metadata: authors: 0: author: Fuchs,Alan
count: Indonesia
docna: 0: docna: The distributional effects of tobacco
            taxation: the evidence of white and clove cigarettes in Indonesia
docty: Policy Research Working Paper
subtopic: Cancer,Health Care Services Industry,Tobacco Use and Control,Law and Justice Institutions,Disease Control & Prevention,Public Health Promotion
teratopic: Industry,Health, Nutrition and Population,Law and Development
abstracts: cdata!: Despite the well-known positive impact
            of tobacco taxes on health outcomes, policy makers hesitate
            to use them because of their possible regressive effect,
            that is, poorer deciles are proportionally more negatively
            affected than richer ones. Using an extended cost-benefit
            analysis to estimate the distributional effect of white and
            clove cigarettes in Indonesia, this study finds that the
            long-run impact may be progressive. The final aggregate
            effect incorporates the negative price effect, but also
            changes in medical expenditures and additional working
            years. The analysis includes estimates of the distributional
            impacts of price rises on cigarettes under various scenarios
            using 2015–16 Indonesia National Socioeconomic Surveys. One
            contribution is to quantify the impacts by allowing price
            elasticities to vary across consumption deciles. Overall,
            clove cigarette taxes exert an effect that depends on the
            assumptions of conditional price elasticity. If the
            population is more responsive to tobacco price changes, then
            people would experience even more gains from the health and
            work benefits. More research is needed to clarify the
            distributional effects of tobacco taxation in Indonesia.
geo_reg_key: 517198,517191
repnme: repnme: The distributional effects of tobacco
            taxation: the evidence of white and clove cigarettes in Indonesia
display_title: The distributional effects of
            tobacco taxation: the evidence of white and clove cigarettes
            in Indonesia
listing_relative_url: /research/2018/08/30355652/distributional-effects-tobacco-taxation-evidence-white-clove-cigarettes-indonesia
topicv3: Cancer m1326342 1431,Tobacco Use and Control m1326794 1439,Mortality m1504365 3661,Health m1504661 3480,Tobacco Taxes m1504803 3496
publishtoextweb_dt: 2018-08-17T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population,Law and Development
no_of_pages: 31
model_extractions: WPS8558 Policy Research Working Paper 8558 The Distributional Effects of Tobacco Taxation The Evidence of White and Clove Cigarettes in Indonesia _Alan Fuchs Giselle Del Carmen_ Poverty and Equity Global Practice August 2018
Policy Research Working Paper 8558

# **Abstract**

Despite the well-known positive impact of tobacco taxes on health outcomes, policy makers hesitate to use them because of their possible regressive effect, that is, poorer deciles are proportionally more negatively affected than richer ones. Using an extended cost-benefit analysis to estimate the distributional effect of white and clove cigarettes in Indonesia, this study finds that the long-run impact may be progressive. The final aggregate effect incorporates the negative price effect, but also changes in medical expenditures and additional working years. The analysis includes estimates of the distributional impacts of price rises on cigarettes under various scenarios using 2015–16 Indonesia National Socioeconomic Surveys. One contribution is to quantify the impacts by allowing price elasticities to vary across consumption deciles. Overall, clove cigarette taxes exert an effect that depends on the assumptions of conditional price elasticity. If the population is more responsive to tobacco price changes, then people would experience even more gains from the health and work benefits. More research is needed to clarify the distributional effects of tobacco taxation in Indonesia..

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
# **The Distributional Effects of Tobacco Taxation: The Evidence of White and Clove Cigarettes in Indonesia**

**Alan Fuchs and Giselle Del Carmen**<sup>**1**</sup>

JEL Codes: H23, H31, I18, O15

> 1 Fuchs: Poverty and Equity Global Practice, World Bank (email: <u>afuchs@worldbank.org); Del Carmen: Poverty and</u> Equity Global Practice, World Bank (email: gdelcarmen@worldbank.org). 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 authors are grateful to Patricio Marquez, Daniela Paz, Maria Eugenia Genoni, Sailesh Tiwari, Jaffar Al Rikabi, Federico Gil Sander, Pandu Harimurti, and Abdillah Ahsan for offering comments and support. 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 (WHO) 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 (Ng et al. 2014) and is among the major preventable causes of disease and premature death globally (Doll and Hill 1956; 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 middleincome countries and are less likely to be informed about the adverse health effects of tobacco use relative to individuals in high-income countries. Indonesia is a major producer and consumer of tobacco; 68.1 percent of men ages 15 or above and 2.5 percent of women use some form of tobacco (Zheng et al. 2018). Kretek cigarettes, often referred to as clove cigarettes, are the most popular tobacco products in the country. As of 2015, an estimated 40 percent of the adult population smoked kretek cigarettes (World Bank 2017a). They are made of a combination of tobacco and ground clove mixed with special sauces unique to each brand (Arnez 2009; Malson 2003). It is estimated that more than one million farmers grow cloves in Indonesia, which accounts for more than 70 percent of the world’s cloves (World Bank 2017b).

Over more than a decade, the Indonesian government has been gradually raising excise taxes on tobacco products. Yet, the taxes remain below the recommended WHO threshold. While the WHO recommends total taxes on tobacco products—that is, specific excise taxes, local taxes, and the value added tax—should be at least 75.0 percent of the retail price, the average rate in Indonesia is 57.4 percent (WHO 2017b). The current cigarette excise tax encompasses 12 tiers. Each tier is based on the type of product, the number of cigarettes produced, and the per unit retail price. Inevitably, such a system leads to substitution toward lower-priced products and thus has limited impact on smoking reduction. Moreover, the Indonesian government has a preferential tax structure for hand-rolled kretek factories because these employ a significant share of kretek workers and account for more than half of the factories in the tobacco industry. As of 2018, the excise tax on hand-rolled kreteks was 24.0 percent of the minimum retail price, while the excise tax on machine-made kreteks was 45.8 percent, depending on the production volume and range of retail prices. In addition to excise taxes on the cigarettes sold in Indonesia, the government also applies a 9.1 percent value added tax, which is collected directly from manufacturers (Ahsan et al. 2013).

Even though the government has considered reforming the excise tax structure by reducing and consolidating the number of tiers and raising taxes, it is concerned this might affect the livelihoods of people in the kretek value chain. A recent World Bank (2017a) study finds that the tax increase would reduce employment in the handmade kretek industry by approximately 8 percent. Some households would lose as much as 27 percent–52 percent of total household income. Nonetheless, the reduction in employment in this industry is equivalent to 1.4 percent of the potential revenue gains from increasing cigarette taxes. The study estimates the annual revenue gain would be Rp 12.9 billion, while the employment loss in the handmade kretek industry would be 24,000 jobs.<sup>2</sup> Thus, the tax and price increase would not only reduce tobacco use and related health care costs, it would generate additional revenue that would more than compensate for the income loss associated with a reduction in employment and production in the kretek industry (World Bank 2017a).

Similarly, reforms in the cigarette excise tax structure have also raised concerns about the possible effects on tobacco farmers. Nonetheless, tobacco leaf is a relatively small agricultural subsector in Indonesia, representing roughly 0.30 percent of the agricultural sector and 0.03 percent of gross domestic product (GDP) (Indonesia Ministry of Agriculture 2010). Another World Bank (2017c) study analyzes the socioeconomic conditions of tobacco farmers in Indonesia and suggests that tobacco is not profitable for most farmers and that there is widespread poverty and food insecurity among tobacco farmers, as well as evidence of child labor during school

> 2 The average annualized kretek income is estimated at Rp 15,500,000, and the projected decrease in wage income is 48.9 percent. The loss of 24,000 jobs in the kretek industry implies a total income loss of Rp 182 billion, equivalent to 1.4 percent of the revenue gain.

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hours. Thus, given the economic and social costs of tobacco growing, additional tax revenues from a reformed tobacco excise system could be used to help tobacco farmers switch to more profitable alternative crops.

Although there are over 225,000 deaths annually from tobacco-related diseases in Indonesia, nearly 470,000 children ages 10–14 and 53 million adults ages 15 or older continue to use tobacco daily. In 2016, 21 percent of male deaths in Indonesia were caused by tobacco; this is higher than the average in countries with a medium human development index.<sup>3</sup> Similarly, Indonesia is among the few countries that has not signed or ratified the WHO Framework Convention on Tobacco Control (WHO 2017c). This policy creates an attractive space for the tobacco industry to continue growing because there is limited regulation of tobacco use.

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 implies that higher tobacco prices because of higher taxes induce a behavioral response involving a reduction and cessation in tobacco consumption that are 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. The third channel is a rise in incomes because of gains in years of employment because of the prolongation of life expectancy. To assess the impact of these effects, this paper estimates the price elasticity of tobacco, simulates upper- and lower-bound 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. Section 4 presents 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. Literature review**

Blecher and van Walbeek (2008) estimate that, because the mean incomes in countries grew more quickly than average tobacco prices, cigarettes became more affordable in developing countries between 1997 and 2006. This indicates that, as smoking prevalence increases, these countries are more likely to bear the major health impacts of tobacco consumption. Furthermore, low- and middle-income countries are experiencing a rise in noncommunicable diseases. Kristina et al. (2015) estimate that, in Indonesia, cancer mortality attributable to smoking represented 74,440 deaths (30.6 percent of total cancer deaths), of which 95.0 percent were men. Cancer deaths attributable to smoking were responsible for 1.2 million years of potential life lost. This has negative consequences for human capital development and imposes an increasing economic burden because smoking decreases earnings potential and labor productivity (WHO 2015a).

Several studies have quantified the economic costs of smoking, though most have been carried out in highincome countries. Annual direct health care costs attributable to tobacco use amounted to US$2.2 billion in Indonesia in 2015, which is equivalent to 2.5 percent of GDP (Kristina et al. 2018). In Bangladesh, the overall economic cost of tobacco use is estimated at Tk 110 billion (US$1.9 billion) or over 3 percent of GDP in 2004

> 3 See “The Tobacco Atlas: Indonesia,” American Cancer Society and Vital Strategies, Atlanta, <u>https://tobaccoatlas.org/country/indonesia/.</u>

3
(WHO 2007). In contrast, annual tobacco-related health costs are estimated at US$81 billion in the United States, nearly US$7 billion in Germany, and US$1 billion in Australia (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 GDP (US$1.9 trillion in purchasing power parity U.S. dollars). The highest share, according to these authors, was in high-income countries (US$1.1 trillion in purchasing power parity dollars), where the tobacco epidemic is the most advanced.<sup>4</sup> Nearly 40 percent of these costs are concentrated in developing countries, reflecting the substantial burden experienced by this group of countries. Earlier estimates of Lightwood et al. (2000) indicate that the gross health costs of tobacco in highincome countries are between 0.1 percent and 1.0 percent of GDP. Likewise, Pichón-Riviere et al. (2014) estimate the annual direct costs of tobacco-related disease in the Chilean health system at approximately 0.6 percent of GDP.

Tobacco price increases are also associated with expansion in productive life years. Verguet et al. (2015) analyze the health effects of a 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, PichónRiviere 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. On Indonesia, Ahsan et al. (2013) estimate that, if taxes were increased to 57 percent of the retail price, 1.96 million tobacco-related deaths would be averted. Similarly, if taxes were raised even more, to 70 percent, the number of deaths averted would increase to over 5 million.

Similarly, exposure to secondhand smoke has a strong relationship with many respiratory diseases among children and adults (DHHS 2004, 2014; Mason, Wheeler, and Brown 2015; Öberg et al. 2011). In Indonesia, more than 97 million nonsmokers are constantly exposed to secondhand smoke (Barber et al. 2008). According to WHO (2017a), secondhand smoke is responsible for over 890,000 premature deaths per year. In the United States, exposure to secondhand smoke costs an estimated US$5 billion annually in direct medical costs and over US$5 billion more in indirect medical costs, that is, disability and lost wages (Behan, Eriksen, and Lin 2005). In the state of Indiana, the health-related costs of secondhand smoking have been estimated at more than US$1.3 billion annually (Mason, Wheeler, and Brown 2015). McGhee et al. (2006) estimate the cost of direct medical care, long-term care, and productivity losses because of secondhand smoke exposure in Hong Kong SAR, China, at approximately US$156 million annually.

Tobacco taxation has been recognized as one of the most effective tobacco control strategies. In high-income 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. Djutaharta et al. (2005) estimate that a 10 percent increase in price would reduce total cigarette consumption in Indonesia by 6 percent. The decrease would be higher (7 percent) among low-income households and lower among wealthier ones (3 percent). Moreover, the study estimates that a 50.0 percent tax increase would raise tobacco tax revenues by 27.5 percent. A similar study by Barber et al. (2008) estimates that raising cigarette taxes to 50 percent of the retail price can decrease the expected mortality rate by 2 percent–5 percent. Adioetomo, Djutaharta, and Hendratno (2005) simulate the impact of a 10.0 percent tax increase and find that the subsequent increase in cigarette prices (4.9 percent) causes a 3.0 percent decline in consumption.

Other tobacco control interventions are also relevant in decreasing demand, but have a smaller impact on tobacco consumption. Several studies have found that public health awareness campaigns contribute to a

> 4 Goodchild, Nargis, and Tursan d’Espaignet (2018) estimate the economic cost of smoking-attributable diseases at US$15 billion in low-income countries, US$359 billion in lower-middle-income countries, and US$354 billion in upper-middleincome countries, all in purchasing power parity U.S. dollars.

4
reduction in cigarette use, but the impact has generally been small and, in some cases, temporary.<sup>5</sup> Levy, de Almeida, and Szklo (2012) estimate that the vast reduction in tobacco use in Brazil was mostly caused by higher tobacco prices (46 percent of the impact) and, to a lesser extent, by smoke-free policies (14 percent). WHO (2015b) argues that smoke-free environments are the only way to mitigate the harmful impacts of secondhand smoking.

Policy makers can design an effective tobacco taxation system based on information on the price elasticity of tobacco products. Tax increases tend to generate more impact on tobacco consumption in low- and middleincome countries relative to high-income ones (WHO 2015b). Several studies have estimated the relationship between tobacco prices and consumption. Chaloupka and Grossman (1996) and Lewit and Coate (1982) estimate the elasticity among the under-18 population in the United States at between −1.44 and −1.31 and, among adults ages 18 years or older, at between −0.27 and −0.42. Cigarette price elasticities across income groups in India range from −0.83 for the lowest income group and −0.26 for the highest (Selvaraj, Srivastava, and Karan 2015). Gallus et al. (2006) estimate a price elasticity of −0.46 for 52 countries in Europe. In the United Kingdom, price elasticity is estimated at −0.5 and, in Hungary, between −0.44 and −0.37 (Szilágyi 2007; Townsend, Roderick, and Cooper 1994). Denisova and Kuznetsova (2014) estimate price elasticities in Ukraine by income deciles, ranging from −0.44 for the lowest income group to −0.11 for the highest. Fuchs and Meneses (2017a) also estimate decile-level price elasticities in Ukraine and find a higher average price elasticity (−0.45), ranging from −0.33 for the richest income group and −0.59 for the poorest. In Moldova, Fuchs and Meneses (2018) find a price elasticity of -0.51 for the poorest decile and -0.26 for the richest.

The price elasticity of cigarettes in Indonesia ranges from −0.26 to −0.76. Bird (1999) uses annual aggregate data on Indonesia from 1970 to 1994 to estimate a price elasticity of the demand for cigarettes at −0.43 in the long run and −0.60 in the short run. Similarly, Djutaharta et al. (2002) use aggregate time series data on Indonesia from 1970 to 1996 to estimate an elasticity of −0.57, while, in 1970–2001, they the elasticity at −0.35. Based on the 1999 Indonesia National Socioeconomic Survey, Adioetomo, Djutaharta, and Hendratno (2005) estimate a total price elasticity of cigarette demand of −0.61 and find that price elasticity decreased in absolute terms at higher income levels: the price elasticity is −0.67 among the low-income group, −0.33 among the middle-income group, and −0.31 among the high-income group. For clove cigarettes, de Beyer and Yurekli (2000) use a log-linear model with 1980–95 data to estimate a price elasticity of demand of −0.51. Nasrudin et al. (2013) use the Indonesian Family Life Survey to estimate cigarette price elasticity: −0.15 and −0.16 among the first two income deciles and −0.20 to −0.28 among the third to ninth deciles. Witeolar, Rukumnuaykit, and Strauss use the household panel of the Indonesian Family Life Survey in 1997 and 2000 to estimate a price elasticity of −0.80.

Two key factors that determine tobacco price elasticities are age and income. Young adults and individuals in low-income groups are more responsive to price changes relative to their peers. This makes them particularly susceptible to tobacco tax increases because they tend to be less dependent on nicotine, more affected by peer effects, and possess less disposable income (Jha and Peto 2014). Several studies in the United States have consistently shown that younger groups show higher elasticities relative to older ones (CDC 1998; Chaloupka and Grossman 1996; Lewit and Coate 1982). Barkat et al. (2012) estimate that a uniform excise tax accounting for 60 percent of the retail price of cigarettes would reduce youth smoking prevalence by 27 percent, preventing over 2 million youth from taking up smoking.

# **3. Model**

The impact of an increase in tobacco taxes in Indonesia 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 could affect household income. First, assuming tobacco consumption does not change,

> 5 Atkinson and Skegg (1973); Bardsley and Olekalns (1999); Stavrinos (1987); Sumner (1971); Townsend (1987); Townsend, Roderick, and Cooper (1994); Witt and Pass (1981).

5
tobacco taxes directly affect household income because the share of household budgets allocated to tobacco purchases rises as the tax increases. Second, household medical expenses could decrease as a result of reduced tobacco consumption. Third, households could also 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) + higher income (C) (1) For further details on the methodology, see annex A or refer to Fuchs and Meneses (2017a).

# **4. Data and descriptive statistics**

## **Tobacco prices**

Data on the household consumption of and expenditure on tobacco products in Indonesia come from the 2015–16 Indonesia National Socioeconomic Surveys. The surveys ask households about daily expenditure and consumption for different tobacco products. These questions allow an estimation of the average price paid by households for tobacco products and of average price elasticities among expenditure groups. However, the survey is not based on a good representative sample of higher-income groups because nonresponse rates among these groups are high.

Table 1 shows significant price variation across deciles and minimal variation by cigarette type. For instance, the poorest decile in 2016 paid an average Rp 495 for clove cigarettes, whereas the richest decile spent Rp 909. For white cigarettes, the variation across deciles is within a similar range. Using Euromonitor data, one can compare the estimated prices from the expenditure survey.<sup>6</sup> The most expensive 20-pack of cigarettes in 2017 cost Rp 24,300, while the cheapest cost Rp 17,200 (figure 1). Moreover, while nominal cigarette prices increased from 2011 to 2017, the real prices remained relatively unchanged (figure 2).

**Table 1 – Mean and Median Price per Stick Paid by Households, by Cigarette Type and Decile, 2016**

|_Price_|_Type_|_1_|_2_|_3_|_4_|_De_<br>_5_|_cile_<br>_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Mean|Clove cigarettes<br>White cigarettes|495<br>523|530<br>559|557<br>571|587<br>615|630<br>633|691<br>714|741<br>746|781<br>792|831<br>812|909<br>884|
||Clove cigarettes|469|511|539|574|618|671|730|733|795|863|
|Median|White cigarettes|479|535|556|597|633|689|730|730|771|821|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 2016. _Note:_ Prices are in 2010 real rupiah. Deciles are constructed using per capita household expenditure.

> 6 See Euromonitor International (database), Euromonitor International, London, http://www.euromonitor.com/.

6
<!-- Start of picture text --> Figure 1 – Mean Cigarette Price, by Year Figure 2 – Mean Cigarette Price, by Year and Pack<br>and Pack Size (2010 prices) Size, Real vs. Nominal Prices<br>20,000 25,000<br>20,000<br>15,000<br>15,000<br>10,000<br>10,000<br>5,000 5,000<br>0<br>0<br>2011 2012 2013 2014 2015 2016 2017<br>2011 2012 2013 2014 2015 2016 2017 Year<br>Year Real Prices 12-pack Real Prices 20-pack<br>12-pack 16-pack 20-pack Nominal Prices 12-pack Nominal Prices 20-pack<br>Price (rupiah) Price (rupiah)<br><!-- End of picture text --> _Source:_ 2011–17 data, Euromonitor International (database), Euromonitor International, London, http://www.euromonitor.com/. _Note:_ Real prices are by pack size in 2010 prices. Euromonitor data do not allow a distinction between clove and white cigarettes.

# **Tobacco price elasticity by decile**

Although several models have been tested to estimate decile-level elasticities, the fixed effects with controls model is preferred. (See annex B for more on the methodology and the various iterations of the estimation of tobacco price elasticities by decile.) The estimated average white cigarette price elasticity in Indonesia is −0.63, which is within the estimated elasticities found in the literature on developing countries (a range of −0.4 to −0.8), as well as previous estimates on Indonesia. As expected, lower-income deciles exhibit higher white cigarette price elasticities relative to richer deciles. For instance, the poorest decile has a medium-bound elasticity of −0.77, whereas the richest has an elasticity of −0.55 (table 2). The standard error of these estimates is approximately 0.10, producing a 95 percent confidence interval of −0.20, +0.20.<sup>7</sup> The effect of a tax increase under different scenarios is shown by simulating a lower- and upper-bound elasticity for each decile. The former tends to involve income groups that would not change consumption patterns, such as rural residents or older individuals, while the latter tends to show a longer-term scenario, reflecting the effect the tobacco tax would have on younger individuals. After a few decades, only these would still be alive; the total average effect of the price increase would therefore be approximated more accurately by the upper-bound price elasticity.

**Table 2 – White Cigarettes: Price Elasticities, by Decile**

|_Price elasticity_|_1_|_2_|_3_|_4_|_Dec_<br>_5_|_iles_<br>_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|−0.567|−0.498|−0.468|−0.444|−0.422|−0.403|−0.393|−0.382|−0.366|−0.345|
|Medium bound|−0.767|−0.698|−0.668|−0.644|−0.622|−0.603|−0.593|−0.582|−0.566|−0.545|
|Upper bound|−0.967|−0.898|−0.868|−0.844|−0.822|−0.803|−0.793|−0.782|−0.766|−0.745|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 2015–16. _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: the age, education, and gender of the household head, the share of individuals by age-group in each household, and urban status. The model also controls for year fixed effects. Only 2015– 16 survey years were used to estimate price elasticities because previous surveys asked about the number of cigarette packs consumed, whereas the most recent years ask about quantities of individual cigarettes.

In contrast, average price elasticities are lower for clove cigarettes than for white cigarettes (−0.52). The poorest decile shows a medium-bound price elasticity of −0.64 for clove cigarettes, which is lower than the −0.77 for white cigarettes (tables 2–4, figures 3–4).

> 7 To maintain comparability with similar studies, the same average upper and lower bounds have been applied to all deciles. Applying individual bounds by decile produces minimal changes in the results.

7
**Table 3 – Clove Cigarettes: Price Elasticities, by Decile**

|_Price elasticity_|||||_Dec_<br>|_iles_<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|
||_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|Lower bound|−0.440|−0.386|−0.354|−0.334|−0.318|−0.302|−0.293|−0.283|−0.272|−0.256|
|Medium bound|−0.640|−0.586|−0.554|−0.534|−0.518|−0.502|−0.493|−0.483|−0.472|−0.456|
|Upper bound|−0.840|−0.786|−0.754|−0.734|−0.718|−0.702|−0.693|−0.683|−0.672|−0.656|

_Source:_ Estimates based on Indonesia National Socioeconomic Surveys 2015–16. _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: the age, education, and gender of the household head, the share of individuals by age-group in each household, and urban status. The model also controls for year fixed effects. Only 2015– 16 survey years were used to estimate price elasticities because previous surveys asked about the number of cigarette packs consumed, whereas the most recent years ask about quantities of individual cigarettes.

**Table 4 – All Cigarette Products: Price Elasticities, by Decile**

|_Price elasticity_|_1_|_2_|_3_|_4_|_De_<br>_5_|_ciles_<br>_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|−0.447|−0.345|−0.313|−0.293|−0.276|−0.261|−0.251|−0.241|−0.230|−0.213|
|Medium bound|−0.647|−0.545|−0.513|−0.493|−0.476|−0.461|−0.451|−0.441|−0.430|−0.413|
|Upper bound|−0.847|−0.745|−0.713|−0.693|−0.676|−0.661|−0.651|−0.641|−0.630|−0.613|

_Source:_ Estimates based on Indonesia National Socioeconomic Surveys 2015–16. _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: the age, education, and gender of the household head, the share of individuals by age-group in each household, and urban status. The model also controls for year fixed effects. Only 2015–16 survey years were used to estimate price elasticities because previous surveys asked about the number of cigarette packs consumed, whereas the most recent years ask about quantities of individual cigarettes.

**Figure 3 – White Cigarettes: Price Elasticity, Indonesia**

**Figure 4 – Clove Cigarettes: Price Elasticity, Indonesia**

<!-- Start of picture text -->
-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>-0.8<br>-0.8<br>-1.0<br>-1.0<br>1 2 3 4 5 6 7 8 9 10<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Decile<br>Lower bound Medium bound Upper bound<br><!-- End of picture text -->

_Source:_ Estimates based on Indonesia National Socioeconomic Surveys 2015–16. _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.

# **Mortality and morbidity**

Data on mortality, years of life lost, and morbidity are obtained from the Global Burden of Disease Study.<sup>8</sup> Ischemic heart disease, chronic respiratory diseases, and tuberculosis are among the most prevalent diseases among men and women in Indonesia. In 2016, approximately 153,108 deaths were attributed to tobacco-related diseases, of which nearly 88 percent were men (135,368) (table 5). Nearly 2.2 million cases of tobacco-related disease were reported in Indonesia the same year (table 6). A total of 4,155,652 years of life lost were estimated for all tobacco-related deaths in 2016 (table 7).<sup>9</sup>

> 8 See 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.

> 9 Years of life lost is the estimated average number of years a person would have lived if he or she had not died prematurely.

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**Table 5 – Tobacco-related Deaths, by Gender and Age-Group, Indonesia, 2016**

||||**Female**|||||**Male**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**25-34**|**35-54**|**55-74**|**75+**|**Total**|**25-34**|**35-54**|**55-74**|**75+**|**Total**|
|**Total Tobacco-related deaths**|**148**|**3,797**|**8,649**|**5,147**|**17,740**|**3,901**|**40,160**|**69,136**|**22,172**|**135,368**|
|Ischemic heart disease|65|1,808|5,092|2,720|9,684|1,705|18,017|26,742|7,890|54,354|
|Cerebrovascular disease|38|1,264|2,714|2,107|6,123|1,165|14,888|27,204|7,634|50,892|
|Tuberculosis|11|153|357|283|804|810|4,279|5,626|2,162|12,876|
|Chronic obstructive pulmonary disease|9|184|282|31|507|76|916|5,057|3,281|9,329|
|Tracheal, bronchus, and lung cancer|12|199|114|3|328|68|1,196|3,051|839|5,153|
|Cervical cancer|4|62|17|0|83|-|-|-|-|-|
|Lip and oral cavity cancer|3|32|17|0|52|18|150|179|52|399|
|Larynx cancer|0|20|25|1|46|5|111|348|90|554|
|Stomach cancer|2|18|7|0|27|8|103|194|55|359|
|Nasopharynx cancer|1|16|7|0|24|11|167|244|38|460|
|Colon and rectum cancer|1|16|6|0|24|10|48|50|13|121|
|Esophageal cancer|1|15|7|0|22|3|96|152|31|282|
|Bladder cancer|0|4|3|0|7|2|37|118|52|209|
|Other chronic respiratory diseases|0|3|1|0|4|4|14|4|1|23|
|Leukemia|0|2|1|0|3<br>|8|48|51|10|117|
|Liver cancer due to other causes|0|1|1|0|2|7|90|118|24|240|

_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.

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**Table 6 – Tobacco-Related Events, by Gender, 2016**

|_Event_|_All_|_Women_|_Men_|
|---|---|---|---|
|Ischemic heart disease|673,152|269,411|403,741|
|Tuberculosis|622,909|244,059|378,849|
|Chronic obstructive pulmonary disease|500,616|166,757|333,859|
|Cerebrovascular disease|334,295|158,159|176,136|
|Cervical cancer|25,192|25,192||
|Colon and rectum cancer|16,695|8,260|8,435|
|Tracheal, bronchus, and lung cancer|13,227|3,372|9,854|
|Stomach cancer|12,642|5,616|7,025|
|Lip and oral cavity cancer|6,429|3,395|3,034|
|Bladder cancer|6,177|1,750|4,426|
|Leukemia|4,675|2,034|2,641|
|Nasopharynx cancer|3,771|919|2,852|
|Liver cancer due to other causes|3,386|370|3,016|
|Larynx cancer|2,694|478|2,216|
|Esophageal cancer|1,729|539|1,190|
|Total tobacco-related events|2,227,589|890,313|1,337,275|

_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.

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

|_Years_|_All_|_Women_|_Men_|
|---|---|---|---|
|30–34|220,583|8,060|212,523|
|35–39|302,657|14,839|287,819|
|40–44|400,801|29,242|371,559|
|45–49|495,617|40,812|454,805|
|50–54|576,637|63,748|512,889|
|55–59|609,999|49,992|560,007|
|60–64|546,819|55,091|491,729|
|65–69|404,476|52,486|351,991|
|70–74|308,666|42,070|266,596|
|75–79|178,786|29,140|149,645|
|80 plus|110,610|22,656|87,954|
|Total|4,155,652|408,136|3,747,516|

_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 most recent study estimating the economic cost of smoking in Indonesia was conducted in 2015. The study estimates the treatment costs of 19 chronic diseases attributable to smoking. Annual direct health care costs attributable to tobacco use amount to US$2.2 billion (equivalent to 2.5 percent of GDP in 2015). The treatment costs of hypertension, chronic obstructive pulmonary diseases, and ischemic heart disease showed the highest cost burden (Kristina et al. 2018). Similarly, a 2008 study estimated the economic loss attributable to tobacco use at US$18.5 billion (NIHRD 2009). Kristina et al. (2015) estimate the indirect mortality costs of premature cancer deaths and years of potential life lost attributable to smoking in Indonesia. They estimate smokingattributable cancer mortality at nearly a third of total cancer deaths (30.6 percent) and the years of life lost at 1.2 million. Furthermore, cancer mortality costs related to smoking amounted to US$1.3 million in 2013.

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# **Baseline descriptive results**

Table 8 summarizes the most important indicators, including total monthly household expenditures and the share of expenditures on smoked tobacco products, for 2016. As of 2016, there was at least one smoker in 64 percent of all households. The share was highest among households in deciles 4–6 (an average 69.1 percent) and lowest in high-income households (52.0 percent). Most households consume clove cigarettes (59.7 percent) rather than white cigarettes (5.6 percent). On average, households spent 6.8 percent of their total incomes on white and clove cigarettes. Lower-income households spent the highest share. For instance, the poorest decile spent 7.5 percent relative to less than 4.0 percent among the richest. The results do not significantly differ if one analyzes the expenditures for tobacco products using 2010 as the base year (see annex C).

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# **Table 8 – Baseline Descriptive Results, 2016**

|_Indicator_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|_All_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Average Household Monthly per capita Expenditure (Rupia)|233,370|310,272<br>|374,363<br>|449,352|532,701|631,259|764,097|947,436|1,286,670|3,952,778|_1,031,795_|
|Proportion of exp. on all tobacco productsa|7.5|8.8|9.2|9.5|9.5|9.3|8.5|7.6|6.2|3.3|_6.8_|
|Proportion of exp. white cigarettes|0.3|0.4|0.5|0.6|0.6|0.7|0.7|0.8|0.8|0.6|_0.6_|
|Proportion of exp. clove cigarettes|7.2|8.3|8.7|8.9|8.9|8.6|7.8|6.8|5.3|2.7|_6.2_|
|Households that smoke any tobacco product (%)|57.1|64.0|66.6|69.3|69.5|68.5|66.9|65.0|60.5|51.9|_63.9_|
|Households that smoke white cigarettes (%)|2.3|3.3|3.7|4.6|4.9|5.5|5.9|7.1|8.6|9.5|_5.6_|
|Households that smoke clove cigarettes (%)|55.3|61.8|64.0|66.1|65.9|64.5|62.4|59.6|53.5|43.4|_59.7_|
|Woman-headed households (%)|16.5|15.0|14.6|13.8|14.2|14.2|15.0|14.6|15.2|17.1|_15.0_|
|Age, household head|50.3|48.9|47.9|47.8|47.7|47.4|47.6|47.1|46.4|45.4|_47.7_|
|Share of kids ages 0-5 in household (%)|12.0|10.7|10.7|10.1|9.6|8.8|8.2|7.8|7.0|5.3|_9.0_|
|Share of individuals ages 6-17 in household (%)|22.4|22.2|22.1|20.9|20.2|19.7|18.1|17.1|15.3|12.2|_19.0_|
|Share of adults ages 18-49 in household (%)|39.2|42.4|44.5|45.9|47.1|48.5|49.4|51.1|54.4|58.0|_48.1_|
|Share of individuals ages 50+ in household(%)|26.4|24.7|22.7|23.1|23.1|23.0|24.3|24.0|23.3|24.5|_23.9_|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 2016. _Note:_ Deciles were created using per capita household expenditure.

a. Proportional to total budget per household in each decile; households that smoke = 1 if a household member reports spending on tobacco products.

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# **5. Results**

To analyze the distributional effects of an increase in tobacco taxes, the effect is estimated on prices, medical expenditures, and years of working life, aggregating these three indicators into a single measure. The price elasticities estimated in tables 2–4, including the lower- and upper-bound elasticities, facilitate an understanding of 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 lower-, medium-, and upper-bound elasticities. Using the price elasticities and the shares of household expenditure on tobacco by decile, the effects of an increase in tobacco prices can be simulated (see annex A, equation A2). To show the effect of the elasticities on prices, tables 9 and 10 also include estimates of a complete pass-through scenario, whereby the increase in prices is completely transferred to consumers without a reduction in consumption. For instance, if one assumes that the prices for tobacco products rise by 25 percent, given the medium-bound elasticity for clove cigarettes in the bottom decile (−0.64) in table 3 and the proportion of clove cigarette expenditures among the bottom decile (4.3 percent) in table 8, the expected decrease in household expenditures would be 0.36 percent (table 10).<sup>10</sup> This represents a loss in welfare because consumers would devote a higher share of their incomes to the purchase of the same amount of tobacco, thereby reducing the consumption of other goods. In contrast, the analysis for white cigarettes implies an overall smaller decrease, whereby, for example, the same medium-elasticity analysis for the first decile yields a 0 percent expected decrease in household expenditures. These results hold for all analyzed scenarios. The effect for white cigarettes appears to be relatively progressive, that is, it affects upper-income groups in a larger proportion, whereas, for clove cigarettes, the effect is U shaped, with an increasing effect from decile 1 through 6 that then reverses so that the top decile is less affected than the poorest decile (lower- and medium-bound elasticity cases; see figure 5 and table 10).

**Table 9 – Direct Effect of a Price Increase through Taxes, White Cigarettes (%)**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_De_<br>_5_|_ciles_<br>_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Complete pass-through|−0.07|−0.11|−0.12|−0.15|−0.16|−0.17|−0.18|−0.19|−0.21|−0.14|
|Lower-bound elasticity|−0.02|−0.04|−0.05|−0.07|−0.07|−0.08|−0.09|−0.10|−0.11|−0.08|
|Medium elasticity|0.00|−0.01|−0.02|−0.03|−0.03|−0.04|−0.05|−0.05|−0.06|−0.05|
|Upper-bound elasticity|0.02|0.01|0.01|0.01|0.00|0.00|0.00|0.00|−0.01|−0.01|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 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 10 – Direct Effect of a Price Increase through Taxes, Clove Cigarettes (%)**

|_Pi hk i_|||||_De_|_ciles_|||||
|---|---|---|---|---|---|---|---|---|---|---|
|_rce soc scenaro_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|Complete pass-through|−1.81|−2.09|−2.18|−2.22|−2.23|−2.14|−1.94|−1.70|−1.33|−0.67|
|Lower-bound elasticity|−0.81|−1.08|−1.22|−1.29|−1.34|−1.33|−1.23|−1.10|−0.88|−0.46|
|Medium elasticity|−0.36|−0.56|−0.67|−0.74|−0.79|−0.80|−0.75|−0.67|−0.55|−0.29|
|Upper-bound elasticity|0.09|−0.04|−0.13|−0.18|−0.23|−0.26|−0.26|−0.25|−0.21|−0.12|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 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.

> 10 The analysis assumes the prices of both clove and white cigarettes increase by 25 percent. However, given the complex tobacco-tax structure in Indonesia, there may be a scenario whereby prices increase less for clove cigarette than for white cigarettes.

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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 highly affected by consumption taxes. One of the main motivations of this study is 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 price increase for tobacco through higher taxes generates negative income variations across all groups in the population because overall prices increase. For white and clove cigarettes, 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 of a more comprehensive approach, including benefits through lower medical expenses and an increase in working years, the overall monetary effect is estimated.

The different types of cigarettes and elasticity assumptions generate four particular results. For white cigarettes, the overall effect is always positive, but not necessarily progressive. For clove cigarettes, the lower-bound elasticity generates an income loss among all decile groups of the population. The middle-bound elasticity generates income losses across all groups except the first two deciles. The upper-bound elasticity generates income gains across all groups, but particularly lower-income groups. This means that the effect of taxes on the different income groups is exacerbated if the variation in price elasticities among income deciles is greater.

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 the price increases; the lowerbound elasticity tends to represent this behavior more closely. In contrast, younger groups of the population show more elastic demand, similar to the upper-bound elasticity. After a few decades, one should expect the impact of the tax policy to resemble the upper-bound elasticity scenario.

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