# Front-of-Pack Labeling in Chile: Effects on Employment, Real Wages, and Firms’ Profits after Three Years of Its Implementation

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

- (a)
- Focus groups with low- and middle-income mothers suggest that after initial implementation of the law, profound changes in attitudes were found toward food purchases driven both by the knowledge mothers gained from these labels and by children telling their mothers not to purchase products with warning labels [3,5].
- (b)
- Among high-in purchases (i.e., purchases in products with at least one front-of-package warning label), relative to the counterfactual statistic, there were notable relative declines of 23.8% of calories purchased, 36.7% of sodium purchased, and 26.7% of sugar purchased. There were larger absolute reductions in sugar from high-in beverages and larger reductions in calories, saturated fat, and sodium from high-in foods [6].
- (c)
- An evaluation comparing the nutritional profiles of products before and after the first year of Chile’s FOP law found significant reductions in the proportion of products required to carry warning labels, suggesting that companies reformulated products to improve their health profiles and avoid the FOP warning label requirement [7].
- (d)
- Research shows that the percentage of TV ads for foods high in energy, saturated fat, sugar, or sodium decreased from 41.9% before the regulation to 14.8% after the regulation’s implementation, resulting in a 44% decrease in exposure to “high-in” foods advertisement in children and a 58.0% decline for adolescents during the first year of the law implementation [8]. Total ads, however, did not change as they were shifted in the first phase to nonchild TV shows [9].

## 2. Materials and Methods

#### 2.1. Data

#### 2.2. Methods

**i**classes of economic activities shown in Appendix A, Table A2 (or the control group) at month

**t**; and $\mathit{A}{\mathit{E}}_{\mathit{i}\mathit{t}}$ is aggregate employment for the

**i**classes of economic activities shown in Appendix A, Table A2 (or the control group) at month

**t**. Nominal variables (total sales, wages, cost on raw materials, and capital goods) are deflated using the Consumer Price Index and expressed in constant pesos of December 2019 [15].

**Y**is the dependent variable (i.e., aggregate employment, average real wages, average employment, gross margin of profits, or number of firms) at time

_{t}**t**. In the case of aggregate employment, average employment per firm, and wages, their natural logarithm is used.

**X**is a categorical variable identifying the treatment period (where

_{it}**i**= 1 if between July 2016 and June 2018, and

**i**= 2 from July 2018 to June 2019);

**Z**is a dichotomous variable identifying the treated group (0 if control group);

**T**are trend variables (

_{it}**i**= 1 is the trend since the start of the first intervention period; and

**i**= 2 is a trend since the start of the second intervention period); and

**D**is a set of dichotomous variables for calendar months (to adjust for seasonality). We considered dummy variables accounting for an outlier month (July 2015) and for a tax change affecting sugar-sweetened beverages (SSB) (October 2014) but finally dropped them because of a lack of statistical significance in all models. IMACEC

_{j}**is the not seasonally adjusted non-mining Monthly Economic Activity Index (Índice Mensual de Actividad Económica, IMACEC) produced by the Central Bank of Chile, and included as a proxy for aggregate economic activity [17].**

_{t}## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Table 1. | Nutrient or Energy | Phase 1: 26 June 2016 | Phase 2: 24 Months after Beginning of Phase 1 (27 June 2018) | Phase 3: 36 Months after Beginning of Phase 1 (27 June 2019) |
---|---|---|---|---|

Solid foods | Energy, kilocalories (kcal)/100 g (g) | 350.0 | 300.0 | 275.0 |

Sodium, milligrams (mg)/100 g | 800.0 | 500.0 | 400.0 | |

Total sugars, g/100 g | 22.5 | 15.0 | 10.0 | |

Saturated fats, g/100 g | 6.0 | 5.0 | 4.0 | |

Liquid foods | Energy, kcal/100 milliliters (mL) | 100.0 | 80.0 | 70.0 |

Sodium, mg/100 mL | 100.0 | 100.0 | 100.0 | |

Total sugars, g/100 mL | 6.0 | 5.0 | 5.0 | |

Saturated fats, g/100 mL | 3.0 | 3.0 | 3.0 |

Classification (ISIC Revision 4 code and Spanish translation in parenthesis) |

Solid food industries |

Slaughterhouse operation (10101 Explotación de mataderos) |

Production of fishmeal (10201 Producción de harina de pescado) |

Processing and preserving of meat (10102 Elaboración y conservación de carne y productos cárnicos) |

Production of fish meal (10201 Producción de harina de pescado) |

Processing and preserving of salmonids (10202 Elaboración y conservación de salmónidos) |

Processing and preserving of other fish (in land) (10203 Elaboración y conservación de otros pescados, en plantas situadas en tierra (excepto barcos de factoría) |

Processing and preserving of crustaceans and molluscs (10204 Elaboración y conservación de crustáceos, moluscos, invertebrados acuáticos y otros productosacuáticos, en plantas situadas en tierra (excepto barcos factoría) |

Processing and preserving of other fish (factory ships) (10205 Actividades de elaboración y conservación de pescado, realizadas en barcos factoría) |

Production of food based on sea algae (10206 Elaboración y procesamiento de algas) |

Processing and preserving of fruit and vegetables (10300 Elaboración y conservación de frutas, legumbres y hortalizas) |

Manufacture of vegetable and animal oils and fats (10400 Elaboración de aceites y grasas de origen vegetal y animal) |

Manufacture of dairy products (10205 Actividades de elaboración y conservación de pescado, realizadas en barcos factoría) |

Manufacture of grain mill products (10610 Elaboración de productos de molinería) |

Manufacture of starches and starch products (10620 Elaboración de almidones y productos derivados del almidón) |

Manufacture of bakery products (10710 Elaboración de productos de panadería) |

Manufacture of sugar (10720 Elaboración de azúcar) |

Manufacture of cocoa, chocolate and sugar confectionery (10730 Elaboración de cacao, chocolate y de productos de confitería) |

Manufacture of macaroni, noodles, couscous and similar farinaceous products (10740 Elaboración de macarrones, fideos, alcuzcuz y productos farináceos similares) |

Manufacture of prepared meals and dishes (10750 Elaboración de comidas y platos preparados) |

Manufacture of other food products n.e.c. (10790 Elaboración de otros productos alimenticios n.c.p.) |

Beverage industries |

Distilling, rectifying and blending of pisco (11011 Elaboración de pisco (industrias pisqueras)) |

Distilling, rectifying and blending of spirits, exept pisco (11012 Destilación, rectificación y mezclas de bebidas alcohólicas; excepto pisco) |

Manufacture of wines (11020 Elaboración de vinos) |

Manufacture of malt liquors and malt (11030 Elaboración de bebidas malteadas y de malta) |

Manufacture of soft drinks; production of mineral waters and other bottled waters (11040 Elaboración de bebidas no alcohólicas; producción de aguas minerales y otras aguas embotelladas) |

**Table A3.**Changes in the number of firms and the gross margin of profit (including expenditures on capital goods) after two stages of FOP regulations.

Variables | Number of Firms (1) | Gross Margin of Profits (2) |
---|---|---|

0.0029 *** | 0.0015 * | |

$\mathrm{Trend}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{pre}\text{-}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{1}}$) | (0.0003) | (0.0008) |

−0.0025 | −0.0049 | |

$\mathrm{Difference}\mathrm{in}\mathrm{levels}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{pre}\text{-}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{4}}$) | (0.0034) | (0.0069) |

0.0001 | 0.0002 | |

$\mathrm{Difference}\mathrm{in}\mathrm{trends}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{pre}\text{-}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{5}}$) | (0.0001) | (0.0003) |

−0.0230 *** | −0.0088 | |

$\mathrm{Change}\mathrm{in}\mathrm{level}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{2}}$) | (0.0075) | (0.0097) |

0.0025 *** | −0.0003 | |

$\mathrm{Change}\mathrm{in}\mathrm{trend}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{3}}$) | (0.0005) | (0.0006) |

−0.0015 | 0.0142 | |

$\mathrm{Difference}\mathrm{in}\mathrm{level}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{6}}$) | (0.0125) | (0.0162) |

0.0033 *** | −0.0003 | |

$\mathrm{Difference}\mathrm{in}\mathrm{trends}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{7}}$) | (0.0008) | (0.0008) |

$\mathrm{Change}\mathrm{in}\mathrm{level}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{after}\mathrm{sec}\mathrm{ond}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{8}}$) | −0.0063 | 0.0070 |

(0.0062) | (0.0115) | |

$\mathrm{Change}\mathrm{in}\mathrm{trend}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{after}\mathrm{sec}\mathrm{ond}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{9}}$) | −0.0025 *** | −0.0002 |

(0.0007) | (0.0014) | |

$\mathrm{Difference}\mathrm{in}\mathrm{level}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{after}\mathrm{sec}\mathrm{ond}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{10}}$) | −0.0045 | −0.0301 |

(0.0095) | (0.0192) | |

$\mathrm{Difference}\mathrm{in}\mathrm{trends}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{after}\mathrm{sec}\mathrm{ond}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{11}}$) | 0.0006 | 0.0010 |

(0.0009) | (0.0019) | |

Logarithm non-mining IMACEC | 0.3147 ** | −0.6631 * |

(0.1432) | (0.3988) | |

Constant | 8.3632 *** | 3.0357 * |

(0.6528) | (1.8170) | |

Difference between the treatment and control groups in comparing trends of the pre.intervention and the second intervention period (β7 + β11) | 0.0037 *** | 0.0007 |

(0.0006) | (0.0017) | |

Observations | 156 | 156 |

## References

- Corvalán, C.; Reyes, M.; Garmendia, M.L.; Uauy, R. Structural responses to the obesity and non-communicable diseases epidemic: The Chilean Law of Food Labeling and Advertising. Obes. Rev.
**2013**, 14 (Suppl. S2), 79–87. [Google Scholar] [CrossRef] - Reyes, M.; Garmendia, M.L.; Olivares, S.; Aqueveque, C.; Zacarías, I.; Corvalán, C. Development of the Chilean front-of-package food warning label. BMC Public Health
**2019**, 19, 906. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Corvalán, C.; Reyes, M.; Garmendia, M.L.; Uauy, R. Structural responses to the obesity and non-communicable diseases epidemic: Update on the Chilean law of food labelling and advertising. Obes. Rev.
**2019**, 20, 367–374. [Google Scholar] [CrossRef] [PubMed] - Taillie, L.S.; Busey, E.; Stoltze, F.M.; Dillman Carpentier, F.R. Governmental policies to reduce unhealthy food marketing to children. Nutr. Rev.
**2019**, 77, 787–816. [Google Scholar] [CrossRef] [PubMed] - Correa, T.; Fierro, C.; Reyes, M.; Dillman Carpentier, F.R.; Taillie, L.S.; Corvalan, C. Responses to the Chilean law of food labeling and advertising: Exploring knowledge, perceptions and behaviors of mothers of young children. Int. J. Behav. Nutr. Phys.
**2019**, 16, 21. [Google Scholar] [CrossRef] [PubMed] - Taillie, L.S.; Bercholz, M.; Popkin, B.; Reyes, M.; Colchero, M.A.; Corvalán, C. Changes in food purchases after the Chilean policies on food labelling, marketing, and sales in schools: A before and after study. Lancet Planet.
**2021**, 5, e526–e533. [Google Scholar] [CrossRef] - Reyes, M.; Smith Taillie, L.; Popkin, B.; Kanter, R.; Vandevijvere, S.; Corvalán, C. Changes in the amount of nutrient of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: A nonexperimental prospective study. PLoS Med.
**2020**, 17, e1003220. [Google Scholar] [CrossRef] - Correa, T.; Reyes, M.; Taillie, L.S.; Corvalán, C.; Dillman Carpentier, F.R. Food Advertising on Television Before and After a National Unhealthy Food Marketing Regulation in Chile, 2016–2017. Am. J. Public Health
**2020**, 110, 1054–1059. [Google Scholar] [CrossRef] - Paraje, G.; Colchero, A.; Wlasiuk, J.M.; Sota, A.M.; Popkin, B.M. The effects of the Chilean food policy package on aggregate employment and real wages. Food Policy
**2021**, 100, 102016. [Google Scholar] [CrossRef] - La Tercera. Ley de Etiquetado: Alerta En Las Pequeñas Empresas. La Tercera, 1 August 2016. Available online: https://www.latercera.com/noticia/ley-de-etiquetado-alerta-en-las-pequenas-empresas/ (accessed on 8 July 2021).
- Castillo, J. ¿Será útil El Nuevo Etiquetado Frontal De Alimentos Y Bebidas? Merca2.0, 28 January 2020. Available online: https://www.merca20.com/sera-util-el-nuevo-etiquetado-frontal-de-alimentos-y-bebidas/ (accessed on 9 July 2021).
- Expansión. 5 Cosas Que Debes Conocer Sobre El Nuevo Etiquetado Frontal. Expansión, 19 October 2019. Available online: https://expansion.mx/empresas/2019/10/01/5-cosas-que-debes-conocer-sobre-el-nuevo-etiquetado-frontal (accessed on 8 July 2021).
- Servicio de Impuestos Internos. Homologación Completa de Actividades Económicas Con El CIIU.CL 2012, SII ed.; Servicio de Impuestos Internos: Santiago, Chile, 2013.
- Instituto Nacional de Estadísticas. Informalidad Y Condiciones Laborales. Available online: https://www.ine.cl/estadisticas/sociales/mercado-laboral/informalidad-y-condiciones-laborales (accessed on 4 September 2020).
- Instituto Nacional de Estadística. Índice de Precios al Consumidor. Available online: https://www.ine.cl/estadisticas/economia/indices-de-precio-e-inflacion/indice-de-precios-al-consumidor (accessed on 1 December 2020).
- Linden, A. Conducting interrupted time-series analysis for single- and multiple-group comparisons. Stata J.
**2015**, 15, 480–500. [Google Scholar] [CrossRef] [Green Version] - Banco Central de Chile. Cuentas Nacionales de Chile. Métodos Y Fuentes de Información; Banco Central de Chile: Santiago, Chile, 2017. [Google Scholar]
- Linden, A. A comprehensive set of postestimation measures to enrich interrupted time-series analysis. Stata J.
**2017**, 17, 73–88. [Google Scholar] [CrossRef] [Green Version] - Abadie, A.; Diamond, A.; Hainmueller, J. Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. J. Am. Stat. Assoc.
**2010**, 105, 493–505. [Google Scholar] [CrossRef] [Green Version] - Hollingsworth, A.; Wing, C. Tactics for Design and Inference in Synthetic Control Studies: An Applied Example Using High-Dimensional Data; SSRN: Rochester, NY, USA, 2020. [Google Scholar]
- Larrain, L. Fascismo, Alimentos Y Libertad de Expresión. 2016. Available online: https://lyd.org/centro-de-prensa/noticias/2017/01/columna-luis-larrain-diario-financiero-fascismo-alimentos-libertad-expresion/ (accessed on 21 September 2021).
- Marinello, S.; Leider, J.; Powell, L.M. Employment impacts of the San Francisco sugar-sweetened beverage tax 2 years after implementation. PLoS ONE
**2021**, 16, e0252094. [Google Scholar] [CrossRef] [PubMed] - Powell, L.M.; Wada, R.; Persky, J.J.; Chaloupka, F.J. Employment Impact of Sugar-Sweetened Beverage Taxes. Am. J. Public Health
**2014**, 104, 672–677. [Google Scholar] [CrossRef] [PubMed] - Guerrero-López, C.M.; Molina, M.; Colchero, M.A. Employment changes associated with the introduction of taxes on sugar-sweetened beverages and nonessential energy-dense food in Mexico. Prev. Med.
**2017**, 105, S43–S49. [Google Scholar] [CrossRef] [PubMed] - Corvalán, C.; Correa, T.; Reyes, M.; Paraje, G. Impacto de La Ley Chilena de Etiquetado En El Sector Productivo Alimentario; Food and Agricultural Organization: Santiago, Chile, 2021. [Google Scholar]
- Taillie, L.S.; Reyes, M.; Colchero, M.A.; Popkin, B.; Corvalán, C. An evaluation of Chile’s Law of Food Labeling and Advertising on sugar-sweetened beverage purchases from 2015 to 2017: A before-and-after study. PLoS Med.
**2020**, 17, e1003015. [Google Scholar] [CrossRef] - Araya, S.; Elberg, A.; Noton, C.; Schwartz, D. Identifying Food Labeling Effects on Consumer Behavior; SSRN: Santiago, Chile, 2018. [Google Scholar]
- Barahona, N.; Otero, C.; Otero, S.; Kim, J. Equilibrium Effects of Food Labeling Policies; SSRN: Rochester, NY, USA, 2021. [Google Scholar]
- Resultados Encuesta Financiera de Hogares 2017 Report; Banco Central de Chile: Santiago, Chile, 2018; Available online: https://www.bcentral.cl/documents/33528/42310/Principales+Resultados_EFH_2017.pdf/1eb385fe-98f3-d013-69e4-38596d828033?t=1569946173177 (accessed on 3 December 2020).
- Jacobs, A. In Sweeping War on Obesity, Chile Slays Tony the Tiger. The New York Times, 7 February 2018. Available online: https://www.iatp.org/documents/junk-food-junk-provisions (accessed on 3 December 2020).
- Shekar, M.; Popkin Barry, M. Obesity: Health and Economic Consequences of an Impending Global Challenge; The World Bank: Washington, DC, USA, 2020. [Google Scholar]
- Popkin, B.M.; Reardon, T. Obesity and the food system transformation in Latin America. Obes. Rev.
**2018**, 19, 1028–1064. [Google Scholar] [CrossRef] [PubMed] - Reardon, T.; Tschirley, D.; Liverpool-Tasie, S.; Fanzo, J.; Dolislager, M.; Sauer, C.; Dhar, R.; Vargas, C.; Popkin, B.M. The Food System Transformation and Its Association with the Underweight and Overweight and Obesity Burden in Sub-Saharan Africa; Elsevier: Amsterdam, The Netherlands, 2019. [Google Scholar]

**Figure 1.**Chile’s multipronged Obesity Prevention Program. * HEFSS: high energy, (saturated) fat, salt, or sugar.

**Figure 2.**Aggregate employment, real wages, average workers per firm, and gross margin of profits in food and beverage industries, January 2013–June 2019.

**Table 1.**Descriptive statistics for aggregate employment (number of workers), average real wages (in constant Chilean pesos), average number of workers (number of workers per firm), and gross profit margins (as proportion of sales).

Aggregate Employment (Workers) | Average Real Wages (Constant Pesos) | Gross Margin of Profits (as Proportion of Sales) | Average Employment Per Firm (Workers Per Firm) | ||||||
---|---|---|---|---|---|---|---|---|---|

Treated | Control | Treated | Control | Treated | Control | Treated | Control | ||

Pre-intervention (January 2013–May 2016) | Mean | 184,971 | 184,867 | 635,964 | 635,864 | 0.053 | 0.053 | 9.60 | 9.60 |

Std. Dev. | 3739 | 2462 | 22,425 | 21,292 | 0.035 | 0.017 | 0.39 | 0.38 | |

First intervention (July 2016–May 2018) | Mean | 196,067 | 187,304 | 667,339 | 666,658 | 0.074 | 0.053 | 8.70 | 8.87 |

Std. Dev. | 8200 | 2458 | 11,274 | 9827 | 0.069 | 0.021 | 0.43 | 0.41 | |

Change (%) | Mean | 6.0 | 1.3 | 4.9 | 4.8 | 39.6 | 0.0 | ||

Second intervention (July 2018–May 2019) | Mean | 209,663 | 184,576 | 675,431 | 690,590 | 0.047 | 0.052 | 7.98 | 8.26 |

Std. Dev. | 3514 | 2171 | 15,606 | 15,989 | 0.024 | 0.017 | 0.22 | 0.08 |

Aggregate Employment | Average Real Wages | Average Employment | Gross Margin of Profits | |
---|---|---|---|---|

VARIABLES | (1) | (2) | (3) | (4) |

$\mathrm{Trend}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{pre-intervention}({\mathit{\beta}}_{\mathbf{1}}$) | −0.0003 | 0.0030 *** | −0.0032 *** | 0.0011 |

(0.0004) | (0.0003) | (0.0004) | (0.0007) | |

Difference in levels of dependent variable for | −0.0068 | −0.0020 | 0.0010 | −0.0064 |

$\mathrm{groups}\mathrm{pre-intervention}({\mathit{\beta}}_{\mathbf{4}}$) | (0.0051) | (0.0088) | (0.0071) | (0.0066) |

Difference in trends of dependent variable for | 0.0004 | 0.0001 | −0.0001 | 0.0003 |

$\mathrm{groups}\mathrm{pre-intervention}({\mathit{\beta}}_{\mathbf{5}}$) | (0.0004) | (0.0004) | (0.0004) | (0.0003) |

Change in level of dependent variable after first | 0.0229 *** | −0.0196 *** | 0.0597 *** | −0.0093 |

$\mathrm{intervention}({\mathit{\beta}}_{\mathbf{2}}$) | (0.0060) | (0.0064) | (0.0128) | (0.0084) |

Change in trend of dependent variable after first | −0.0018 *** | −0.0008 ** | −0.0038 *** | 0.0003 |

$\mathrm{intervention}({\mathit{\beta}}_{\mathbf{3}}$) | (0.0003) | (0.0004) | (0.0007) | (0.0005) |

Difference in level of dependent variable for groups | −0.0169 | 0.0047 | −0.0253 | 0.0159 |

$\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{6}}$) | (0.0146) | (0.0101) | (0.0166) | (0.0156) |

Difference in trends of dependent variable for | 0.0043 *** | −0.0006 | 0.0003 | −0.0008 |

$\mathrm{groups}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{7}}$) | (0.0011) | (0.0006) | (0.0010) | (0.0008) |

Change in level of dependent variable after second | 0.0077 | −0.0013 | 0.0428 *** | 0.0001 |

$\mathrm{intervention}({\mathit{\beta}}_{\mathbf{8}}$) | (0.0066) | (0.0049) | (0.0118) | (0.0093) |

Change in trend of dependent variable after second | −0.0004 | 0.0027 *** | −0.0000 | −0.0004 |

$\mathrm{intervention}({\mathit{\beta}}_{\mathbf{9}}$) | (0.0005) | (0.0004) | (0.0009) | (0.0009) |

Difference in level of dependent variable for groups | 0.0341 | −0.0129 * | 0.0152 | −0.0196 |

$\mathrm{after}\mathrm{second}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{10}}$) | (0.0220) | (0.0070) | (0.0231) | (0.0170) |

Difference in trends of dependent variable for | −0.0068 *** | −0.0001 | −0.0059 *** | 0.0009 |

$\mathrm{groups}\mathrm{after}\mathrm{sec}\mathrm{ond}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{11}}$) | (0.0016) | (0.0006) | (0.0019) | (0.0015) |

Logarithm non-mining IMACEC | 0.3976 ** | −0.3566 *** | 0.2619 | −0.5461 |

(0.1682) | (0.1026) | (0.1614) | (0.3487) | |

Constant | 10.3198 *** | 14.9560 *** | 1.1371 | 2.5279 |

(0.7663) | (0.4706) | (0.7355) | (1.5885) | |

Observations | 156 | 156 | 156 | 156 |

**Table 3.**Changes in labor market and firms’ profit outcomes considering one FOP regulation intervention (July 2016).

Variables | Aggregate Employment (1) | Average Real Wages (2) | Average Employment (3) | Gross Margin of Profits (4) |
---|---|---|---|---|

$\mathrm{Trend}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{pre-intervention}({\mathit{\beta}}_{\mathbf{1}}$) | −0.0002 | 0.0030 *** | −0.0030 *** | 0.0010 |

(0.0004) | (0.0003) | (0.0004) | (0.0006) | |

$\mathrm{Difference}\mathrm{in}\mathrm{levels}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{pre-intervention}({\mathit{\beta}}_{\mathbf{4}}$) | −0.0068 | −0.0020 | 0.0010 | −0.0064 |

(0.0048) | (0.0088) | (0.0064) | (0.0065) | |

$\mathrm{Difference}\mathrm{in}\mathrm{trends}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{pre-intervention}({\mathit{\beta}}_{\mathbf{5}}$) | 0.0004 | 0.0001 | −0.0001 | 0.0003 |

(0.0004) | (0.0004) | (0.0004) | (0.0003) | |

$\mathrm{Change}\mathrm{in}\mathrm{level}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{2}}$) | 0.0192 *** | −0.0247 *** | 0.0422 *** | −0.0076 |

(0.0060) | (0.0075) | (0.0124) | (0.0071) | |

$\mathrm{Change}\mathrm{in}\mathrm{trend}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{3}}$) | −0.0015 *** | −0.0003 | −0.0020 *** | 0.0001 |

(0.0002) | (0.0003) | (0.0004) | (0.0003) | |

$\mathrm{Difference}\mathrm{in}\mathrm{level}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{6}}$) | −0.0114 | 0.0090 | −0.0161 | 0.0199 |

(0.0147) | (0.0105) | (0.0178) | (0.0182) | |

$\mathrm{Difference}\mathrm{in}\mathrm{trends}\mathrm{of}\mathrm{dependent}\mathrm{variable}\mathrm{for}\mathrm{groups}\mathrm{after}\mathrm{first}\mathrm{intervention}({\mathit{\beta}}_{\mathbf{7}}$) | 0.0039 *** | −0.0011 ** | −0.0006 | −0.0013 ** |

(0.0007) | (0.0005) | (0.0007) | (0.0006) | |

Logarithm non-mining IMACEC | 0.3439 ** | −0.3399 *** | 0.1518 | −0.5249 |

(0.1679) | (0.1132) | (0.1620) | (0.3271) | |

Constant | 10.5644 *** | 14.88 *** | 1.6384 ** | 2.4315 * |

(0.7648) | (0.5183) | (0.7380) | (1.4904) | |

Observations | 156 | 156 | 156 | 156 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Paraje, G.; Montes de Oca, D.; Wlasiuk, J.M.; Canales, M.; Popkin, B.M.
Front-of-Pack Labeling in Chile: Effects on Employment, Real Wages, and Firms’ Profits after Three Years of Its Implementation. *Nutrients* **2022**, *14*, 295.
https://doi.org/10.3390/nu14020295

**AMA Style**

Paraje G, Montes de Oca D, Wlasiuk JM, Canales M, Popkin BM.
Front-of-Pack Labeling in Chile: Effects on Employment, Real Wages, and Firms’ Profits after Three Years of Its Implementation. *Nutrients*. 2022; 14(2):295.
https://doi.org/10.3390/nu14020295

**Chicago/Turabian Style**

Paraje, Guillermo, Daniela Montes de Oca, Juan Marcos Wlasiuk, Mario Canales, and Barry M. Popkin.
2022. "Front-of-Pack Labeling in Chile: Effects on Employment, Real Wages, and Firms’ Profits after Three Years of Its Implementation" *Nutrients* 14, no. 2: 295.
https://doi.org/10.3390/nu14020295