163, 1332) | 1245 (1177, 1312) | -3 (-63, 57) | -0.2% | |
Jul–Dec 2014 | 437 (401, 474) | 387 (361, 413) | -11.5% | 1289 (1202, 1375) | 1251 (1178, 1323) | -38 (-103, 28) | -2.9% | |
All 2014 | 434 (398, 469) | 389 (365, 413) | -10.2% | 1268 (1184, 1353) | 1248 (1180, 1315) | -20 (-79, 38) | -1.6% | |
Medium SES | ||||||||
Jan–Jun 2014 | 480 (457, 504) | 461 (442, 480) | -4.1% | 1557 (1493, 1620) | 1533 (1483, 1583) | -24 (-73, 26) | -1.5% | |
Jul–Dec 2014 | 503 (477, 528) | 465 (447, 483) | -7.5% | 1591 (1527, 1654) | 1542 (1488, 1596) | -49 (-101, 4) | -3.1% | |
All 2014 | 491 (467, 515) | 463 (446, 480) | -5.8% | 1574 (1511, 1636) | 1537 (1488, 1587) | -36 (-83, 11) | -2.3% | |
High SES | ||||||||
Jan–Jun 2014 | 564 (527, 602) | 560 (530, 590) | -4 (-31, 23) | -0.8% | 1902 (1813, 1991) | 1946 (1865, 2027) | 44 (-32, 120) | 2.3% |
Jul–Dec 2014 | 573 (533, 613) | 551 (521, 580) | -22 (-51, 6) | -3.9% | 1919 (1827, 2011) | 1957 (1876, 2038) | 38 (-43, 120) | 2% |
All 2014 | 569 (531, 607) | 555 (527, 584) | -13 (-39, 12) | -2.3% | 1911 (1821, 2000) | 1952 (1874, 2029) | 41 (-32, 115) | 2.2% |
Source: Authors’ own analyses and calculations based on data from Nielsen through its Mexico Consumer Panel Service (CPS) for the food and beverage categories for January 2012–December 2014.
Bold numbers:
As can be seen in
Monthly trends in predicted volume purchased (g/per capita) of (A) taxed and (B) untaxed foods compared to post-tax counterfactual. Source: Authors’ own analyses and calculations based on data from Nielsen through its Mexico Consumer Panel Service (CPS) for the food and beverage categories for January 2012–December 2014.
Overall, low SES households bought less taxed food before and after the tax compared to their higher SES counterparts but showed the greatest response to the tax (
Probability | Amount | Total | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Post-Tax Counterfactual (Probability/ Month) | Post-Tax Observed (Probability/Month) | Observed vs. Conterfactual | Post-Tax Counterfactual (g/capita/month) | Post-Tax Observed (g/capita/ month) | Observed vs. Conterfactual | Post-Tax Counterfactual (g/capita/month) | Post-Tax Observed (g/capita/ month) | Observed vs. Conterfactual | ||||
Absolute Difference (Probability/ Month) | Percent Difference | Absolute Difference (g/capita/ month) | Percent Difference | Absolute Difference (g/capita/ month) | Percent Difference | |||||||
Taxed | ||||||||||||
Salty snacks | 0.78 (0.76, 0.79) | 0.72 (0.71, 0.74) | -6.5% | 123 (116, 130) | 124 (118, 129) | 0 (-5, 6) | 0.4% | 94 (88, 100) | 88 (84, 93) | -6.3% | ||
Cereal-based sweets | 0.87 (0.86, 0.88) | 0.84 (0.84, 0.85) | -2.7% | 267 (254, 279) | 260 (251, 269) | -7 (-16, 3) | -2.5% | 230 (219, 242) | 218 (210, 227) | -5.2% | ||
Ready-to-eat cereals | 0.56 (0.54, 0.58) | 0.54 (0.53, 0.55) | -4% | 210 (200, 219) | 215 (208, 222) | 5 (-4, 14) | 2.5% | 119 (112, 126) | 117 (112, 122) | -2 (-8, 4) | -1.6% | |
Non-cereal-based sweets | 0.52 (0.5, 0.54) | 0.49 (0.48, 0.51) | -5.1% | 138 (131, 145) | 143 (138, 149) | 6 (-1, 12) | 4.2% | 71 (66, 75) | 70 (67, 73) | -1 (-5, 3) | -1.2% | |
Untaxed | ||||||||||||
Sugar and sugar substitutes | 0.37 (0.35, 0.39) | 0.35 (0.34, 0.36) | -5.9% | 496 (464, 528) | 481 (455, 507) | -15 (-50, 20) | -3.1% | 182 (167, 198) | 166 (154, 178) | -8.9% | ||
Cereals | 0.94 (0.93, 0.94) | 0.94 (0.93, 0.94) | 0.00 (-0.01, 0.01) | 0.0% | 439 (423, 455) | 436 (424, 447) | -4 (-17, 10) | -0.8% | 410 (394, 426) | 406 (395, 418) | -3 (-16, 10) | -0.8% |
Dairy | 0.96 (0.95, 0.96) | 0.95 (0.95, 0.96) | 0.00 (-0.01, 0.00) | -0.5% | 595 (572, 618) | 603 (586, 620) | 8 (-10, 27) | 1.4% | 569 (545, 592) | 574 (557, 591) | 5 (-13, 24) | 0.9% |
Processed fruits and vegetables | 0.46 (0.44, 0.48) | 0.47 (0.45, 0.48) | 0.01 (-0.01, 0.02) | 1.8% | 245 (227, 263) | 247 (236, 259) | 2 (-11, 15) | 0.9% | 116 (105, 127) | 119 (111, 127) | 3 (-5, 11) | 2.6% |
Salty snacks | 0.33 (0.32, 0.35) | 0.32 (0.31, 0.33) | -5.0% | 68 (63, 73) | 73 (70, 76) | 5 (1, 10) | 7.7% | 22 (20, 24) | 23 (21, 24) | 1 (-1, 2) | 2.7% | |
Non-cereal-based sweets | 0.39 (0.37, 0.41) | 0.39 (0.38, 0.4) | 0.00 (-0.01, 0.02) | 0.5% | 203 (187, 219) | 217 (206, 227) | 14 (-2, 29) | 6.7% | 80 (73, 88) | 86 (81, 91) | 6 (-2, 13) | 7.2% |
Other | 0.85 (0.84, 0.86) | 0.86 (0.85, 0.86) | 0.01 (-0.01, 0.02) | 0.6% | 290 (279, 301) | 311 (302, 320) | 21 (11, 30) | 7.2% | 245 (234, 255) | 264 (256, 272) | 8.0% |
Bold numbers
Source: Authors’ own analyses and calculations based on data from Nielsen through its Mexico Consumer Panel Service (CPS) for the food and beverage categories for January 2012–December 2014.
Among untaxed foods, there were only significant declines in the volume of sugar and sugar substitutes purchased compared to what was expected based on the pre-tax trend (-8.9%,
For the first full year after Mexico’s taxes on SSBs and nonessential energy-dense food taxes, we find significant changes in the observed per capita volume of household purchases of taxed foods compared to the counterfactual (i.e., what was expected based on pre-tax trends). Overall, we find that taxed foods declined by 25 g/capita/month (-5.1%), whereas untaxed food purchases did not change (-0.3%). Moreover, we find much larger declines for lower SES households (-10.2%), whereas medium SES households changed by 5.8% and high SES households did not change.
Empirical evidence on the effect of food and nutrient taxes is limited. With regards to Denmark’s short-lived saturated fat tax, one study of household food purchases found a 10%–15% reduction in purchases of butter, blends, margarines, and oils in the first 9 mo of implementation, when the increase in price of these products was 8%–22% [
The reduction of 25 g/capita/month represents 70 to 110 kcal (energy density is at least 275 kcal/100 g, but based on the ENSANUT 2012, the mean energy density for the intake of taxed foods is 430 kcal/100 g). Although in absolute terms this reduction is small, the purchases captured in Nielsen only represent a fraction of all household purchases, and real absolute change in energy intake from taxed food might be larger.
The changes in taxed foods were for salty snacks and cereal-based sweets. Interestingly, for salty snacks, all the change was due to changes in probability of purchasing, suggesting that, for this item, people prefer to decrease the frequency of purchases rather than the amount. Moreover, we saw smaller-than-expected increases in the volume of sugar and sugar substitutes, suggesting that households are not necessarily substituting sugary home-prepared foods or beverages for pre-packaged taxed sweets.
Lower SES households were more responsive to the tax than middle SES households, while higher SES households showed no statistically significant change in purchases, consistent with results of the evaluation of Mexico’s SSB tax [
A great complexity of implementing a food tax is to define the characteristics of the foods subject to it. If only selected unhealthy foods are taxed, individuals can substitute with other unhealthy untaxed foods; on the other hand, if the tax categorization is too broad, many relatively healthy products will also be affected, increasing the cost of food without the public health benefit [
This work had several important limitations. First, we were unable to capture and analyze all foods that households purchased, including unpackaged produce, chocolates, candies, tortilla, and bread from bakeries. However, even for foods that were collected consistently in the Nielsen CPS, we captured only 474 g/capita/month of taxed foods in 2014. This is lower than what we would expect an average person to purchase, particularly if we compare to Euromonitor retail sales of 1,236 g/capita/month (excluding chocolates and bread from bakeries) or to the National Institute of Statistics and Geography’s (INEGI’s) manufacturer’s industry survey of 1245 g/capita/month (excluding chocolate) (
Our model and counterfactual comparisons allowed us to examine what happened post-tax compared to what would have happened if the pre-tax trends had continued. However, this comparison assumes that pre-tax trends would have continued, which may not have been the case, and we cannot rule out that these results may have been influenced by other concomitant changes unrelated to taxes, including economic trends and anti-obesity and public health campaigns and regulations. [
This evaluation of Mexico’s nonessential food and SSB taxes shows that the volume of taxed food purchases declined over what was expected, and that these results were similar in direction and magnitude to declines in SSBs in response to the SSB tax. Declines after the tax were statically significant among low and medium SES households and for selected food subcategories (salty snacks and cereal-based sweets). Our results can orient Mexican policymakers, who every year decide on the continuation of the tax, as well as policymakers from others countries currently considering the implementation of foods taxes. However, the impact of this tax on overall energy intake, dietary quality, and food purchase patterns, as well as how these changes relate to weight status, remains to be studied.
(DOCX)
Monthly trends in predicted volume purchased (g/capita/month) of (A) taxed and (B) untaxed foods comparing to post-tax counterfactual by SES.
(DOCX)
Monthly trends in predicted total volume purchased (g/capita/month) of taxed food subcategories: (A) salty snacks, (B) cereal-based sweets, (C) ready-to-eat cereals, (D) non-cereal-based sweets.
(DOCX)
Monthly trends in predicted total volume purchased (g/capita/month) of untaxed food subcategories: (A) sugar and sugar substitutes, (B) cereals, (C) dairy, (D) processed fruits and vegetables, (E) salty snacks, (F) non-cereal-based sweets, (G) other.
(DOCX)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
We thank Donna Miles for exceptional data management and programming support; Denise Ammons for graphics support; Tania Aburto, Lilia Pedraza, Juan Carlos Salgado, Nancy López Olmedo, and Emily Ford Yoon for excellent research assistance; and Frances L. Dancy for administrative assistance. We thank Shu Wen Ng and Arantxa Colchero for their insight and feedback on the study design and analysis. We also thank and recognize the invaluable contributions and reviews by members of our independent evaluation advisory committee.
All tables and figures were produced by authors; the data are owned by The Nielsen Company, 2016. The Nielsen Company is not responsible for and had no role in preparing the results reported herein.
Consumer Panel Services
National Health and Nutrition Survey
National Institute of Statistics and Geography
National Institute of Public Health
institutional review board
socioeconomic status
sugar-sweetened beverage