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).

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