metadata: authors: 0: author: Fuchs Tarlovsky,Alan
1: author: Matytsin,Mikhail
2: author: Obukhova,Olga
count: Russian Federation
docna: 0: docna: Tobacco Taxation Incidence : Evidence from
            the Russian Federation
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Cancer,Tobacco Use and Control,Disease Control & Prevention,Public Health Promotion,Communicable Diseases,Tuberculosis,Leprosy,Cholera
teratopic: Industry,Health, Nutrition and Population
abstracts: cdata!: Despite the well-known positive effects
            of tobacco taxes on health outcomes, policy makers avoid
            relying on such taxes because of their possible regressive
            impact. Using an extended cost-benefit analysis to estimate
            the distributional effect of cigarettes in the Russian
            Federation, this paper finds that the long-run impact may in
            fact be progressive. The methodology applied incorporates
            the negative price effect caused by an increase in tobacco
            taxes, combined with a presumed future reduction in medical
            expenditures and a rise in working years caused by a
            reduction in the rate of smoking among the population. The
            analysis includes estimates of the distributional impacts of
            price rises on cigarettes under various scenarios, based on
            information taken from the Russia Longitudinal Monitoring
            Survey -- Higher School of Economics for 2010–16. One
            contribution is the quantification of impacts by allowing
            price elasticities to vary across consumption deciles.
            Overall, cigarette taxes exert a positive long-term effect
            on household incomes, although the magnitude depends on the
            structure of the conditional price elasticity. If the
            population is more responsive to tobacco price changes, then
            it would experience greater gains from the health and
            extended work-life benefits.
geo_reg_key: 517208,517191,82689,517198
repnme: repnme: Tobacco Taxation Incidence : Evidence from
            the Russian Federation
display_title: Tobacco Taxation Incidence :
            Evidence from the Russian Federation
listing_relative_url: /research/2018/10/30556290/tobacco-taxation-incidence-evidence-russian-federation
topicv3: Tuberculosis m1326131 1419,Cancer m1326342 1431,Tobacco Use and Control m1326794 1439,Health m1504661 3480,Tobacco Taxes m1504803 3496
publishtoextweb_dt: 2018-10-29T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population
no_of_pages: 27
author_keys: 0: author_key: 1107738
1: author_key: 1160937
2: author_key: 1645197
model_extractions: WPS8626 Policy Research Working Paper 8626 Tobacco Taxation Incidence Evidence from the Russian Federation _Alan Fuchs Mikhail Matytsin Olga Obukhova_ Poverty and Equity Global Practice October 2018
Policy Research Working Paper 8626

# **Abstract**

Despite the well-known positive effects of tobacco taxes on health outcomes, policy makers avoid relying on such taxes because of their possible regressive impact. Using an extended cost-benefit analysis to estimate the distributional effect of cigarettes in the Russian Federation, this paper finds that the long-run impact may in fact be progressive. The methodology applied incorporates the negative price effect caused by an increase in tobacco taxes, combined with a presumed future reduction in medical expenditures and a rise in working years caused by a reduction in the rate of smoking among the population. The analysis includes estimates of the distributional impacts of price rises on cigarettes under various scenarios, based on information taken from the Russia Longitudinal Monitoring Survey—Higher School of Economics for 2010–16. One contribution is the quantification of impacts by allowing price elasticities to vary across consumption deciles. Overall, cigarette taxes exert a positive long-term effect on household incomes, although the magnitude depends on the structure of the conditional price elasticity. If the population is more responsive to tobacco price changes, then it would experience greater gains from the health and extended work-life benefits.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at afuchs@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Tobacco Taxation Incidence: Evidence from the Russian Federation**

**Alan Fuchs, Mikhail Matytsin and Olga Obukhova**<sup>**1**</sup>

JEL Codes: H23, H31, I18, O15

> 1 Fuchs: Poverty and Equity Global Practice, World Bank (email: afuchs@worldbank.org). Matytsin: Poverty and Equity Global Practice, World Bank (email: mmatytsin@worldbank.org). Obukhova: Ministry of Health Russian Federation (email: <u>obuhova@mednet.ru) The authors are grateful to Patricio Marquez, Sevil Salakhutdinova, Daria</u> Khaltourina, Polina Kuznetsova, and Daniela Paz for providing inputs, comments and support for this paper. Support in the preparation of this report has been provided by the World Bank’s Global Tobacco Control Program, co-financed by the Bill and Melinda Gates Foundation and Bloomberg Philanthropies. The findings, interpretations, and conclusions in this research note are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.
# **1. Introduction**

The World Health Organization estimates that tobacco kills more than 7 million people worldwide each year (WHO 2017a). It is the second leading cause of death and disability worldwide and is among the major preventable causes of disease and premature death globally (Doll and Hill 1956; Ng et al. 2014; Wynder and Graham 1950). Diseases associated with tobacco use include lung cancer, stroke, ischemic heart disease, and respiratory diseases (DHHS 2004). Nearly 80 percent of the world’s smokers live in low- and middle-income countries and are less likely to be informed about the adverse health effects of tobacco use relative to individuals in high-income countries. The Russian Federation is one of the largest producers and consumers of tobacco; 50 percent of the men and almost 14 percent of the women smoke (30 percent overall), above the average in the countries with high human development indexes.<sup>2,3</sup> Tobacco taxes are often considered regressive because the share of household budgets allocated to tobacco products is larger among low-income households than among high-income households. This paper shows that, if behavioral and indirect effects, especially health-related effects, are taken into account, this is no longer valid. The long-run benefits of not smoking offset the costs associated with tobacco taxes among low-income groups and the overall population. Potential benefits include a reduction in medical expenditures and an increase in healthy life years, factors that translate into economic benefits that outweigh the losses created by tax increases if consumers quit or never start smoking.

This paper describes and quantifies the medium- and long-run effects of tobacco tax increases on aggregate household welfare through three channels. The first channel implies that higher tobacco prices because of higher taxes induce a behavioral response involving a reduction and cessation in tobacco consumption that is manifest particularly among people who discontinue smoking and younger individuals who do not start smoking. The second channel is associated with a reduction in medical expenses associated with the averted treatment costs of tobacco-related diseases, and the third is a rise in incomes because of gains in years of employment derived from an extension in life expectancy. To assess the impact of these effects, this paper estimates the price elasticity of tobacco, simulates upper- and lowerbound scenarios, and calculates the welfare gains among various income groups.

The study is structured as follows. Section 2 reviews the literature on the health effects of tobacco, the economic costs associated with tobacco-related diseases, tobacco tax policies, and price elasticities. Section 3 presents the model, and section 4 provides an overview of the data used to estimate the impact of the tobacco tax. Section 5 examines the results. The final section concludes with a discussion on policy implications.

> 2 2016 Global Adult Tobacco Survey (GATS), Russian Federation Factsheet:

> <u>http://www.who.int/tobacco/surveillance/survey/gats/rus_factsheet_2016.pdf?ua=1</u>

> 3 The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The measure was introduced and measured by the United Nations Development Programme (UNDP).

2
# **2. Literature review**

Stefler et al. (2018) estimate that the population share associated with smoking among cancer deaths in Russia was 25 percent.<sup>4</sup> Close to 300,000 Russians died because of tobacco-related illnesses (GBD 2016). A 2002 study by WHO suggests that tobacco-related diseases may shorten the healthy years of life by 13.6 years<sup>5</sup> (Mathers et al. 2002). This has negative consequences on human capital development and imposes a growing economic burden because smoking decreases earnings potential and labor productivity.

Several studies have quantified the economic cost of smoking, though most have been carried out in high-income western countries (Guindon et al. 2007). Goodchild, Nargis, and Tursan d’Espaignet (2018) find that tobacco-related diseases accounted for 5.7 percent of global health expenditure in 2012 and that the total economic costs of smoking, including health expenditure and productivity losses, were equivalent to 1.8 percent of the world’s gross domestic product (GDP) (US$1.85 trillion in purchasing power parity U.S. dollars). According to these authors, upper-middle-income countries, such as Russia, experience almost a third of this burden.<sup>6</sup> Estimates for the economic burden in the case of Russia vary between US$8 billion and US$17 billion (Potapchik and Popovich 2014; Goodchild, Nargis, and Tursan d’Espaignet 2018).

Tobacco price increases are also associated with expansion in productive life years. Verguet et al. (2015) analyze the health effects of price increases in China and conclude that a 50 percent rise in prices would result in 231 million life years gained over 50 years and would have a significant impact among the poor. In contrast, Pichón-Riviere et al. (2014) estimate that tobacco use in Chile would reduce life expectancy by nearly 4.0 years among women and 4.3 years among men. For Russia, Maslennikova et al. (2013) estimate that, if taxes were raised to 70 percent of the retail price, along with other policies, such as banning smoking in public places, 3.7 million tobacco-related deaths would be averted, although this may be a low prediction because secondhand smoking effects were not taken into account. According to the WHO, secondhand smoke is responsible for over 890,000 premature deaths a year (WHO 2017a).

Tobacco taxation has been recognized as one of the most effective tobacco control strategies. In highincome countries, a 10 percent increase in the price of cigarettes is associated with a decrease in the demand for cigarettes of approximately 4 percent (World Bank 1999). In low- and middle-income countries, an equivalent increase is associated with an average 6 percent reduction in cigarette consumption (Chaloupka et al. 2000). Higher taxes have the additional benefits of reducing exposure to secondhand smoke and increasing government revenues. Maslennikova et al. (2013) estimate that a 10 percent rise in price would reduce cigarette consumption among people under 35 by 3 percent and among people over 35 by 1 percent. This may be because price elasticities for tobacco products are higher among young people. Lance et al. (2004) use data from three waves of the Russia Longitudinal Monitoring Survey–Higher School of Economics (RLMS-HSE) (1996, 1998, and 2000) to estimate the demand for cigarettes among men and find that the price elasticity of smoking participation was between –0.106 and –0.050. However, young people (ages 13–19) were found to be much more price sensitive; the total

> 4 <mark>‐ Randomly selected participants living in mid size towns in Belarus, Hungary, and Russia provided information on smoking habits, alcohol consumption, vital statistics, and cause of death (if applicable) of male relatives (fathers, siblings, and partners). The population attributable risk (PAR) fraction of cancer deaths (ages 35–79) because of smoking, alcohol consumption, or both was estimated between 2001 and 2013. Among 72,190 men, 4,702 died of cancer.</mark>

> 5 This number might be overestimated due to alcohol abuse effect. Some twenty years ago the number of life years lost was estimated as 5-7 years (Gerasimenko, 2001).

> 6 Goodchild, Nargis, and Tursan d’Espaignet (2018) estimate the economic cost of smoking-attributable diseases at US$354 billion in upper-middle-income countries (purchasing power parity U.S. dollars).

3
elasticity was –0.345. Additionally, recent decreases in tobacco consumption have occurred in lockstep with increased prices of cigarettes from the pass-through of excise and ad valorum taxes.<sup>7</sup>

## **3. Model**

In this paper, the impact of raising tobacco taxes in Russia is estimated using an extended cost-benefit analysis as in other studies (Pichón-Riviere et al. 2014; Verguet et al. 2015). The paper analyzes three factors to estimate how tobacco taxes affect household incomes. First, assuming tobacco consumption does not change, tobacco taxes directly affect household income because the share of household budgets allocated to tobacco purchases increases as the tax increases. Second, household medical expenses decrease as a result of a reduction or widespread cessation in tobacco consumption. Third, households experience a positive income change because of additional years of labor recovered through the extension of life expectancy. The aggregate effect of a tax policy is estimated as follows: Income effect = change in tobacco expenditure (A) + lower medical expenses (B) + rise in income (C) (1) For further details on the methodology used, see annex A; refer also to Fuchs and Meneses (2017a).

# **4. Data and descriptive statistics**

## **Data**

For the current analysis, we mainly used the data provided by the RLMS-HSE. This is a rich set of data on individuals and households and covering incomes, expenditures, and health status as well as attitudes toward smoking. The RLMS-HSE is an annual survey that allows researchers to construct panels to follow the same individuals over a few years at a time. The latest available wave of the survey when the analysis was conducted was the 2016 round.

The information on attitudes toward smoking is available on households and individuals. In the family questionnaire are questions about how much households spend on cigarettes and how many packs were purchased during a recent period. This allows the unit values (individual prices) of cigarettes to be calculated for all the households that were involved in smoking. There are also questions on how much the respondent smokes and how much the respondent spends on cigarettes.

Another source of information is cigarette prices in communities. This information is also collected through the survey. Prices for four types of cigarettes are collected in this section of the questionnaire: brands that are domestic, imported, cheaper, or more expensive. However, the variation in prices among households is relatively low. This is why the information is not helpful in the analysis.<sup>8</sup> The analysis was run and is presented below by deciles that were defined using per capita expenditures (unless specified otherwise).

> 7 Sakharova, Antonov, and Salagay (2017) observe a 25 percent decrease in consumption in 2005–14 that correlates with government action on the tobacco epidemic.

> 8 As a robustness check, we tried a few modules in which the prices in communities were used as instruments. However, the results appeared to be statistically unstable.

4
# **Smoking prevalence in Russia**

Despite the gradual decrease in recent years, smoking is still common in Russia (figure 1, panel a). According to RLMS-HSE data, 29 percent of the population ages 16–75 were smoking in 2016. Smoking is three times more popular among men (45 percent) than among women (15 percent). Smoking prevalence among men has been falling over the last decade and has declined by 10 percentage points since 2010, while, among women, prevalence has remained stable.

Smoking is common across the entire expenditure distribution (figure 1, panel b). While there is no specific pattern in smoking among men across deciles, smoking among women is clearly more popular among the more well-off population. Smoking in Russia involves mostly filter cigarettes; the share among all cigarettes smoked is between 90 percent among the lower deciles to 100 percent among the richer deciles. Men smoke an average of five or six packs a week, while women smoke an average of four packs a week; these numbers are rather stable across the distribution.

<!-- Start of picture text --> Figure 1 – Smoking Prevalence, Russian Federation<br>(ages 16–75)<br>a. Over time b. By decile<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 1 2 3 4 5 6 7 8 9 10<br>both men women both men women<br>c. By age-group d. By educational attainment<br>60 60<br>50 50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10 0<br>0 below general above higher<br>16-24 25-34 35-44 45-54 55-64 65+ secondary secondary general<br>secondary<br>both men women both men women<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles have been established using per capita household expenditures.

# **Tobacco price elasticity by decile**

Although several models have been tested to estimate elasticities by decile, the pooled cross-sectional model with year fixed effects and demographic controls at the household level is preferred (table 1; see 5
annex B for more on the methodology and the various iterations of the estimation of tobacco price elasticities by decile.).<sup>9</sup> The estimated average cigarette price elasticity in Russia is −0.52, which is within the estimated elasticities found in the literature (−0.4 and −0.8), as well as those previously estimated for Russia.<sup>10</sup>

**Table 1 – Cigarette Price Elasticities, by Decile**

|_Price Elasticity_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|-0.48|-0.41|-0.38|-0.34|-0.32|-0.31|-0.29|-0.26|-0.25|-0.21|
|Medium|-0.68|-0.61|-0.58|-0.54|-0.52|-0.51|-0.49|-0.46|-0.45|-0.41|
|Upper bound|-0.88|-0.81|-0.78|-0.74|-0.72|-0.71|-0.69|-0.66|-0.65|-0.61|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles were created using per capita household expenditure. Lower- and upper-bound elasticities show average differences of −0.2 and +0.2, respectively, with the medium-bound elasticity. Demographic controls include area, macroregion, and year fixed effects.

# **Mortality and morbidity**

Data on mortality, years of life lost, and morbidity have been obtained from the Global Burden of Disease Study. Ischemic heart disease, stomach cancer, and stroke are among the most prevalent diseases among men and women in Russia. In 2016, approximately 243,252 deaths were attributed to tobacco-related diseases, of which nearly 86 percent were men (208,451) (table 2). Similarly, more than 2.5 million cases of tobacco-related disease were reported in Russia the same year (table 3). A total of 5,979,509 years of life lost were estimated for all tobacco-related deaths in 2016 (table 4).

> 9 To make this paper comparable to the other cases analyzed by the authors in the past (Chile, Ukraine, Moldova, South Africa, Indonesia, Bangladesh and Bosnia Herzegovina) it was decided to use household level data to estimate price elasticities. However, a full replication of the analysis using individual level data is also performed and included in the annex. Although there is a difference in levels, both analyses yield similar and consistent results in terms of trends and incidence.

> 10 Only partial elasticity of consumption is estimated and used here unless specified otherwise. Using the full elasticity, including the effect on participation, would make the results more precise, but, because of computational difficulties, this step was omitted on the assumption that participation in smoking is less sensitive to relatively small changes in prices.

6
**Table 2 – Tobacco Related Deaths, by Gender and Age-Group, Russian Federation, 2016**

||||**Women**|||||**Men**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**25-34 **|**35-54 **|**55-74**|**75+ **|**Total **|**25-34 **|**35-54**|**55-74**|**75+**|**Total**|
|**Total Tobacco-related deaths**|**397**|**6,549 **|**19,779 **|**8,076 **|**34,801 **|**1,814 **|**36,357 **|**126,200 **|**44,080 **|**208,451**|
|Bladder cancer|0|8|43|32|**83**|1|195|1,588|792|**2,576**|
|Cervical cancer|3|115|111|23|**252**|-|-|-|-|**-**|
|Chronic obstructive pulmonary disease|2|180|1,239|1,338|**2,760**|18|1,168|9,072|6,987|**17,245**|
|Colon and rectum cancer|1|55|234|103|**392**|2|259|1,433|444|**2,138**|
|Esophageal cancer|0|31|143|82|**256**|2|696|3,091|592|**4,381**|
|Ischemic heart disease|192|3,164|9,777|3,533|**16,665**|1,081|18,878|52,413|17,682|**90,055**|
|Larynx cancer|0|36|112|45|**194**|2|645|2,747|534|**3,927**|
|Leukemia|0|7|17|5|**29**|5|230|737|177|**1,149**|
|Lip and oral cavity cancer|1|52|138|54|**245**|4|781|2,252|360|**3,397**|
|Liver cancer due to other causes|0|2|7|3|**12**|1|83|339|88|**510**|
|Nasopharynx cancer|0|10|18|4|**32**|1|91|227|28|**347**|
|Stomach cancer|1|66|215|92|**374**|5|855|3,690|900|**5,450**|
|Tracheal, bronchus, and lung cancer|3|460|1,970|801|**3,234**|21|4,783|26,023|6,958|**37,785**|
|Tuberculosis|37|141|43|3|**223**|231|1,301|775|44|**2,350**|
|Stroke|156|2,224|5,712|1,958|**10,049**|441|6,391|21,814|8,495|**37,141**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

**Table 3 – Tobacco-Related Events, by Gender, 2016**

||**All**|**Women**|**Men**|
|---|---|---|---|
|Bladder cancer|17,898|4,051|13,847|
|Cervical cancer|15,718|15,718|0|
|Chronic obstructive pulmonary disea|334,223|83,351|250,873|
|Colon and rectum cancer|73,256|39,421|33,835|
|Esophageal cancer|7,943|1,510|6,433|
|Ischemic heart disease|1,200,833|651,505|549,328|
|Larynx cancer|9,221|748|8,473|
|Leukemia|10,843|5,276|5,567|
|Lip and oral cavity cancer|11,517|3,944|7,573|
|Liver cancer due to other causes|1,845|475|1,370|
|Nasopharynx cancer|845|171|674|
|Stomach cancer|40,978|18,991|21,987|
|Tracheal, bronchus, and lung cancer|53,673|10,624|43,049|
|Tuberculosis|82,910|24,279|58,631|
|Stroke|673,818|404,503|269,315|
|**Total**|**2,535,522**|**1,264,569**|**1,270,953**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

_Note_ : Incidence is defined as the number of new cases of a given disease during a given period in a specified population. It is also used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases, new or old, in the population at a given time.

7
**Table 4 – Years of Life Lost, by Gender, 2016**

||**All**|**Women**|**Men**|
|---|---|---|---|
|30-34|120,406|21,587|98,819|
|35-39|185,642|36,704|148,938|
|40-44|300,647|52,518|248,129|
|45-49|415,610|65,885|349,726|
|50-54|773,506|104,615|668,891|
|55-59|1,109,687|141,986|967,701|
|60-64|1,221,584|169,004|1,052,579|
|65-69|917,030|125,746|791,284|
|70-74|347,145|47,219|299,926|
|75-79|416,913|51,640|365,273|
|80 plus|171,339|29,945|141,395|
|**Total**|**5,979,509**|**846,848**|**5,132,661**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

# **Tobacco-related medical costs**

The total tobacco-related medical expenses in the case of Russia used in this analysis have been calculated using national data. The direct health care costs related to the treatment of tobacco-related diseases include the costs of medical care provided in outpatient facilities, hospitals, and day-care hospitals. It is calculated by disease using the information on the number of cases and the weighted average costs of the treatment for each case. The diseases considered are as follows:

- Diabetes

- Cardiovascular disease

- Respiratory disease

- Cancer

- Tuberculosis

The number of diseases associated with tobacco smoking is calculated through the use of the indicator generally accepted in the available literature on population attributable risk (PAR). The PAR is calculated based on data on smoking prevalence and on the indicator of the relative risk (RR) of disease in smokers vs. nonsmokers. For the purposes of this paper, the RR has been taken from previous studies. The details of the calculations and the sources of information are summarized in annex C. The total direct tobaccorelated medical costs in Russia in 2017 are estimated at the level of 418 billion rubles.<sup>11</sup>

> 11 Alternatively, as a robustness check, the simulations have been performed using data from a cross-country comparable database. The most complete and standard study estimating the economic cost of smoking in Russia is Goodchild, Nargis, and Tursan d’Espaignet (2018). The study uses a cost of illness approach to estimate the economic cost of smoking attributable-diseases in 152 countries. Annual direct health care costs attributable to tobacco use in Russia in 2012 amounted to 526 billion rubles.

8
## **Baseline descriptive results**

Table 5 summarizes the most important indicators, including total monthly household expenditures and the share of expenditures on smoked tobacco products in 2016. As of 2016, there was at least one smoker in each of 40 percent of all households. The share was highest among households in deciles 6–7 (an average 46 percent) and lowest in low-income households (32 percent). On average, households spent 0.9 percent of their total income on cigarettes, and lower-income households spent the highest share. For instance, the poorest decile spent 1.3 percent relative to 0.5 percent among the richest.

**Table 5 – Baseline Descriptive Results, 2016, by Decile**

|_Indicator_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Expenditure per capita (Rub., monthly)|1,760|2,932|3,754|4,594|5,451|6,553|7,857|9,653|13,109|29,712|
|Expenditure per hh (Rub., monthly)|6,942|11,821|14,327|16,450|18,697|21,647|28,081|30,709|38,848|81,548|
|Prop. of exp. on tobacco products, %|1.3|1.1|0.9|0.9|0.9|1.1|0.8|0.8|0.7|0.5|
|HH that smoke tobacco, %|32|40|37|39|39|47|45|44|41|40|
|HH Size|3.9|4.0|3.8|3.6|3.4|3.3|3.6|3.2|3.0|2.8|
|Area (regional center), %|21|29|29|36|41|43|45|47|54|58|
|Female, %|0.55|1.55|2.55|3.55|4.55|5.55|6.55|7.55|8.55|9.55|
|Age 16-34, %|35|37|37|36|32|38|35|33|34|31|
|Age 35-54, %|35|31|32|31|32|33|33|36|34|36|
|Age 55+, %|30|32|31|34|35|29|32|31|32|34|
|Higher education, %|10|16|15|19|19|21|23|25|32|38|
|Years smoking (for smokers)|25|25|24|24|25|24|25|25|25|25|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Deciles have been established using per capita household expenditure.

# **5. Results**

To analyze the distributional effects of an increase in tobacco taxes, we estimate the effect on prices, medical expenditures, and working life years, aggregating these three into a single measure. The price elasticities estimated in table 1, including the lower- and upper-bound elasticities, allow us to understand how the results could change under different assumptions.

# **Tobacco price increase**

Income changes that arise from an increase in tobacco prices are estimated for each decile based on low-, medium-, and upper-bound elasticities. Using the price elasticities and the share of household expenditure on tobacco by decile, we can simulate the effects of an increase in tobacco prices (see annex A, equation A.2). To show the effect of the elasticities on prices, table 6 also includes estimates of a complete passthrough scenario (i.e. assuming a price elasticity equal to zero), whereby the increase in prices is completely transferred to consumers without a reduction in consumption. For instance, if we assume that the prices for tobacco products rose by 25 percent, given the medium-bound elasticity for cigarettes for the bottom decile (−0.68) in table 1 and the proportion of cigarettes expenditures for the bottom decile (1.3 percent) in table 5, the expected increase in household expenditures would be 0.01 percent (table 6). This represents a loss in welfare because consumers would devote a higher share of their incomes to purchasing the same amount of tobacco, thereby reducing the consumption of other goods. These results hold for all the scenarios analyzed.

9
**Table 6 - Direct Effect of Price Increase through Taxes (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Complete pass-through|-0.09|-0.08|-0.06|-0.07|-0.07|-0.09|-0.06|-0.06|-0.05|-0.03|
|Low-bound elasticity|-0.03|-0.04|-0.03|-0.04|-0.04|-0.05|-0.04|-0.04|-0.04|-0.02|
|Medium elasticity|-0.01|-0.02|-0.02|-0.02|-0.02|-0.03|-0.02|-0.03|-0.02|-0.02|
|Upper-bound elasticity|0.01|0.00|0.00|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table shows the share of total consumption for each decile. Complete pass-through refers to elasticity equal to zero; consumers pay all the increased prices, and this does not affect the quantity purchased.

**Figure 2 – Income Gains: Direct Effect of Tobacco Taxes (Increase in Expenditure because of tobacco taxes), by Decile**

<!-- Start of picture text --> 0.04<br>0.02<br>0.00<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.10<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Complete pass-through Low-bound elasticity<br>Medium elasticity Upper-bound elasticity<br>Source: Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016.<br>Note: Estimates assume a price shock of 25 percent.<br>Income Gains (%)<br><!-- End of picture text -->

# **Medical expenses**

Table 7 and figure 3 report the income effect of a reduction in medical expenses. As noted above, the model assumes that the health effects of tobacco-related diseases will immediately diminish with the reduction in tobacco consumption. Even though this assumption is implausible in the short term because changes in the effects of tobacco-related diseases take some time to materialize, it provides an upperbound estimate of the effects of tax increases. The overall results indicate that the reduction in medical expenditures is highly progressive, disproportionally benefiting lower-income groups. This derives from two factors: (1) the higher price elasticity and (2) a lower income base that massively benefits from the reduction in medical costs.

10
**Table 7 - Reduction in Medical Costs (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|1.13|0.71|0.50|0.41|0.34|0.34|0.23|0.18|0.14|0.05|
|Medium elasticity|1.60|1.05|0.76|0.65|0.55|0.56|0.40|0.33|0.24|0.10|
|Upper-bound elasticity|2.07|1.39|1.02|0.89|0.76|0.78|0.56|0.47|0.35|0.15|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey 2016. _Note:_ The table reports the share of total consumption for each decile.

**Figure 3 – Income Gains: Medical Costs of Tobacco Taxes (Reduction in Medical Expenditures), by Decile**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **Income gains deriving from an increase in working life years**

We estimate the cost of the working life lost because of tobacco consumption, assuming that the impact of lower tobacco use on health and work-generated income is direct. The 6.0 million years of life lost attributed to tobacco consumption are distributed using the occurrence of mortality profile. For each death, working years lost are divided across deciles proportionately to the number of households that consume tobacco in each income group. The results show that the reduction in tobacco consumption and the expected reduction in work years lost have a positive impact on welfare that represents a transversal income bump among all decile groups (table 8 and figure 4).

**Table 8 – Years of Working Life Lost (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.35|0.36|0.32|0.32|0.31|0.38|0.32|0.31|0.31|0.27|
|Medium elasticity|0.49|0.53|0.49|0.51|0.51|0.63|0.54|0.55|0.55|0.53|
|Upper-bound elasticity|0.64|0.70|0.66|0.70|0.71|0.88|0.76|0.79|0.79|0.79|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total consumption for each decile. Years of life lost have been estimated using all deaths from tobacco-related diseases.

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**Figure 4 – Income Gains: Production during Years Lost, by Decile, by Decile**

<!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text -->

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **Net effects: Total distributional impact**

In this subsection, we add the previous results to estimate the aggregate effect of an increase in tobacco taxes (see equation 1). The aggregate effect of an increase in tobacco taxes is positive and progressive; in the long run, poorer deciles benefit more than richer ones. The positive effect of reduced medical expenses and years of life gained offsets the negative price effect.

Under any tobacco price elasticity scenario, the overall effect for cigarettes is always positive and progressive (table 9 and figure 5). Moreover, the benefits are amplified if we compare the lower-bound elasticity with the upper-bound elasticity. Apparently, a population in which medical expenses are high and which is sensitive to tobacco price changes will reduce consumption sufficiently to allow work and health benefits to offset cost increases. This is exactly what happens in Russia. Furthermore, the assumptions in this model do not include other possible policies, such as smoking cessation programs, antismoking advertising, youth outreach, or policies financed through the new tax revenue. Therefore, these results are in line with the literature, showing the important role that taxation plays in lowering tobacco usage.

**Table 9 - Net Effect on Household Expenditures (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|1.44|1.03|0.78|0.69|0.61|0.67|0.51|0.45|0.41|0.31|
|Medium elasticity|2.08|1.56|1.23|1.14|1.04|1.16|0.91|0.85|0.77|0.62|
|Upper-bound elasticity|2.71|2.09|1.68|1.59|1.47|1.66|1.31|1.25|1.13|0.93|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total consumption for each decile.

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**Figure 5 – Total Income Effect: Direct and Indirect Effects of Tobacco Taxes (Tobacco price increase, medical expenditure, and working years gained) , by Decile**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **6. Discussion**

Despite the wealth of research on the negative effects of tobacco consumption and on the benefits of various public policy mechanisms aimed at reducing tobacco use, questions remain about the progressivity or regressivity that these entail. The implementation of tobacco taxes is considered one of the most effective ways to discourage tobacco use. Nonetheless, this policy has a direct impact on household incomes, especially among low-income households that allocate a larger proportion of their budget to buying smokes. Moreover, the net effect of an increase in tobacco taxes depends on the price elasticity of this product across different segments of the population. The price elasticity determines the magnitude of the income shock and the benefits gained from a decline in tobacco consumption.

To assess the net welfare gains from this policy, one must look beyond the direct impact on household income and consider other benefits of lower tobacco consumption, including a reduction in medical costs and an increase in the potential working years associated with good health. Thus, it is critical to justify the maintenance or intensification of the use of tobacco taxes by means of a demonstration of the aggregate monetary gains or losses generated. Moreover, the policy should focus on low-income households that are more likely to smoke and, hence, tend to be the most affected by consumption taxes. One of the main motivations for this study is the ability to weigh the main costs and benefits of tobacco taxation to determine if, in the end, the policy is regressive.

The results show that, considered by itself, a tobacco price increase through higher taxes generates negative income variations across all groups in the Russian population because overall prices increase. These negative effects are particularly acute in the scenario of lower-bound elasticity, and they are more moderate as elasticity expands in absolute terms. Based on the assumptions behind a more comprehensive approach, including benefits through lower medical expenses and an increase in working years, the overall monetary effect reverses and is positive and progressive among all deciles.

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The three price elasticity scenarios mimic the short- versus the long-term effects of tobacco taxes. There is evidence that adult smokers will only present small changes in their behavior if prices increase; the lower-bound elasticity would tend to represent this behavior more closely. In contrast, younger groups of the population would show more elastic demand, similar to the upper-bound elasticity. After a few decades, we expect the impact of the tax policy to resemble the upper-bound elasticity scenario.

# **References**

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Policies on Smoking Prevalence and Smoking-Attributable Deaths in Russia.” _Tobacco Control_ 23 (6): 484–90.

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# **Annexes**

# **A. Model**

This section describes the partial equilibrium approach used to simulate the impact on consumption of an increase in the price of cigarettes. This approach is used to evaluate the first-order effects of a change in prices. It relies mainly on household expenditure patterns. The focus is on the impacts of a rise in the price of cigarettes because this has been the focus of the potential reform of tobacco taxes.

To assess the distributional impact of an increase in the price of cigarettes, the simulation allows for differences in the responses across consumption deciles to reflect the fact that poor households likely have different price elasticities relative to households with more resources. The different elasticities, combined with the initial consumption patterns across deciles, explain whether a price reform will be more regressive, more neutral, or more progressive.

The loss of real consumption arising from the price increases in a product _i_ is obtained as follows: where ωij is the share of product _i_ in total household expenditure for a household in a decile _j_ ; ∆pi is the price increase; and ∆ωij is the change in the consumption of the good that depends on the price elasticity of the product.<sup>12</sup>

## **Change in tobacco expenditure**

To estimate the variation in cigarette consumption after the price increase, the model considers the change in prices (∆pi), the tobacco price elasticity (εj) for decile _j_ , and the share of cigarette expenditure in period 0 (ωij). The change in expenditure for each household in each decile is presented as a share of total expenditure and averaged by decile to quantify the overall impact, as follows:

## ∆ Expenditurei,j=((1 + ∆P)(1 + εj ∗∆P)-1) `∗`

## **Medical expenses**

The change in medical expenses associated with tobacco-related diseases is estimated using equation (A.3), where the cost of treatment of tobacco-related diseases for income decile i is obtained from administrative data. The cost of tobacco-related medical expenses is distributed across income decile i according to the share of households that consume tobacco in decile i. Equation (A.3) shows the income gains associated with the reduction in medical expenses because of reduced tobacco consumption over the long term.

## ∆ Medical expenditurei,j=((1 + εj ∗∆P)-1) `∗`

A reduction in tobacco consumption in the long run would be strongly related to a reduction in tobaccorelated diseases. The model assumes that the health effects of tobacco-related diseases will immediately diminish with the reduction in tobacco consumption. Even though this assumption is implausible in the

> 12 For a detailed discussion of the methodology, see Coady et al. (2006); Kpodar (2006).

16
short term because changes in the effects of tobacco-related diseases take some time to materialize, it provides an upper-bound estimate of the effects of tax increases.

# **Increase in working life years**

The model estimates the impact on income arising from the increase in working years (equation A.5). To estimate the increase in working years, the years of life lost ( _YLL_ ) from tobacco-related diseases are distributed across deciles (i) proportionally to the number of households that consume tobacco (equation A.4). Subsequently, the income lost is estimated as the average income per household in decile i. Overall, the model anticipates that income will increase as the number of years lost drops off because of the decline in premature deaths associated with the decline in tobacco consumption.

Working Yearsi = (YLLi ∗Share of Smokersi)/Populationi (A.4) The total income gains in each income group are estimated by adding the results of the increase in tobacco expenditures, the reduction in medical treatments, and the gain in working years (equation 1 in the main text).

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# **B. Tobacco Price Elasticity by Decile**

Let Qid be defined as the average quantity smoked per day by individual i in income decile d; P the average price per cigarette (unit value of tobacco use); Di the consumption decile of individual i; and Xid the individual characteristics. Then, the smoking intensity equation is written as follows: ln Qid = β0 + β1lnP∗Di + β3Xid + μid (B.1) The empirical analysis of equation (B.1) assumes a log-log relationship among smoking intensity, price, and income. lnQid is observed if and only If the individual from a given decile d is a current smoker.

Several models were tested before deciding on the final elasticities used. All relied on the RLMS-HSE in 2010–16. Table B.1 considers the main equation using cross-sectional estimates and some that use the panel feature of the data. All the models were tested without controls and then adding demographic controls and year fixed effects. Among control variables included in the household level model were urban status, macroregion and year fixed effects. For the individual level cross-section and panel models, the controls included age, education, number of years smoked, urban status, macroregion, and year fixed effects.

**Table B.1 Regression Results, by Model**

|_Both_|<br>_1_|<br>_2_|<br>_3_|<br>_4_|<br>_5_|<br>_6_|<br>_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Cross-section (ind level)|-0.25|-0.23|-0.22|-0.21|-0.20|-0.21|-0.20|-0.20|-0.19|-0.19|
|Panel (RE)|-0.14|-0.13|-0.13|-0.12|-0.12|-0.12|-0.12|-0.12|-0.11|-0.11|
|Panel (FE)|-0.07|-0.06|-0.06|-0.05|-0.05|-0.05|-0.05|-0.05|-0.04|-0.04|
|Panel (HT)|0.01|0.05|0.05|0.03|-0.03|-0.11|0.01|0.01|0.04|0.06|
|Cross-section (HH level)|-0.68|-0.61|-0.58|-0.54|-0.52|-0.51|-0.49|-0.46|-0.45|-0.41|
|Common prices|-0.02|-0.03|-0.03|-0.03|-0.03|-0.03|-0.02|-0.02|-0.03|-0.02|
|_Men_|||||||||||
|Cross-section (ind level)|-0.22|-0.21|-0.20|-0.18|-0.17|-0.17|-0.17|-0.16|-0.16|-0.16|
|Panel (RE)|-0.13|-0.12|-0.12|-0.11|-0.10|-0.10|-0.10|-0.10|-0.10|-0.10|
|panel (FE)|-0.07|-0.06|-0.06|-0.05|-0.05|-0.05|-0.05|-0.04|-0.04|-0.04|
|_Women_|||||||||||
|Cross-section (ind level)|-0.33|-0.30|-0.27|-0.30|-0.31|-0.31|-0.31|-0.29|-0.29|-0.28|
|Panel (RE)|-0.17|-0.16|-0.14|-0.15|-0.15|-0.14|-0.14|-0.15|-0.14|-0.13|
|Panel (FE)|-0.07|-0.07|-0.04|-0.05|-0.05|-0.03|-0.04|-0.05|-0.03|-0.03|

_Source_ : Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16.

_Note_ : Demographic controls are included on every model. For the HH level cross-sectional demographic controls include: urban status, macro region and also controls for year fixed effects. For the individual level cross-section and panel models: gender, age, education, , number of years smoked, urban status, macroregion, and year fixed effects.

The total expenditures were deflated using the consumer price index, while the prices for cigarettes were deflated using the average prices provided by the Russian Federal State Statistics Service, Rosstat. To adjust for spatial variation in prices, all monetary variables were spatially deflated using the fixed basket index provided by Rosstat.

The deciles were defined based on per capita total expenditures. The panel model was estimated in both specifications, with fixed and random individual effects. According to the Hausman test, the hypothesis of no correlation between individual random effects and other regressors should be rejected, and the model with random effects provides biased estimates.

Self-reported prices are potentially endogenous because the decision on the brand of cigarettes to buy is taken together with the decision on the quantity of smoking. A few techniques were used to try to address 18
this, as follows: (1) using communal prices as regressor and (2) using communal prices as an instrumental variable. However, because of lower variation in communal prices, these estimates led to uninterpretable results. This is why they were not used in the additional analysis.

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# **C. Estimating direct health care costs associated with the provision of medical care related to tobacco-related diseases**

The direct health care costs related to treatment of tobacco-related diseases include costs of medical care provided in outpatient facilities, hospitals, and day-care hospitals for patients with the following diseases:

- Diabetes

- Cardio-vascular disease

- Respiratory disease

- Cancer, and

- TB

When calculating direct health care costs related to cases associated with smoking, the following were used:

- Tariffs calculated in accordance with the guidelines for paying per DGR-based completed treatment case, 2017 version<sup>13</sup> ;

- Official data from statistical surveys forms:

- No 7 "Information about malignant neoplasms", table 2000 "Information on newly diagnosed malignant neoplasms"

- No 12 "Information on the number of diseases registered with patients living in the area served by a medical organization"<sup>14</sup>

- No 14 “Information on performance of health facilities providing inpatient health care”

- No 14 DC “Information on performance of health facilities providing inpatient health care in day-care hospitals”

- Standards of financial costs calculated for the Ministry of Health of the Russian Federation based on the Ministry of Finance methodology, for tuberculosis;

- Standards of financial costs for high-tech medical care according to the Order of the Government of the Russian Federation of 19.12.2016 N 1403 “About the Program of the state guarantees of free rendering medical care to citizens for 2017 and for planning period 2018 and 2019";

- Number of cases of high-tech medical care in 2017;

- Values of the relative risk (RR) of smoking-related disease based on scientific publications, the results of scientific research available in open sources of information;

- Correction factor to the cost of outpatient visit according to the Letter of the Ministry of Health of the Russian Federation of 23.12.2016 No 11-7/10/2-8304 "On the formation and economic justification of the territorial program of state guarantees of free provision of medical care to citizens for 2017 and the planning period of 2018 and 2019;

- Ratio of outpatient visits in the completed outpatient case according to the Letter of the Ministry of Health of the Russian Federation of 23.12.2016 No 11-7/10/2-8304 "On the formation and economic justification of the territorial program of state guarantees of free provision of medical care to citizens for 2017 and the planning period of 2018 and 2019;

- Average standards of financial costs per unit of volume of medical care according to the Order of the Government of the Russian Federation of 19.12.2016 No 1403 “About the program of the state

> 13 Letter No. 11-7 / 10 / 2-8080 of the Ministry of Health of the Russian Federation, FMHIF #13572 / 26-2 / and dated November 21, 2012, "On Guidelines for Medical Care Payments using MHI Proceeds" (Calculation Methodology).

> 14 Ordinance of Rosstat of July 21, 2016 #355 "On the approval of statistical tools for the organization of federal statistical surveys in public health by the Ministry of Health of the Russian Federation.”

20
guarantees of free provision of medical care to citizens for 2017 and for the planning period 2018 and 2019."

- The average cost of drug provision at the outpatient stage, in rubles, calculated on the basis of the data of the analytical review of the Russian pharmacological market for 2017, prepared by DSM Group, for diseases of the circulatory system, respiratory system and malignant neoplasms. For diseases of diabetes mellitus, the average cost of drug provision is determined according to scientific research. For diseases of tuberculosis in an outpatient setting, drug therapy is included in the standard of financial costs.

In accordance with the Methodology, the size of the base rate for DRGs in the 24-hour and day hospitals was determined on the basis of the average standard of cost of hospitalization according to the Program of the state guarantees of free medical care to citizens for 2017 and for the planning period 2018 and 2019 (Rub 28,767.4), except for expenses on high-tech medical care, hemodialysis services (calculations were made according to the Federal compulsory health insurance Fund data received for performance of research work). Thus, the size of the base rate for the 24-hour hospital was 22,000 rubles and for the day hospitals, 10,000 rubles.

As a tariff for the cost of outpatient treatment, the average standard for the cost of outpatient visits in accordance with the Program of state guarantees for the provision of free medical care to citizens for 2017 and for the planning period of 2018 and 2019, equal to Rub 376.2 rubles, is taken.

Based on the results of the Global Adult Tobacco Survey (GATS),<sup>15</sup> the prevalence of smoking in Russia in 2016 was 30.5%.

To calculate the number of diseases associated with tobacco smoking, the generally accepted indicator in available literature, indicator of population attributable risk (PAR), was used. PAR is calculated based on the data on smoking prevalence and on the indicator of the relative risk of disease in smokers vs. nonsmokers (RR).

Calculation of the indicator of the relative risk of disease in smokers vs. non-smokers was not done, and values of the RR were taken from the open sources:

- Diabetes: during long-term follow-up: the Malmö Preventive Project. J Intern Med. 2006 Aug;260(2):134-41; http://ex-diabetic.com/vashe-zdorove/caharnyj-diabet-i-kurenie.html

- Cardio-vascular diseases:  How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. https://www.ncbi.nlm.nih.gov/books/NBK53012/

- Respiratory diseases:  Краснова Ю.Н. Влияние табачного дыма на органы дыхания//Сибирский медицинский журнал, Том 137, No6, стр.11-16.2015 г.

- Cancer:  Ying-Ying Wang, Wei Zhang, et.al. Population attributable risks of cigarette smoking for deaths of all causes, all cancers and other chronic diseases among adults aged 40-74 years in urban Shanghai, China. Chin J Cancer Res. 2015 Feb; 27(1): 59–65.; Белялова Н.С., Белялов Ф.И. Факторы риска и профилактика рака. Часть 1. Клиническая медицина 2005;11:17-21.

- TB:  http://www.who.int/tobacco/resources/publications/ru_tfi_tb_tobacco_factsheet.pdf

Population risk of smoking (RAR) was calculated by the formula:

# RAR = ((p0+p1*RR)-1)/(p0+p1*RR)

> 15 Global Adult Tobacco Survey: Overview, 2016. https://static1.rosminzdrav.ru/system/attachments/attaches/000/036/485/original/GATS-%D0%BA%D1%80%D0%B0%D1%

82%D0% BA%D0% B8%D0% BE7af5a0856d91% 82%D1% 87%D0% B5%D1% 82-%D1% 80%D1% 83%D1% 81.pdf? 1511269867 21
where _p0_ – proportionof non-smokers _p1_ – share of smokers at the moment RR – the relative risk of diseases in smokers in relation to non-smokers.

The following algorithm was used to calculate the **cost of inpatient treatment in 24-hours** and **day care hospitals** for diseases caused by smoking:

1. define average base rate per inpatient treatment case in 24-hours and day care hospitals (averaging rates specified in regional tariff agreements);

2. extract information from statistical survey forms on the total number of cases of treatment for ICD10 codes in question; 3. defining the values of RR based on available open sources 4. relate the disease classes with PAR indicators- the shares of diseases associated with smoking by disease (attributable risk of smoking); 5. determine the number of treatment cases triggered by smoking (the number of treatment cases under ICD-10 codes, multiplied by the relative attributable risk of smoking); 6. on diseases of the circulatory system, respiratory system, and malignant neoplasms: determination of the clinical profile group on the basis of decoding the DRGs provided in hospital and day hospitals, and the determination of the DRG weight on the profile of diseases; 7. for diabetes – determination of DRGs, which include the treatment of diabetes and the calculation of the average DRG weight for the disease; 8. calculate price per treatment case based on profile group (average base rate multiplied by average disease-profile weight); 9. determine the cost of treatment for diseases associated with tobacco smoking as the number of treated cases caused by tobacco smoking, multiplied by price per case under respective DRG profile group and multiplied by PAR.

Overall, the cost is determined by formula: where _Cost_ – the cost of inpatient treatment for diseases associated with smoking; NзаболМКБ – number of registered diseases according to the ICD-10 code; Кз<sup>КПГ</sup> – average weight of DRG profile groups;

- _БС_ – average DRG base rate for inpatient care;

- PAR – population attributable risk for ICD-10 code (the proportion of diseases associated with smoking).

For the calculation of health care losses from tuberculosis caused by tobacco, the calculation was made according to the following formula: where Cost TB – the costs for the treatment of diseases associated with tobacco; NзаболМКБ – number of registered diseases according to the ICD-10 code; NFZ – the standard of financial expenses of treatment of patients with tuberculosis in the conditions of 24-hours and day hospitals; 22
PAR –population attributive risk for ICD-10 (proportion of tobacco-associated diseases).

The calculation of the costs of high-tech medical care related to tobacco-associated diseases was carried out in accordance with the following algorithm: CostHTМC = NHTМC * PAR * NFZ HTМCav, where: CostHTMC – the cost for high-tech medical care related to tobacco-associated diseases; NHTМC – number of cases of high-tech medical care according to ICD-10 codes; NFZHTMCav – Average standard of financial expenses per high-tech medical care case; PAR – population attributive risk for ICD-10 (proportion of tobacco-associated diseases).

The calculation of the cost of outpatient treatment of diseases caused by smoking was carried out in accordance with the following algorithm:

- extraction of the data from the form of statistical observation No 12 "Data on the number of diseases registered in patients living in the area of service of the medical organization" on the total number of cases of diseases for the considered ICD-10 codes;

- calculation of PAR – shares of diseases associated with smoking in the context of diseases (population attribute risk of smoking);

- determination of the number of cases of treatment related to smoking: the product of the number of cases of treatment according to the ICD-10 code and the PAR;

- determination of the correction factor to the cost of an outpatient visit taking into account the specialty, in accordance with the Letter of the Ministry of Health of Russia of 23.12.2016 N 117/10/2-8304 “On the formation and economic justification of the territorial program of state guarantees of free provision of medical care to citizens for 2017 and the planning period of 2018 and 2019”;

- determination of costs for the outpatient treatment of diseases associated with tobacco as a product of the number of cases of diseases related with tobacco, fee per cases in the outpatient setting, including drug coverage, the values of the population attribute risk.

The calculation of the costs of drugs for treatment of outpatient cases was carried out according to the data on the volume of costs of the pharmaceutical market in the commercial segment (p. 15) and within the preferential drug provision (p. 41, 43), presented in the analytical review of the pharmacological market of Russia for 2017, prepared by the DSM Group, for each of the considered classes of diseases, except tuberculosis, and the number of cases of diseases.

In total, the costs/losses associated with outpatient treatment of tobacco-related diseases are determined by formula 3: Outpatient costs = N cases * (KP * T visit + T drugs)* PAR, (3) where – Outpatient costs costs of outpatient treatment of tobacco-related diseases; N cases – number of diseases of the specified class of diseases; KP – correction factor to the cost of a visit taking into account the specialty corresponding to the disease; T visit – average norms of costs of outpatient treatment; Kr – ratio of outpatient visits in the completed outpatient case; – T drugs average cost of drug provision at the outpatient stage; PAR – population attributive risk for ICD-10 (proportion of tobacco-associated diseases).

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The total direct losses to the health care system for the treatment of tobacco-related diseases were calculated by summing up the losses in the inpatient and outpatient sectors.

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# **D. Robustness: Simulation Using Data on Individuals**

**Table D.1 – Cigarettes Price Elasticities, by Decile**

|_Price Elasticity_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|-0.032|-0.014|0.005|0.015|0.020|0.017|0.018|0.026|0.029|0.036|
|Medium|-0.232|-0.214|-0.195|-0.185|-0.180|-0.183|-0.182|-0.174|-0.171|-0.164|
|Upper bound|-0.432|-0.414|-0.395|-0.385|-0.380|-0.383|-0.382|-0.374|-0.371|-0.364|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles were created using individual expenditure. Lower- and upper-bound elasticities show on average differences of −0.2 and +0.2, respectively, with the medium-bound elasticity. Demographic controls include: area, macro region and year fixed effects.

**Table D.2 - Direct Effect of Price Increase through Taxes (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Complete pass-through|-0.59|-0.46|-0.31|-0.31|-0.26|-0.29|-0.22|-0.20|-0.14|-0.08|
|Low-bound elasticity|-0.57|-0.45|-0.31|-0.31|-0.27|-0.29|-0.23|-0.21|-0.15|-0.09|
|Medium elasticity|-0.42|-0.34|-0.23|-0.24|-0.20|-0.22|-0.17|-0.16|-0.11|-0.07|
|Upper-bound elasticity|-0.27|-0.22|-0.16|-0.16|-0.14|-0.15|-0.11|-0.11|-0.08|-0.05|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table shows the share of total consumption for each decile. Complete pass-through refers to elasticity equal to zero; consumers pay all the increased prices, and this does not affect the quantity purchased.

**Table D.3 - Reduction in Medical Costs (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.09|0.03|-0.01|-0.02|-0.02|-0.02|-0.01|-0.02|-0.01|-0.01|
|Medium elasticity|0.66|0.42|0.27|0.21|0.18|0.18|0.14|0.12|0.08|0.04|
|Upper-bound elasticity|1.24|0.80|0.55|0.44|0.39|0.39|0.30|0.25|0.18|0.09|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total consumption for each decile.

**Table D.4 – Years of Working Life Lost (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.03|0.01|0.00|-0.01|-0.02|-0.02|-0.02|-0.03|-0.03|-0.04|
|Medium elasticity|0.20|0.21|0.17|0.17|0.17|0.21|0.19|0.20|0.18|0.20|
|Upper-bound elasticity|0.38|0.41|0.35|0.35|0.36|0.43|0.41|0.42|0.40|0.45|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total individual consumption for each decile. Years of life lost have been estimated using all deaths from tobacco-related diseases.

**Table D.5 - Net Effect on Household Expenditures (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|-0.45|-0.41|-0.32|-0.34|-0.31|-0.33|-0.26|-0.25|-0.19|-0.14|
|Medium elasticity|0.45|0.29|0.21|0.14|0.15|0.17|0.17|0.16|0.15|0.18|
|Upper-bound elasticity|1.34|0.99|0.74|0.62|0.61|0.67|0.59|0.57|0.50|0.49|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total consumption for each decile.

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