Do Stronger Tax Systems Increase the Effectiveness of Cigarette Taxes? Evidence from Cross-Country Data

Do Stronger Tax Systems Increase the Effectiveness of Cigarette Taxes? 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Evidence from Cross-Country Data Estelle Dauchy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9200426/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This paper examines whether the design and consistency of cigarette tax systems influence the effectiveness of tobacco taxation in reducing cigarette consumption. Using cross-country panel data for countries surveyed by the World Health Organization between 2014 and 2024, we use the Cigarette Tax Scorecard (CTS) and its components to evaluate how different features of tax systems affect cigarette consumption and influence price responsiveness. In a first step, we estimate the association between CTS scores and per capita cigarette consumption using both contemporaneous and lagged CTS measures to capture short- and longer-term effects while mitigating potential endogeneity arising from the inclusion of price-related components in the score. Higher overall CTS scores are associated with significant reductions in consumption, with a one-unit increase in the CTS reducing consumption by approximately 5–5.5 percent. In a second step, we examine whether countries that consistently maintain strong tax systems experience greater demand responses to price increases. Using two-way fixed effects models and two-stage least squares estimates that instrument cigarette prices with specific excise taxes, we find that countries with consistently high CTS scores exhibit substantially larger price elasticities of demand than countries with weaker tax systems. This effect is particularly pronounced in low- and middle-income countries. Overall, the results suggest that maintaining comprehensive and consistent tax systems over time substantially enhances the effectiveness of tobacco taxation policies. JEL codes: I12, H23, H25, C23 Cigarette taxation Tobacco demand Price elasticity Cigarette Tax Scorecard Excise taxes Cross-country panel data Figures Figure 1 1. Introduction Cigarette smoking contributes significantly to the global non-communicable disease burden [ 2 ]. Over the past 16 years, global smoking rates have decreased from 22.3% to 16.4% [ 3 ]. Excise tax policies aimed at increasing cigarette prices have been an important contributor to this reduction [ 4 ], and have been recognized by the World Health Organization [ 5 ] as an especially effective mechanism for reducing cigarette smoking [ 6 , 7 ]. Historically, the WHO has advised countries on cigarette tax policies by using the share of taxes in retail prices as a guiding benchmark. In 2008, as part of its "best-buy" measures to reduce cigarette demand, the WHO recommended that countries aim for a total tax burden of 75% of the retail price of the most-sold domestic cigarette brand [ 8 ]. This target, derived from a 1999 World Bank report, was based on the success of high-income countries using taxation to reduce smoking [ 2 , 5 , 9 , 10 ]. In 2010, the WHO introduced a new benchmark, recommending a 70% excise tax burden [ 11 ]. While the recommended tax burdens differ slightly, both emphasize the importance of taxes constituting the majority of a cigarette's retail price. However, the WHO’s biennial Report on the Global Tobacco Epidemic (RGTE) continues to use the 75% total tax incidence benchmark to evaluate countries’ cigarette tax policies [ 3 ]. While a high tax incidence is a well-established strategy for reducing smoking, other factors beyond the tax incidence can also influence the effectiveness of cigarette taxation in reducing demand [ 5 , 9 ]. For example, research indicates that the excise tax structure—defined by the tax base and whether uniform or tiered rates are applied—affects both average cigarette prices and price dispersion. From a public health perspective, higher prices reduce demand, while narrower price gaps reduce incentives for people who smoke to down-trade to cheaper brands to avoid tax hikes, encouraging reductions in consumption instead [ 12 ]. Studies show that tax structures characterized by uniform rates and a greater emphasis on specific taxes tend to produce higher average prices and narrower price dispersion compared to those relying more on ad valorem taxation or tiered rates, making these tax structures more effective in lowering cigarette demand [ 13 – 15 ]. However, the long-term effectiveness of specific taxes depends on regular adjustments for inflation. Without such updates, the real value of taxes declines over time, diminishing their impact [ 5 , 9 , 12 ].Therefore, to maintain their effectiveness, regulators should adjust excise taxes annually, typically using a measure like the consumer price index [ 11 , 16 ]. Additionally, price increases need to outpace income growth to ensure continued reductions in consumption [ 17 , 18 ]. If income growth surpasses tax-driven price increases, cigarettes may become more affordable, limiting the impact of tax hikes [ 17 ]. To help countries maximize the impact of taxation on reducing cigarette demand, the WHO Framework Convention on Tobacco Control (FCTC) has provided best practice guidelines [ 16 ]. These include adopting uniform tax structures, prioritizing specific taxes, adjusting rates annually for inflation and income growth, and ensuring that taxes make up most of the retail price [ 19 ]. To evaluate countries’ performance in relation to these guidelines, Economics for Health developed the Cigarette Tax Scorecard (CTS), a tool that assesses cigarette tax systems across four metrics: (1) cigarette prices, (2) changes in cigarette affordability the share of taxes in cigarette retail prices, and (4) the excise tax structure [ 17 ]. The CTS gives a score for each metric, and an overall score that reflects how well a country’s system aligns with best practices. A higher overall score points to a better, more FCTC-compliant, cigarette tax system. Beyond evaluating the overall quality of countries' cigarette tax systems, the CTS also facilitates research on how different elements of these systems collectively impact cigarette demand. To date, only one peer-reviewed study has utilized CTS data to examine the relationship between the overall quality of tax systems and smoking behaviour. Focusing on the impact of the overall CTS score on adult per-capita cigarette consumption in 97 countries from 2014 to 2020, Ngo et al (2024) [ 4 ] find that each unit increase in the overall tax score was associated with a significant 9% reduction in per-capita cigarette consumption. While this provides evidence about the association between more FCTC-compliant tax systems and lower cigarette consumption, the study does not investigate the relative impacts of each policy component on cigarette-smoking behaviour, nor does it evaluate how specific aspects of the overall score enhance the effectiveness price increases in reducing cigarette demand . Since cigarette tax systems comprise multiple interacting dimensions (e.g., tax structures, cigarette prices, cigarette affordability and tax burdens), empirical evidence on the relative impacts of these aspects of a country's tax system—both independently and in combination—can offer valuable insights into the mechanisms through which cigarette tax policy reduces cigarette demand. In this study, we explore how each of the four CTS components independently and cumulatively affect per-capita cigarette consumption using data from a sample of 99 countries over the period 2014–2024. Additionally, we investigate how the overall strength of excise tax systems moderates the effectiveness of price increases in reducing cigarette consumption. We also assess whether maintaining a high tax share in the retail price, as recommended by WHO, is sufficient to ensure effective reductions in cigarette consumption. In so doing, this study provides new evidence on the role of different components of cigarette excise tax systems in reducing cigarette consumption and underscores the public health benefits of adopting tax systems that align with best practice recommendations. 2. Methods 2.1 Data The dependent variable in this analysis is country-level adult per-capita cigarette sales volumes, which serves a proxy for cigarette demand. This variable was constructed using tax-paid cigarette sales volumes data obtained from Euromonitor International [ 1 ] and data on the adult population (aged 15 and older), derived from the United Nations [ 20 ]. The Euromonitor dataset includes information for 99 countries, of which 46 are high-income countries (HICs) and 53 are low- or middle-income countries (LMICs). Our primary independent variables are the individual scoring components and overall average scores reported in the most recent (fourth) edition of the CTS, covering the years 2014, 2016, 2018, 2020, 2022, and 2024 [ 21 ]. The CTS evaluates cigarette tax policies globally across four key components: (1) cigarette price, (2) changes in cigarette affordability tax share of price, and (4) tax structure. Each component is scored on a scale from 0 to 5, with 5 representing the strongest performance. The overall score for each country is calculated as the average of these four component scores, ranging from 0 (lowest) to 5 (highest). Information on how each component score is calculated can be found in Appendix 1. In addition to the individual and overall CTS scores, the analysis also incorporates the price of a 20-pack of the most-sold cigarette brand in each country, as well as the total cigarette tax incidence for the years 2014, 2016, 2018, 2020, 2022 and 2024. These data are sourced from the most recent editions of the WHO RGTE (WHO 2015, 2017, 2019, 2021b, 2023, 2025) [ 3 ]. The WHO reports cigarette prices for each country-year in nominal terms, but to maintain consistency with the CTS method for accounting for inflation and purchasing power differences across countries (see Appendix 1), these prices are converted into constant 2018 international dollars using conversion factors from the World Development Indicators (WDI) database [ 22 ] To address the potential endogeneity of retail prices, the analysis also instruments cigarette prices using the excise tax amount levied on a 20-pack of cigarettes. This variable is constructed by multiplying the excise tax share of the retail price reported by the WHO by the nominal retail price of the most-sold cigarette brand, and then converting the resulting nominal tax values into constant 2018 international dollars using the same WDI adjustment factors [ 22 ]. Lastly, all models include country-level per capita Gross Domestic Product (GDP), measured in constant international dollars (BY = 2021), as a proxy for income and obtained from the World Development Indicators (WDI) database [ 22 ]. The analysis also employs World Bank country income classifications, distinguishing between high-income countries (HICs) and low- and middle-income countries (LMICs) [ 22 ]. 2.2. Statistical methods We begin by estimating the impact of both individual and overall tax scores on cigarette demand using a two-way fixed effects model, as presented in Eq. ( 1 ): $$\:\text{l}\text{n}\left({Sales}_{it}\right)=\alpha\:+{\beta\:}_{k}{Score}_{it}^{k}+\gamma\:{\text{l}\text{n}(Income}_{it)}+{\mu\:}_{i}+{\mu\:}_{t}+{e}_{t}$$ 1 In this equation, \(\:{Sales}_{it}\:\) represents adult per-capita cigarette consumption (in number of cigarette sticks) for country i at time t . The dependent variable is log-transformed to allow the relationship between countries’ cigarette tax scores and cigarette consumption to change proportionately. The term \(\:{Score}_{it}^{k}\) denotes either the overall score or one of the individual components of the scorecard (i.e., k =overall score, price score, tax share score, tax structure score, or change in affordability score). The coefficient \(\:{\beta\:}_{k}\) captures the relationship between each individual and the overall tax score, \(\:k,\:\) and per-capita cigarette consumption. Equation ( 1 ) also controls for per-capita GDP in constant PPP dollars ( \(\:{Income}_{it}),\:\) in logarithmic form, to capture the income effect. Additionally, it controls for year and country fixed-effects that capture factors that are invariant within countries over years ( \(\:{\mu\:}_{i})\) and unobserved factors that uniformly affect countries over time \(\:{(\mu\:}_{t}).\:\) Across all models, standard errors are clustered at the country level to account for intertemporal correlation within countries. Because three of the scorecard components (price, tax share and change in affordability) depend on the price of a 20-pack of the most-sold cigarette brand domestically (Appendix 1), they are likely endogenous. To address this, Eq. 1 is also estimated using the first lag of the each individual score and the overall score ( \(\:{Score}_{i,t-1}^{k}\) ) instead of the contemporaneous score ( \(\:{Score}_{it}^{k}\) ). Given that TCS data are only available on a biennial basis, the first lag represents the individual and overall scores that prevailed two years prior to the sales data used as the dependent variable. We also present results from both the contemporaneous and lagged models, with the k scores interacted with binary indicators for country income groups (HICs and LMICs). This interaction approach allows us to examine heterogeneous effects across income-group contexts, testing whether the responsiveness of per-capita tobacco consumption to the k scores differs systematically between HICs and LMICs. Next, we investigate whether and how the strength of the overall cigarette tax systems moderates the impact of price increases on per-capita cigarette sales, using the model shown in Eq. 2 . $$\:{Sales}_{it}=\alpha\:+{\beta\:}_{1}\text{l}\text{n}\left({Price}_{it}\right)+{\beta\:}_{2}{\text{l}\text{n}(Price}_{it})*{CTS\:index}_{i}^{overall}\:+\gamma\:{Income}_{it}+{\mu\:}_{i}+{\mu\:}_{t}+{e}_{t}$$ 2 where, \(\:{CTS\:index}_{i}^{overall}\) is a dummy identifying countries where the overall tax score is above the median in all years, constructed as follows: \(\:{CTS\:index}_{i}^{overall}=\upharpoonleft\:({score}_{it}^{overall}>{Median}_{t}\:,\:\forall\:t)\) , where \(\:\upharpoonleft\:\left(.\right)\:\) is the indicator function and \(\:{Median}_{t}\) is the cross-country median of the overall tax score in year t. The coefficient \(\:{\beta\:}_{2}\:\) captures the price elasticity of cigarette demand in countries where the tax score is above the median in all years, while \(\:{\beta\:}_{1}\) captures the price elasticity of cigarette demand in countries where the tax score is below the median in one or more years. Other control variables are defined in Eq. ( 1 ). In line with the historical emphasis on higher tax shares in retail prices, we also explore whether the WHO’s recommendations for a higher tax share enhances the effectiveness of price increases. To test this, we estimate equation $$\:{Sales}_{it}=\alpha\:+{\beta\:}_{1}\text{l}\text{n}\left({Price}_{it}\right)+{\beta\:}_{2}{\text{l}\text{n}(Price}_{it})*{CTS\:index}_{i}^{tax\:share}\:+\gamma\:{Income}_{it}+{\mu\:}_{i}+{\mu\:}_{t}+{e}_{t}\:\:$$ 3 In this equation, \(\:\:{CTS\:index}_{i}^{tax\:share}\:\) is a dummy defined similarly to \(\:\:{CTS\:index}_{i}^{overall}\) but identifies a country where the tax share sub-score is above the median in every year. The coefficient \(\:{\beta\:}_{2}\:\) captures the price elasticity of cigarette demand in countries where the tax share sub-score is above the median in all years, while \(\:{\beta\:}_{1}\) captures the price elasticity of cigarette demand in countries where the tax share sub-score is below the median in one or more years. Other control variables are as defined in Eq. ( 1 ). In addition to estimating Equations 2 and 3 as standard two-way fixed effects models (TWFE) with country-level clustered standard errors, we also use the specific excise tax amounts (in constant PPP $ ) as instruments for prices in a two-stage least squares (2SLS) regression to address the endogeneity of the price variable. To enable comparison between TWFE and 2SLS specifications, we presents results for 2 samples: (i) all countries with observations (99 countries with 589 observations) and all countries with a specific excise tax (83 countries with 466 observations). As a large body of empirical literature has documented higher price elasticity of cigarette demand among lower-income populations, we also estimate specifications that include additional interactions by country income groups (high-income countries, HICs, and low- and middle-income countries, LMICs) to explore potential heterogeneous effects. Evidence consistently shows that smokers with lower incomes tend to be more responsive to price changes, implying larger reductions in consumption following tax-induced price increases [ 9 , 11 , 23 – 25 ]. 3. Results Summary statistics for variables included in the regressions are presented Table 1 for the full sample (Panel A) and the sample used in 2SLS regressions (Panel B). Figure 1 shows the average overall CTS scores (on a scale from 0 to 5) for the 99 countries in the analysis sample from 2014 to 2024. [TABLE 1] [FIGURE 1] Among the countries included in the specifications, the average overall has barely improved from 2.2 in 2014 to 2.3 in 2024 (Figure A2.1 ). Breaking it down by income group, the average score for LMICs rose modestly from 1.6 in 2014 to 1.9 in 2024. In contrast, HICs saw a decline in their average overall scores, from 2.9 to 2.8. Figure A2.1 in Appendix 2 provides a breakdown of average scores for the individual CTS components in the analysis sample in 2014 and 2024. The change in affordability scores emerges as the weakest component, falling from 1.4 in 2014 to 0.5 in 2024 overall. The largest part of this reduction stems from HICs, where the affordability score dropped from 2.2 in 2014 to 0.3 in 2024 (vs. a reduction from 0.9 to 0.6 in LMICs). Among the sampled LMICs, the strongest performing CTS component is the excise tax structure, where the score increased 3.2 in 2014 to 3.5 in 2024 on average across all countries. For HICs, the price score became the best rated component in 2024, standing at 3.7, slightly higher than the tax structure (3.5) and the tax share (3.6) scores. Trends in per-capita cigarette sales and constant PPP prices (base year = 2022) or price sub-scores from 2008 to 2024, for HICs and LMICs in the analysis sample are shown in Figures A2.2 and A2.3, respectively. While a clear negative relationship between prices and sales is evident, this relationship weakens between 2020 and 2024, likely due to the COVID-19 pandemic, during which real prices remained stable while sales continued to decline. This temporary decline in the price scores among sampled countries is similar to the trend across all CTS countries (Figure A2.4 ). Effects of the CTS Score and Its Components on Cigarette Demand Table 2 presents estimates from Eq. ( 1 ) using the overall tax system score (columns 1 and 6) and its components. Columns 1–5 use current tax scores, while columns 6–10 employ lagged scores to address simultaneity bias. From column 1, a one-unit increase in the overall tax system score reduces per-capita cigarette sales by 5.5% ( p < 0.01). This effect persists with the lagged score (column 6), though the reduction is smaller at 4.8% ( p < 0.05). Among the components, the price score shows the strongest and most statistically significant association with per-capita consumption. A one-unit increase in the contemporaneous price score leads to a 6.3% reduction in per-capita consumption (column 2; p < 0.01), and a 6.6% reduction in the lagged model (column 6; p < 0.01). The tax structure score also has a significant negative effect, but only in the contemporaneous specification, where a one-unit increase results in a 3.7% reduction ( p < 0.05) in per-capita consumption. Though the affordability score is the least performant component, it is statistically significant in in contemporaneous model only, with a one-unit increase resulting in a 1.0% reduction ( p < 0.1) in per-capita consumption.. The tax score is not significant in any model. The income elasticity of demand is consistently estimates at roughly 0.5 ( p < 0.05 across all models). [ Table 2 ] Tables 3 and A3.1 presents results for the models interacting TCS scores with country income classification, using the contemporaneous score (Table 3 ) or the lagged score (Table A3.1 ). In the contemporaneous specification, the negative relationship between overall and price scores and p.c. consumption is consistent and significant across income groups. However, the effects are larger in LMICs than in HICs. [ Table 3 ] From column 1, a one-unit increase in the overall score leads to a 7% reduction in per-capita cigarette consumption in LMICs ( p < 0.01) and 4.7% in HICs ( p < 0.05). From column 2, a one-unit increase in the price score reduces per-capita cigarette sales by 7.8% in LMICs ( p < 0.05) and 5.2% in HICs ( p < 0.01). These differences persist in models using the lagged scores to mitigate reverse causality (Table A3.1 : columns 6 and 7). Notably, from column 6, the relationship between higher overall TCS scores and reduced consumption remains significant in LMICs (a 9% reduction per unit increase, p 0.1). From column 7, the effect of the price score remains significant in both groups, with a one-unit increase associated with a 9.1% reduction in LMICs ( p < 0.01) and a 4.3% reduction in HICs ( p < 0.01). The significance of overall average effect of the affordability score (Table 2 ) is essentially driven by LMICs, where a one-unit increase in the score reduces consumption by 1.3% (column 4; p < 0.1) in the contemporaneous model and by 2.0% in the lagged model (column 9; p < 0.05). However, no significant effect for the change in affordability score is observed for HICs in either specification. In contrast, while the average effect of the tax share score (Table 2 ) was not statistically significant, it becomes significant among HICs, where a one-unit increase in the score reduces consumption by 3.5% (column 3; p < 0.1) in the contemporaneous model. However, no significant effect for the change in the tax share score is observed for LMICs. Likewise, the majority of the negative effect of the tax structure score is driven from HICs, where one-unit increase in the score reduces consumption by 4.6% (column 5; p < 0.1), in the contemporaneous model only. Moderation of Price Effects by the CTS Score Table 4 presents the results from Eq. ( 2 ). Columns 1 to 4 show results from the TWFE estimates, and columns 5 to 6 present estimates from 2SLS where the specific tax is used as an instrument for current prices. For ease of comparison between samples, TWFE models are estimated across all countries with non-missing observations (columns 1 and 2) and among countries with non-missing specific taxes (columns 3 and 4). To facilitate the interpretation of the results, estimates of the full effects of all regressions with interactions are presented in Appendix table A3.4. [ Table 4 ] Each column provides estimates of the price and income elasticity of demand only (even columns) or with added interactions between prices and the dummy that identifies countries where the overall CTS scores is larger than the median in each year from 2014 to 2024 ( \(\:CTS\:index\) , odd columns). When we use TWFE, a 1% increase in the price reduces cigarette demand by 0.22% ( p < 0.01, column 1) in the full sample and 0.32% (Table 4 ; column 3; p < 0.01) in the sample limited to countries with a specific tax. Using taxes as instruments for prices in the 2SLS specification produces a much larger price elasticity of demand [ 26 ] estimate of -0.56 (Table 4 ; column 5; p < 0.01). The instrument is also found to be strongly correlated with prices in the first-stage regression (t = 7.10), corresponding to an F-statistic above 50, well above the conventional weak-instrument threshold of 10 [ 27 ]. When we interact prices with the dummy ( \(\:CTS\:index\) ) that identifies countries consistently above-average CTS overall scores consistently above-average (Table 4 ; columns 2, 4, and 6), a one-unit increase in the index increases the negative impact of the price on p.c. cigarette sales, as reflected by the significant negative coefficient on the interaction terms. Results from the TWFE models using the full sample (Table 4 ; column 2) imply that the PED of cigarettes is -0.17 (p < 0.01) in countries with a low CTS index, but this effect increases by 0.5 (p < 0.01) units in countries with a high index where the PED is estimated as -0.67 (or -0.17+-0.5; Table A3.4 ; p < 0.01). These results imply that in countries with a low CTS overall score in all years, p.c. cigarette demand is much less sensitive to prices than in countries with high CTS index in all years. Results from the TWFE model in the limited sample (Table 4 ; column 4) confirm this difference: a 1% increase in the price of cigarettes reduces p.c. consumption by 0.24% (p < 0.01) and − 0.65 (-0.24+-0.41; p < 0.01) in countries with low and high CTS index, respectively. These differences are statistically significant (Table A3.4 ). This association is confirmed in the 2SLS specification (Table 4 ; column 6): a 1 percent increase in the price implies a -0.49% (p < 0.05) reduction in p.c. consumption in countries with a low CTI index, and a -0.81% (-0.49+-0.32%; p < 0.10) reduction in countries with a high CTS index, also a statistically different. Full marginal price effects are computed using the Stata lincom command, which estimates linear combinations of regression coefficients to recover the total effects implied by the interaction terms. The resulting estimates are reported in Appendix Table A3.4 . As in the models without interactions, the implied PED is much larger in the 2SLS model than in the TWFE model, as shown in Appendix Table A3.4 : the full PED is estimated as -0.44 (p < 0.01) and − 0.65 (0.01), respectively in the TWFE and 2SLS models, revealing that linear fixed effect models tend to underestimate the price effect due to the simultaneity between prices and demand, while this bias is reduced in 2SLS models. Estimates of Eq. ( 2 ) are further disaggregated by country income groups in Table 5 , which shows results of the same specifications as in Table 4 , with additional interactions between the variables of interest and dummies that identify HICs and LMICs. Columns 1 and 3 (TWFE) and 5 (2SLS) show the estimates of the price and income effects on cigarette demand, disaggregated by income groups, while columns 2 and 4 (TWFE) and 6 (2SLS) add interactions of the price and income variables with the dummy that identifies countries with a high CTS score in all years (CTS index), in addition to interactions with country income groups. The full marginal effects are reported in Appendix Table A3.4 within income groups and overall. [ Table 5 ] Focusing on specifications limited to the countries with a specific tax (Table 5 ; columns 3 to 6), the effect of cigarette prices on p.c. consumption is larger (in absolute value) in HICs than for LMICs, though the difference is not significantly different, as revealed by the overlapping confidence intervals. As previously shown in Table 4 , the 2SLS model estimates a much larger price elasticity of demand [ 26 ] than the TWFE specifications. For instance in LMICs, a 1% increase in the price reduces p.c. demand by -0.29% (Table 5 ; column 3; p < 0.01) and − 0.56% (Table 5 ; column 5; p < 0.1), respectively in TWFE and 2SLS models, confirming that standard linear models underestimate the PED of cigarette demand. In addition in HICS, the estimate of the PED of cigarette demand is larger than in LMIC: a 1% increase in the price of cigarettes reduces p.c. demand by -0.48% (p < 0.01) and − 0.63% (p < 0.1), respectively in TWFE and 2SLS models. These differences between HICs and LMICs are not statistically significant (Appendix Table A3.4). However, when we add an interaction between the price and the CTS index (Table 5 , columns 2, 4, and 6), the PED significantly increases in LMICs but not in HICs, as revealed by the interaction term. In contrast, among HICs, the PED is not significantly different between countries with a high or low CTS index. In LMICs with a low CTS score in all years, a 1% increase in the price of cigarettes reduces demand by -20% (TWFE, column 4, p < 0.01), but this effect is increased by -0.72 pp (p < 0.05) in LMICs with a high CTS index, implying a full effect of -0.92%(-0.20+-0.72, p < 0.01) in LMICs with a high CTS index. Results from the 2SLS model produce similar differences between countries with a low CTS index, where the PED is -0.42 (Table 5 ; column 6; p < 0.01), while this effect is increased by -0.77 pp in countries with a high CTS index, implying a full effect of -1.31% (-0.42+-0.77, p < 0.01). These impacts between LMICs with high or low CTS scores are significantly different. The full PED in LMICs is estimated at -0.31 (p < 0.01) and − 0.65 (p < 0.01), respectively for TWFE and 2SLS models (Appendix table A3.5) In HICs, the PED is not significantly different in countries with high or low CTS indices: in the TWFE model (Table 5 ; column 4) a 1% increase in the price of cigarettes leads to a reduction in demand by -0.48% (p < 0.01) or -0.42%, respectively in TWFE and 2SLS models, but this effect does not significantly change in countries with a high CTS index, as reflected by the insignificant interaction term between \(\:Lnp{r}_{HIC}\) and \(\:CTS\:index\) (-0.02 in TWFE and − 0.33 in 2SLS, p > 0.1). These results provide evidence that that LMICs that support a high overall tax score in all years benefit from a larger responsiveness of cigarette demand to prices, while this improving effect of the CTS score is inexistant in HICs: overall in LMICs a high CTS index increases the price elasticity of demand (in absolute terms) by about 4.2 pp based on the 2SLS model. Affordability and Tax Share Impacts We also estimate of the incremental association between affordability and consumption, moderated by the overall CTS score (Appendix Table A3.2). Although we find a large affordability elasticity of cigarette demand (AED), we do not find evidence that countries with a high CTS index exhibit a larger AED than countries with a low CTS. In Appendix Table 3.2, the TWFE model (column 4) estimates a significant negative interaction between affordability and the CTS index, suggesting that countries with a consistently high CTS score experience a stronger demand response to affordability changes than countries with lower CTS score. However, this interaction is no longer significant in the 2SLS model (column 6). Finally, because the WHO places particular emphasis in the tax share sub-score when assessing countries’ policy performance in reducing tobacco consumption, we construct a tax share index analogous to the overall CTS index, as presented in Eq. ( 3 ). This index is defined as a dummy variable equal to one for countries whose CTS tax share sub-score remains consistently above the median in all years. We then estimate the association between cigarette prices and consumption shown in Eq. ( 3 ), interacting prices with the tax share index (Appendix Table A3.3). The results indicate no additional effect of the tax share index on the PED, suggesting that maintaining a consistently high tax share in the retail price is not sufficient, by itself, to enhance the effectiveness of tax policy beyond its average effect. In Table A3.3 , the interaction term between prices and the CTS index is not significantly different from zero in the TWFE model (column 4) across countries with a specific tax, nor in the 2SLS specification using specific taxes as instruments for prices (column 6). We also replicated the analyses in Tables A3.2 and A3.3 by country income groups, but found no significant interaction effects between affordability or prices with CTS indices. To avoid redundancy, we do not report these results. Instead, we present estimates of the full PED and AED from our preferred specification—the 2SLS model—interacted with either the CTS overall index or the CTS tax share index, both overall and by country income group. Estimates of the PED or AED for countries with a CTS index equal to zero are obtained directly from the baseline coefficients reported in Tables 4 and 5 (PED by CTS overall index) and are presented in Panel A of Appendix Table A3.5. Correspondingly AED estimates based on the CTS overall index are derived from Table A3.2 and reported in Panel B, while PED estimates based on the CTS tax share index are derived from Table A3.3 and reported in Panel C. Estimates of the PED or AED for countries with a CTS index equal to one, as well as overall effects, are computed separately using the Stata command lincom . Across all models, the PED is consistently estimated around − 0.7 overall and not differ significantly between HICs and LMICs. The AED is estimated approximately around − 0.75 and likewise does not differ significantly across country income groups. 4. Discussion This study evaluates in depth the importance of maintaining a consistent, comprehensive, and strong cigarette tax system to maximise the potential of tax policy to reduce tobacco use. We do this in two steps. First we obtain the Cigarette Tax Scorecard and its components for all countries surveyed by the WHO, from 2014 to 2024, and evaluate the relative potentials of each component of the tax system to reduce p.c. consumption. We find that a higher overall tax system score has can significantly reduce consumption, and that this effect is persistent over years, as suggested by the larger impact of the lagged CTS scores than the contemporaneous score. A one unit increase in the overall CTS can reduce consumption by between 5 and 5.5% both in the short- and long-terms (more than one year). Among the components of the overall CTS score— price levels, affordability trends, tax system designs, and tax shares in the retail price— the price sub-score has the strongest association with reductions in cigarette consumption. A one unit increase in the price sub-score is associated with an approximately 6.5% reduction in consumption in both the short and long term. In contrast, the tax share and affordability sub-scores show the weakest associations with consumption. The overall effect is also significantly more pronounced in LMICs than in HICs. A one-unit increase in the overall CTS score reduces cigarette demand by approximately 9% in the long term and 7% respectively in the short-term. However, in HICs the overall CTS score has no statistically significant effect beyond the short-term. Likewise the effect of the price sub-score on consumption is largely driven by LMICs, as a one-unit increase in the price score reduces consumption by nearly twice as much in the long term in LMICs as in HICs. Our second step evaluates whether countries that consistently maintain a high overall CTS score experience greater effectiveness of excise tax increases in reducing cigarette consumption. We find that do this by constructing a dummy that identifies countries where the overall CTS score is above average in every period, and interact it with price levels to estimate the marginal impact of a consistently high CTS on the demand effect on price increases [ 26 ]. We find that a countries with persistently high CTS scores exhibit substantially larger price responsiveness. Based on our preferred 2SLS specification, the PED is nearly twice as large in countries with a high CTS index (-0.82) as in countries with low CTS index (-0.49). This finding suggests that maintaining strong and consistent tobacco tax policies enhances the effectiveness of price increases in reducing cigarette demand. I addition, the ability of the overall CTS score to improve the effectiveness of tax policy appears to be concentrated in LMICs. LMICs with a consistently high CTS score exhibit a PED of above unity (-1.3), compared with − 0.54 in LMICs with lower CTS scores. In contrast, the difference is much smaller in HICs: countries with a consistently low CTS score have a PED of -0.4, compared with − 0.7 in countries with consistently high CTS scores, and this difference is not significant. These result provide strong support for the view that countries seeking to maximize the effectiveness of excise tax policy should aim to maintain strong and consistent tobacco tax systems over time, rather than weakening tax policy in the short term. This study is not without limitations. First, the identification of a causal effect of prices on cigarette demand relies on the assumption that specific excise taxes are exogenous to cigarette consumption. This assumption is motivated by the fact that specific taxes are strongly associated with cigarette prices but are determined through policy decisions rather than by contemporaneous supply or demand conditions. However, the exogeneity of tobacco taxes has been questioned in the literature, as governments may adjust taxes in response to public health concerns, fiscal pressures, or trends in tobacco consumption [ 28 , 29 ]. If tax changes are correlated with unobserved determinants of demand, the exclusion restriction may be violated. Nevertheless, in the context of cross-country panel analyses, excise taxes remain one of the most commonly used instruments for cigarette prices because they represent the primary policy driver of retail price variation across countries and over time [ 9 , 24 ]. Moreover, the strong first-stage relationship observed in the data supports the relevance of the instrument. The paper also finds a larger price elasticity of demand [ 26 ] among high-income countries (HICs) than among low- and middle-income countries (LMICs). Several factors may contribute to this pattern. First, the estimates rely on repeated cross-country data, which may introduce measurement error in prices and consumption and reduce the precision of elasticity estimates relative to country-specific studies. Second, the sample period (2014–2024) spans the COVID-19 pandemic, which disrupted cigarette markets in many countries through lockdowns, supply-chain disruptions, and changes in purchasing behaviour, affecting both prices and consumption patterns [ 30 , 31 ]. Third, the rapid expansion of novel nicotine products during the last decade may have altered substitution patterns and therefore the responsiveness of cigarette demand to price changes, as evidence suggests that e-cigarettes and other novel products can act as substitutes for cigarettes [ 32 , 33 ]. Finally, differences in income growth and affordability trends across income groups may also play a role, as rising incomes in LMICs can offset price increases and dampen observed price responsiveness [ 18 , 34 ]. 5. Conclusion This study provides new evidence on the importance of maintaining a strong and consistent cigarette tax system to maximize the effectiveness of tobacco taxation policies. Using cross-country data from 2014–2024, we show that countries with high overall CTS scores experience lower cigarette consumption and stronger price responsiveness. Importantly, the results indicate that countries that consistently maintain a strong tax system obtain greater reductions in consumption from price increases. This effect is particularly pronounced in LMICs, where a high CTS index is associated with substantially larger price elasticities of demand. In contrast, we find no evidence that maintaining a high tax share in price alone enhances the effectiveness of tax policy beyond its average effect. We also find that though maintaining a high CTS score over time can significantly increase the responsiveness of consumption to prices, it has no marginal impact on the responsiveness of consumption to affordability. Likewise, maintaining a high tax share in the retail price of cigarettes alone is not sufficient to maximize the effectiveness of tobacco tax policy. These findings suggest that governments seeking to maximize the public health impact of tobacco taxation should focus not only on increasing prices but also on maintaining comprehensive and consistent tax policies over time. Declarations Data availability Replication data and Stata code will be deposited in a public repository upon publication. Some original data sources [1] are subject to third-party licensing restrictions; in these cases, processed datasets sufficient to replicate the analysis will be provided. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements: The author thanks Samantha Filby for excellent assistance with the initial data analysis and programming, and for helpful comments and editing of earlier versions of the manuscript. Any remaining errors are the author’s responsibility. Funding : This research was supported by the African Capacity Building Foundation (ACBF). Author Contribution E.D. is responsible for the entire project. References International, E. (2025). Cigarettes: Market sizes and company shares by country, in Passport Global Market Information Database . Euromonitor International. Reitsma, M. B., Kendrick, P. J., Ababneh, E. (2021). Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and initiation among young people in 204 countries and territories, 1990–2019 . The Lancet Public Health , 6(7): p. e472 – e481. World Health Organization. (2025). WHO report on the global tobacco epidemic 2025: Warning about the dangers of tobacco . World Health Organization: Geneva. Ngo, A., et al. (2024). As countries improve their cigarette tax policy, cigarette consumption declines. Tobacco Control , 33 (e1), e91–e96. NCI and WHO., The economics of tobacco and tobacco control . NCI Tobacco Control Monograph. Vol. 21 (2016). Bethesda, MD: National Cancer Institute and World Health Organization. Jha, P., Marquez, P. V., & Dutta, S. (2019). Tobacco taxes: A win–win measure for fiscal space and health . World Bank Group: Washington, DC. World Health Organization. (2021). WHO Technical Manual on Tobacco Tax Policy and Administration . World Health Organization. World Health Organization. (2008). WHO report on the global tobacco epidemic, 2008: The MPOWER package . Geneva. International Agency for Research on Cancer, Effectiveness of Tax and Price Policies for Tobacco Control . IARC Handbooks of Cancer Prevention, Tobacco Control. Vol. 14 (2011). Lyon: International Agency for Research on Cancer. Cruces, G., Falcone, G., & Puig, J. (2022). Differential price responses for tobacco consumption: implications for tax incidence. Tobacco Control , 31 (Suppl 2), s95–s100. World Health Organization. (2010). Technical manual on tobacco tax administration . World Health Organization: Geneva. World Health Organization. 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P. H. C. S. A. (2021). The Role of Parallel Trends in Event Study Settings: An Application to Environmental Economics. Journal of the Association of Environmental and Resource Economists , 8 (2), 235–275. Stock, J. H., & Yogo, M. (2005). Testing for Weak Instruments in Linear IV Regression. Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg (pp. 80–108). Cambridge University Press. D.W.K. Andrews and J.H. Stock, Editors. DeCicca, P., Kenkel, D., & Liu, F. (2013). Who pays cigarette taxes? The impact of consumer price search. Review of Economics and Statistics , 95 (2), 516–529. Lovenheim, M. F. (2008). How far to the border? The extent and impact of cross-border casual cigarette smuggling. National Tax Journal , 61 (1), 7–33. Maloney, S. F. (2021). Impacts of COVID-19 on cigarette use, smoking behaviors, and tobacco purchasing behaviors . Drug And Alcohol Dependence , 229. Jackson, S. E., et al. (2022). Moderators of changes in smoking, drinking and quitting behaviour associated with the first COVID-19 lockdown in England. Addiction , 117 (3), 772–783. Cotti, C., Nesson, E., & Tefft, N. (2018). The relationship between cigarettes and electronic cigarettes: Evidence from household panel data. Journal of Health Economics , 61 , 205–219. Pesko, M. F., Courtemanche, C., & Maclean, J. (2020). The effects of traditional cigarette and e-cigarette taxes on adult tobacco product use. Journal of Risk and Uncertainty , 60 , 229–258. Chaloupka, F. J., Straif, K., & Leon, M. E. (2011). Effectiveness of tax and price policies in tobacco control. Tobacco Control , 20 (3), 235–238. Tables Table 2:Impacts of tax system scores on per-capita cigarettes sales Current First lag (2 years) VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Score_overall -0.054*** -0.048** -0.088 - -0.021 -0.086 - -0.010 Score_price -0.063*** -0.066*** -0.097 - -0.029 -0.101 - -0.032 Score_tax share -0.026 -0.014 -0.069 - 0.017 -0.055 - 0.028 Score_affordability -0.010* -0.010 -0.022 - 0.001 -0.023 - 0.002 Score_tax structure -0.037** -0.019 (-0.071 - -0.002) (-0.050 - 0.012) Ln(GDP per capita) 0.507** 0.507*** 0.535** 0.542** 0.540*** 0.577** 0.597*** 0.611** 0.593** 0.624*** (0.101 - 0.912) (0.125 - 0.888) (0.121 - 0.950) (0.129 - 0.954) (0.135 - 0.945) (0.129 - 1.024) (0.173 - 1.021) (0.137 - 1.085) (0.130 - 1.056) (0.165 - 1.083) Constant 1.466 1.491 1.130 1.019 1.112 0.685 0.524 0.255 0.417 0.148 (-2.592 - 5.525) (-2.301 - 5.283) (-3.017 - 5.277) (-3.081 - 5.120) (-2.948 - 5.173) (-3.806 - 5.177) (-3.709 - 4.758) (-4.503 - 5.013) (-4.207 - 5.040) (-4.467 - 4.764) Obs. -0.054*** 589 589 589 589 482 484 490 491 488 R-squared -0.088 - -0.021 0.492 0.471 0.472 0.477 0.451 0.470 0.447 0.451 0.447 N. of id 100 100 100 100 100 100 100 100 100 100 Notes: Confidence intervals in parentheses, clustered at the country level. *** p<0.01, ** p<0.05, * p<0.1. All regressions include country and year fixed effects Table 3: Impacts of tax system scores on per-capita cigarettes sales, by income group (LMIC vs HIC) Current VARIABLES (1) (2) (3) (4) (5) Score_overall X LMIC -0.070*** (-0.119 - -0.021) Score_overall X HIC -0.047** (-0.084 - -0.010) Score_pricel X LMIC -0.078*** (-0.084 - -0.010) Score_pricel X HIC -0.052*** (-0.084 - -0.020) Score_tax share X LMIC -0.017 (-0.091 - 0.058) Score_tax share X HIC -0.035* (-0.074 - 0.004) Score_affordability X LMIC -0.013* (-0.028 - 0.002) Score_affordability X HIC -0.009 (-0.029 - 0.011) Score_tax structure X LMIC -0.033 (-0.076 - 0.010) Score_tax structure X HIC -0.046** (-0.093 - -0.000) Ln(GDP per capita) X LMIC 0.629*** 0.638*** 0.608** 0.632*** 0.620*** (0.186 - 1.07) (0.208 - 1.070) (0.146 - 1.070) (0.166 - 1.10) (0.177 - 1.06) Ln(GDP per capita) X HIC 0.380 0.393 0.465 0.465 0.463 (-0.229 - 0.990) (-0.178 - 0.963) (-0.161 - 1.09) (-0.16 - 1.09) (-0.158 - 1.08) Constant 1.495 1.405 1.120 0.948 1.111 (-2.84 - 5.83) (-2.70 - 5.51) (-3.35 - 5.60) (-3.43 - 5.33) (-3.31 - 5.53) Observations 583 583 583 583 583 R-squared 0.496 0.504 0.481 0.482 0.486 Number of id 99 99 99 99 99 Notes: Confidence intervals in parentheses, clustered at the country level. *** p<0.01, ** p<0.05, * p<0.1. All regressions include country and year fixed effects. Table 4: Impacts of prices on per-capita cigarettes sale—direct, and moderated by overall tax system score POLS 2SLS VARIABLES (1) (2) (3) (4) (5) (6) All countries Countries with specific excise Countries with specific excise Ln(real price) -0.219*** -0.168*** -0.322*** -0.237*** -0.563*** -0.494*** (-0.327 - -0.112) (-0.271 - -0.065) (-0.468 - -0.176) (-0.378 - -0.095) (-0.864 - -0.263) (-0.783 - -0.205) Ln(real price) X CTS index -0.501*** -0.410** -0.323* (-0.812 - -0.190) (-0.732 - -0.088) (-0.691 - 0.045) Ln (GDP per capita) 0.591*** 0.564*** 0.749*** 0.705*** 0.713*** 0.678*** (0.224 - 0.959) (0.232 - 0.897) (0.235 - 1.263) (0.232 - 1.177) (0.445 - 0.981) (0.411 - 0.946) Constant 0.887 1.515 -0.387 0.356 0.428 1.008 (-2.778 - 4.553) (-1.808 - 4.837) (-5.532 - 4.758) (-4.402 - 5.113) (-2.406 - 3.262) (-1.894 - 3.910) Observations 586 586 466 466 466 466 R-squared 0.513 0.543 0.553 0.573 0.526 0.546 Number of id 99 99 83 83 83 83 Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. All models are estimated with standard errors clustered at the country level. 95% confidence intervals are presented in parenthesis. All regressions include country and year fixed effects (not reported). TWFE = Two-way fixed effects. 2SLS = Two-stage least squares using the specific excise tax as instrument for price variables. Specifications in columns (3)-(6) are limited to countries with excise tax systems that includes a specific excise (mixed or not). The “tax share index” is defined as a dummy equal to one if a country has an CTS tax share sub-score above the cross-country mean in all years. Prices are converted in international PPP dollars, obtained from the World Health Organization’s Global Health Observatory. GDP per capita is obtained from the World Bank World Development Indicators database (Base year = 2021). Table 5: Impact of cigarette prices on per-capita cigarette sales--moderated by overall tax system score, by income group POLS 2SLS VARIABLES (1) (2) (3) (4) (5) (6) All Countries with specific excise All Ln(real price) X LMIC -0.220*** -0.171*** -0.291*** -0.205*** -0.557*** -0.540*** (-0.339 - -0.101) (-0.280 - -0.061) (-0.438 - -0.145) (-0.335 - -0.074) (-0.898 - -0.215) (-0.878 - -0.203) Ln(real price) X HIC -0.259*** -0.174** -0.481*** -0.477*** -0.636*** -0.423* (-0.438 - -0.079) (-0.306 - -0.041) (-0.781 - -0.182) (-0.757 - -0.197) (-1.032 - -0.240) (-0.853 - 0.007) Ln(real price) X CTS index X LMIC -0.770** -0.724** -0.778* -1.398 - -0.142 -1.345 - -0.104 -1.620 - 0.063 Ln(real price) X CTS index X HIC -0.330** -0.021 -0.330 -0.658 - -0.002 -0.388 - 0.346 -0.860 - 0.200 Ln(GDP per capita) X LMIC 0.661*** 0.617*** 0.715** 0.667*** 0.774*** 0.746*** (0.250 - 1.071) (0.247 - 0.987) (0.170 - 1.259) (0.178 - 1.156) (0.454 - 1.094) (0.427 - 1.065) Ln(GDP per capita) X HIC 0.402 0.455* 0.744** 0.753** 0.586*** 0.552*** (-0.136 - 0.941) (-0.061 - 0.971) (0.068 - 1.420) (0.086 - 1.421) (0.185 - 0.987) (0.149 - 0.955) Constant 1.534 1.709 -0.051 0.177 0.898 1.401 (-2.353 - 5.420) (-1.873 - 5.292) (-5.476 - 5.374) (-4.997 - 5.350) (-2.154 - 3.951) (-1.712 - 4.514) Observations 586 586 466 466 466 466 R-squared 0.517 0.550 0.556 0.583 0.529 0.535 Number of id 99 99 83 83 83 83 Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. All models are estimated with standard errors clustered at the country level. 95% confidence intervals are presented in parenthesis. All regressions include country and year fixed effects (not reported). TWFE = Two-way fixed effects. 2SLS = Two-stage least squares using the specific excise tax as instrument for price variables. Specifications in columns (3)-(6) are limited to countries with excise tax systems that includes a specific excise (mixed or not). The “tax share index” is defined as a dummy equal to one if a country has an CTS tax share sub-score above the cross-country mean in all years. Prices are converted in international PPP dollars, obtained from the World Health Organization’s Global Health Observatory. GDP per capita is obtained from the World Bank World Development Indicators database (Base year = 2021). Additional Declarations No competing interests reported. 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Evidence from Cross-Country Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCigarette smoking contributes significantly to the global non-communicable disease burden [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Over the past 16 years, global smoking rates have decreased from 22.3% to 16.4% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Excise tax policies aimed at increasing cigarette prices have been an important contributor to this reduction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and have been recognized by the World Health Organization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] as an especially effective mechanism for reducing cigarette smoking [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, the WHO has advised countries on cigarette tax policies by using the share of taxes in retail prices as a guiding benchmark. In 2008, as part of its \"best-buy\" measures to reduce cigarette demand, the WHO recommended that countries aim for a total tax burden of 75% of the retail price of the most-sold domestic cigarette brand [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This target, derived from a 1999 World Bank report, was based on the success of high-income countries using taxation to reduce smoking [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2010, the WHO introduced a new benchmark, recommending a 70% excise tax burden [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While the recommended tax burdens differ slightly, both emphasize the importance of taxes constituting the majority of a cigarette's retail price. However, the WHO\u0026rsquo;s biennial \u003cem\u003eReport on the Global Tobacco Epidemic\u003c/em\u003e (RGTE) continues to use the 75% total tax incidence benchmark to evaluate countries\u0026rsquo; cigarette tax policies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile a high tax incidence is a well-established strategy for reducing smoking, other factors beyond the tax incidence can also influence the effectiveness of cigarette taxation in reducing demand [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, research indicates that the excise tax structure\u0026mdash;defined by the tax base and whether uniform or tiered rates are applied\u0026mdash;affects both average cigarette prices and price dispersion.\u003c/p\u003e \u003cp\u003eFrom a public health perspective, higher prices reduce demand, while narrower price gaps reduce incentives for people who smoke to down-trade to cheaper brands to avoid tax hikes, encouraging reductions in consumption instead [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies show that tax structures characterized by uniform rates and a greater emphasis on specific taxes tend to produce higher average prices and narrower price dispersion compared to those relying more on \u003cem\u003ead valorem\u003c/em\u003e taxation or tiered rates, making these tax structures more effective in lowering cigarette demand [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the long-term effectiveness of specific taxes depends on regular adjustments for inflation. Without such updates, the real value of taxes declines over time, diminishing their impact [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].Therefore, to maintain their effectiveness, regulators should adjust excise taxes annually, typically using a measure like the consumer price index [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, price increases need to outpace income growth to ensure continued reductions in consumption [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. If income growth surpasses tax-driven price increases, cigarettes may become more affordable, limiting the impact of tax hikes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo help countries maximize the impact of taxation on reducing cigarette demand, the WHO Framework Convention on Tobacco Control (FCTC) has provided best practice guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These include adopting uniform tax structures, prioritizing specific taxes, adjusting rates annually for inflation and income growth, and ensuring that taxes make up most of the retail price [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo evaluate countries\u0026rsquo; performance in relation to these guidelines, Economics for Health developed the Cigarette Tax Scorecard (CTS), a tool that assesses cigarette tax systems across four metrics: (1) cigarette prices, (2) changes in cigarette affordability the share of taxes in cigarette retail prices, and (4) the excise tax structure [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The CTS gives a score for each metric, and an overall score that reflects how well a country\u0026rsquo;s system aligns with best practices. A higher overall score points to a better, more FCTC-compliant, cigarette tax system.\u003c/p\u003e \u003cp\u003eBeyond evaluating the overall quality of countries' cigarette tax systems, the CTS also facilitates research on how different elements of these systems collectively impact cigarette demand. To date, only one peer-reviewed study has utilized CTS data to examine the relationship between the overall quality of tax systems and smoking behaviour. Focusing on the impact of the overall CTS score on adult per-capita cigarette consumption in 97 countries from 2014 to 2020, Ngo et al (2024) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] find that each unit increase in the overall tax score was associated with a significant 9% reduction in per-capita cigarette consumption.\u003c/p\u003e \u003cp\u003eWhile this provides evidence about the association between more FCTC-compliant tax systems and lower cigarette consumption, the study does not investigate the relative impacts of each policy component on cigarette-smoking behaviour, nor does it evaluate how specific aspects of the overall score enhance the effectiveness price increases in reducing cigarette demand .\u003c/p\u003e \u003cp\u003eSince cigarette tax systems comprise multiple interacting dimensions (e.g., tax structures, cigarette prices, cigarette affordability and tax burdens), empirical evidence on the relative impacts of these aspects of a country's tax system\u0026mdash;both independently and in combination\u0026mdash;can offer valuable insights into the mechanisms through which cigarette tax policy reduces cigarette demand.\u003c/p\u003e \u003cp\u003eIn this study, we explore how each of the four CTS components independently and cumulatively affect per-capita cigarette consumption using data from a sample of 99 countries over the period 2014\u0026ndash;2024. Additionally, we investigate how the overall strength of excise tax systems moderates the effectiveness of price increases in reducing cigarette consumption. We also assess whether maintaining a high tax share in the retail price, as recommended by WHO, is sufficient to ensure effective reductions in cigarette consumption. In so doing, this study provides new evidence on the role of different components of cigarette excise tax systems in reducing cigarette consumption and underscores the public health benefits of adopting tax systems that align with best practice recommendations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThe dependent variable in this analysis is country-level adult per-capita cigarette sales volumes, which serves a proxy for cigarette demand. This variable was constructed using tax-paid cigarette sales volumes data obtained from Euromonitor International [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and data on the adult population (aged 15 and older), derived from the United Nations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Euromonitor dataset includes information for 99 countries, of which 46 are high-income countries (HICs) and 53 are low- or middle-income countries (LMICs).\u003c/p\u003e \u003cp\u003eOur primary independent variables are the individual scoring components and overall average scores reported in the most recent (fourth) edition of the CTS, covering the years 2014, 2016, 2018, 2020, 2022, and 2024 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The CTS evaluates cigarette tax policies globally across four key components: (1) cigarette price, (2) changes in cigarette affordability tax share of price, and (4) tax structure. Each component is scored on a scale from 0 to 5, with 5 representing the strongest performance. The overall score for each country is calculated as the average of these four component scores, ranging from 0 (lowest) to 5 (highest). Information on how each component score is calculated can be found in Appendix 1.\u003c/p\u003e \u003cp\u003eIn addition to the individual and overall CTS scores, the analysis also incorporates the price of a 20-pack of the most-sold cigarette brand in each country, as well as the total cigarette tax incidence for the years 2014, 2016, 2018, 2020, 2022 and 2024. These data are sourced from the most recent editions of the WHO RGTE (WHO 2015, 2017, 2019, 2021b, 2023, 2025) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe WHO reports cigarette prices for each country-year in nominal terms, but to maintain consistency with the CTS method for accounting for inflation and purchasing power differences across countries (see Appendix 1), these prices are converted into constant 2018 international dollars using conversion factors from the World Development Indicators (WDI) database [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo address the potential endogeneity of retail prices, the analysis also instruments cigarette prices using the excise tax amount levied on a 20-pack of cigarettes. This variable is constructed by multiplying the excise tax share of the retail price reported by the WHO by the nominal retail price of the most-sold cigarette brand, and then converting the resulting nominal tax values into constant 2018 international dollars using the same WDI adjustment factors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLastly, all models include country-level per capita Gross Domestic Product (GDP), measured in constant international dollars (BY\u0026thinsp;=\u0026thinsp;2021), as a proxy for income and obtained from the World Development Indicators (WDI) database [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The analysis also employs World Bank country income classifications, distinguishing between high-income countries (HICs) and low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Statistical methods\u003c/h2\u003e \u003cp\u003eWe begin by estimating the impact of both individual and overall tax scores on cigarette demand using a two-way fixed effects model, as presented in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{n}\\left({Sales}_{it}\\right)=\\alpha\\:+{\\beta\\:}_{k}{Score}_{it}^{k}+\\gamma\\:{\\text{l}\\text{n}(Income}_{it)}+{\\mu\\:}_{i}+{\\mu\\:}_{t}+{e}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Sales}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents adult per-capita cigarette consumption (in number of cigarette sticks) for country \u003cem\u003ei\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e. The dependent variable is log-transformed to allow the relationship between countries\u0026rsquo; cigarette tax scores and cigarette consumption to change proportionately.\u003c/p\u003e \u003cp\u003eThe term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Score}_{it}^{k}\\)\u003c/span\u003e\u003c/span\u003e denotes either the overall score or one of the individual components of the scorecard (i.e., \u003cem\u003ek\u003c/em\u003e=overall score, price score, tax share score, tax structure score, or change in affordability score). The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}\\)\u003c/span\u003e\u003c/span\u003e captures the relationship between each individual and the overall tax score, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k,\\:\\)\u003c/span\u003e\u003c/span\u003eand per-capita cigarette consumption.\u003c/p\u003e \u003cp\u003eEquation (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) also controls for per-capita GDP in constant PPP dollars (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Income}_{it}),\\:\\)\u003c/span\u003e\u003c/span\u003ein logarithmic form, to capture the income effect. Additionally, it controls for year and country fixed-effects that capture factors that are invariant within countries over years (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i})\\)\u003c/span\u003e\u003c/span\u003e and unobserved factors that uniformly affect countries over time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{(\\mu\\:}_{t}).\\:\\)\u003c/span\u003e\u003c/span\u003eAcross all models, standard errors are clustered at the country level to account for intertemporal correlation within countries.\u003c/p\u003e \u003cp\u003eBecause three of the scorecard components (price, tax share and change in affordability) depend on the price of a 20-pack of the most-sold cigarette brand domestically (Appendix 1), they are likely endogenous. To address this, Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is also estimated using the first lag of the each individual score and the overall score (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Score}_{i,t-1}^{k}\\)\u003c/span\u003e\u003c/span\u003e) instead of the contemporaneous score (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Score}_{it}^{k}\\)\u003c/span\u003e\u003c/span\u003e). Given that TCS data are only available on a biennial basis, the first lag represents the individual and overall scores that prevailed two years prior to the sales data used as the dependent variable.\u003c/p\u003e \u003cp\u003eWe also present results from both the contemporaneous and lagged models, with the \u003cem\u003ek\u003c/em\u003e scores interacted with binary indicators for country income groups (HICs and LMICs). This interaction approach allows us to examine heterogeneous effects across income-group contexts, testing whether the responsiveness of per-capita tobacco consumption to the \u003cem\u003ek\u003c/em\u003e scores differs systematically between HICs and LMICs.\u003c/p\u003e \u003cp\u003eNext, we investigate whether and how the strength of the overall cigarette tax systems moderates the impact of price increases on per-capita cigarette sales, using the model shown in Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Sales}_{it}=\\alpha\\:+{\\beta\\:}_{1}\\text{l}\\text{n}\\left({Price}_{it}\\right)+{\\beta\\:}_{2}{\\text{l}\\text{n}(Price}_{it})*{CTS\\:index}_{i}^{overall}\\:+\\gamma\\:{Income}_{it}+{\\mu\\:}_{i}+{\\mu\\:}_{t}+{e}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CTS\\:index}_{i}^{overall}\\)\u003c/span\u003e\u003c/span\u003e is a dummy identifying countries where the overall tax score is above the median in all years, constructed as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{CTS\\:index}_{i}^{overall}=\\upharpoonleft\\:({score}_{it}^{overall}\u0026gt;{Median}_{t}\\:,\\:\\forall\\:t)\\)\u003c/span\u003e \u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\upharpoonleft\\:\\left(.\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eis the indicator function and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Median}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the cross-country median of the overall tax score in year t. The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\:\\)\u003c/span\u003e\u003c/span\u003ecaptures the price elasticity of cigarette demand in countries where the tax score is above the median in all years, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e captures the price elasticity of cigarette demand in countries where the tax score is below the median in one or more years. Other control variables are defined in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with the historical emphasis on higher tax shares in retail prices, we also explore whether the WHO\u0026rsquo;s recommendations for a higher tax share enhances the effectiveness of price increases. To test this, we estimate equation\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Sales}_{it}=\\alpha\\:+{\\beta\\:}_{1}\\text{l}\\text{n}\\left({Price}_{it}\\right)+{\\beta\\:}_{2}{\\text{l}\\text{n}(Price}_{it})*{CTS\\:index}_{i}^{tax\\:share}\\:+\\gamma\\:{Income}_{it}+{\\mu\\:}_{i}+{\\mu\\:}_{t}+{e}_{t}\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this equation,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{CTS\\:index}_{i}^{tax\\:share}\\:\\)\u003c/span\u003e\u003c/span\u003eis a dummy defined similarly to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{CTS\\:index}_{i}^{overall}\\)\u003c/span\u003e\u003c/span\u003ebut identifies a country where the tax share sub-score is above the median in every year. The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\:\\)\u003c/span\u003e\u003c/span\u003ecaptures the price elasticity of cigarette demand in countries where the tax share sub-score is above the median in all years, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e captures the price elasticity of cigarette demand in countries where the tax share sub-score is below the median in one or more years. Other control variables are as defined in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to estimating Equations \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e as standard two-way fixed effects models (TWFE) with country-level clustered standard errors, we also use the specific excise tax amounts (in constant PPP\u003cspan\u003e$\u003c/span\u003e) as instruments for prices in a two-stage least squares (2SLS) regression to address the endogeneity of the price variable. To enable comparison between TWFE and 2SLS specifications, we presents results for 2 samples: (i) all countries with observations (99 countries with 589 observations) and all countries with a specific excise tax (83 countries with 466 observations).\u003c/p\u003e \u003cp\u003eAs a large body of empirical literature has documented higher price elasticity of cigarette demand among lower-income populations, we also estimate specifications that include additional interactions by country income groups (high-income countries, HICs, and low- and middle-income countries, LMICs) to explore potential heterogeneous effects. Evidence consistently shows that smokers with lower incomes tend to be more responsive to price changes, implying larger reductions in consumption following tax-induced price increases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eSummary statistics for variables included in the regressions are presented Table\u0026nbsp;1 for the full sample (Panel A) and the sample used in 2SLS regressions (Panel B). Figure\u0026nbsp;1 shows the average overall CTS scores (on a scale from 0 to 5) for the 99 countries in the analysis sample from 2014 to 2024.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[TABLE 1]\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[FIGURE 1]\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong the countries included in the specifications, the average overall has barely improved from 2.2 in 2014 to 2.3 in 2024 (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eA2.1\u003c/span\u003e). Breaking it down by income group, the average score for LMICs rose modestly from 1.6 in 2014 to 1.9 in 2024. In contrast, HICs saw a decline in their average overall scores, from 2.9 to 2.8.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eA2.1\u003c/span\u003e in Appendix 2 provides a breakdown of average scores for the individual CTS components in the analysis sample in 2014 and 2024. The change in affordability scores emerges as the weakest component, falling from 1.4 in 2014 to 0.5 in 2024 overall. The largest part of this reduction stems from HICs, where the affordability score dropped from 2.2 in 2014 to 0.3 in 2024 (vs. a reduction from 0.9 to 0.6 in LMICs). Among the sampled LMICs, the strongest performing CTS component is the excise tax structure, where the score increased 3.2 in 2014 to 3.5 in 2024 on average across all countries. For HICs, the price score became the best rated component in 2024, standing at 3.7, slightly higher than the tax structure (3.5) and the tax share (3.6) scores.\u003c/p\u003e \u003cp\u003eTrends in per-capita cigarette sales and constant PPP prices (base year\u0026thinsp;=\u0026thinsp;2022) or price sub-scores from 2008 to 2024, for HICs and LMICs in the analysis sample are shown in Figures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eA2.2\u003c/span\u003e and A2.3, respectively. While a clear negative relationship between prices and sales is evident, this relationship weakens between 2020 and 2024, likely due to the COVID-19 pandemic, during which real prices remained stable while sales continued to decline. This temporary decline in the price scores among sampled countries is similar to the trend across all CTS countries (Figure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eA2.4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of the CTS Score and Its Components on Cigarette Demand\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents estimates from Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using the overall tax system score (columns 1 and 6) and its components. Columns 1\u0026ndash;5 use current tax scores, while columns 6\u0026ndash;10 employ lagged scores to address simultaneity bias.\u003c/p\u003e \u003cp\u003eFrom column 1, a one-unit increase in the overall tax system score reduces per-capita cigarette sales by 5.5% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This effect persists with the lagged score (column 6), though the reduction is smaller at 4.8% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong the components, the price score shows the strongest and most statistically significant association with per-capita consumption. A one-unit increase in the contemporaneous price score leads to a 6.3% reduction in per-capita consumption (column 2; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a 6.6% reduction in the lagged model (column 6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The tax structure score also has a significant negative effect, but only in the contemporaneous specification, where a one-unit increase results in a 3.7% reduction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in per-capita consumption. Though the affordability score is the least performant component, it is statistically significant in in contemporaneous model only, with a one-unit increase resulting in a 1.0% reduction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) in per-capita consumption.. The tax score is not significant in any model. The income elasticity of demand is consistently estimates at roughly 0.5 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 across all models).\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eTable \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e and A3.1 presents results for the models interacting TCS scores with country income classification, using the contemporaneous score (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e) or the lagged score (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003eA3.1\u003c/span\u003e). In the contemporaneous specification, the negative relationship between overall and price scores and p.c. consumption is consistent and significant across income groups. However, the effects are larger in LMICs than in HICs.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eTable \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom column 1, a one-unit increase in the overall score leads to a 7% reduction in per-capita cigarette consumption in LMICs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 4.7% in HICs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). From column 2, a one-unit increase in the price score reduces per-capita cigarette sales by 7.8% in LMICs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 5.2% in HICs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eThese differences persist in models using the lagged scores to mitigate reverse causality (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003eA3.1\u003c/span\u003e: columns 6 and 7). Notably, from column 6, the relationship between higher overall TCS scores and reduced consumption remains significant in LMICs (a 9% reduction per unit increase, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while for HICs, the reduction is not statistically significant (2.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1). From column 7, the effect of the price score remains significant in both groups, with a one-unit increase associated with a 9.1% reduction in LMICs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a 4.3% reduction in HICs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eThe significance of overall average effect of the affordability score (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e) is essentially driven by LMICs, where a one-unit increase in the score reduces consumption by 1.3% (column 4; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) in the contemporaneous model and by 2.0% in the lagged model (column 9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant effect for the change in affordability score is observed for HICs in either specification.\u003c/p\u003e \u003cp\u003eIn contrast, while the average effect of the tax share score (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was not statistically significant, it becomes significant among HICs, where a one-unit increase in the score reduces consumption by 3.5% (column 3; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) in the contemporaneous model. However, no significant effect for the change in the tax share score is observed for LMICs. Likewise, the majority of the negative effect of the tax structure score is driven from HICs, where one-unit increase in the score reduces consumption by 4.6% (column 5; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1), in the contemporaneous model only.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModeration of Price Effects by the CTS Score\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results from Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Columns 1 to 4 show results from the TWFE estimates, and columns 5 to 6 present estimates from 2SLS where the specific tax is used as an instrument for current prices. For ease of comparison between samples, TWFE models are estimated across all countries with non-missing observations (columns 1 and 2) and among countries with non-missing specific taxes (columns 3 and 4). To facilitate the interpretation of the results, estimates of the full effects of all regressions with interactions are presented in Appendix table A3.4.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eTable \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEach column provides estimates of the price and income elasticity of demand only (even columns) or with added interactions between prices and the dummy that identifies countries where the overall CTS scores is larger than the median in each year from 2014 to 2024 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CTS\\:index\\)\u003c/span\u003e\u003c/span\u003e, odd columns). When we use TWFE, a 1% increase in the price reduces cigarette demand by 0.22% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, column 1) in the full sample and 0.32% (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e; column 3; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the sample limited to countries with a specific tax. Using taxes as instruments for prices in the 2SLS specification produces a much larger price elasticity of demand [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] estimate of -0.56 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e; column 5; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The instrument is also found to be strongly correlated with prices in the first-stage regression (t\u0026thinsp;=\u0026thinsp;7.10), corresponding to an F-statistic above 50, well above the conventional weak-instrument threshold of 10 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen we interact prices with the dummy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CTS\\:index\\)\u003c/span\u003e\u003c/span\u003e) that identifies countries consistently above-average CTS overall scores consistently above-average (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e; columns 2, 4, and 6), a one-unit increase in the index increases the negative impact of the price on p.c. cigarette sales, as reflected by the significant negative coefficient on the interaction terms. Results from the TWFE models using the full sample (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e; column 2) imply that the PED of cigarettes is -0.17 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in countries with a low CTS index, but this effect increases by 0.5 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) units in countries with a high index where the PED is estimated as -0.67 (or -0.17+-0.5; Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003eA3.4\u003c/span\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results imply that in countries with a \u003cem\u003elow\u003c/em\u003e CTS overall score in all years, p.c. cigarette demand is much less sensitive to prices than in countries with \u003cem\u003ehigh\u003c/em\u003e CTS index in all years. Results from the TWFE model in the limited sample (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e; column 4) confirm this difference: a 1% increase in the price of cigarettes reduces p.c. consumption by 0.24% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026minus;\u0026thinsp;0.65 (-0.24+-0.41; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in countries with low and high CTS index, respectively. These differences are statistically significant (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003eA3.4\u003c/span\u003e). This association is confirmed in the 2SLS specification (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e; column 6): a 1 percent increase in the price implies a -0.49% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) reduction in p.c. consumption in countries with a low CTI index, and a -0.81% (-0.49+-0.32%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) reduction in countries with a high CTS index, also a statistically different. Full marginal price effects are computed using the Stata \u003cem\u003elincom\u003c/em\u003e command, which estimates linear combinations of regression coefficients to recover the total effects implied by the interaction terms. The resulting estimates are reported in Appendix Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003eA3.4\u003c/span\u003e. As in the models without interactions, the implied PED is much larger in the 2SLS model than in the TWFE model, as shown in Appendix Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003eA3.4\u003c/span\u003e: the full PED is estimated as -0.44 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026minus;\u0026thinsp;0.65 (0.01), respectively in the TWFE and 2SLS models, revealing that linear fixed effect models tend to underestimate the price effect due to the simultaneity between prices and demand, while this bias is reduced in 2SLS models.\u003c/p\u003e \u003cp\u003eEstimates of Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) are further disaggregated by country income groups in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which shows results of the same specifications as in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, with additional interactions between the variables of interest and dummies that identify HICs and LMICs. Columns 1 and 3 (TWFE) and 5 (2SLS) show the estimates of the price and income effects on cigarette demand, disaggregated by income groups, while columns 2 and 4 (TWFE) and 6 (2SLS) add interactions of the price and income variables with the dummy that identifies countries with a high CTS score in all years (CTS index), in addition to interactions with country income groups. The full marginal effects are reported in Appendix Table A3.4 within income groups and overall.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eTable \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFocusing on specifications limited to the countries with a specific tax (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e; columns 3 to 6), the effect of cigarette prices on p.c. consumption is larger (in absolute value) in HICs than for LMICs, though the difference is not significantly different, as revealed by the overlapping confidence intervals. As previously shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the 2SLS model estimates a much larger price elasticity of demand [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] than the TWFE specifications. For instance in LMICs, a 1% increase in the price reduces p.c. demand by -0.29% (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e; column 3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026minus;\u0026thinsp;0.56% (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e; column 5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), respectively in TWFE and 2SLS models, confirming that standard linear models underestimate the PED of cigarette demand. In addition in HICS, the estimate of the PED of cigarette demand is larger than in LMIC: a 1% increase in the price of cigarettes reduces p.c. demand by -0.48% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026minus;\u0026thinsp;0.63% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), respectively in TWFE and 2SLS models. These differences between HICs and LMICs are not statistically significant (Appendix Table A3.4). However, when we add an interaction between the price and the CTS index (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e, columns 2, 4, and 6), the PED significantly increases in LMICs but not in HICs, as revealed by the interaction term. In contrast, among HICs, the PED is not significantly different between countries with a high or low CTS index. In LMICs with a low CTS score in all years, a 1% increase in the price of cigarettes reduces demand by -20% (TWFE, column 4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but this effect is increased by -0.72 pp (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in LMICs with a high CTS index, implying a full effect of -0.92%(-0.20+-0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in LMICs with a high CTS index. Results from the 2SLS model produce similar differences between countries with a low CTS index, where the PED is -0.42 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e; column 6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while this effect is increased by -0.77 pp in countries with a high CTS index, implying a full effect of -1.31% (-0.42+-0.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These impacts between LMICs with high or low CTS scores are significantly different. The full PED in LMICs is estimated at -0.31 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026minus;\u0026thinsp;0.65 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively for TWFE and 2SLS models (Appendix table A3.5)\u003c/p\u003e \u003cp\u003eIn HICs, the PED is not significantly different in countries with high or low CTS indices: in the TWFE model (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e; column 4) a 1% increase in the price of cigarettes leads to a reduction in demand by -0.48% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) or -0.42%, respectively in TWFE and 2SLS models, but this effect does not significantly change in countries with a high CTS index, as reflected by the insignificant interaction term between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Lnp{r}_{HIC}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CTS\\:index\\)\u003c/span\u003e\u003c/span\u003e (-0.02 in TWFE and \u0026minus;\u0026thinsp;0.33 in 2SLS, p\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\u003c/p\u003e \u003cp\u003eThese results provide evidence that that LMICs that support a high overall tax score in all years benefit from a larger responsiveness of cigarette demand to prices, while this improving effect of the CTS score is inexistant in HICs: overall in LMICs a high CTS index increases the price elasticity of demand (in absolute terms) by about 4.2 pp based on the 2SLS model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAffordability and Tax Share Impacts\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe also estimate of the incremental association between affordability and consumption, moderated by the overall CTS score (Appendix Table A3.2). Although we find a large affordability elasticity of cigarette demand (AED), we do not find evidence that countries with a high CTS index exhibit a larger AED than countries with a low CTS. In Appendix Table\u0026nbsp;3.2, the TWFE model (column 4) estimates a significant negative interaction between affordability and the CTS index, suggesting that countries with a consistently high CTS score experience a stronger demand response to affordability changes than countries with lower CTS score. However, this interaction is no longer significant in the 2SLS model (column 6).\u003c/p\u003e \u003cp\u003eFinally, because the WHO places particular emphasis in the tax share sub-score when assessing countries\u0026rsquo; policy performance in reducing tobacco consumption, we construct a tax share index analogous to the overall CTS index, as presented in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This index is defined as a dummy variable equal to one for countries whose CTS tax share sub-score remains consistently above the median in all years. We then estimate the association between cigarette prices and consumption shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), interacting prices with the tax share index (Appendix Table A3.3). The results indicate no additional effect of the tax share index on the PED, suggesting that maintaining a consistently high tax share in the retail price is not sufficient, by itself, to enhance the effectiveness of tax policy beyond its average effect. In Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003eA3.3\u003c/span\u003e, the interaction term between prices and the CTS index is not significantly different from zero in the TWFE model (column 4) across countries with a specific tax, nor in the 2SLS specification using specific taxes as instruments for prices (column 6).\u003c/p\u003e \u003cp\u003eWe also replicated the analyses in Tables \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003eA3.2\u003c/span\u003e and A3.3 by country income groups, but found no significant interaction effects between affordability or prices with CTS indices. To avoid redundancy, we do not report these results. Instead, we present estimates of the full PED and AED from our preferred specification\u0026mdash;the 2SLS model\u0026mdash;interacted with either the CTS overall index or the CTS tax share index, both overall and by country income group. Estimates of the PED or AED for countries with a CTS index equal to zero are obtained directly from the baseline coefficients reported in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e (PED by CTS overall index) and are presented in Panel A of Appendix Table A3.5. Correspondingly AED estimates based on the CTS overall index are derived from Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003eA3.2\u003c/span\u003e and reported in Panel B, while PED estimates based on the CTS tax share index are derived from Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003eA3.3\u003c/span\u003e and reported in Panel C. Estimates of the PED or AED for countries with a CTS index equal to one, as well as overall effects, are computed separately using the Stata command \u003cem\u003elincom\u003c/em\u003e. Across all models, the PED is consistently estimated around \u0026minus;\u0026thinsp;0.7 overall and not differ significantly between HICs and LMICs. The AED is estimated approximately around \u0026minus;\u0026thinsp;0.75 and likewise does not differ significantly across country income groups.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study evaluates in depth the importance of maintaining a consistent, comprehensive, and strong cigarette tax system to maximise the potential of tax policy to reduce tobacco use. We do this in two steps. First we obtain the Cigarette Tax Scorecard and its components for all countries surveyed by the WHO, from 2014 to 2024, and evaluate the relative potentials of each component of the tax system to reduce p.c. consumption. We find that a higher overall tax system score has can significantly reduce consumption, and that this effect is persistent over years, as suggested by the larger impact of the lagged CTS scores than the contemporaneous score. A one unit increase in the overall CTS can reduce consumption by between 5 and 5.5% both in the short- and long-terms (more than one year). Among the components of the overall CTS score\u0026mdash; price levels, affordability trends, tax system designs, and tax shares in the retail price\u0026mdash; the price sub-score has the strongest association with reductions in cigarette consumption. A one unit increase in the price sub-score is associated with an approximately 6.5% reduction in consumption in both the short and long term. In contrast, the tax share and affordability sub-scores show the weakest associations with consumption. The overall effect is also significantly more pronounced in LMICs than in HICs. A one-unit increase in the overall CTS score reduces cigarette demand by approximately 9% in the long term and 7% respectively in the short-term. However, in HICs the overall CTS score has no statistically significant effect beyond the short-term. Likewise the effect of the price sub-score on consumption is largely driven by LMICs, as a one-unit increase in the price score reduces consumption by nearly twice as much in the long term in LMICs as in HICs.\u003c/p\u003e \u003cp\u003eOur second step evaluates whether countries that consistently maintain a high overall CTS score experience greater effectiveness of excise tax increases in reducing cigarette consumption. We find that do this by constructing a dummy that identifies countries where the overall CTS score is above average in every period, and interact it with price levels to estimate the marginal impact of a consistently high CTS on the demand effect on price increases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We find that a countries with persistently high CTS scores exhibit substantially larger price responsiveness. Based on our preferred 2SLS specification, the PED is nearly twice as large in countries with a high CTS index (-0.82) as in countries with low CTS index (-0.49). This finding suggests that maintaining strong and consistent tobacco tax policies enhances the effectiveness of price increases in reducing cigarette demand. I addition, the ability of the overall CTS score to improve the effectiveness of tax policy appears to be concentrated in LMICs. LMICs with a consistently high CTS score exhibit a PED of above unity (-1.3), compared with \u0026minus;\u0026thinsp;0.54 in LMICs with lower CTS scores. In contrast, the difference is much smaller in HICs: countries with a consistently low CTS score have a PED of -0.4, compared with \u0026minus;\u0026thinsp;0.7 in countries with consistently high CTS scores, and this difference is not significant. These result provide strong support for the view that countries seeking to maximize the effectiveness of excise tax policy should aim to maintain strong and consistent tobacco tax systems over time, rather than weakening tax policy in the short term.\u003c/p\u003e \u003cp\u003eThis study is not without limitations. First, the identification of a causal effect of prices on cigarette demand relies on the assumption that specific excise taxes are exogenous to cigarette consumption. This assumption is motivated by the fact that specific taxes are strongly associated with cigarette prices but are determined through policy decisions rather than by contemporaneous supply or demand conditions. However, the exogeneity of tobacco taxes has been questioned in the literature, as governments may adjust taxes in response to public health concerns, fiscal pressures, or trends in tobacco consumption [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. If tax changes are correlated with unobserved determinants of demand, the exclusion restriction may be violated. Nevertheless, in the context of cross-country panel analyses, excise taxes remain one of the most commonly used instruments for cigarette prices because they represent the primary policy driver of retail price variation across countries and over time [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, the strong first-stage relationship observed in the data supports the relevance of the instrument.\u003c/p\u003e \u003cp\u003eThe paper also finds a larger price elasticity of demand [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] among high-income countries (HICs) than among low- and middle-income countries (LMICs). Several factors may contribute to this pattern. First, the estimates rely on repeated cross-country data, which may introduce measurement error in prices and consumption and reduce the precision of elasticity estimates relative to country-specific studies. Second, the sample period (2014\u0026ndash;2024) spans the COVID-19 pandemic, which disrupted cigarette markets in many countries through lockdowns, supply-chain disruptions, and changes in purchasing behaviour, affecting both prices and consumption patterns [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Third, the rapid expansion of novel nicotine products during the last decade may have altered substitution patterns and therefore the responsiveness of cigarette demand to price changes, as evidence suggests that e-cigarettes and other novel products can act as substitutes for cigarettes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Finally, differences in income growth and affordability trends across income groups may also play a role, as rising incomes in LMICs can offset price increases and dampen observed price responsiveness [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides new evidence on the importance of maintaining a strong and consistent cigarette tax system to maximize the effectiveness of tobacco taxation policies. Using cross-country data from 2014\u0026ndash;2024, we show that countries with high overall CTS scores experience lower cigarette consumption and stronger price responsiveness. Importantly, the results indicate that countries that consistently maintain a strong tax system obtain greater reductions in consumption from price increases. This effect is particularly pronounced in LMICs, where a high CTS index is associated with substantially larger price elasticities of demand. In contrast, we find no evidence that maintaining a high tax share in price alone enhances the effectiveness of tax policy beyond its average effect. We also find that though maintaining a high CTS score over time can significantly increase the responsiveness of consumption to prices, it has no marginal impact on the responsiveness of consumption to affordability. Likewise, maintaining a high tax share in the retail price of cigarettes alone is not sufficient to maximize the effectiveness of tobacco tax policy. These findings suggest that governments seeking to maximize the public health impact of tobacco taxation should focus not only on increasing prices but also on maintaining comprehensive and consistent tax policies over time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReplication data and Stata code will be deposited in a public repository upon publication. Some original data sources [1] are subject to third-party licensing restrictions; in these cases, processed datasets sufficient to replicate the analysis will be provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cbr\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The author thanks \u003cstrong\u003eSamantha Filby for\u003c/strong\u003e excellent assistance with the initial data analysis and programming, and for helpful comments and editing of earlier versions of the manuscript. Any remaining errors are the author\u0026rsquo;s responsibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was supported by the African Capacity Building Foundation (ACBF).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eE.D. is responsible for the entire project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational, E. (2025). \u003cem\u003eCigarettes: Market sizes and company shares by country, in Passport Global Market Information Database\u003c/em\u003e. Euromonitor International.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReitsma, M. B., Kendrick, P. J., Ababneh, E. 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Effectiveness of tax and price policies in tobacco control. \u003cem\u003eTobacco Control\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), 235\u0026ndash;238.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cimg width=\"697\" height=\"661\" 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/aL0X8QVRvxaEv01ArRqLGgc26uxULNXRbvIgECnTbwWQ1KNBfKpqnhj9PfKRQ4R8tZYqNfqqx1l9ERp/a7OSg1pERAEQRBJdXxzOHvDgAX+Bb+vOF+CoxR+vQ5CtR1n7Wb+N+9rXY7c+3GChTRHLgmMZDulmmAhYbxzg4W8+8kxJ/LMNXGeDFRDQ1Ho0d+fynEf8pxyQpdh+5fnehGXk+T7rNLqS82esyutD1lgQcf5zwQLCc5s2r51HHDxC3pesOC3t97XSXnQcPyy+vmIzqoQPGKC99N3Qvsder9dVNAwTOvgYfyd6HrzvsILBRd1wYK3X39FAj/XXFh5IcfcSAALSbUU0ueBnA7B96cY3xzFG1PHd8uChcgx1wMLaZEMWxEshKs/eHP6HndOR3N1OZjTPJXA35f1XlKEQV6wEKZWBGkNSccO7u+/sKXx9GsW0KtRgUf6D2ltssBCvL9dVerG+Z0CdMhfvDG5fwALEARBUCJYCNaevyaue5+k8Guo1TrFHFJ/PXqrx7/usZd6r3r2/Yv/jwolZ2Xnhodcy04228Z3otwX/bpzJM3JjCs/HMg6Fn8w+/3X23/YP1o5y/LMo7ENVohIgA4iQOBjwkJTq9XqM+H4ecWTfHgSjUM8RF+0oRIgsC+Mgm1HtZ9cc6LZ+3rS/tNBVr6iojGnu+3s42OaHCXjgwIxkiDPOSXtx3Fmv6aaZ6Po3+DXi9/3lD5o5+XHxij4kq4ao69b9BSfa2JYfDAXPusLdQ+dRv+reMK5feYtqRfsj/Xdc0KF2gjpx+Ch+n70A9+GQ5Gke41VX/6O7jwMCwBq9NPrDyXv8/slb8/D8NXLNkbpGkm2E4sQyucSWx7SPY5qnNKhUrwIowogMPAgzI0nuF3Z+cSuH6HYoud0Wfb3lpZmvx4exx33xGUl9eaBeH6WNA8uiPNAhAJsfP0aDUE7ZhuNVIjg+rwg9z3ox6c6/dCaW1L6grz/fsef2Uy9+gOrRcAd47itjj66ujRbYbaKn9fRDv/yn3Rcfl5GsJ+s1QSS9hO7/oN0BuWcFpzv8LySr+WO2B8GBwx+7IT0Ap1jizCgvrDkLR3ZW1m81a/REF+SMq0Nt5tfE8K9NmaP/kc5UiIsAEnL53hxRbYftu8Zq3mKLzXpjW2zdtAvxhg/v/C4LBrj7qPfZSBCjE4Q+1dlK00gFQKCIAhSgYVYYUFNZ11epYFarRMsgkB0rLmjEub/UnI+rfq8qh/Kdeo19qXncGafb16wwGsssBepOcs8Ke6bjVFaRAXbJoIIdKNmt4+FywGy8bTbD0TLA/Y7iaGDJ8p9sVb/fv/Lo9lPlJqgNyf++j8oxz6jKOcgRRsjx75ZFm2hsoM498TChXnmuWo/DCokzelh+zfM9SJem7p9SL2ehJUH+sbI/Pf/2x3/A/lgFHOtz6ls2/tE8EYoPS9CiMRVEdx9hWAi9jf/S33MwU9ZSUEbLrj9jO1P6qc8nsxmpkk+FG2mXPHBdbyZ7eyq+Uy4ekGl9UMVEGLOWtI+E8cpAy55jm1Km765wfrW6d0iwgDutMswiUVXMKggFk5McrjZCgaq8e2HMJK9K60XkuZBo7t0uw82GhWd8VVBBbFon9/3xSfU/TiU0g/1ihd7FSsj0PrSIy1r5pSh2H8z4XfupMqrLdDKodBWnhNNK+eYrdTn1e7Z1HhLtf+l+s5H+p3wSuJ5KVd88J3YPpCz7M/pD6KVO/pXVfAcZkreS1vJgRU2jJaHVB77przH9uZczV4N5s+P+RjQysLf8nPpa2P0t4kiDITxVqQ9LPvXQHSelH7kwaNXIvsqz2/PoRc9GwUrP0Qq/27/inOHsn97ov5BEARBEAYBmholpQxA0LjkpYpU7SdVDqRfOZ8+oSpWCEHQVryW9/5AFWESXMuP0/rSiqpeAgRBEARBAAvQdnox9Iro5aufAUGDin/dTotI8ELBc9T7gCBoc65lFgFRaz52f1L6hZdm4F7LWekZEARBEASphUGApsfJK5EzcOKgSckvdhgVP1SpbdsPAHRB0NaWX2gxKoKoatO07cM6qS0QBEEQBKmFQYC2rKLCjnr1FyBopM5ImOfvF+0T596a49zMoEKemi4QBG2OovoE7rW8sDQvAoTgWj7M6owgpQmCIAiCABag7fgy2Lbm/aKEdIPa7R6gAjTxOcicDos+HSuOSel5y24+jPkIQdN1Lbcs+lSsCCgrbMiuZUQqQBAEQRDAAgRBEARBEARBEARBmycMAgRBEARBEARBEARBAwuDAEEQBEEQBEEQBEHQwMIgQBAEQRAEQRAEQRA0sDAIEARBEARBEARBEAQNLAwCBEEQBEEQBEEQBEEDC4MAQRAEQRAEQRAEQdDAwiBAEARBEARBEARBEDSwMAgQBEEQBEEQBEEQBA0sDAIEQRAEQRAEQRAEQQMLgwBBEARBEARBEARB0MDCIEAQBEEQBEEQBEEQNLAwCBAEQRAEQRAEQRAEDSwMAgRBEARBEARBEARBAwuDAEEQBEEQBEEQBEHQwMIgQBAEQRAEQRAEQRA0sDAIEARBEARBEARBEAQNLAwCBEEQBEEQBEEQBEEDC4MAQRAEQRAEQRAEQdDAwiBAEARBEARBEARBEDSwMAgQBEEQBEEQBEEQBA0sDAIEQRAEQRAEQRAEQQMLgwBBEARBEARBEARB0MDCIEAQBEEQBEEQBEEQNLAwCBAEQRAEQRAEQRAEDSwMAgRBEARBEARBEARBA2vqT6DnNHfNWeZJSuh52+mVt7vBks533ONw/Hjneqdt76tSepoQ69Jir3cLLqBobOwSOUMI3bDaa/Po9xY4L0re8s6r2bsPc3Sy400IMab5XNp27SGr7hyZ5vMYpRy79kDNbh/rdLs3YDwgCIIgCEoECy2LnHJfoK4li25QWj3ddJwt57T3HLtMCdng/RzGoeb7mrSDlee4Sec7ynFIEpsn0TEAFmRnxL+Opg8sTGO/tZxDizwPsDDx8V6fZrDQ6y3eYlHyPv331VdvJuT/Kz4HaX35EdV5sXPfS8mb7t8+iz03K4decP9vJOPgXqPPGfL+3Xtwo9vd0QcB2rUF0yQfhu0oPV9rLjeTbCK3p9R6vbHUvb3fvvRpg9pvNTrdGzHnIQiCIAhSgoXIwaAnvJeLkn2mc/z49fx398XjEHcoqdU6sRVfavlX12Ec6uU6XfHO32qdmmT/8x436XxHNQ56YKO4YMGp1w8CqkDQ9lIIFQSAwKLA7Kr5DIcG5ebj9yc56CFgcJ+fjU5nZM53cM+9KgN/FehwbPqwD3eWW/xvDByw7Wl96REZdCy57Q23fX1haZ61Z+fLzoH9Nru0asn799pT630V0IAgCIIgCAr/Y61tzctgoe9v/gvNynYDC95LJSGXxvnFf1THBVjYXOejQipvASxA0PaSF3FhtU7JznRwj77A79HM4U7aB3Ps2fNxlBEbLFpBZ58cQPjAQQ0QRFiwtmjd6/73pzIsic5XHRHB+jNjNd1xemUG8waCIAiCoNxgIXJa1X+fdrDgnXvVfpWf46TgySDHBVjYPPnRJUgDgaDtJN/J9h3vvnue52jT9ygl54Pn39tJEQmjBgve/ZZWX9OJEOCgQBcs+OkV1kXVvv32bF9HH5X3tbZi72EAIy16A4IgCIIggIX5NHAQ1WLYWo7VsA61X3iMvsy2DdMSJgBPBj0uwMJmQgXUl4Cg7aSw8KQiWiECC9abzeX6ve69bz2tfsKowYKf2sDSMOiGac39fWNp9baMe/NVUprrS8WQIUJWVAKHFP55xiMT2HjNsXQJ7ziotwBBEARBUE6wEItYEByraCUCXtSPFX+qnpYdXnk1Aa9uA/8CRCtnkxxhtl2FkrNRIar+tsM61P5XIftlds6R45y9r2Er6o/iuKMCC8yOvn2igp1eFXBpHohgodlzdoV1OQLbNJ3erj4b1q0j/NzYPh1n9mt2s/f1vPaW51Hs+G67f0/I/yEXHRXHIVakVJjjWXOYAyBVYVMeYZK1IofO+A56neQZX1W/5H739aPb/ZLu9SrP09CebLy73Ruivvr7YfuOn0P89+T+xm01u7Rakdux3Hh2XvxvSecVFq5zjyvvJ+283P285jlmq0u3scJ2ngMYnKc4n1nevjifVcfgxfHCAoBuu3pz+d5JHJtV/PcdZvc+ZjcfVs2frDaq8U64Bl6Lihz6+xL73Hd+rtOrY6Ok/uk4+IET/VlScUYOFth8CZ6Rn6bVOJDBQgQH0gok99dP6HYXvxSlYAh/q7R+qFqdgdd48Io80so5XoCR1Vhwx+WqGK2QCRaCqARSqr+tggc8ogFRCxAEQRAE5QYL4qoDoTPF6wO47blDyV4CZadOXk3Asq2n7LazT3ZU5a/APOyc75u9mPr1COKO/LBggfVP3F/ogGYUUxwWLIziuKMAC77d3W0Cm/jLrdGeai6I88C05k6KdleBEc+G0vxIWgEjy95986hef9xZc272zpkdo+fsCvudEFXA+xNCBc05HGsr7TtrRQ7d8R3kOskzvkqwpei3Rj/WuWOetn9/lYDIqa3X6w+yfkYrCLi/V+1X61b9cd7/WOSO5DwF43/RK44XfLkNbPVZkPteifcxzImv8P6M6ryU85B99Xb7FsISz6G0LvK+BvP5orxSRbRUo+vkuW09B7FK/lvMoQzGQ3HsJzq9zi2DHttrFx9PVugvtrJDVpuk8e533uk6gyVsm+AaeEIGIsH5rYfnN2c9zbcRbSQ6w17/2GoFKeegMU8/S2ovgoU4KOivtzCOGgtrjnOzAIk+iyIm+mscBAUo3+P9syz6MoPXcqSDWDdCBQdCsDBARAMEQRAEQQALMbAgOi/iqhCiI8SdALEugBjZIDo3SY5RkjPst+//+quCH0N9qVcU44sKVY4v5H2Y444SLHC7qGo7qFarSANBsm14f+R9s/3Kjq+uvbMiO7LGgEU49IEP3TmcABay5/Hw46vaf57xzTuXkhztyAlWO5BqaNX/NT3cvxSdEMIDhYMfjJf3Zbm/P66tlM56vJ+jOq9oP+r27O8mMT+U/6YaD+6oxlcjCPavAiwjOjY/Z9UXdjZ/OADIapM1fry/yasYuH2qtF7oO3fJqVUdQ+jfI2n9S53/0T6tpHu1CBbiy0v2Q45RgwXleKWsUBGLXPDubf2rQQQA0V/CUgEHQnCQELGQFdEAQRAEQRDAwjz/6lQqld6NfTFja2FLodusPXOCam3nkPTCcio/WIi39/aRGjnR72ANAhaYsyI7ZuJKDYNEIoz7uKMEC76t1O2j/kR/TwMLcvuoP1EUAt+HHGqta2+dGg8qWBDuK0g9GWgODwAWRjW+quskz/iOHizEHflMB1wGCwkAIfq930ENbLVeay43xd959MNowELO80qIcPD6pAkFxC/mOuMwqmPLkRKq+aPTJgss+OfnFQ6sJFwDF9URJ0lgIYouyOpf5koKGSkBKrAQ3y5ezHHcYCEGFzynvr+IJC/4WK3Sl8IIB0Vb8RxKdmuNz5eVleaeMOohLTIiY9wgCIIgCAJYGLhwYZS/7MOIQcFCvJ5Dsvrb5wMLYvFEtcM9niKOwx53VGBBdExVTrrKNmmOvap9VPDQz4VX1WDIY28dsJAEAHS+5KfO4ZxgYZTjmwTgdMZ3u4CFTFttEbAg7it1PgfHUYEFnQiAURxb/ALO5o+qOKBOm6T+io6/CoLI456W8qACC/39s15PK3A4KrAg2OGqWMxxlDUWkuBEFJHQ3+cg5SSspxCkhVxNSp/gtTGE2h7nWOoJS8Vh0QxJUREACxAEQRAEjRws+LmytWNeuLfn2DjlYSMWxLx3nX4MV1sg6yVv9CssDHvcUYGFNEdZBh2DggXfARRSaYLaDLG6DTnsrbsqRV+/FaknuedwTrAwyvFNGlud8d1OYEG2VWNpqRKO4RYBC2ItCFWRvYT7QTwdICUlZJTHDubPQlTXwJs/fy9vm9UmcbxTzoNLrnGQFyzonsOowYIMDZgTziPRxl3QMKyhIfQ5BB0SQIjqJSSnT8TmY0Z9BYAFCIIgCIJGDhai4nrxUOxhwULWl15vf03767Fw3QHAglw8MclJVuXHD6Nhjzt6sJDcXg7l1wML/fvzq72ra3XksbcuWAjbBcdRpZ7knsMDg4XhxzcNLGSN73YBC1EBwnjY+1ZLhcj6Si/fv6JzoBt8JQhV3YVxHVuaP+tJxTODNguqNtlgITkCRU6VGAQsJPTv7Sy4MCxYYGq6/TfEYomTAgusYKWQ3hCfM/GaCnmKLfLaC7R+9FFVbQaABQiCIAiCRgoW0pzIkdVYSHGu25bVG6bGgirfXlasQv2I0iFGcdzR11hIHmf/7+Lyoho1FoTijaziv7ykYnItgmx764IFcV6x+gmq1JPcc3jgGgvDj28SgNMd32kHC9k5/FuwxkLKEobufH6ib7wp/ZW4SgerJaEMQx/Rsb1ztuzvpUUe6LTRtE9Kf9jfo3PJXWMhvX9W5vzPWbxRtY+omGN6GsMI4fRzSSkXKrAQgoAMsMCXkcxqh+KNEARBEASNDCykfY0dBVgQ0wVYaLfjrN0cvkCxL2uCcz6oQ52Vbz+OIo6jOO44VoVIAwWiU5y9PKgUeaJw6P128T7q2lsXLMj7VC3hmXsOp7TPWhVi2PFNjOzRHN8tBxYS9p8EFtK+fG9FsBCmN/Cw/I60yg6bz8F4+Puy3s9a5nJEx16gdO9LfFWIvZS+JI+n76D6EQQ6bbLAgk7qhnJFDE2wkNE/K2s88y43mWKTq5MACz6E2vuSXIwxvpKD9LcABKSlQoRQQQMWYLlJCIIgCIISwQJ7QWNL8eWpKxAWGnSdNv6Fmn1Z5l/dmMPEq3MH681f63dgeCh6/1ddsTBdWv0BdozQEa87R7L6zfsirwTQ/wLX3FWh5Gx4zGDNe/GF0/+73niN7LgJ55t3HPrGmVbO8nQAr6gXczglhzzWN9dZ5O15GL562cboHEJ7K+CVjr2T5tGgYCjPHJadezFiIG3s84xvnusk7/gmOSmqfov9iDvmPB1BL0Uosmk8fSHY/2fJjr97nSSs/uDZKnCMJVs9wm0VnNdF/7yWv5N93Nh5ZTqGSfvpd25VRfvi23DHNmnus/7L9QRGcWwRBvAUjHAcpCUpeRu/BkK8jTCPwvFOXFbSvQb4PPCuAbZv154656eyUUr/LmjXuPCd5MSICr8/dD0pgkR2tkcFFvyUBLpu2c2H+Xkca9t7q1X7yaTzCtMYKod+GNnGHQ+2yoPVPCVDADZ+bJ/u399XzbUkcQihU7MBgiAIgqACgYXQuUpZeUEluZo/tVon2Jdm0ZGy23/9H5QV/92XSbUjGf9S63/dI+eTKt9HX3hjFbVXdPqrOl74kphUYFFwBHXBwiiPm3S+ecZB7SjZ+yKYESwvmgBAwnzsFLvwdszxdpxmec4yT4rzJMnpTdpv4soRGQ60N8e0VthIm8MR2BHHmRdJ1Bn7rPFNPL+U66TZc3blHV8ZKvT3+68fz329ZlT7l8dFeb9x95/0O3dc5H1KtvKcVuZcinAiOu7iExnn9dkw55XkxLL5bJrkQ3E+y6sWBI7f+1mrOAxw7IW0Y/M0AjZ/xJUBaKX1w1jtBLfN0lKjktRGPd79znXkwArXgOCsJ55fpfVCko0aq0u3ZfUvS+FxpXEMv8pL/Ulzpke53KQHa/zx+kw1XinQK2b3pO04hGB/98arkx4B1rdtae4M0iAgCIIgCIqBBQwCBEHQ5ogByqSv0P4qGPSJSRQELLKCZRq1UicKP1YaaRUQBEEQBBVTGAQIgqBNEP9anlbXwftyPaIaL1CyvJQUKS0D6heLVphRpFVAEARBEARhECAIgjZBfupTVARR6fDa9mHdsH5ocHHIM4lVHaZVXm0Far2PJSYhCIIgCFIJgwBBELQJiuoT+MUHxdoYa45zc9u2H5Brl0DjUwgXWJ0SwJzYuLQt+jSgAgRBEARBacIgQBAEbZI8gOA6bbECiJSeF1cDgCYrx649wFZJQeRCNB41u30M8xGCIAiCoDRhECAIgiAIgiAIgiAIGlgYBAiCIAiCIAiCIAiCBhYGAYIgCIIgCIIgCIKggYVBgCAIgiAIgiAIgiBoYGEQIAiCIAiCIAiCIAgaWBgECIIgCIIgCIIgCIIGFgYBgiAIgiAIgiAIgqCBhUGAIAiCIAiCIAiCIGhgYRAgCIIgCIIgCIIgCBpYGAQIgiAIgiAIgiAIggYWBgGCIAiCIAiCIAiCoIGFQYAgCIIgCIIgCIIgaGBhECAIgiAIgiAIgiAIGlgYBAiCIAiCIAiCIAiCBhYGAYIgCIIgCIIgCIKggYVBgCAIgiAIgiAIgiBoYGEQIAiCIAiCIAiCIAgaWBgECIIgCIIgCIIgCIIGFgYBgiAIgiAIgiAIgqCBhUGAIAiCIAiCIAiCIGhgYRAgCIIgCIIgCIIgCBpYGAQIgiAIgiAIgiAIggYW+981CIIgCIIgCIIgaGr0f4YzCwEsQBAEQRAEQRAEQQAL0PYACxgECCocTfwseCAZGI9C2PvTwN4zGI9C2f2TwO7XYTwKYe+PA3t/DuNRKLv/KbD7/wnjUSi7//8AFqAtOTcxCBAEsAABLEAACxDAAgSwAAEsQNDAcxODAEEACxDAAgSwAAEsQAALEMACBA08NzEIEASwAAEsQAALEMACBLAAASxA0MBzE4MAQQALEMACBLAAASxAAAsQwAIEDTw3MQgQBLAAASxAAAsQwAIEsAABLEDQwHMTgwBBAAsQwAIEsAABLEAACxDAAgQNPDcxCBAEsAABLEAACxDAAgSwAAEsQNDAcxODAEEACxDAAgSwAAEsQAALEMACBA08N6ex0y2LnCKEbthOr5xnu+PHO9e37doxSsl5Wl9eyWrfc5q75izzJCX0fN5jQRDAAgSwkKw1x7nZvR8/RAlZt5q9+zAftx9YcJ/VzxmErs8urVqwL8ACBLAgPQP+L+167UHTMK7sPvDofvLKKzPCvePZGaN09a7Fla+Sa9dw7wBYgLYbWPCdeW8Sx2S11+Zjznhv8Rb3DeKS2EbHiZ8EWBDPIatPPccuuy+8G377/MeCoGkHCwzE2ZS8Nci1HFw/6+G2JftMp9u9AeM/fWDBA7IWfTqyJ92w7ObDzJ5t2z48iF3Zc2IvpS/xuTgIWGhb5HlhLitEN0ql0rteXzu9WwAW0m08R8mbhjCetH70Uff/Mm2ytmLvcefG1ehar7/d6HRvBFjY+mAhuMe/EbP73Ue/Kzp4aXY3CblsxO1+E2yxdcFCYO/ThhE+E1x7d7qk0ci8R6wuufY2yB/CubKj/s78ovOFgaHCWudm9xnwc8MwvL4ALAAsQAUDC0xO294XOtvMUTh+/Pqkmxd34mtt59BWOuG1tjWv6yB5N+ESOQOwABURLKghgXVpsdv9kqbT57V3nYoKxn06wUIEl6xLjaXV20TQ4M0jBTBi88WqO0d05pdj04fZfgaNWGBRZa7HepHdo2vN5aa4D8dplu2q+Qyf7+49/5EiObeDRCzEIYFr8253R9Y2PjxgY1y5BIAwXWBBggRXfEhQ/t2806WZdq+Qn/oOavl3s51uFXbf+mBBAQl8e3ccM8t+zQr5yYzB7inl39212B2Zs79c3/mQO48+lsECBLAAFQAs+DcBuuJNZqt1KqvdqCMVABYgaPJgQY5c0Iz2WUekwvSDhTTH3/ubwr4MKrE5MgmwwI9H/K/iSoDltGsLfD4WCS4MAhbkyIWsqIUYiBAiFaDpAgty5EJW1EIsWgGRClMHFuTIhayohVi0wpCRCgALAAsQwELcaQhTHaxLiz11eKl/06Ivb0VnHGABwgNpELBAX67Xrcd1YAGDila9/rgX3QSwMNVgIc1pZ8+Cqmk/I9qXgwJdB34SYMG77y9a9/rznm6wr+oAC8nX+l5KfxFc659lwYIl137Btb4OsDDtYIH+g2v3JzxnMwMWuPf4Y57dDeOqAbAwlWCBpSH49jY+yYIFzPmv1WcfZ7UQjB31twEWABYgaGRggYmnOSQ5594XS2q/rEqVYKGrVUpPi3mwLGxWvOGJBRaddu1Q6KC4+0srqJi1bxkssNSOCiVnvbbU3V/b2ZcHLIR9419yrdaJpPQQCJp2sDDrOF/zrwc/J1557TNnk1Zfajte2tR6GlgIrp/1tOsnuKZfi1/Ty99R3S9Yn7wwfEre9/ZXaf0Q4bmjAAvqlAfv70GNheBF9aW+mgfCdoGtHgqjB1z7BBFwYwcLQtTNZ6TSeqEv+kKIauB9i86LvBk7r5LtOlKdG0UwIv8+7WBhdmnp62y82BfKcvPx+1W2Ca71f2gu1+/1ohYksMCuXZaKYhLzQ54iweeAaZIP2X6D6/U9D0ZVDrnXKxyLzQQL9tLS/41HrJQb3W+owt2Z3Wu09vOFpfq8aZArKrDQW1m8tWqa/xTVbihtWHcv/UfmOPbVZmDac+jFTrdzkzvnXufblA90vwlHc7xgwT569C6bGm8wmJTk2LNaCJZp/cy1932mYVxOAwtOs3bQnROXuQ1Lexb+c6Pb/Xz8eW05HqBw27C/L9V3PiKDhVV3/sxVzf+y0zA/kGsssL9ZpvmPvDaDO7f+uPvuo0cbQcRFeAzT+LfyQvebq0vzlapJfs3OkR2P2Pa2KG4KsABtK7AQFjZMqLPgfbGUijrGtrNap9h2kePuF4EU/81kWnMnm05vl//yaV0KnBVlQcWsfctggYEEXv+BvQBxwCC2TQMLLYueoHa7x8+fOUlZtScgaJrBArsGwlSoBEeTp0CF6RAJ7RKun5gDG+6DXdOBk8dTMpgj6r0cVcl/CwvJWvZTHA6KX8Jh78HBQiythVbO1pvL96bNGx4ZoIpY8AGAX6vBf/mjT1BKfzVo8cY8YCEOAeK1A7zClGwuBvOOQYZgLnqgIAYXKodekFMD/HO2LjaWurdvMbsPARZWK2y8DBJ+ve4DJixagV3rq0uzFRksxGs1+FEi3vVbJf+VOx2WNfc0n09LwbGSIAY0KbCwanm2YLn0CZEILFphZ31phTlsHiCQ2nFwMFM+9IJ7nd0kpllwWMDAQ5h64W//+Whb899Qs2FCYMEd52WbHmOOfVLUAoso8O1tW146RAJYaFfMH+y0mz1mcw4Z3P3+WdyvX6ehcmH+8MpXut3ODe4z4Hsmpf/CIAEHC/EikaU/imCB/21mz8EXGbDgKR0zARjpdDs3uu8EL/EUjz17Zn9YX1iaf8Xd73aLjABYgLYVWEhy2r0X0d7iLRVSeUuVJsGdEpWjL0Y/pNVxSHL2dfedlAqhgiVJx/L2IfUtbUwgaLuAhSAV6mLgLFQS26WABX79iMeXoYFwTcfgQHD9eo5r3LGMR1HwdvJxABbyby9/zTetub/nhRx1wYL/e9z5l20+brCgSofwfrPaz8tzkYME7ugG8/kqm89yVIITONhbbZ4NCxaCa/0CH6/+MQrahWMTBxBRvYb4qhC82KMIEQLbfOqDG3yl3kywENj9vEFKG7KDH2vn2l0FFhh4YGBCjHjoNa17mVMn1m7wvoQHx7mrs1Jjx+HQApEKkwML3e7il1w7fOjZQYoOENt59k4AC2vt2r4Zq/UcsX/0Oen+fppHQzzevnPeMMzL4jHkNtzuETAorYvtVXCAHZsBDLFOhL+iBPlE1Y7sOfgzcpvzFwALELSFwILooMtf6NnvLKQ54QX1kNJJlyAChwSqVIskZ19330lgQbXfpGMlLb05ruU1IWirgIU08Cde+2lgIVoxIvH6eUS4pmMOowoYqACEGO0AsDAcWPDHs7mLp5jI6QJZYCEECIq5MKkaC0lgIWvJSn4eEWyI12jgKSBbceWTYcGCGEkgO/xsLKnVfpr9FkYnaIIFVXRCuA+AhU0HC9zB96IW9hx6USzqxwABtZpPs9/ClAYJLPhfqs0rHBaIYEHeX+DsfczmztHmnS1K7V+gXsNkwQJz2rnD7jrdL4pwgNlnp9V6iqUPhJEECrAQrRihvo+W/u/N789R402jNHdG3lYFC5LAgtO2GjKcUAED1T6D/v+eRTsALEDQFgQLURHHfqdb54t9mBvNUxuGBAu6+04r3siBAe9/KmzIWBUDgrYrWEiKWmDpDWGbBLAQOpk5HX7VNQ2wMDmwEDoNUvSCbF8VWAhtoaptsIlgIe9cVBWm9PZJ7be2Um2FUYKFpKgF91p/ircBWNh+YCEpaqFdMZ/kbZLAQt99u157kBrkqhHUUpBXHwiXraTlc2yZS6RATB4sBNEjfVELLL3Bs7f77ySwEEKAysILIpQQJaYwyG3ygIX+dwKvXoNf0wFgAYKmGyyIAIA72d7DyJw7mVZjgBdf9NYdt9vHwtoEIwALOvtOAwtyOoXqWCFQAViACgoWvi9AuPDalwq2JoGFEEpoOnMJ1zQiFjYJLIQvdBZ9moRLEkqO9hYFC3KNhdBp1p2LvL2QDtHMsbTmNIKF4Fr3Uhe40+9BALr3JT4GAAvbDyzEHP4ABvh2jyIK0sCCV0Ohav7YMOhVa7bpFeFWRUB4+3Gd2gorrkcqlwAWNgcsePcyP+rgUx61wBxx197/wOtfJIEFDiVU0GAcYIEXdmRzqzy78Ohy886mYRiIWICg7QAWwroEwdKTvHBbZvugwKLo6A8LFnT3nR2xkJ4KEf2WvNwmBG13sBAV9XOvg273SyxaQYxUyoxYCLbTuL+ExRuF6xdgYUJgwbFrDyRFhoWOumDjrQoWVKtCiHNRLOaYJgYSeH0AHzRYb2bN42kHC1EhRn+cGFQS7QWwsD3BQgAOrnCHn0Wp1JjdeQh6AliQizd694CEVIjg/n1sZ7X6C2oY64ZQyBGaLFiIiia69u44ZrtqPllrPna/5JgnRiwYRuUi2051Hx8VWJCLN/rvBEiFwHUAbRuwEDnjrqNut3uy86EGAnGHfBRgIc++M2ssaBRvTFtu01s+S1riEoK2G1iIXQd79z4tRyrp1FhIu36SAATAwqTBAn2YAaOk5Qa96JMssKBoN2mwoKqvIMxF5SoWfC6Kv4eFCl0HeLlZa/qFH69tyfk1KrAQXOte1AK17RPsWhdTPwAWtidY8OweRC3Qev2Hvt37AYIIFqIVIFwn0+nS8LpJqbHAazaExxIKPEKTAwtMPGrBtfffWjvdZ7oAeXRqLIgFFMXtLHu+WzGMj1Tb6oKF6DcPYOwM7x0ACwAL0PYCC6HznrHUYlSTYfRgIc++s1aFEH9PXRWCF/cSlszzXqQs82QSXIGg7QQWQic/+XpKXhWCb5dw/UR1HAAWNhssJC0Zysc4KxVCdOBlgKCqWzBqsCDWhZCPHwEHcq1kt9b4efpLI5rPqFY+4UUcS6XSu1uxaOM4wEK4aoN3rR99VIQpAAvbFywEQMCbR7LDrwILUW0GP8ohDSz0pVYoVomAXScLFkIn3bN3HBKkgQVxux2zB/8Tj1Txn+k7/44dp1Wd+am8UoMIAcTjqcBCMD8+CCIqABYAFqDtChZ0l1kU23EnxGnb+6qUnubOP3tRZevQp0YEhBCBXKu1nUO59t3sfT1K36AbfHuv4rm7T3k1C9WxuBJXhkDtBWibgYXg+rgoR+KkpTWEtRBY+LRiacLElSECECAuRRitEqG+X9hV8qrYLgYgEpxigIUcYIGNI62crTeX743SG4JVIuQaGkLag79OuX2Y/T1KnaEbfD/M4Xft+Vo4H4N9efddtm/3mNnpMv7cFPcb9t1plhkc4JEKteZyUzXnE1eG8Jzc/miE0Mku2W+rijYG/X+P9V83xWKrgIVgPC9Y9eXvyMsM+pCgP22E2dGfYxX3Wu/eLj0/Lxge0Fluhdd1lfxXL/pBABTRmMbhBDQZsMBqIri2+siaPdoRnb6kCATP7t7qD2yOlX+3v7PyZeG+/YZn3wBELC/Ye6um+c9+gb1DL7oOnmW3j+1lqwTsrC89Encyg9UovH12vwy4MB6wENj7w9rdR/9jQ4AHaWkNgb0/ZraZP7zyFdk2iStDMIc/qNngp1qUNu7a3/nGK67dnaZ10J0b/2QYhv+M2jH7DoMWwb3jAzZndh84ssjaRrCBRbX4EMJp1u1we/c4Kx27areX99q1mVcMoV0MQATHAFiAoC0KFqKX+OyaA+xmUaHkrHcxUHqeOevi6hK19l8f5oAgUrTfCAr0L+2Yve8IDEQF4YL9BO3jL1jJx+I34FiV+qC4XFrhSgiaJrAgOveR4itBeEtMCtdFFGkgvVwoai1kXT+hg8mvUdcxFFekqLUXH+i7X1itUzyCIXYP2aJ58FsfLPg1Ftg9M3LSfXuplpv0HfWgsCNzrAWoFF+ykm4w59WzlWl+aNnNh/m+dMFC1lKRXh9p9TQDDmlwKZiLD0WrXbh9E/qjBAeuw5wUZTGNYCECB+J4xleC8JaY9ApV+jAgWjFCvtbrby8tzX7dr8sgPj9ZuqTxVuwYlUMvLNV3PmJEc/SaByg2cdyKBBZEEBCNf3wlCG+JSXvpYQ4AoqgE6doLIheCd7HfeH+n9CNrYand7Xa+xKMRrMZSu2nNPO8VhnS3Kx/oftMDECFUiPbJ/wb7jgYsROBAvN7iK0F4S0wye3OHPFwxQrL3jvo7cq0F9z7KVmm44rct/bE823yERy8wscKLFZO86x7f+/vuu48eda//FcM0/61cbz7i3nNvWl3hACI6HgcEQaHPd73+l0of7V44+u1ub3GHDyFKG+VGu+PeY96YEc9vz8GfefcYI7w+rnlwRIh6AFiAoC0GFiAI2n5gAQJYgKba7rkjFqBigQWoWGAB2lZ2B1iAABYgCAJYgAAWIIAFCGABAliAABYggAUIgqb7gQSwUCx7AywU0+4AC8WyN8BCMe0OsFBMuwMsQAALEAQBLEAACxDAAgSwAAEsQAALEMACBEHT/UACWCiWvQEWiml3gIVi2RtgoZh2B1gopt0BFqCtOjeTKmpDEARBEARBEARBW1AACxDAAgRBEARBEARBEASwAG0TsIBBgKDC0USkQhTL3kiFKKbdkQpRLHsjFaKYdkcqRDHtjlQICGABgiCABQhgAQJYgAAWIIAFCGABAliAIGi6H0gAC8WyN8BCMe0OsFAsewMsFNPuAAvFtDvAAgSwAEEQwAIEsAABLEAACxDAAgSwAAEsQBA03Q8kgIVi2RtgoZh2B1golr0BFoppd4CFYtodYAECWIAgCGABAliAABYggAUIYAECWIAAFiAImu4HEsBCsewNsFBMuwMsFMveAAvFtDvAQjHtDrAAASxAEASwAAEsQAALEMACBLAAASxAAAsQBE33AwlgoVj2Blgopt0BFoplb4CFYtodYKGYdgdYgAAWIAgCWIAAFiCABQhgAQJYgAAWIIAFCIKm+4EEsFAsewMsFNPuAAvFsjfAQjHtDrBQTLsDLEAACxAEASxAAAsQwAIEsAABLEAACxDAwkTltO19Nbt9bJzH6DnNXXOWeZISet52emVMjOlVyyKnCKEbo7Dj8eOd623LespuO/sAFiCABQhgAQJYgAAWIIAFCBoCLHgOVomcCSaxIOvSYq93y/icRHqClOwzTae3a3xQwS5TQjb88xmNQwptD7Dgz4/mrgolZ2l9eQVgAZLvi227dsw0yYfeeLr3qk63e4POnKrXrccpJefdefVIkh28/Vv0aff+tM7vT7TS+qHOMQAWxvGsaO6ybet7zN5pdkt71liUvO/ZglbOzi6tVvra9BZvsQi50P+sDVSqv93odG4EWBizrVcWb3Vt9Z7B75W0fM61l6Vrc3btuu8vT4nXrtVcbrn/aaS0vRq2bbhtr10zcl7nAAujuKdXzSd3GuSq4Y1laYPuWfjbRqd7U975Y9u1VdM0PqD1o4+SRuO6UbQFWBj389xyXDv8m2G41/2O2XfmF50v6Gy7tta52b05fGBE71Zs7vzxrkXnTvLKKzPx58D8X1VM8q57jE9Jqfzbuw53vyq3AViAtj1YCC+etjXvO+Hjd8CX63SFWq0TnePHr5/EDcUHJwAL29cpcF/q686RYebHdoELAAujmU++0+A6AXbzYcdZu1n7HkrprzjMTHJQvTlHyVsMVjSWVm8LHdvgtzxwAWBheK0tWvfq2C1te2YH37kkhtOuLbB/l5uP3y/uh7dLBAuVQy8QovcSCrAwoK1X7D0mIVf6x59u+PZKd/iDa/cNozTnXru925l9l117M3BAZ13HUXAixLb7O6t3eG2bblujvy3AwgTeAyl5XbSF5/RzW3a6n9e7x9f2ufeK/04NY53BCXr30e8mwYI8bQEWxqfVJfeaN8jvDVLaKM8uPLro9Hbkub8v13c+OGOE15+vHbPvzDY6X5TtbRjkT7sPHFlsuHZ22lbDMIw/796/8q28cAFgAdoWYCH4mnLJe7Edo8PPXr7HHQ0BsFAssUiGYcAAn/tWe20eYKHgjocPWNdFp3+wexz5LMlB9f9O1+Wv2hxoWM3efQALmwMY2HjmAQthFIIHBSKntGmR5w1iXWx0uzv4byxCpdZcbsr7Zs+ovZS8yeyue1yAhYGdyzdM+9Ca60h6kSGx6AUvYiT96/WyTY8ZhnWh4XRLceeD/U6vzna6VW5D/ze9tgAL476vM6fPvHxXZ6UmXqerPmi6XD7Q/WYe5y9wIj/WgQV52gIsjN7upkH+YOy45+35wytfyQKHqnvGXtP8mc3mTcr84FENM3sOvkhuc/5CeDd9dsaoXJjvODvzvJMBLEAACzmPMcmvwwAL21ss+iX4yrgy9H7GDNUAFra2okgF69Jit/ulYeBEGlhwbPqwEiz498eLeRxbgIXNBQu+LclnSdEJfF/Mts2m+vnjzzvrfRFCACyMwb6uE1mznQdk5yK47j4y3HeENGc/bKcAEGEkRPnQC8x5ZG0rmm0BFibxnrDzIcMwr8hgIXAIz++czZemALCw9RVFKlQu5XXsRdvNVFs/FWFBwvzyohrk6AQexUDvPvI3xP7R53LYHWABKh5Y4DnILJeYfellL0csX91z8qzWieQX7riDr6zxIPSBO47y7+HxhRoKqvQKGSzIx+MOaXj+0u9p58lyaVnkBWvDCkSGOZQpX76ddu1Q1Gd/rNLG2z++/QCvBcDrAvBj1drOobzHEc+J7TNsq2F73XFn/axSeloMNRXTFfKel1yE0//6RF/uz1OOziGrDwqncmPaoxYAFoYAkCwVgTkXivz4UYIF/ndSab0Qc0b9aK6LeaAGwMLmgQUeaRDMGUtxP7kqRzIkwgmrdUo3DQJgYfRqWeQ5g8G+FLAQAgEFLAjTHkjlIotQYPbXbQuwMIFrO3DuyZ5DLxLb/lz898pH806X5kp/AliYhuf56RmjtO7VQshZ0yS+j6BeQuPo/Z1u98aE58Br7rGuynUXWM0FBjfkSAaABQhgQXXBVcmr3GGzLDusrs9BgOygRQ59fxpEzNl3X7DUL+LWJV7oMWwvFH9kzrHsWKrAwvdDwtj/pdsvtMQKBPq/951nvf64fJ6kar9ar9cfZMf0vlJ4zrE61YMVrKR2u8f7l9TnpHGm1epLbBUN71iCIy6PddpxnDXnZhGsmNbcSTaG/vmkp6iEY+naqA/8CHYLi2YG7UT7sr7mPa+0IpyBk9YXsZDVh8T5r5h/AAtFePEMnH3PwRtu7LLAQgQx3HkbFGwMINhredIgABY2FyxExRhTwIJ7T82KRGBObZ40CICFcYEF5ug7iY4+wMLUO5qvG+71Xdqz8J+ZTVgaTNU0/8mLNsqZAw+wMBUg6c9kz8Gf5XHo4/dwHwrEijYqCjJGxR1LG0lgIW/UBMACVDiwIDyMT8mOGnf0ZActa/+hI6j4u1fsUXAcfWe2P7UhCRaowEKSQ6rahwqWJJ1PEljxjieNSZazmzmmijHTPY4KCGRJBR9CmKKIMlHNC3Fc85zXoHbM6oMO+AJYKMRL51v+fGl/J4o+Sq7wPwxY8Od4cxdLe/CPQc9TWj09SE0HgIVNBAsp8CCCDulgwW9nvZEnDQJgYbTiKQ5e1f6UqJEICJDPygvdb4iRKDIsyNMWYGFCdmb1NAg579fTKH1EqfX6/s7KHYOGyAMsbOnnuRdpsLvR+J/r1X/3Y8MwPonAwMpX80QwOI7zl+167UHTMC77kCG+IkQaPIigA8ACBLCgBRaUDrfwpVjpLCY4skmOIw9557+J0Qr94ff8y3bkHG4VsMAhTJLSagQk9VV1brrHyVubIM8XfT9CQj3e4vZ5ziuvHXX70G+j6a7FAbAwsGPhOfklu7XGV2Vw2va+cHWIHJEEOmChDy4MmIIBsDDdYME7ptV+Pk8aBMDCiK9/1wYGYUUWnZJeWzb25d9xp9RP66OPe0vMub9zWJCnLcDCJsAF9oVZqrkAsLAN7uPC8pA7Zg/+J/f+60UMOc26bRrGH1hkwe4Dj+3PG6USS40QVoUAWIAAFjYJLGQ5dUnOrrddLG8+ObJhFA7pOMCCmEIw0I0y5Uu7GDWS5zi5wUIw7nmLJPbVZNAAC/J5DWJH3T4ALEBh0UbFUo9hikSOZSB1wIKfl0lfYrVEWOqSb7d8AANgYQuDBc1UiEHSIAAWRgsVq6b5zyyVRdcGx9r23nAlCVZjqNL64XLd+g4Ls/dy+AXncbmvbfNvg7afyG0BFsar4J778zuby62mRZ92nf1PGFwoH3jsm0iF2D4KizaylSAWnS8obPFnBgbkv+WDC0HtBnfepIEFpEJAAAubDBZUfZCXEpxGsDBs/n6a8yweL89xBgULuufAiy16hRjt9rGwzoMmWJDHcRCwoNMHgAUoDSxE0Qz6K0Xo1lhgc5b/3Z2bCxwu5IlcAFjYRLCQEpUQggW2bGmnc2Py9vnTIAAWRqeWRZ+qeWAn/1fr2H4q5KcsCkFOexi2LcDC6KACq7Gwsx6t/uA0awe9go4DRC4ALEwpWBgwgqDfnuYfOFhI22cIFrwIh+4Xc9gdYAECWMhyQHUdU/FLtb9kU+WtWE7/NEcsDJi/n/1lX17xIvs4A4MFjfkhF05MAku65zWIHXX7ALAAhfBAARai+gujAwt8iUI5OiFptQiAha0JFtJWheD7SlsVYtA0CICF0WjZpsd21pdWBhn/mB1TijoO0xZgYYS29pabJJ+UD3S/qVgOsG+1CICFKb6Pc0dfARaiiIPKxUHBgg8LzP/9rsUVr4hj2qoQfLnJvEUkARagwoAFp14/KDqsucCCJrgQt/e+MCftR+EAquovbLUaC+ov63Y5aRlEPQc8Agm6xxm4xkJGockkuDEYWBisVkaePowK/gAsTLfaFnleBQ9CsDDCVAj/WP2RCeGxcqxMAbCweWBBhEReZXlhm6TfpXvcQGkQAAsjcDTbtQX2LBw2UoEXfjTY9ZyyVCVvW9FsC7Aw4vt7hfzEYI6fFJnAIxlmGAAEWNg2cu+tz84YlQsyPOBg4brSPWcGSYUI7UntN3iNhQBcPTjj2nj3/pVviWAh6XeABQhggf/dsp/KDPtPiUyQncV0x5RulEqld1Vfj5NWNODnoLMqRFI/x7oqBC+gKCwFyfrHwvXTvpJnrbShXGkh4zh5wYIILbyxDJbdjKCTdYTtOwIQ+mBhGDuqwEKePvRtg+Umi+lUJsAAniaRx9nUjViQ/65KkQBY2NpgIUyHkCITmhZ53iDWxaQ0h2HSIAAWhocK1Go/LUcq+MsP2j9udLo36uwnWt2BrpcXVlPTGvK0BVgYg82DiAXZuVelSAAsbIfnuV9Lgd7d6YpjztMk5N/zqFkxf1BrPna/asnJmT0HXxQjE5IAB8ACVAiwwF6G5RUVopfr5i7m1PU5elXyauISggpA4f8tO9Q8bR9xJzM6dtJqEeKXdlYoTeV8Np3eLl7gr1qlL0Vr1tpnnDXn5jAKwGqd6AMT7vZiuL6qbb9jLinDmY1gAd3g5xCeL62cle2VdZxYP3OAhXBpSXmlCUrO83FX2YZV169Sepr3gY2d3ex9Pc95JdpRAES+De0HmM10+yDvJy0aA2Bhe6tt0ae9udhcbob3PSlawZuHlLzvzc+E1AgODkjVflUV5RDVbaAb9ebyvXzet236hHs/uaibcgGwMFqFdrPsXybajZL3mO1FIMCBhNVcbrHrzt8PXU+LRhgmDQJgYTioYETXjGLVpGjJycDevzFo+ay4YoR3re6rPUgNcpXQ8rm0wo99bXNGKgAsjMjR9B2/82xFgNkDR+/nIezuPfd7BnP8nC7ldmFtKyb5NbO7+LsKVJBK/X9x7wWf14EaOm0BFkb4PK+YPzAM88ruA0e/3Wg0rltdWbxVFa0Q2Ptdz94dx+SrtzAocB0tvz9/eOUrbHlKb764+7yzebTVUEAJnvaw+8CRRfZ3P1rBvDzIChQAC9BUgwXREUyXKt897rSGkQSh4pCCb5flzKq+WKv6zaqph5X+gwJ9MagQOv/qZR0jmOKvV88Ag3cOlJ73iv0JDqp4nmJUQLhUnON8TdW27yVDXJ1A0ec0sMAiEIIK8t7+TWvuZBJ4STqO2t764f888kE8dwZRZJgTAgg2lm3nkJjCwsGA7nll2THcNrBhnj5ED3/X7jnqiwAsbFPnsm4d8ZeY9Oe2ZTcf7gOVCWAhLALZv8Rr3xdwH8yazwj2YvP+73XTLQAWRqdoBYd0uyWBBR9c1hZC27O/L3VvTzvmMGkQAAvjgQpESlGQwUIUdeBes5R+xEBSp3v8hsT3Ks22AAuTUfDu8l/8pT6Da7x8z7N8OULJ0VSChVW/RsZlQ75XKKIRgq/jWm0BFsb2PP+fTMO4HCwv+sfy7MKjHbW9Y2DBv6dbDX95Sv8ZXSrP/mR/p/tlBhkSjyduUyr/lkOJAa53gAVoesHCpOU7k9Odx745Y5YvugDnlcOxCIDDtEcrACwU8CYPsFBUuwMsFMveAAvFtDvAQjHtDrAAASzk0Xb5OgywMP3npRtFA7AAASxAAAsQwAIEsAABLEAAC1sRLgih6xDAwqQV1g5R1MMAWIAAFiCABQhgAQJYgAAWIGiLgwUmVlCP5f7DWOkKa1ewYnDbKMpjM8/Li1SwrKfk1S0AFiCABQhgAQJYgAAWIIAFCIrNzayCjBAEQRAEQRAEQdAWEsACBLAAQRAEQRAEQRAEASxA2wQsYBAgqHA0EakQxbI3UiGKaXekQhTL3kiFKKbdkQpRTLsjFQICWIAgCGABAliAABYggAUIYAECWIAAFiAImu4HEsBCsewNsFBMuwMsFMveAAvFtDvAQjHtDrAAASxAEASwAAEsQAALEMACBLAAASxAAAsQBE33AwlgoVj2Blgopt0BFoplb4CFYtodYKGYdgdYgAAWIAgCWIAAFiCABQhgAQJYgAAWIIAFCIKm+4EEsFAsewMsFNPuAAvFsjfAQjHtDrBQTLsDLEAACxAEASxAAAsQwAIEsAABLEAACxDAAgRB0/1AAlgolr0BFoppd4CFYtkbYKGYdgdYKKbdARYggAUIggAWIIAFCGABAliAABYggAUIYGHT5bTtfTW7fWwc+25Z5BQhdMN2emVMEGhc6vUWb7HMuZOd48evF38/frxzvW1ZT9ltZx/AAgSwAAEsQAALEMACBLAAbRuw4Dk7JXImmMSCrEuLvd4tk+xwy6InSMk+03R6uwAWIP25GojS89RqnRAd+rzbDHKMECg4zV1zlnlSbE9p9bQ4n1mbCiVnaX15BWBh64nZp163HqeUnGc2yjOObO60Lfo0JWTdtwHdqDWXm33HYOCJkIuJc8y9B3a63RsAFiZpd7tsUfK+f41Xzs4urVYGvUe17dpDpkk+DGz5dqPTuVGeY7ZtfY+1cefYI4NcqwALw2ltxd7j2vs9g90vafmca28rjx16K4u3RjY8+ih55ZWZpPngvtc85d4TrvJ7Aq00/7bR6d4IsDDha9y1WdU0/8ng98xS+dz+zsqX815/a2udm93J8qERPWtdlTbuOrxSI9euJe5rub7zIcMwr9y1uPLVtHYAC6PRqmvvikneNYzQ3r91baQ99tH9ev6vqnw/3j66X0263uPPAcsxTePf3O0+Iztm35ltdL8IsAAVAiyEN8u2Ne8+/DY2y/FertOVJIcN2uQX7rpzZCsdi72Yuw/2S+JcDV7gTvCXN3kO590mf3u77F0/VuuU03O3DSIWnHbtEPtdBAkcXowDLgAsDOFssHsgpb/y74PkWh6nz7MpJW8xKNBYWr2N/RbYfl3eDzsOib2USqq0XtA9LsDCCOy+aN3LxpFBIDburt0WmH3Kzcfvz+Vs+veAq+zeYNnNhzudfjDPjjXoHANYGNFzxrWB6xR+YjWPttwhNJZde7N/lxe632D/1tneteG/cICYBBaCe8IbRmnuzP7O6h3Mzh6QCH5rdLo3ASxM6Br3YcB5o+++W/7dvNOlea5BHxCE15+vHfV3XHt+PhtGlDYAFiYAFZbsPaZBft9v79Ifdx94bH8WGIie1bV9rq3/tPvAkcVGo3Gd07YahmH8eff+lW8l7YOBCP/YpY3y7MKji05vR957PMACtC3AQvAV7ZL3tWzCzr3/oj35CAkoWyzKY1xf1wc9VhRV0A8Q/KiU4KuvMnJBb5u87cVoGDkVwoNm0nnx681qr80DLGw9wMDGMY/T59j0YfcednGx2/1S/+90XfwCzqIaam3nkLxvDiesZu++HPYGWBjGyfSvwwukcugF0alsWuR5w7Vno9vdoQsnPEdTAEs6MANgYVPs/dGMZ+/IMXDv388ZpHKx4TilvIAiCSz4fzevzHa61RhYXHGdHkKu6IIMgIXh1a6YT97JwGFgJwZ4OGgoH+h+U9fRZPfovZT+3PZsqv/lu10hP/G/nAMsjFvBc/S0Obfwfff+7cG7WPTCjtl35hedL2jCqA9m9hx8kdzm/IVwr3h2xqhcmO84O+V7NwMRpkH+YLBjHF75St7oCIAFCGBhdA/6S5NyXiF9MYc4+Kq2spWOleb0B05hX0RB3m3ytJfbymAhKRLDO+cRX28AC5MHC2FqgyKFIfiKvc4iWbwvlm7bZlMdEea3td6X4QTAwvjkg5/+6IQ8jn8UqWBdygMiABY24Zlm02MsFF526mOQgOg5mVlgwb2/HzMMelUGC+weUCHko52zySkUAAuTuM97X6M/zgMW2DYzVus5Ytufy3Mcs1r/8Rw13jKM0jrAwnjFohVq9vJhFmGgAgV+1IhzZ5YNlus7H5xx54ccncCjGOjdR7rE/tHnoucAj1SoXFJBB4AFCGBBw7lr27VjLBeZfXVlL1csd9xzDq3WiXwv8f3Om79/+wGej85z08Pc5bZzSNUX1paHn4vnwvPfKaHn1cdytw/CU708yIS0jHDfPId+iPQN3ePqtBvEHk7dOsLTX1jBTMeZ/RpzgH3iS19W5n4L41ml9HT0d7ohOs+6/dE5Vh6wEKYk5AALqm3ytg+jGKRUCA2HZGOUUQsAC5sAFjg8UICFMEWCRWRlAAPPyQ0ABMDC+OV/fSRvsuuY5dgrYYEUyZBnHwALW8/eXhoCiyBKiCIgUiTDMGCB/53sOfQiEZwc/3frQsPplnLYG2Bh1A6oa3NKKr/RTYUI5s/rXo2GUvlcfWHpvk73eGotHM+ZpdbPmyudO7xtARY2VX60QelqFljgUQ8znr3ctsL1zQGCGMkQPAdeC/etCaoAFiCABfGiq5JXuRNoWXZY6Z5/edZxliLnLZ4GIe+fVqsvMceX9UsEDOwYcrE905o7yYrl+f3w9xs5gilfst0XejF0nTuJ8ZsSPUHtdo+3Y5AhywnOPPeM44bthKKW8nEHsYf8tZzXFBAjBviX+b4wfqGeQHj8wAahTXL2J+lYecHCZkQsxGFDUOCR2i9nggV+zUnzDGChmGChbZHn80ImgIXBFaZBpIGFjCgEDgiyAATAwpax90cGs3cSWCCVS7rpEFlgIQIZ7r2ksuAVbPSAvGn+sx8hoz9fABZGr3a5/D3bK9qpZ4dVf45cNmIfQMrn7uokF21kKRi15mP3d7qdGwEWtgpYUKcx9AEhMbpBARbEyAQexUD2HPyZmDYBsAABLORMheBfacWX4dDp0nCWso6ZtK/QiRO2S4IBWY6lCCDEfnnwQti/1xdp37JDnWe8dY/rt+t3blWpA7r2SCoe6BWv0wALKjigaptnfowKLGxWjYU+uMDORYBQecAawMJ0gYUIHrhzXaqPoAsW/Huh9WaeNAiAhSEdzRR4EEGHZLAQRSuQz6x2+zt21XwmvP4yVpYAWNiE6zoFHkTQYXRgwWvj5/J/5K8+QT+i1Hq9sdS9fYBibgALo7ruvdUhyK8NWj47yKoQjuP8ZXtf7UHTMK4YKStCsJB8arWeYtEqYbQDwMLmXf8BLNh599HvEilNov/ZkJzWEEEH/29RtAL5dHej8T/Xq//ux4ZhfBKtRJG9igTAAgSwkOJgil+zNV/sEtsmOZsqpy8rR1+1TZ4vxqEzmaA8dQh0jytGK8g2iZzYyCnVtYfo0KYt7Zk0/n7ERNzhVgGDPPNjWLDgp17QXtKqEHm3GeQYvE20ckR2NMuol0AFWJg8WBC38RzRoHhfMF+e4L+nQQMOLgdwOAAWNgksRG3ItZLdWuPRKsfa9t5wdYhm7z6VTQEWigEW+uCCIloCYGFyioooCstEsogDkr/IXiw1QloVIlbk8do1A2BhKzzXWVSBdV6n/kEesBDcOz5g1/eO2YP/yX1eePPAadZt0zD+wKIe8qxEAbAAASwMARayohvSnE35a/hAYCHoa5YzK6YtjPCFNvO4qsgMHbiiY48wwoONGa2eVgEGHWe/r/7DRMGC+0JfKr0bixJIOJe82wxyDPFaYvUnWH0Jf5vkiASAhe0BFnzgZu+zKHk/Wq++9UNWx8SzScYSkoOkQQAsjBEsaKRChG3YShCdzo0qcOD+7W35bwALWw8sjCMVInI+6T+wpUybFn3ac0Lde0N5YfUbSIXYHDGbOM3aQddx9CMOSulLRWrBBQYMhKgFtiTlzvrSCv8yDrCwydf+Wufmqmn+r3ZK2oouWJD/5t072L93zL4z2+h8sR9muDb0/tb9IsACBLCwhcGCfOxhwEJWX0edC6993DGCBc8RCos3RvUpxOOkjT8vhhkWfuR1HzYpYmHU2wxyjNh8MedOOmvOzWH9jwRbAyxsH7CQBAy8UPmUJSQHTYMAWBjyPpwSlZAGDXTaZEU8ACyMyaa8YGIsotCPEkiLSgjBQqn+tutk3jQKsMBrLLgO5iP878vN2oLraHySN3IBYGH0imomuA6iZgFH9fOCOY/mZQ4WvBQIav+DHMEAsLB5albMH7BaF9qrf0hRCUqwEICE1TSwkLIfgAUIYGEMYGHQVAiVQzYUWMg451Hnwused9xgge8nFnEg15XQKN6YBImKDha8YqP8ukqYOwAL2xcspBV17DveAGkQAAvDKW1FB52ijCE8UICFaN8AC1sFLHS7nRuSVoUItxvhqhD+cpPJS1vKq0UALExefmpE5eIwYMF3LM1/5WCBRSv48Cg5dZaQ8u9cR9PMOibAwvCSo0e03xcTVoWQCzWG8GDHPW/LYCGqv+DOMYAFCGChv41Trx8UnaOhwYJm8cZksNBfX2CgGgsaxRd56oX6671dFpda1D7vjONG7VJqBigKWA5iD9X4qMY/CbIUGSywtnWr/ni4woYAFtKgFIo3bl+wEFy7FwOntZL6cjtgGgTAwvDylvh0be1X6Y9snfS7rKZrO4NYF2V4EIKFhIgHgIVNcjJs19kn/c5+0u/DgIVWhfzUMPohBo9kmLGapwAWNt/pNOjcm4OmQoSOJrXfSNsHIhY26f7ethp31peONHJABWFuPDhjkI9371/5lnh9q35PWm2Cg4XrSvecQSoEBLCg+rtlPyX+bViwoAIEKmezb1WIoK+iEzoIWBCBgfd7sCRiBFKsI7KTLVf7Z/tlKQF5vzjrHjdptYu0MdAp3ig6wvHt08FC0hf4ooMFm9KXY8BKjlhQFeDEcpPbEixEK0H4xfuy77uDpUEALAyvMOpAikxIAga6gICnSSTNIYCFTbX3RzNSZIL7PH7OIJWLuvUVdMACj1igs/G/q1IkABYmL+7s1xhMylm1XxRfUjJtHwALmwMVdrJVOWw7ds2srizeWts593edDJjEIxFm9hx8UVxCUgUReBQDvftIl9g/+lz0HPDTJuTfARagQoAFb6lBaZWB6OJo7pKXJ/RulFXyquwMhg6mZkqF317tvEXOPN2otZ1DMYePVs7yfvqV+JMjCuIOMbnG98V/95Z4lFd5YIX3pHNIXBnCdQzF/eg4x7rHFYsI8v2qohXy2EPcnhch5DYWnVwRAvgpE/YDYt0AfhxWtI4VKwzHwt2OwZI880N1rKT5w/oaRnxoRork3SZPe3FOsvMOayw4zXIanODnPOjXaoCFMb2QBF+rSdV+VU5h8GzLCjSy+48EA4K0ooe89IeM5QZj18OAaRAACyMCSYGTzwrsMTv49qfr8ooOge3fY7YVgUPbok+z9nx7717KwFJKfYZwjln2L5PaACyMCS4EQMBqHm0xmORHK9CrfnTKNdnev2HLEqqAg7cdW2GgUv+lqi6D94z3ajrQjfrC0n1s38FKMY8bhnWh4XRLOewNsDDo9e07iOeNUvmsZ4dXXpnx7FA1n9xpNU/IUSOsfSVYjpKnSHjvmCwChZbf39/pfpn/xqDCnc2lZhaYAFiYPFQwDOPPiau43d3pcrsH9n7Xs7eUnsKBwe4DRxZZ1IMfrWBeVq3y0KyUfuD+7cruA0e9tqtLi7eydIq80QoAC9DUgwXRcU1XfNm9vm3cl2NxpYGsavjy8VXOePgV2273xCX8xCKD6v7HjxstzaheHpJHHYjnSq3WCVVdg1g9gqBwIf8yHYEC/a/iusdl56867iD24BELzPGNjq8+djju7st0CCHEc6X0PAM1YtpGrf3XhweZH6pj6c3V5PHOu80gx4jNjXAliGiuJq0g4Y1HjpomAAtjdjh4TYT+pWTDr8oqsBBFKATXg+tg6tp0mDQIgIVRvojWFkLbM3AQLBmqgEp9YMEHBbUHorlDNyy7+bCqrka02kTyHANYGL+WfXv7dqDlc42l7u3y+CeBhWgFCcmGs/2RC96ztmr+WFzikJbvebbR6d6Y8zoHWBhQwXvWf4nZgFbeqC8szauAgAoseNe4uJJEYMf9ndU7dK5bgIWtAxUIKf3Rq5sQ2CANLPD9+ctGuu9WpfJv5w+vfCXJfk7d+p92GsZlf2nZ0h/LswuPus+Bm/KeA8ACNNVgYSvIBwj9ECJPePxWUtuyeqPKmYem0EEVUiFS2xByaZTRCgALBbzJAywU1e4AC8WyN8BCMe0OsFBMuwMsQAALw0r19XYawULeQo5Q8ZQWpQOwAAEsQAALEMACBLAAsIDxgAAWhoULQhj8NIEFXhdALsQIQaLCeiVW68SYHkgACwALEMACBLAAASxAAAsQVFywwMQKAbL6ASFoYBcXK6I2wjx0CNoMeZEKlvXUOOETwALAAgSwAAEsQAALEMACBI14bmYVZIQgCIIgCIIgCIK2kAAWIIAFCIIgCIIgCIIgCGAB2iZgAYMAQYWjiUiFKJa9kQpRTLsjFaJY9kYqRDHtjlSIYtodqRAQwAIEQQALEMACBLAAASxAAAsQwAIEsABB0HQ/kAAWimVvgIVi2h1goVj2Blgopt0BFoppd4AFCGABgiCABQhgAQJYgAAWIIAFCGABAliAIGi6H0gAC8WyN8BCMe0OsFAsewMsFNPuAAvFtDvAAgSwAEEQwAIEsAABLEAACxDAAgSwAAEsQBA03Q8kgIVi2RtgoZh2B1golr0BFoppd4CFYtodYAECWIAgCGABAliAABYggAUIYAECWIAAFiAImu4HEsBCsewNsFBMuwMsFMveAAvFtDvAQjHtDrAAASxAEASwAAEsQAALEMACBLAAASxAAAsQBE33AwlgoVj2Blgopt0BFoplb4CFYtodYKGYdgdYgAAWIAgCWIAAFiCABQhgAQJYgAAWIICFTZfTtvfV7PaxaelvyyKnCKEbttMrq8+ndogSsuHdJGjlLGuXtc1W1PHjnetty3rKbjv7cHEBLEAACxDAAgSwAAEsQAALEMBC3GEskTPBJBZkXVrs9W6ZrJNOT5CSfabp9HZtB7DAz6dz/Pj1Pccu+4CBblT+Pfl/TxtYYOo5zV0VSs7S+vIKLjCAhe0odp1alLzPQeDs0mpFa7ve4i0WIRf776OB2H2g271BvO+2Lfq0e09Y99vQjVpzuZnXbgALo7u32bb1PdMkH7r3t0fy2iHPvBn2WAALQ9p6ZfHWqmn+s8HvlbR8rrHUvV3XDuzadZ/tT4nXrtVcbrn/aSQdL7D3B7R+9FHyyiszA9gbYGEkdie/NozA7qXyudlOt5pktyTbtyvmkzsNctXw7FHasBpLbR2bLtd3PmQY5pW7Fle+Sq7pHRNgYTitLtl7XJu/69r8U9fev73rsP7YC/b+wU7DuOLfL0p/3H3g6GKj0Yjdd9fWOje7z/8PjOj9K64ds+/MNrpfBFiACgEWwgujbc1zx3czHN7lOl2hVusEc8K3i4PCxlN0wtkYM2AzDnDivdjWnSPjPi8OogAXABa2m/zrk3zGHHz2bxZtxP5tNXv36W6bCBYqrRe4TbxriJK3GGxoLK3eFhxrgTkqeR1NgIUR2H3RupdS+iseWZbXBmx7ZgffuSQGsyX7d7n5+P3yfoY9FsDCkM9JHwB+ZPRdo+XfNRynpPX8o+QNozTnXrs9D0Ys+9fuVTrbDw16vr3/hUMIVRuAhQk4mCv2HpOQy/12L22UDzz2TR2bBLZ/ndl+f6f7Ze9ab9YOmga5Qu8++l0iOZsKx/NDwz0ewMKknue1fYZB/rz7wBEPBDhtq2EYhvvvR/fnsPfp60r3nJk/vPIVZjO2D9Mw/kDvPtIl9o8+Jx3rT4nP/z0Hf0Zuc/4CYAEqFFgIHriX+Bf2yb/QTz5CYgJOyrVJOeAscmJSx+JzxWqvzeNCA1jYRg7HRWK1Tolj17bI8969qdv9Utr2LPqg1nYOJbycvCXCCcemD7v7vCjv0/+drutGSQAsjB4wsPHM4+wH8+YCqRx6Qfzy2XTnjeHauNHt7hjVsQAWRvGcpE/5kUG+rdhX7AA0fFZe6H4j6+v1sk2PGYZ1oeF0YxBiuc5+p1f9L+D99mSAwT3GJwALkxcHAqZ9aM29Hm/idg+jF0r1dxqd7uez9uNHHFQuzHccU7RxGIlweKWWBAzaFfIT76s5wMJk7uVBBMHMnoMvig69+5787Ixvw51Z913Xrg+6bT+S2/q/m5fvWnTu5Ndys1L6wZ3Noy05koHNvb2UvGYtdL+Z57oHWIAAFkbgpG63L+AsAmNSYGGSx4odcxMgFASwMA75Tn1/dAKPREhzANk9rNlUR3n5kUvW+xwihABDSo2I2pJ1GW4ALGxdsMDnjRydkLUvgIWtI+70Z4GFMNqhVH/bdURvitnT/yJ+hZQPvaD6cg2wsHli0Qo1e/kBedwD5/O8F0XQWaml2T6MOPBt/3l5/6ZBLs/sOfQise0++7Cv2Wa1/uM5arxlGKV1gIVJvJ8y2EM+lqMTeBQDvbvT1Ygw+cDYcc/b84vOF+LP6fm/cu39ew4tWNtmc23PK4rr2m9beVcHZAAsQIUHC17ukV07Rik5z75cs5diln/vObhW64T2y5z34p5S/LBuHeEpGqyoo+PMfo2H/Pt9sB/gef+8BkCYs6z4gujtUyyoGPRXPufw/MJ2dENux443Z5knKaHnef+jWgrqmhWqbfIcU25vU/qyMp9b6meV0tNRG7rBxlDZV9epketuqCIT+LaIWgBYmHaFqQnudSFHCwzi7Pc5nsK24f4UYCHqR3aEBMDC5oOF4GvUm8G8sRTz5qocyQCwsAXt7jqGlFjvZaVChPBAARbCFAlSuShHMwAsbF35kQSlq1lgIUylUICFMEXCqFyUoxk8B5VaP2+udO7w2wAsTOh5fnrGG2vnTnGsWc0FEQok+kMBPFCBhWj/nr1TgQGLbrjOaj6fJw0CYAEqJFjwLqwqeTV0PC07XC2Afz3XcTgjB1adBiF/FWcOMo9ukPtAq9WXGHjwCyVGgEHuByuoSO12j+8zyKOOOeNhv1yHgP/Gz4v91g8Q+sGIKoogbRudY6bDGXXEQnjMYL8iNPCBUHNXCBFiY82264cfffMlo28QwMJWV1R4MQUs5HD2Yy+uFnlevAcBLGwfsBCmQaSBBdeWqnQIgIUt5Fxa1uPsus+yA8DCdgULroPodGma/QcFC6zQY6352P2dbudGgIUJ3cfDQoos7UQNFtzr9FIaFBgVWGCpFzUWCZXzmgdYgAoHFoSL5pTsvHMnV8fhTDtmUpFAr8hkX0HE/uOFDrWwb6+t1E52tCOHPg47WF89WKECEJpgIW0b3WPmBQsq0CO3D+0g9MkrppmSWpEFhSCAhakBCynwIIIO+cGCv631prhdBA/ca1JKuwBYmDKwkAIPIugAsLBlr3tWX4GS9wxaPquzKkQED/rrMQAsTK0Den4ns0lKWHwMHrCirAfi+fJJYIE5sdRqPcX2HbUBWBi30uBBBB3SwUIED8inu/evfEth70ywwI5VIZXXGhJsAliAABZSpHJaxS/kmi9myrai45q2ikKSU61y4DkISZKXTpHjS/yowMKwX//TwIIfkRHvnwrGhL+59vdSRaj9ctY8SFtqEwJYKDpY4CCzb2WAcAUJ1+kMVoXw06DoE/x3gAWABYCF8cl9dj0XXxqObiQVXlQBAraKxP7Oyh2sfXDtPu4X52OrSwAsTMX17uXbVz7KilaIt2d2YLb3V4UIbP8993dvTnCw4KdJ0Z/bbE5du2YALEwXWBDs/SfProsr/yO3o2vvVT4PUsGCu/1MtfXTvGkQAAsQwMIQYCEruiFMBWBOM62eVgGGNKdajKgQUw10YIdOIcSRgYUcx8wLFuRjx2o4SGMRghdaOasThQCwALCw7cHCEKkQchqEKKdt77MoeT+spVJp/ZDVk/HsJyxNCbAwpWABqRBbXux5GCzzetWH6v0pDioda9t7vUgH717rX7vLdes77Gs2YQX8ULxx61/rzME0zX/0HH9yTfv6c5r7bNf2v/FWkyClDbpn4W+X6rXveI6mULyRFQ/cWV9a4XMBYGFrgAXdVIjI3nW7ahq/Dlb0+GNpz8J/9u1t/DlrCclB0yAAFiCAhTGCBe/CDos3+oDBtOZOiv3TTQPQjQrI0/9Rg4VxRCz4+/cLRoYFMHldCTl9hKdeaKY3ACwALGwLsJASlZBWEyF7n/E0CB0QoVqZAmBhi4KFlKiEECy486bR6dwIsDChazmMKBCjEdOjEcLaCa6zkVXAMfFZWCE/Zc5H0soSAAtbS37tg959g9iif1/+UpI8RcJLgaD2P4i1GAAWJgyNEqISQrCgqJ2gfa17S1aST+QUCbkPg6ZBACxAAAtjSoWQnfHYl3a5bkJqxILv+OrWBFDVZpgYWBhw+cY8xRvTgI5XV6FafUm3LwALAAvbQWmrQoRpCzlXhUhKg8i47+QGGAALmwcW0laF4PvCqhBbHywEz7Ln/PoI+cFCWlFHgIWtJzmaYBiFRR13REUdg6UOP0lLuxXTJgAWxvI8V64KwZebzIo2SH5O86KOs+/MNjpfTH7+D54GAbAAFQosOPX6QdEpHxosDLDEpeyUZ4OFCCTwUP8kB9xbgjEsZJi9ssXIayxorqahCxaSYEpSjQW+TGjaOGXtGwJYmEZ5y0IqogWSfs9SWhpEwn1QuSoFwMLWBQvi/Cg3H79f3Cbpd4CFLeps2vSYQefe0kmFUFy7HxkZ8AJgYYvc55u1g1Z96cgoIhWCL+MfeqsPHF6ppUUhIGJh8vCIpafsPvDoftHWSb/nsHe02kTK9sOkQQAsQIUBC97fLfsp8W/DggWV8y/eiOtW/XG5L/4x+8GCKqyfL03Z51Azp1lYcpIdi6UK9Bd5dI8TLKMZwRXrCG83ylUhdI+ZByxEwCIdLHg2E4o1qlaJSJwvWG4SYGEbKHTupSgDPz0hX32FPGkQYrREXngBsLD5YCFMh5AiE5ruvDGIdVFVXwFgYWuJr+jAlgXMk28frQRB18sLq99I2xZgYWtABWo1n5YjFdjqIFVz7r90ckClaJWI0nr5wGPfzLIpwMKE7+UBBJjZc/BFMWrAT2OoXNCpr6CwnxcFsfvAY6lQYtg0CIAFaNuABe+LtZdq0O/ksxx9eelH70Krkldlh1ZcXUAnCsFv3+/Ahg64ux9etJH3Q7maAasf0HYOxbZVFCFMXBlC2GdUa0BaNYKS8+J5iZEG/Nj8+OFxhBSEtG10j6l8aRFgjp82Yj/A2otLaXIbsaJxVUpP876tOXf9X+/4H8gHMpSICmeqV+XgxxwkwgICWNiSLyNB2kOtudz0rhXvqzNdFx1+7/plRRfZvSUBHOikQQTpXQ956Q/uvvJGKgAsjMHxCKIMiGX/UpWOEtj+PWYvERhwSGA1l1vM5uK8SZoD4rFUNRgAFsYGDz9iy0vWF5bmefX+lkWf2mk1TxASdxYCe//GW45SSJHwrt19tQepQa4SWj7H0mCy7rfu8/SYVwCuUv9l3qgIgIXRQAW/kn/CimB3H/0uBw6eY2iSXzO7yytGeLav1x40DeOKUSqfu6uzUtOBUQALm/E899Medh84sthwbetHK5iXZTAQ2Ptdz94SDPCf05bj2vuyZ+/D3a9mQaRh0yAAFqCpBwui85muyPlXbuO+SIurOHCnNCtMnu9L9WWfRSw4TrPsFx4MqqdbrRPK4o12u+e+IJxIKvIovdBH9RqCgoZyWx7FIJ6/eOzQmZeXq1T8nvY3GdakHTNN4bm7L70iCIgBC0rPM5ghRiTceiv53/jxOCTot2N/iobXZsCaEBDAwpZ9AWVLrXqrQPjXEl8OMg9YSEuDiCIUguuxudwc5hoCWBiBwxmu4NB3z45FEySBhWDeLMTnTff2YY4FsDB6ee8UVfPH4jKTlFZe55AhASSFYCGKUHC3p/QjBpI63eOp9VCiwpCSvXNGLgAsjA8qsNUdRECgAgtRhIJ7vy2VPrIWltruPeDGPHMPYGEznudWw4MC7JotlX87f3jlKzIIUoGFKELBt/fuhaPf1rX3sGkQAAvQ1IOFrSAfDgyWq6+71CI0whfxAEwgWgFgAdp0ewMsFNPuAAvFsjfAQjHtDrBQTLsDLEAAC8Nq0C/gAAuTVVKECQSwAAEsQAALEMACBLAAASxAAAtbAy5IofwAC1tHYa2NYPUICGABAliAABYggAUIYAECWIAAFracWHFBVvNAt31YD6Bqv4p8//HJi1SwrKfkFSsggAUIYAECWIAAFiCABQhgAdrWczOrICMEQRAEQRAEQRC0hQSwAAEsQBAEQRAEQRAEQQAL0DYBCxgECCocTUQqRLHsjVSIYtodqRDFsjdSIYppd6RCFNPuSIWAABYgCAJYgAAWIIAFCGABAliAABYggAUIgqb7gQSwUCx7AywU0+4AC8WyN8BCMe0OsFBMuwMsQAALEAQBLEAACxDAAgSwAAEsQAALEMDCpivvcpPQdKhlkVOE0A3b6ZUxHgALEMACBLAAASxAAAsQwAK0jcDC8eOd6+0SOdNfjdS6tNjr3TJZ55OeICX7TNPp7VL1s23XjlFKzov9NK25k03HKfccu2zVnSNxRzat2irdKJVK7zKI4ThrN+uNSSBKz1OrdaJz/Pj1mGgACwALEMACBLAAASxAAAsQwAJUaLDAtda25ikhG5vl/C3X6UqSs86gAe9bre0cEts47doh/2/kGq0vr8S3a+6yCLnEtxP/5jjN8pxlnuSwQN5W3p6PCYMOHgAJ4AQc5eEkAyEIYAECWIAAFiCABdgdYAFgAeMBTSlY6PUWb/Gc6JJ9ZtJf4hnUSIqQiKBCcgQFBwAqOJD1pTwNTESRC/3bhxERmzBe20lsHFV2gwAWIIAFCGABAlgAWABYAFiAIICFXMdVRgzwPmlEBnhwwmqdygsWIrDRH4GQBhaStoH0xaJUkqJFIIAFCGABAliAABYAFgAWABYgaJuDBbHmgdVem2eRBRVKznqOotU6oXtc30FXO+fc8dTpk9d/q90bBCzEaioIcCINLIjpGVn7btv2A2xsmAPNoiv4OKlSNLjESAo+pnwMxLFn+wzbaoxTuG24b7ohp6CwPlYpPS3WoxDTFfKeE/s7SzuhhJ5nY+WNK6Uv99WuEPqf1QcIYAECWABYAFgAWIAAFiCABQiaYrDgOYZV8ip3+izLfspuO/tEGMBggw6c8B33/jQH0dkf5ou2btHAEGIIfRk2YkEeJ1qtvsSKRbLxFZ1xeaxYDQdqt3vcDgwccMfbWXNuFgtL+sUre7v8/qcX3AzPx2qd4vsOzzsAKiEwCdqIdmD9zHtOEYDpHys+hv21MdL7gIsbYAECWABYAFgAWIAAFiCABQiacrAQd9rjzl7ocCvSEvIcM0qDGM6Z1AULKlAwqhoLSWMSOtDCPlQpHSrHWgYCOlLBBzbOHgwI+qACQyoAkOecksYxCSzo9gECWIC9ARYAFjAeAAsQwAIEsABBUw4WVA6g+LU585gpbcUv3UlgQYQPokTnc9RgwU8DoL089RWSnGLV/rOWyeT7yFufIByrDLv40RFqACBum+ec8oIF3T5AAAuwN8ACwALGA2ABAliAABYgqOBgIc1Z1AELoiOaVO9gNGCBXCuVSu/Gah7Q6mmWgqAzTmlf28WoDzFVQXfstcFCMJ55vvj31WPQAAvyOQ0CFnT7AAEswN4ACwALGA+ABQhgAQJYgCCAhWSwIEYjZOwrattfY2BcNRbyKM2BFsdQN6pgGLCgF0niF1v0CjHa7WNhjQdNsCDPi0HAgk4fIIAF2BtgAWAB4wGwAAEsQAALEFRwsJDVNkoLSC9KmNb3ca0KMSqwIPYvrZjlyMBChn3lwonf/36+VAjVmOcFC7p9gAAWYG+ABYAFjAfAAgSwAAEsQNCUgQWnXj8oOrxDg4WMY4rpEGkO9LBgIWmFh8mBhQgkcJii/opvl/mSiwPXWEhJLUkCG4OBhezID9U+8vQBAliAvQEWABYwHgALEMACBLAAQVMEFry/W/ZT4t+GBQsqJzTZ6SfXam3n0KjBQlSfod/hHjVY6FtBIei3crUF5nALS06yvrDUAN6PvGBBhBbe+QTLg4bjULeO2E57nyqlJA0s6JxTHrCQlNYCsACwAAEsQAALAAsYD4AFCGABgrY4WGCOm+9g9zv5LN+dOYZ9zmKVvJq4DKFmSoXfPt1xF51/05o7KRZNXHOcm8O/S8dk/fadVIUj7TTLfg6/72iroEW0PbnGIwWGAgvCcUJnm1bOqutCKFaFCJxqtm1aZEMaPPKWlpRXmqDkPBs7Z825maeE8P06bXtfldLT/PgMHNnN3tfznJMYLSGOswih/CKN9gO6fagvLh7k54IlKAEWABYAFgAWIIAFCGABAliAoE0EC7H6AqlSr5YgOr1RAUSu7FoBfF9ZziF3PEMHU+gXW6GBgQMRKmQt25i0XfqYDBa5EH6Zt9u9lkVP8P0xSKKCL30rIQQFDFlbdd+yx1ncdwRUgnGwWid4P2LwgdLzDAREYCCCCLrnJKazqJcDDballbMcGOn0IQ5JhosoAViAABYggAUIYAECWIAAFiBoCLCwFeQ7qfrO8bRJZ1lFnNPgaltWb7vOHYAFCGABAlgAWABYAFiAABYgCGBBU160Q44VKeCE45yYxIKWEMACwAIEsAABLEAACxDAAgQVGCyEcEEIiYcTjnNKEk+NkWtn4IEEsACwAAEsQAALEMACBLAAQQUGC0ysUB+rJ7CdDBHWnqjar26XiIzteE4ACxDAAgSwAAEsQAALEMACBG0DsLCdJK6GMEihRZwTBLAAASxAAAsACwALAAsQwAIEASxAEASwAAEsQAALEMACBLAAASxAAAsQBAEsQAALEMACBLAAASxAAAsQwAIEQdv7gQSwUCx7AywU0+4AC8WyN8BCMe0OsFBMuwMsQAALEAQBLEAACxDAAgSwAAEsQAALEMACBEHT/UACWCiWvQEWiml3gIVi2RtgoZh2B1gopt0BFiCAhXHLW43AnDspL214/HjnetuynrLbzj4YHcIDCWABYAECWIAAFiCABQhgAYImDBY8x7xEzsSXDxRE6XlqtU5whz5v+0G3CYGC09w1Z5knxfaUVk83nd4usU2FkrO0vrwCw0MACwAL8j2kXrcep5ScZ/eItLHpOXbZouR9/75UOTu7tFrJdyy97VmfqpS+Ft0DK2cbS6u3ASxs1hwZ3O7s+da26NOUkHXfFnSDVlo/7HS7N+i0rTWXm3mvV4CFAe3sL5f8kZH0LlKqv93odG/KsZ/Pou3pxmynW5Vt2VtZvNW2re+ZJvmA1o8+Sl55ZWaA6xxgIckW3vjWVk3T8Me30bhuFG0Tr/WK+eROg1z151Bpg+5Z+NtGVz1nVpfsPVWT/Now3HlSKp+7q7NSI9euGTnsDrAgjymzYb32/3Rt+G909uh302wYjP+77vh/6o7/b+86vPLVPOPPj1cPj9f5LrF/9LmUufGDnYZxxb8vlP64+8DRxUbOOQawAE09WBBfdN0H5SX2cLSdXplfKC2LnuAPTf77IO0HO4Zddl/ANojVOuX03G2DiAWnXTvEfhdBAocXgAsQwALAAtda25qnlP7Ku48wKFlffiRpbFhbNn7M0WP/ZvcZ9m+r2btP91g62wdOycV+x8a6tNjtfglgYcJzZNG6l40jd/Bduy0wu5Wbj9+fdR2x585eSt4kJfsMB0PsOWdT8pb3W6dzo9zWKM2FbdmxGGRIm5cACyN0Ql1bG9HY9aty6AVCsh3/ZZse85yVDCjBjufef/6FgyTXEQJYGOn9vbbPHd//zsaXOfr07mRHM0/bpGvdva5fZ9fv/k73y+x69UBF8Jtr+8/Lx3PnyMfWgc63mc2dZu0g+3f5QPebunMAYEFpw/8PNQxmw8/o3Z1umr3d8f7z7gNHPOfeaVsNwzDcfz+6X3f8+493pKsCC8G9/bXrSvecmT+88hUGL9jxTMP4Q9I2AAvQtgcLUVRBPxBoWeSU/+C0z/RHLui1H2Qb/ze/rZwKsVynKzJECF7YL1nttXlMAAhgAeMhO/1JDlzo7FutU+Lf2xZ5Xsfhz7M9+2JdazuHeLsAuHqgQRdiACyMyNH07XbBdyijL1lN124GsS42ut0d2VCCrs8urVqx/fpQ/CqzJ7ezY9OHiWKf/u/9+wBYGL1aFn3KB0jxr5aB0/hGrfnY/fLf1A4m/YXt2kv3PsuBBsDC+BxO5rTrwII8bfu3My97UQfCHFldsfeYBrksAoO1tc7N7uT4cKbs3ldsO7RZu0J+YhiVi/Mdx9SZOwALqTb8cxJYCMb/g5k9B18ktzl/IfgZz84YlQvu+O/M844UHO9PSZBgub7zQXe/H8n79X9358yic2ee6x5gAdr2YCF4Kb8mRxrkaZ93G7mtDBa80NW6c6T/AqcrMtCAIIAFgIU0sOA7d/3RBVnbjWp73g5gYbLidpOjE3gUg57dFWAhABZ8+xBgeF82oygGEULofi0HWBgcIjWbvbLKnmuuc0iJ9V7DcUo6kGDGaj6fxykFWJh+sOA6iQ8ZhnlFBgscIuwU9ue3JZ/I0Ql5jw2wMBhYCMb/Yzk6IWu7QcAChxjGjnvenm10vhi/t8//lWmQ38uAA2ABKjxYCFMSNMGCqv0g24RRDFIqROoDPNgPohYggAWMh46DH3ytfMvLkZZy64P7ybociTDK7aN21vtIhZicwjQG325WsrOf/AWbAwi5nf97FJ0Q7k8BFsIUCWJdyoqQAFgYj1hqgwcLMsAOj2zwcqhp+Vx9YWleVUsDYGH7gQW+Hdlz6EUxCsH/3fta7UUhhCkTRmn9rsPxmgos5991NP8wI+0DYGF0YCEY/9MzbPxZpED/+Od29NPAAocHxo7Zd2SwwFMkZvwoFe0oCYAFaNuDhc2IWIjDhqDAI7VfzgQLQToEe5HHJIAAFqAssBDVPEgBAynpEMNuz9S2rCfkeyLAwngVRhGkgYUMZz+CE+7cCgo28sKcYhoEwMLWVssiz9Waq/dlpUGwyAbXe7wSLz5dPjebkRYBsDD9YCEEBu49t7Rn4T+zgo2sxkLVNP+ptrD6DTkNwiCljSSwYJDKJZ10CICF/GAhjCBg458AFoLx13b0ARYgaMRgYbNqLPTBBVZwx2730uBCdAz3Rb7XuwUTAQJYgFLBQorzH0GDFLAwxPZefQW2GgFWhZg8WEiBBxF0yHb2gxoZFyIAXj0t2zKCB/1pFwALWwEwWW/ojj2T4zh/2bZrD1GDzZ/kFSEAFrYPWPDsuLJ4q3utn/ciVkqljyi1Xt/fWblDtHvgvF5WwYMIOgAsjAsspMGDCDqMDixE8IB8unv/yrfE6xtgAQJYkJx+b/kUm/Z0ow/S2g+6DW8TrRzRDx9kiUUfMREggAVoK4IFv7CjtFxdzuUtARY2Hyz0wQVFBIQ3B3naBNvnUvd24fn3hD8PABY2ZR7wmgkkv8MfS41IWaoSYGF7gIU+uMC+ineUUQkACwUBC+LfCSn/br658j+y+RDc21e99Bn2O8ACVFywQK6VSqV3Y1ECtHq66fR2DdN+0G3EF70qpafZWvTh0mwJEQkACxDAAsDCSMCCTirEENuze2KwfO56CE01crYBFsYMFjRTIbgN91L6ElttIADgn7Hnj5gKwXWsbe/1IlSCNix9wqlbR6I6DSjeOGnppkFkwwW6nhS1ALCwPcBCcK3//M7mcqtp7XyaFWhkcKF84LGwSGMaWEAqxOaChXGkQnA5zbpdNY1f+8vQlv64Y8/Cf16q177Dlrgkew7+DMUboQKDBT2HPG/7QbcRwQIr3uisOTdzOJFURwFgAQJYAFjQBgspUQUhGEhx+IfdXhdgACyMGCykRCVENRHsvpoI/Y4GeVOcV067tuDbRR25oHJsVSkSAAuTmgP50iCU+/HAgXkFYGH7ggVeY2Fn3bVhsJ3TrB30v0hH9RTSohJCsLDDi275vIbdARZygoW0qIQQLOy45+35RecLOY+XChYS7u3PzhjkEzlFAmABAljYQmCBpUCEBRoTohYAFiCABYAFXbCQtqoD327QVSHC7SutF7Js4qdGACxMSmmrQiSt9iAra7nKrO3TijoCLEwALAyRBhGbL15RR/PfABa2L1jIWkKSrxaRtipE0soSAAujAwtpq0Lw7fJGEAwCFuJFHbtfzHm9AyxAAAujPgZrW7fqj/NaCiJYSCvQiOKNEMACwEIesCA6iCx8Xef3UW8ftkUqxESVBAaSfpfVtMjzXgi8BCZCaGE1TyWBhbRVKQAWJqNh0yBi4IDOvYkaC9sXLLQr5CeGUbrq1VQQ5gsHCTMMIgawIAlCJP0OsDA6sCCM88e7Dzy6XzH+fb+PGiz0rUyR85oHWIAAFsYEFmxKXw6XnlRFLCiKOGK5SQhgAWAhL1gI0xmkyATdKIJht+dRDzoAAmBhdAqdeymywAcG1sWsEHkOIOR5pUqRUIEHw30W5kmBAFgYte2HT4PwAQV9qubV1EiASAALUw8WOBSQt1OlSPB0iJlyBBsiOOGtEGDqXPMAC4OBBe7Yz+w5+KIYmeCnJlQu5KmvkBcsRCtBlK7uPvDY/gGvd4AFaPrBQlDV+hKbzFbdOTLq9nm3CSEErZy1286+sMaC0yynwQm+PKXVXpvHJIAAFiDRASRV+9WkiAAOH1gRvmgbui46+959KFgeUoYF2tszAOFuX28u38vvdW2LPk2t1okBHEyAhWFfUoO0BWY3Nv6i3UR7BLZ/z1saNHBGxaiD+sISs78RrfTQDyb8v9Ue8uppuPvJuwoIwMIIwUJGGkRg798YtHy24Tglf3Uq8pz77/f5EoPBilVP3enNneSoh+U6PeYVdavUf5kU1QCwMLi40++O7//iXnOfH6Ytc0orJvk1s/u806X8HhDVTihtzB44ej9zGINr/XuG76zGV4AIAIZ1oPNt1tY/rnlFLPQIsDCUvT9mNpx31LUSOHzYfeDIYqPRuC4Y/8uysx/Y+13P3gnAx932wRl2vPLs/yvpeP5csBzTMC4bpfK5uw53vzoIVABYgKYeLIgrNcSVvPRjnvaDbiO8hB2LVoLwZVpzJ5NWkHAf4CtZy1FCEMBCQZyHsChi/N6TmBIhrtDAHMil1dsUzoYSLOhs790Lq+Yz4jKTbEUcDhkGsDfAwijAU7u2kGa3JLCQZFP3GfX3IsCKIhTcNpSeZxAjT8oLwMLolZUGIYMF7/3CnydXw2vXuudZtnRo0v3Wr71ArvTdf3JGLgAsqLXqj+9lQx5fRTSCbtsksMCv4znL/C9+5f9g+7I7B7pqWMSKO/orRLAVf8rn9ne6X87zbAZYkOwdrqohLtPMbKiOXHDaVsNz9L3lYMu/nT+88hX5ek8DC2GdhL7jRZELUYSCOydKpY92Lxz9tjsfbhzmPAEWoKkGC1PjIAipEKltCLmEaAUIYAFgoSD2Blgopt0BFoplb4CFYtodYKGYdgdYgAAWNls8KoLWl1dgfKjADySAhWLZG2ChmHYHWCiWvQEWiml3gIVi2h1gAQJY2Eyx2g0eVLBaJ2B4qOAPJICFYtkbYKGYdgdYKJa9ARaKaXeAhWLaHWABAljYLHmRCpb1FCvwCKNDeCABLBTM3gALxbQ7wEKx7A2wUEy7AywU0+4AC9BWnZtysUQIgiAIgiAIgiBoCwtgAQJYgCAIgiAIgiAIggAWoG0CFjAIEFQ4mohUiGLZG6kQxbQ7UiGKZW+kQhTT7kiFKKbdkQoBASxAEASwAAEsQAALEMACBLAAASxAAAsQBE33AwlgoVj2Blgopt0BFoplb4CFYtodYKGYdgdYgAAWIAgCWIAAFiCABQhgAQJYgAAWIIAFCIKm+4EEsFAsewMsFNPuAAvFsjfAQjHtDrBQTLsDLEAACxAEASxAAAsQwAIEsAABLEAACxDAAgRB0/1AAlgolr0BFoppd4CFYtkbYKGYdgdYKKbdARYggAUIggAWIIAFCGABAliAABYggAUIYAGCoOl+IAEsFMveAAvFtDvAQrHsDbBQTLsDLBTT7gALEMACBEEACxDAAgSwAAEsQAALEMACBLAATZl6TnPXnGWepISet51eOet3CGABAliAABYggAUIYAECWICgsYGF48c717cseoISsuFParpRs9vHOsePX9+27QfY/2NgtxJUsMuirThASPodAliA0q6n5i6LkveD8btGaOXs7NJqJc/2VUpfE7dvLK3eltbetq3vmSb5kNaXHxnEZgALo7P9MLZg99xw7qTMG/aMbVv0aff+vM7vz7TS+mGn270BYGFyWlux97j2es9g90taPufay8pj8zzbB21/E7btdKt55xfAwui1XN/5kGGYV+7qrNTcoc20x9pa52bXyB8a0TM2rh31dxqd7ueztylt3LW48lVyLfuYAAuDa3Vl8daKSd41jOAZWSr/9q7DeuOusOEHkg3/eNeicyd55ZWZ+HNg/q/CY3rH635VbgOwABUGLLAXHrtEzhBiXWo6vV0iaPAvSvsMwMLWU2S3OEBI+h0CWIDUjmHk7ImiG1azd1/m9r3FW9yXj4v921uXFrvdL/W9rLSteUrprzgABFjYRCdz0bp3GFuw7ZkdrOZyi23ntGsL7N/l5uP3i/th9+S9lLxplObOcODkAQ1K3vJ+63RuBFiYwLXu2st1Ej6xmkdbzKFcdu3F/l1e6H5Dx8EMt1842s7aPmzbcNu6Ds1y021r6B8LYGFM17zvLJ43mJOvCxbatX2u7T5WQgWmPYdeJLb9uX54EV6riQACYGHEUGHJ3mMa5Pf9EKj0x90HHtufx9l3bfjgjGz3HbPvzDY6X1TMjz/tPnBksdFoXOe0rYZhGH/evX/lW3nhAsACtC3AwnKdrrCJbLXX5pV/A1gAWIAAFrbrNeQ6d9Ru9/iX41j0Arv3ZXxRZl+ha23nUAQq3O0D0JAGJhhgYDYDWNgagIGNZx5bBEDpAqkcekF0TpoWed4g1sVGt7sjvn96lX3d/n4/1LoqgwiAhTFABd9eH8149ope9lsWec4glYsNxylpb+86D+H2FfJTw2Dbd0ti2wprW85uC7AwWbUr5Cf+l2x9sNCumE/e2Vxqyk5i8Px4vcZgkfA3HyTSn9ssQiXnV3KAhaGf56fNuYXvu/ffmzzQIEYv7Jh9Z37R+YLuvvaa5s9sNkdS4ACPapjZc/BFcpvzF8J95dkZo3JhvuPszPN8B1iAtgVYcC+AU0lOqPcwNedOAiwALEAAC9vO2WBh7HXnSIITcZFdQ3lSImRoALCwfcGCY9OHmf1kKKDal9+WrveBhQBO5DkuwMJgWrbpMYNFk0gRAzyygNaPPioCB+X2Rsr2s+72gQOyXNdvC7Awweu8XdtnVus/nqPGWwYpreuABeY4Nptre1TtVlfY1/HKr+edLo3dA9zjzFit5+QoBoCF8YpFK9Ts5cMNAeaJzr8XpcLSGK7pRanMVFs/FWGB8r4QRDXI0Qk8ioHefeRviP2jz+WwO8ACtF3AQnLKg6rGgpcvateOifn81GqdGKRd2IaS8+4L1orTrh3y2kv9CX8PQpJUx8vrmKf1LXLQhTAooU880kM1dml91TnfIGf7dCwsW3KAABYggIXxqW2R5wcFC/6XaOt9VSoEwML0gwWe2hDMD2UUghjJwPdPpK/l/u/x6AaAhdEr+JL5hsHgjlTngNVBMAm5Itsm9/ZBdEKetgALE7zGXeeyRq2f71/p3OHbRw8sZDiVD81YzedFgMCjGIJc+3N3Lyzd1+h0b8y7b4CF0cqPIChd1QELPPJhhtdLaBy9v9Ptt2HwHHgt3K8AFljNBZaSIUcyACxAhQALsYJ/tHLWbjv7Mi865rharVN9Trb7W18712HmtRuYEy064rLzblpzJ1lbf3/WpcVe7xb/pkBPeOHKwfHk/Qz0opHRtz64IJxb3DmIalNk9dVZc27OOt/QHsH4in0Q01UAFiCAhXGDBXWdhOxtrSeyrj2AhekFC2EaRBpYcOcOBwZhjQVm76BgIy/4yaJacobKAizkFE9jMJi9kpx9UrmUlA6hv323xNMgdNoCLEzwfl4xn6y511rXvfY8x38EYIGlVdSaj90vOpRBjv9lI/ZBqnzOO9a1XLU1ABZGDhb0UhM4FIjVaVAUZOyLhFCABcO91vOkQwAsQNsCLHDnV/zCzh1eVVvZ6ecP3golZ/u/6Pc7txxCsK/18m+Jzrv0e5Kzras8fQsdfQXEYO3Ftrp9TTtfVc2LwAmJ9QtgAQJYGKsjcjGv0x/WZ8hYFQJgYcrBggIe9EOH+N+C2hsXfIBPz1NaPZ01RwAWRmTfFHgQQYNksJAGBCKQEIAFd27otgVYmIxYygK1Wk8JESVDgwXmVFZI5fSilAYRvlM7zl+299UeNA3jipFzRQiAhRFf/wEA2Hn30e/qRgqFNqx7NrzsQ4b4ihBp8CCCDgALUEHBAn/x8eCAQFrldIPgIXxJ5RArnWmFMx5FSERwQuXQc4WpGglSbTPKvqU68JS+LP6m29e08/UjHOLH4mBBGRECsAABLIz2RcSPRLqYJ1rBj3AQK1Gnp1EALBQLLPTBBUW0A8ACwALAwmgVK6RIrhkjAwuadRRiqRFYFWKTnues3oF1Pm8hRcmGfmqEsCoEwAIEsKApOXohnvvvO95ZznzaV36V45vkaItpF6ManLx9S+qf5xio0iY0+poGFuT+xOpAACxAAAtjFXv5ZyHqg9RWYNdecP9cz1pVAmBhm4KFhL8FDs4vas3lJltJxL9e6UaeFSEAFkYPFnRSIdLAQl8qRApYQCrE5MXqIOysL63wL9WjAguqNIhsuOAeUzNqAWBhRNe+6+BXTfN/tXOmoiTDBWZDP2ohDSwgFQICWFBcRKxWgPylXcz/nwRY0I2QGDdYCPshbMOiE2IpEzn6mgUW2JetOcs8yfpRs9vHwjoNAAsQwMJYxZy+QdKrFPeY9bQaDQALUwwWUqMSArDgPisanc6NEVQgb8ZWimjXFnwb5otcAFgY4HpMiUoInf1S/e1Gx1+iLn37bur2aVEJOscCWBidvBQIav+DGCUwCrCQlQahvt+zr+bmZYCFyapZMX+gC4A0bfgHDhbSohJCsOBFOHS/mOP+DrAATT9YcOrWkSQHNKwFEDjUaU75OMBC1DZe02HSYIGDBF77wH95qLwl9ilPX9PAgly8UXBCABYggIUxii0LyK7JUYxhVvFHgIXpBQtpq0LEV4DwHYispSnTViQAWBheaSs18CUgB10VItx+z6EXs1aFkNsCLIxXLFrBMMLrJUHl383nAATcwcy7nKRf1NH817sO6305B1gYjf3FaJXhfQcGC8z/3YNDr7wyk7YqBF9ukuw5+DOsCgEVDiwwJzfpC538pT78d0bRxKhdv3OrqnGgU2MhyQlXrUOfenPI2TeVw+9FECgiE3T7mhekACxAAAtjhgruNZ33XpIFKZAKsT3BQhosUP3etMjznqMpQQgOKGas5imAhTE7GTY95jr1n5YXut8Qv1In/a7c3lBsX+//XfVb2u8ACxOHTENFLORJg4g5mtR+AzUWJvU8txp31peONEYEFUQb8hoLAbx4cMYgH+/ev/ItcT4k/Q6wABUGLCRFIKhqKkQFCl3HVVqaUox+SFr5gDv2qlUhVA556FSzvwvLOLIHBEsXGMR5ztM3lcNeKpXeVR1Xt6+ZqR8ACxDAwkShAitUK4+dtySgaT+TBAcyXl7fYksJJr+kACxMM1gI0yGEyIQIIlgXxRQJDhvk/atSJAAWxiOezjAjRSa47zPPGaRyMam+gnJ7wVlpVchPDYNtH6U98HSImXJ2W4CF6QILg6RB+DCCLXepDyMAFoaDCjvZKiBSRMnqyuKttZ1zf9fRhDuyVGkVPB1iZs/BF8XIhDzLWwIsQNsTLHhLYFXOiqAgXCVCgg7h0pLyigeUnFcWMxQcaFVEgF/PIflLfxxmSBqw9oJu3xLBQUqbrL6mna+qX07b3lel9DTfB4M9drP3dTF6pNZ2DvXDifjvEMACpIYK8dUc+lZy8Zw+77oKlpLk6Q18WUr2W725fC+/hlmdBhWoiB03cDZJ1X41L7gAWBjxHOC2sOxfqmwR2P49bxlRARhwIGE1l1vM1v5+6DoDSrEQ+LAmA92oLyyxZ4jhF+alTxAJQgAsjBEuBKkIVvNoizmUfrQCvepHl1wzJHv/xqDlsyJwCLdfONoOtzfc7RdW+yIQwrYNt+01t209uS3AwtYACx40MMmvmd2T0iPS0iC8a7pCfuJu//7+TvfL4XVeMZ+8s7nUzPnlGmBhQKhgGMafE5/nd3e6HPYF9n7Xs3fHMbm9GBS4zrXh/OGVr7BrN7DhD+507xuqCAie9rD7wJFF9nc/WsG8vPvAY/vz1nYAWIC2BVjgUQZRwcBouTR5uUnxBqrTlheBjFaZ8IsRquBDpP4aBX2rI0j7GfQBk9Y35YtJSkSDTl91zjcGbig9z+CAmL7h/TtcFjO+lGXS77goABag/FBBXDJSBRa867lqPiPug9LqaQ4ZlPeQsLCjGmAALEzQ0QxXcEi3RRJYCObQQmhP9vel7u2JDo00V0xr7u/zQiWAheG07NvLtzktn2P26otUSgALqu33d1buSLpuvbaGXluAhekAC1lpEE6zdtA0yGWD30vK9zy7v7M6iN0BFkYMFQgp/dGrhRDUuFCBBb4f0zD+YAT36lJ59iceKEqpjRHbplT+LYcSA7zHASxA0w8WIAgCWICmzt4AC8W0O8BCsewNsFBMuwMsFNPuAAsQwAIEQQALEMACBLAAASxAAAsQwAIEsABB0HQ/kAAWimVvgIVi2h1goVj2Blgopt0BFoppd4AFCGABgiCABQhgAQJYgAAWIIAFCGAB2nZzM6l4CQRBEARBEARBELQFBbAAASxAEARBEARBEARBAAvQNgELGAQIKhxNRCpEseyNVIhi2h2pEMWyN1Ihiml3pEIU0+5IhYAAFiAIAliAABYggAUIYAECWIAAFiCABQiCpvuBBLBQLHsDLBTT7gALxbI3wEIx7Q6wUEy7AyxAAAsQBAEsQAALEMACBLAAASxAAAsQwAIEQdP9QAJYKJa9ARaKaXeAhWLZG2ChmHYHWCim3QEWIIAFCIIAFiCABQhgAQJYgAAWIIAFCGABgqDpfiABLBTL3gALxbQ7wEKx7A2wUEy7AywU0+4ACxDAAgRBAAsQwAIEsAABLEAACxDAAgSwAEHQdD+QABaKZW+AhWLaHWChWPYGWCim3QEWiml3gAUIYAGCIIAFCGABAliAABYggAUIYAECWIAgaLofSAALxbI3wEIx7Q6wUCx7AywU0+4AC8W0O8ACBLAAQRDAAgSwAAEsQAALEMACBLAAASxAQ6hlkVOE0A3b6ZUxHtAmPZAAFoplb4CFYtodYKFY9gZYKKbdARaKaXeABWh7gIXjxzvXtyx6ghKy4U9qulGz28c6x49f37btB9j/Y2ABFiCAhe2ontPcZVHyfjB+1witnJ1dWq0Msi92L23btYdMk3zo7atkn+l0uzeIx6pS+pp4rMbS6m0AC5tne9u2vsfsRevLjwxz/Tg2fdh9Dqy7c8eKHaO3eItFyIXQ5rJK9bcbnc6NAAvj19qKvce91t8z2P2Sls8xW+WxeZ7tg7a/Cdt2utW88wtgYUR2X+vc7BrqvBE9J12VNu7qrNTc/9Syyaprz6pJfm0Y7j5K5XPettfU264u6bcFWBitVlcWb7VM8x8Nw/CfkaXyb+cPr3xF187Rs2H+r1wbvuva8FO2j7sOd79KXnllJqltRbMtwAK07cECexG2S+QMIdalptPbJYKG8MUYYAGCABa2pWNplykh6/0OH92wmr37BtuXu63dfNhx1m5WOJgX+49lXVrsdr8EsDBhZ2PRupdS+isO1IcBCxE8oBsyWGDHEezVr8qhFwjRewkFWBjiWnft4DqWn1jNoy3mZCy3awvs3+WF7jd0nI5w+4Wj7aztw7YNt63rUC433baG/rEAFkar5frOh9j4S0DvnUan+3mte0W7ts/d/mPrQOfbzGF0mrWD7N/lA91vyg5knrYAC2MBSB/EARJT+XfzHWen7v09sOGfdh84sthoNK5z2lbDMIw/796/8q0Ee2u1BViACgEWlut0hU1kq702r/wbwAIEASxsQ3lQlZK3qN3u8aiCWPSCFG2Q/iJizXtQwd0mKQKhbdGna23nELeRd6wANOSFGAALowUMbDyHAQtNizzvv8z2gwXP7s3lprxvNv/2UvIms73ucQEWBoQKPvj5aEaCOC2LPGeQysWG45S0t3edh3D7CvmpYbDtuyWxbYW1LWe3BViYzH1+L6U/t72IkWu5r+/AWf3Qs6dth3ZoV8hPmD1dh9Xk12+etgALo1e7Yv7gzubRViO47rzohQA07D7w6H4dR5/DiZk9B18ktzl/Idwrnp0xKhdEQJGnLcACVBiwkBbG7z1MzbmTAAsQBLCw7ZwNxy5bdedIghNxMXASKzr78SMV8kceMCDBbAewML1ggW1vVu1n/Mi/OFhgc6nZVKfI+fPGer/R7e7IYXeAhQG0bNNjrnPxqRwxwCMLaP3oo2lRI972Rsr2s+72gdOyXNdvC7Awgeu7Xds3Y7WeEx39XHMniHaQIw54ZAK9++h3OUDK0xZgYXL2d8f+z7pgwbXhgzOureSIAx6ZQO8+0iX2jz6n2fZveFuABahgYCE55UFVYyHIIz4m1mSgVuvEIO3CNpScd1/sVpx27ZDXXupP+HsQ2qQ6no7849kPVCg5y47Hvhqy/w5rS7SdQ3n6xrafs8yTlNDzMpzRHadRnRsEsACwMBq1LfK8DljgUQ+6ECLJuUQqxHSCBQYOqrT6i8bq0m3CPLB0tvVqMlitU7ppEAALgym4Rt8wWP0Lqc4Bq4NgEnIlLR1Fa/sgOiFPW4CFidn+dQaVWK2D+sLSfZ3u8Rvyb19al+sxsJoLpkEuz+w59CKDFmFbw217OF5TgdVccNv+gbcFWJic/LGvvKsTLRLY8PQMs+Gic6cIC1gdBdeGv+fRCUHE2Wtu26tZbQEWoEKBheBr2wYvJGa3nX2ZFx77MuO+EHHnl6dTsN/62rlOOK/dwBxoEWJE9R18h9q05k6ytv7+rEuLvd4tPvygJ7xw5eB48n5yPSSq5NXQga9WX+JFKkXAwNJCdPoWGzsp6kN3nEZ1bhDAAsDCqMFCdgQCjzggldYLg4x927KeGKToK8DC1gALLM2BRZt0u50bWFpDHrDAwvDzpEEALAwmnsZgMNskOfukcikpHUJ/+26Jp0HotAVYmIBT6Y/5ZSNWW4EX0sxOi+CpDYai0CMHC4ZrT+a0BvPEb5sAFnjbrGseYGGUz3Jr1WZFVq9p2/sDz4YJsCCw4c7A3lptc6S6ASxA0w8WuDMrfjHnTrSqrez08wev55QLzrDfrj/FgjvXLAJA/k10uGMv7tLvotOvqg2RefPwnYG+44WgoO881H2L9yV+rjrjNI5zgwAWMB4jcUQuZjmaUbQCu1bb32GRS6EdMlaWCGs5YFWIqQUL3vPCaj/NtuH1EnTBgj/HrDfypEEALAxo3xR4EEGDZLCQBgQikBCABXdO6LYFWJicHMf5y/a+2oOmYVwxcqwIEYGJfiAQQQf/b2sSaEhrC7AwCai0eKu3UgMtn9VdFSINCETQwf9bYG+ttgALUOHAAn/RjVIC1CH5wUP4UpKT3eccK766R1/5I6dbBRu4wlSNBKm20QUL8rYqSJDWt6RtdMdpHOcGASxgPIZ0RLz7g3UxK1pBXOWhZLfWeKFHp23vC1eHUNRO8KMhxKrV+dMoABY2FywEBeFe4nbLCxa8Y1rt5/OkQQAsACxAwymeGpG9KgTAwnTKL54orgJS+qMXVZARtQCwAEEjBAtccvRCvJ6ADwWyHF7Vl/9BnHcxnWD0jkM6yODRAgOBBY1xGte5QQALGI/B5efM09dyFW1UrB4RpkgkrCzBrv/gXruedwUKgIXNBwusPgK7v/P2ecHCIGkQAAujBws6qRBpYKEvFSIFLCAVYqvBhf66CXnAgpwKkQYWkAqxOXZmS32ahnHZW7Fnx+w784vOFwYFC/Lf0sACUiEggAXFBcly/+Uv5yEwyHCGRwUWdL/8jxIsyMtvDgMW0vo9rnODABYwHoPLy5nXTEFKAwtRNEN6nYZBV5QAWNg8sODZjO59SbR5HrAwaBoEwMJgSotKCJ39Uv3tRqd7U/b23dTt06ISdI4FsDCha96r2m9ezgILaZEGIVjY4dnz81GNhRSwELTVsDvAwogUjL2Wk58WaRDCgh2z78w2Ol+Maixkte1+Mcf9HWABmn6w4NStI0nFw8L6AgEgSAMG4wALUdt4rYJxggV5+c2hwELKOI3r3CCABYzHYJK/Qms6LBdVYCGqv5ANDHQLRQIsbA2w4K3mEEtlUcm6lAQOBk2DAFgYTGkrNfAlIAddFSLcnlX7z1gVQm4LsLCJzqYHecx/zQILadENfAlJorEqhNwWYGGy8lMjKheywELaqhB8CUmy5+DPslaFkNsCLECFAgvMcU76Qhd+Vedggf87o7Bg1K6/eKOq/oJOjQXV35LWoR8eLOjVf0gEC5rjNI5zgwAWMB4DQIV27dAg11sSFAjBgkaKg+eoIhViasBC0nNAN2Jh0DQIgIXBtWzTYyyvvrzQ/YboHCb9rtzeUGxf7/9d9Vva7wALm3DNM8eP2m/oRA8s13c+5Nrtk/KB7jdF51H1e562AAsTvP7Z+NO5N7NSIYK2D84Y5OPd+1e+Jdmw7/c8bQEWoEKBhaQv66paAVHBQdeRlpamFKMfklZT4E63alUIlYMdruDA/i4sy8he5FgV9kGWaktcFSJn35LAgu44jePcIIAFjEd+qMAK1cpjxwraVk37mTSHn9dSkJ1SnuKgu7KEqsgjwML2AwvDpEEALAwuns4wI0UmMMhjkMrFpPoKyu2FaINWhfzUMNj2UdoDT4eYKWe3BVjYHLUr5pM1Bvc0nD6eDuHZU4g2aFfIT5g9xbSHPG0BFiYjHoVQY0BP394fzOw5+KIYbaCKesjTFmABKhZYCJZHEx3gcJUICTqESybKKxhQcl5sKy6byJ1yVbSCX88h+ct93EmXNGB9gsihpxu1tnMo1jd3HHi0gk7fxOgEvq884zTqc4MAFjAe+aBCWlg7dza96zxYHlKOTmB1Gdx7yXqtudzk9045WiFMm3C3rzeX7+X3F7atCmoALExwDvDUBsv+pQoiBbZ/z1saNAEI6IKFYdIgABaGhAtBKoLVPNpiEQN+tAK9Wm4+fr8YQRDY+zdsmToROITbLxxth9sb7vYLq30RCGHbhtv2mtu2ntwWYGF8DqUHc2j5/f2dlS/zZWHbVfPJO917texkMiexYpJfe8sTOl0aq/IfpDJYBzrfZtv5EQjmlfKBx/oiEPK0BVgYncL6CKXyubsaR+9/xR1rz94V8wc7reYJYv/ocwp7+8tRyqt4BKkMuw8cWWw0Gtf5EQjm5d0HHtufYG+ttgALUCHAAv96zl6Gg3XYwyXQ5OUmxRu2TlteBDJaZcJ15O32MRV8EHNT5ZoD3s3Brh1L2s+gYIFFCYhFKk1r7mSevkVLZ6qXiNQZp1GfGwSwgPEYDVQQl4FMAwu+c1p7IFzhgS0zaTcflov72VXzGfF4lFZPc8gwgL0BFoZ1NP3799UkmDQOsDBMGgTAwvBabtcWQpvT8rnGUvf2vkilBLCg2t51WO9IsqXX1tBrC7Awpnu8tyoAuWLwa7t8z7P7O6tKO6SBhb59lZg9u19OsmfQ9rJOW4CF0YGkOWvn3xmGES4zWSpV3qgvLN33isLBTwML/vuB1TAN4w/eihKl8m/nD698JWm5yjxtARagbQ8Wiqi0GgsQBLAATYG9ARaKaXeAhWLZG2ChmHYHWCim3QEWIICFaRTAArQNH0gAC8WyN8BCMe0OsFAsewMsFNPuAAvFtDvAAgSwMI0CWIC24QMJYKFY9gZYKKbdARaKZW+AhWLaHWChmHYHWIAAFqZRYcHKqv0qahlA2+SBBLAAsAABLEAACxDAAgSwAEGjnJtJxcggCIIgCIIgCIKgLSiABQhgAYIgCIIgCIIgCAJYgLYJWMAgQFDhaCJSIYplb6RCFNPuSIUolr2RClFMuyMVoph2RyoEBLAAQRDAAgSwAAEsQAALEMACBLAAASxAEDTdDySAhWLZG2ChmHYHWCiWvQEWiml3gIVi2h1gAQJYgCAIYAECWIAAFiCABQhgAQJYgAAWIAia7gcSwEKx7A2wUEy7AywUy94AC8W0O8BCMe0OsAABLEAQBLAAASxAAAsQwAIEsAABLEAACxAETfcDCWChWPYGWCim3QEWimVvgIVi2h1goZh2B1iAABYgCAJYgAAWIIAFCGABAliAABYggAUIgqb7gQSwUCx7AywU0+4AC8WyN8BCMe0OsFBMuwMsQAALEAQBLEAACxDAAgSwAAEsQAALEMAClCGnXTtECdnwLnpaOWs7vbLO38ahNce5uW3XjrFjWu21+TzbHj/eud7blpLztL68orNNyyKnCKEb4nn1nOauOcs8SQk9L5+vqj0EsAABLEAACxDAAgSwAAEsQNsYLDBns2XRE6Fz7DqFNbt9rHP8uOuE2g+w/y/ygLKxISX7DBuHnmOX/XHyHee0v42jL73e4i02pS8HN59recGC7/T72w4KFqLz9OcKwALAwrSLgbJ63XqcA7e848iuCYuS9zlcnF1arSTda9sWfdq9ftb59UMrrR92ut0bABY2x+62bX3PNMmHrt0f0bU7uw9bhFzg99I+ley3G53OjeJxqpS+Fl6ntHKusdS9HWBhslpbsfe41+l7BrMDLZ9zr1Mrz7WeZ/ug7W/Ctp1uNe99BWBhRHZf69zsGuq8ET0nXZU27uqs1Nz/1LLJqmvPqkl+bRjuPkrlc96219Tbri7ptwVYGL2C8X/XHf9P3fH/7V2HV76aZ/z9e/b8X8X30f0qeeWVmaS2Fc22AAvQtgcL7EXXLpEzhFiXmk5vlwgaghekM0UGC9yJFp3wtbY1z8ar/df2f0j6Gx/LcWm5TlcGAQtRH/XBQvq80QcInvNVd47gIgVY2FIvne71QCn9FYdleRxM4Xr6rNZcbrJ/swgm9m+r2buv75qh5C12T20srd4WOrbBb3ngAsDCCOy+aN07qN3ZtoIN+lU59AJ3WJIhhHWp0e3uAFiY0LPctZnrWH5iNY+2mG2W27UF9u/yQvcbOs5luP3C0XbW9mHbhtvWdWiWm25bQ/9YAAujfl/a+RAb/zj8q7/T6HQ/r3ePr+1zt//YOtD5NnMYnWbtIPt3+UD3m7IDmactwMI4nufe+P9594Eji41G4zqnbTUMw3D//eh+3fEP9vGnvn3sX/lWgr212gIsQIUAC2kOqve3goOFNCd8FA56kcACi2TYjLECWIDyAIIBvlxfJFbrlLhN2yLPM8dxsdv9Unz/dF2OZgjg5boMIgAWJgcY2HjmsTuLOqm1nUNye3Zf3EvJm8yW/G9e2+Zyk/+bwSQOGsrNx+/Pc80CLAwIFfzr9KMZD/hEL/vuM+k5g1QuNhynpL296zyE21fITw2Dbd8tiW0rrG05uy3AwvjlX5P057YXMZLvq7V3f/CjHT707GnboR3aFfITZs/5jmPyazhPW4CFMdzL/fH/YGbPwRfJbc5fCNf5szNG5YI7/juzxj/PPkZxPIAFaNuBhbSwde9has6dLDJY4A68yiFO+xvAwtYZK4AFaFxgwbHpw6roBNW+/LYKsBDAiTzHBVjYPLDA7NVsqu95PiSy3s+KRODHBFiY0PPSpscMNt5SxACPLKD1o4+KwEG5vZGy/ay7ffB10n3WabcFWJjEfb22b8ZqPSc6+vnetfxoBznigEcm0LuPfpcDpDxtARbG8V7sjf/HcnQCj2Kgd3e6WePv7uPBGbYPKeKARybQu490if2jz2m2/RveFmABKhhYSE55UNVYCAsACnn21GqdGKSdXEwwLIQo9SdWIJE5qIrj5VGQ83o6Co2jG2KYfryOQDx8te3Y+5L+ttjr3TLq85f3w/aRBBayzksGC07b3leh5Kyf90vP221nn7w/VZHGJLAgt/fDv6OaEKHo1/7la5T8S+w3q3VK7qNqXCGAha0AFsLUBvcaSIpCECMZ+P5JpfWCuP8gfeqiGN0AsLB1wUImaPJsnv5lVBdAACwMr+A6fcNgUE+qc8DqIJiEXCFSJEPu7YPohDxtARYmZvvXGVRitQ7qC0v3dbrHb8i/fWldrsfAai6YBrk8s+fQiwxahG0Nt+3heE0FlvPvtv0DbwuwMDZbn55h47/o3KkY/9/LkQWZ+xBgAaujIO4jiE57zW17NastwAJUKLAQc6DZigaSY6m88JhD6b48cceXO7miYxi2cx1kXm8gyD0OIUbknPoOpGnNnWRt/f1FzqRXWNJu9/jx5P3khwrBOQfnIPZDdtQHjVgY7fkz+OPXbfAhA+2VSqV35f7qnlfotFN6noXzciDAAQNvm1akUQUW0tqroiT8Wh4+2EpONRl/vQqABWgQsBCmQaSBBSEdIgIR7nwPCjbyon550iAAFrYuWGha5HkxDSJJbct6IqnAJ8DCaMXTGAx2nSY5+6RyKSkdQn/7bomnQei0BVgYv1b9Mb9sxGor8EKa2WkRPLXBUBR65GDBcO3JUhyCeeK3TQALvG3W/QFgYYD7eJCW4I1/AlgIxj8xPaFvHwpYwPcR2FurbY5IRIAFaPrBAnd4xS/w3MFVtZWdXv7g9ZxSwdH32/WHyauccRWYiL3sS7+ngQAdqb72J6UHDAoWRnr+iU59/Bx0zyvpXEMwIEVLqCIT8v6eeEz/5nxJBYlQkwFgYUuDBQU86IcO8b8F+fUXOdijtHqaF3IEWJhusODb3HojLQrBsz8l7zGIj1UhJmTfFHgQQYNksJAGBCKQEIAF956g2xZgYXJyHOcv2/tqD5qGccXIsSJEBCb6gUAEHfy/rUmgIa0twMIYIFIKPIiAQbqjnwYE5H0E9h7qeAAL0LYFC/yFJwyJT0g3CJ1AhQOsdPwVzmL0VTuCE2kOurg8okqDOJ5+5IDa8ZXPbRCwMKrzT9uPCiLonleSk6+CAuMGC0nn4r+EVd5CCgTAwnYCC31wQRHtALAwnWDB24/Vfj7JWfELBYrL3Xm2twAWABYAFiajeGpE9qoQAAsACwALEDQgWBCd01j9AMGpVS29mPLCvaFyiFWOZ6ZjnQEyhn3QxOogjAAsjOr8xdQGHWdc97zSnHwOcvh+JwEWVOepilSBABamBiwk/C2oUP4SS0EKlvT9zKuDglSIqQcLOmkQzP7uM3bBnxvekndvNzqdGwEWNgcs6KRCpIGFvlSIFLCAVIitBhf66ybkAQtyKkQaWEAqxOaCBd1UiDSwIP8tDSwgFQICWFDceIOX3lhUQJqjOxbHWjNCYhDxIoOsHzW7fSysfzDlYEHnvPJED0wCLERAI9qG/XuQNBeABYCFiYGF1KiEACywe0C3e4PwQvsWuwbClSJcJ5PDhTyRCwALWwss6KRBKObHVTZ38hRwBFgY1DbqqITQ2fcAT/em7O27qdunRSXoHAtgYVL3ela137ycBRbSIg1CsLDDs+fnoxoLKWAhaKthd4CFvDZNiRIIwcKOe96eX3S+MMg+QliwY/ad2Ubni1GNhay23S/muL8DLEDTDxacunUkabnAMPc/cJDTHOZxONZR29GuCiAXORQd32kGC7rnlR2xMNlUCPnv/nlUziINAmBhK4OFtFUhVCtAZC1NKa8WAbAwPWAhKw1CJT81wroIsDBepa3UwJeAHHRViHB7Vu0/Y1UIuS3AwubJj0Qw/zULLKRFN/AlJInGqhByW4CFsV3nylUh+HKTZM/Bnw26KgRfQpLvI21VCLktwAJUKLDAHNSkL8NyYb3w3xlFE6N2/cULVXUDdGosJIXQy0spat18FLBipGBhROefVtgwObIg+7wyayxMsHijyi5799KnkQYBsLDVwUIaLFD93rbI8yoIEQIKYWlKgIXpAgu6q0H0zRFqv4VUiPFr2abHWF59eaH7DdE5TPpdub2h2L7e/7vqt7TfARY2417vOn7UfkMnemC5vvMh126flA90vyk6j6rf87QFWBjDde6P88e7Dzy6XzH+fb8n7OPBGdZ2/8q3pH30/Z6nLcACVCiwkBSBoKqpEBVTdB1HaWlKMfohaaUD7iyrVkVIXnIwSMkQlpxkL+Ms5D8p2iIbeowPLIzy/OWaB0nb5DmvrFUh5CUhJwEWYmOmADIQwMJWBAthOoQEBXyIEE+R4LBB3r8qRQJgYXrAQt40CG7zvZS8mRdGACwMJp7OMCNFJvhRI5WLSfUVlNsL0QatCvmpYbDto7QHng4xU85uC7CwOWpXzCdr7NrTcPp4OoRnTyHaoF0hP2H2FNMe8rQFWBjDvTxIZZjZc/BFMVLAvc6fnTEqF3TqHeTZxyiOB7AAbU+w4C19VjkrgoJwlQgJOoRLS8qrM1ByXv2lO3Imk76Ip0UlxGGGpAG+aqv65bTtfVVKT/N9Mgfbbva+LvZNTC+Q+y3/bZTnH60iEYEcVjch7G+QquKsOTfrnpe4T1ZEjtvbAx5W64QaxJBrvO1AvwtpGn5hSfuBvhUzUiI0IICFcYs7/qRqv8rrIsTmJiXvs/ukXE+BA4lac7kZ7Yeuy1EMUU0GulFvLt/Lr/+2TZ8gxLoo7xdgYcJ2t+xfynYXbO8vEamAB2lpEIHNL7Btmc3Z9enZ3KJPs3tt3usVYGEIuBCkIljNoy1mKz9agV4tNx+/X7RdYO/fGLR8VgQO4fYLR9vh9oa7/cJqXwRC2Lbhtr3mtq0ntwVYGI+8dysGc2j5/f2dlS+H117VfPJO914tQwXmJFZM8mtm93mnS2NV/oNUButA59tsO/8LuHmlfOCxvgiEPG0BFsYAF4K0h90Hjiw2Go3rgvG/vPvAY7FohcDe73r2llfxCFIZhH08OKPYR962AAtQIcACjzKIiv5FS2HJy02KN2ydtrwIZLTKhF9QUAUfIvXXU+hb4UDaT+4XDBGOUHqeOcBi+oL379D57l/eMu1v4zj/+FKgdIOlf3hAiPVd2J/OeYn7jNkwaK+GGtnnn/Z7BIiCgqDuS3bT6e1KgkiDLCEKsACwMLDDEa7g0Hc9h1+w08ACh33hPpgDurR6WyLYrJrPCPa6Zlpzf69yaAEWJmL3q2l21wELaWkQKntTar3OIcMA1znAwhBa9lfk8G1Oy+caS93bZTskgQXV9q7DekeSHb22hl5bgIUxQcNm7aBpkCsGv/bK9zy7v7OqtEMaWOjbV4nZs/vlJHsGbS/rtAVYGIPd21bDNIzL3vK+pfJv5w+vfEUGemlgQdjHH2L7uKaGgnnaAixA2x4sQNBWkR8STl9GGgTAAgSwAAEswN4ACwW1O8BCMe0OsAABLEDQqOR9QaT2y0iDAFiAABYggAXYG2ChoHYHWCim3QEWIIAFCBqFeFpI2mojEMACBLBQcLsDLBTL3gALxbQ7wEIx7Q6wAAEsQNCg6jXtr8dqMqBoI8ACBLAAASxAAAsACwALAAsQtFXmpmIFBQiCIAiCIAiCIGirCmABAliAIAiCIAiCIAiCABagbQIWMAgQVDiaiFSIYtkbqRDFtDtSIYplb6RCFNPuSIUopt2RCgEBLEAQBLAAASxAAAsQwAIEsAABLEAACxAETfcDCWChWPYGWCim3QEWimVvgIVi2h1goZh2B1iAABYgCAJYgAAWIIAFCGABAliAABYggAUIgqb7gQSwUCx7AywU0+4AC8WyN8BCMe0OsFBMuwMsQAALEAQBLEAACxDAAgSwAAEsQAALEMACBEHT/UACWCiWvQEWiml3gIVi2RtgoZh2B1gopt0BFiCABQiCABYggAUIYAECWIAAFiCABQhgAYKg6X4gASwUy94AC8W0O8BCsewNsFBMuwMsFNPuAAsQwAIEQQALEMACBLAAASxAAAsQwAIEsABB0HQ/kAAWimVvgIVi2h1goVj2Blgopt0BFoppd4AFCGABgiCABQhgAQJYgAAWIIAFCGABAliAIGi6H0gAC8WyN8BCMe0OsFAsewMsFNPuAAvFtDvAArQ9wMLx453rWxY9QQnZ8Cc13ajZ7WOd48evb9v2A+z/MbAQBLCwHdVzmrssSt4Pxu8aoZWzs0urlTz3z7ZFn3bvn+v8/kkrrR92ut0bMrezaw+ZJvnQ265kn8naBmBh9La3bet7zAa0vvzIMNePY9OHXduvu3PHGmVbgIXRaW3F3uNe6+8Z7H5Jy+fY+OexeZ7tg7a/Cdt2utW88wtgYUR2X+vc7BrqvBE9J12VNu7qrNTc/9Syyaprz6pJfm0Y7j5K5XPettfU264u6bcFWBitVlcWb7VM8x8Nw/CfkaXyb+cPr3xF187Rs2H+r1wbvuva8FO2j7sOd79KXnllJqltRbMtwAK07cECe7m1S+QMIdalptPbJYKG8GV3G4KFnmOXrbpzBH3cHmMFsACwMOjcjoCAKLphNXv3ad0/KXmL3ScbS6u3hc5q8FsSKIiO6x7Hbj7c6fRuyWlvgIVhnY1F615K6a84UB8GLPR6i7e4jssFZs8sWJCnLcDCCK91196uY/mJ1TzaYk7Gcru2wP5dXuh+Q8fpCLdfONrO2j5s23Dbug7lctNta+gfC2BhtFqu73yIjX/sHl+qv9PodD+vda9o1/a5239sHeh8mzmMTrN2kP27fKD7TdmBzNMWYGEsAOmDOEBiKv9uvuPs1L2/Bzb80+4DRxYbjcZ1TttqGIbx5937V76VYG+ttgALUCHAwnKdrrCJbLXX5pV/26ZgoWWRU+6L5Ar6uD3GCmABYCGvOBSgdrvHAUAsekEjgmCtbc0HX54rKnCgghNsGw8qCDBiAHsDLIwQMLDxHAYsNC3yvP8ymw0L3Pvpc7ptARZGBBV8mPPRTOXQC4REL/u+LSoXG45T0t7edR7C7Svkp4bBtu+WxLYV1rac3RZgYTL3+b2U/tz2Ikau5b6+A2f1Q8+eth3aoV0hP2H2dB1Wk9838rQFWBi92hXzB3c2j7YawXXnRS8EoGH3gUf36zj6HE7M7Dn4IrnN+QvhXvHsjFG5IAKKPG0BFqDCgAXmNLIXHNvplZUPU3Pu5HYDCxymbGVneav0cRrGCgJYGMjZSIjECZyIi4Hjl5oSIYS0V1T7kJ3VKFLBurTY7X5pCHsDLGwRsMC2N6v2M37kXzosENsaAAuTe47Z9JjrXHwqRwzwyAJaP/qoCByU2xsp28+62wdOi/vM1G4LsDCB67td2zdjtZ4THf1870B+tIMcccAjE+jdR7/LAVKetgALk7O/O/Z/1gULrg0fnHFtJUcc8MgEeveRLrF/9DnNtn/D2wIsQAUDC8kpD6oaC0Fu8DGxJgO1WicGaRe2oeQ8c16ddu2Q117qT/h7ENqkOl6W/C+U9OW+sGfhWOyLZZXS07GQ6MD5CJyCjdi2VutUlE7i/6aK/lA6JXXriL8/v6aF48x+zar/9YNJfXTWnJuTxor9TewDBwGBg3NJ/j3LRt7+UvqR51iTtDHAAsDCKNS2yPM6YMGPWHDHvtJ6QRx7/3froggPwrQJjf0CLEwHWGD3vCqt/qKxunSbYFtLpy3AwmQUXHdvGAwASnUOWB0Ek5ArRIpkyL19EJ2Qpy3AwsRs/zqDSqzWQX1h6b5O9/gN+bcvrcv1GFjNBdMgl2f2HHqRQYuwreG2PRyvqcBqLrht/8DbAixMTv7YV97ViRYJbHh6htlw0blThAWsjoJrw9/z6AQ/Eoa85ra9mtUWYAEqFFiIOcu0ctZuO/syLzzmVLoONXf6+Fdt9ltfO9d55LUbmOMoOvKyQ25acydZW39/1qXFnp937BWWZOHKwfHk/eR+kfSdgT4nOxyL4NzE/nFY4OVP8z7HgATblp5XRX6oJKeZeCHYrlPO+yT3UXesVBEGfs0MHyDJv2fZMmmsdI+1WTYGWICGBwvZUQURLHDne1CwMYCTr8lpEEkQAmBhesECK9rJ7Nztdm5wXzLfTAMLcluAhcmIpzF4453k7JPKpaR0CP3tuyWeBqHTFmBhAk6lP+aXjdjHEV5IMzstgqc2GIpCjxwsGK49mdMazBO/bQJY4G2z7jMAC6N8llurNiuyek3b3h94NkyABYENdwb21mqr+1wBWIC2BVjgTpz4pZg7f4kOseAQ8gdvhZKzohPot+tPsVA5oyowEXsZl35XOfyjAAuqehOqttGX+ej82La66QK8/3J7L/c6ASzojJXOdv3jnm7LNLCQ/1iTszHAAjSkI3JR19EMoOBFH87S85RWT8u1E0QAYbXb35mzzJOhzXKuQgGwsDXAggeTrfbTbJvg61UiWFC1BViYkH1T4EEEDZLBQhoQiEBCABZcO+u2BViYnBzH+cv2vtqDpmFcMXKsCBGBiX4gEEEH/29rEmhIawuwMAmotHirt1IDLZ/VXRUiDQhE0MH/W2BvrbYAC1DhwAJ/OfYcSoHsyqHooUOd4NT2OYWKr81RhET6V3auMFUjQYPk/ic5xP5X8jgM4W3lcw5/Z6kBDMxQ+2XdL+uqlTiGcdoH2U7XlqMGC5OyMcACNLAjokhjyAUXFKkOUd0Gcq1kt9Z4UUinbe8LV4fQWIUCYGFrgIWgINxL3M5pYCFo+wu5LcACwALAwmQVT43IXhUCYGE65RdPFFcBKf3RiyrIiFoAWICgEYIF0bmO1RHoC/cnG1mOXggPFGAhcqrjX/uT8v95qP7oHYd0h7Wv9oCiD6FDTCtnxa/+Ogq/4LN+0OppGTCMHSxo2nLcYGFcNgZYgAaRnwdPX8sTQcCdzFrbORQs0/uZDArCoo2KlSbCFAmNVSgAFrYGWGBFO9m9jLdPAwtJbQEWNh8s6KRCpIGFvlSIFLCAVIitBhf66ybkAQtyKkQaWEAqxObYmS31aRrGZW8Vnh2z78wvOl8YFCzIf0sDC0iFgAAWFBdk8IJ8TeWMZjmBowILul/VRwkW2JdHP0w5KKjIc/0VfQjTBqR0Am2IExZvjFJQ+HhNCixsdsTCuGwMsAANIi8PPkfqTbhkpeA4BveMz8TIhTSwEEUz6K8UAbCweWDBsyXd+5JoxySwwNs2Op0b5bYAC5ODhUlRCaGzX6q/3eh0b8revpu6fVpUgs6xABYmdM17VfvNy1lgIS3SIAQLOzx7fj6qsZACFoK2GnYHWBiRgrHXcvLTIg1CWLBj9p3ZRueLUY2FrLbdL+a4vwMsQNMPFphzm1RwMPyqHgCCNGAwDrAgpgwM4rjndZbl4o1iW5Xj69VVqFZf0hmTNMckFhmRUdtg5GAho98Ti1gYsY0BFqC8kr8s627Dxj6rUGMIDxRgIaq/ALAwDWCB2zwtfYvZstHt7sjTFmBhPEpbqYEvATnoqhDh9qzaf8aqEHJbgIVNdDY9yGP+axZYSItu4EtIEo1VIeS2AAuTlZ8aUbmQBRbSVoXgS0iSPQd/lrUqhNwWYAEqFFhgDl/SF7rwazIHC8Jygmlf9VTFDfscyb5Cj+n59+roAvU69IOAhSQHN63GAqtBkdXHXC8/AnCZWI2FDFtOssbCKG0MsADlggrt2qFB5lnSspQhLHDvG9wmSStNhG2RCjEVYCHp/p21KoTcFhELk9OyTY+xvPryQvcbonOY9Ltye0Oxfb3/d9Vvab8DLGzCNc8cP2q/oRM9sFzf+ZBrt0/KB7rfFJ1H1e952gIsTPD6Z+NP597MSoUI2j44Y5CPd+9f+ZZkw77f87QFWIAKBRaSvlqr8vCjQnuuAywtTSlGPyStAsAdWl2nM3Ts2d+F5QjZyxlLWdBd3jHLIY4c7Wyw4Ie3RsUa00BK0otl3ao/Lo+5uJLGoGAhKcVBtZ2OLdPTRvSPNWkbAyxAeaACg4Ty2HnLRpr2M2nOPv8iLTulqhQJHsUgt+VpEnkcW4AFgAXYTl88nWFGikxwn4HPGaRyMam+gnJ7IdqgVSE/NQy2fZT2wNMhZsrZbQEWNkftivlkrdm7T8fp4+kQnj2FaIN2hfyE2VNMe8jTFmBhMuJRCDUG9PTt/cHMnoMvitEGqqiHPG0BFqBigYWgAKHoXIarREjQIaorIFXup+S82FZcLpA7k6poBb+eQ/oX/8RVAwbMyxcdYj8VwX7AWXNulvvLKrZXKT3Nj8W2Y2OkWioyKsaYvNJD7EYXjANvy8ZbLGKY1MessRIBCds3T7WoVulLckFOHVuq+tEPVNKPpdPvUdsYYAHShQppoerc2fTmOiXve4VahYiDqD4C3ag3l+/l13fbpk+oVpZgNRzctuu15nLT2y+77nNGKwAsjHgO8HQFy/6lygaB7d9jtk9KWQBYmAK4EKQiWM2jLRYx4Ecr0Kvl5uP3ixEEgb1/w5apE4FDuP3C0Xa4veFuv7DaF4EQtm24ba+5bevJbQEWxudQejCHlt/f31n5Ml/qtV01n7yT3X8lJ5M5iRWT/NpbntDp0liV/yCVwTrQ+Tbbzo9AMK+UDzzWF4GQpy3AwugU1kcolc/d1Th6/yvuWHv2rpg/2Gk1TxD7R59T2NtfjlJexSNIZdh94Mhio9G4zo9AMC/vPvDY/gR7a7UFWIAKARb4l+moaCF/qaYb8nKT4g1bpy0vAhkVKPQLIqrgg5hvKufa99UhkPYziMLilO7LYujci442pedZlXcxGsGv+h45wDyFQFzhQf5b0gOPRSw4TrMcjWP/GMb76JR1xsq/0Vnz4VgF5+f1kZ2TNG46tlSNle6xRGAzaRsDLEDDQIVY8cUEsBDex6rmM+K+TGvu75NAgWPXHvCXmPSPYdnNh/NABYCFETmaPjS9mgSTABa2n5bbtYXQ5rR8rrHUvb0vUikBLKi2dx3WO5LuuV5bQ68twMKY7vHeqgDkisGv7fI9z+7vrCrtkAYW+vZVYvbsfjnJnkHbyzptARZGB5LmrJ1/ZxhGuMxkqVR5o76wdN8rCgc/DSz47wdWwzSMP3grSpTKv50/vPKVpOUq87QFWIC2PViAIAhgAZo6ewMsFNPuAAvFsjfAQjHtDrBQTLsDLEAACxAEASxAAAsQwAIEsAABLEAACxDAAgRB0/1AAlgolr0BFoppd4CFYtkbYKGYdgdYKKbdARYggAUIggAWIIAFCGABAliAABYggAVo283NpGJkEARBEARBEARB0BYUwAIEsABBEARBEARBEAQBLEDbBCxgECCocDQRqRDFsjdSIYppd6RCFMveSIUopt2RClFMuyMVAgJYgCAIYAECWIAAFiCABQhgAQJYgAAWIAia7gcSwEKx7A2wUEy7AywUy94AC8W0O8BCMe0OsAABLEAQBLAAASxAAAsQwAIEsAABLEAACxAETfcDCWChWPYGWCim3QEWimVvgIVi2h1goZh2B1iAABYgCAJYgAAWIIAFCGABAliAABYggAUIgqb7gQSwUCx7AywU0+4AC8WyN8BCMe0OsFBMuwMsQAALEAQBLEAACxDAAgSwAAEsQAALEMACBEHT/UACWCiWvQEWiml3gIVi2RtgoZh2B1gopt0BFiCABQiCABYggAUIYAECWIAAFiCABQhgAdpCOn68c33brh2jhGxY7bV5jAmk8UACWABYgAAWIIAFCGABAliAoM0DC8yRbVn0BHNk/UlNN2p2+1jn+HHXwbUfYP+PgZ0cVLBL5Exwc7kGsAABLIxXPae5y6LkfX7NEVo5O7u0Whn0+m3btYdMk3zo7atkn+l0uzcM2xZgYXy2t23re8wGtL78yKDXj8KWbzc6nRuT2js2fdh9zq6788wCWJic1lbsPe61/p7B7pe0fI6Nfx6b59k+aPubsG2nW807vwAWRmT3tc7NrqHOG9Fz0lVp467OSs39Ty2brLr2rJrk14bh7qNUPudte0297eqSfluAhdFqdWXxVss0/9EwDP8ZWSr/dv7wyld07Rw9G+b/yrXhu64NP2X7uOtw96vklVdmktpWNNsCLEDbHixEjqx1qen0domgIXzZ3YZgoefYZavuHNmq/WtZ5BTAAgSwMP77ACVkPXrZ5KIbVrN332D7cre1mw87ztrNum07nd4tOe0NsDCss7Fo3Usp/RUH6oOChcCWV3Vt2est3uI6ORdYe4CFCV7rrr1dx/ITq3m0xZyM5XZtgf27vND9ho7TEW6/cLSdtX3YtuG2dR3K5abb1tA/FsDCaLVc3/kQG//YPb5Uf6fR6X5e617Rru1zt//YOtD5NnMYnWbtIPt3+UD3m7IDmactwMJYANIHcYDEVP7dfMfZqXt/D2z4p90Hjiw2Go3rnLbVMAzjz7v3r3wrwd5abQEWoEKAheU6XUlyYL2/bVOwwBx390VyZes+CJPtAkEAC8PLg6qUvEXtdo9HCsSiF3JEEKy1rXkPFLjbNJZWbxtVW4CFyQAGNp6DgAUPTuS0pfvsec5/8QVYmBhU8GHORzOVQy8QEr3s+7aoXGw4Tkl7e9d5CLevkJ8aBtu+WxLbVljbcnZbgIXJ3Of3Uvpz24sYyffVWnBWP/TsaduhHdoV8hNmT9dhNfl9I09bgIXRq10xf3Bn82irEVx3XvRCABp2H3h0v46jz+HEzJ6DL5LbnL8Q7hXPzhiVCyKgyNMWYAEqDFjwv4zTDdvplZUPU3Pu5HYDC9xpB1iAABYK7GwkRC0FTsTFwPGr6OzHjz6wLi12u18aVVuAha0NFqJIBetSo9vdoXsss2o/w6IEDYCFyT1PbXrMdS4+lSMGeGQBrR99VAQOyu2NlO1n3e0Dp8V9dmu3BViYwPXdru2bsVrPiY5+vncxP9pBjjjgkQn07qPf5QApT1uAhcnZ3x37P+uCBdeGD864tpIjDnhkAr37SJfYP/qcZtu/4W0BFqCCgYXklAdVjQWxuCAPG6ZW68Qg7cI2lJxnjr7Trh3y2kv9CX8PQptUx8uS/4WSvtwX9iwci32xrFJ6OhYSHTgfwYvkRmxbq3Vq0LoITt064u/Pr2nhOLNf48cSwQI7boWSs/y81S+5yf2e9DhDAAvTrrZFntcBCzzqYdRtARa2Nljwv4KSN/NEHTBgVaXVXzRWl25j8wBgYTIKrrs3DFbTQqpzwOogmIRcIVIkQ+7tg+iEPG0BFiZm+9cZVGK1DuoLS/d1usdvyL99aV2ux8BqLpgGuTyz59CLDFqEbQ237eF4TQVWc8Ft+wfeFmBhcvLHvvKuTrRIYMPTM8yGi86dIixgdRRcG/6eRycEz4DX3LZXs9oCLECFAgsxZ5lWztptZ1/mhcecaNeh5g4nd4LZb33tXMeV125gTqvoyMsOuWnNnWRt/f1ZlxZ7fq6qV1iShSsHx5P3k/tFsm3NqyIWwrEIzk3sH4cFXrEv3ucYkGDb0vOqyA+V5DQTLwSbkEu8TyFYsOynuE2S6i5k9XuzxhkCWJhusJAdVRDcSz4jldYLWWOfpy3AwtYGC3wb3yHVC69uW/RpVrej2+3cwKAEwMJkxNMYvPFOcvZJ5VJSOoT+9t0ST4PQaQuwMAGn0h/zy0bsQxIvpJl93fLUBkNR6JGDBcO1J3Nag3nit00AC7xt1n0GYGGUz3Jr1WZFVq9p2/sDz4YJsCCw4c7A3lptdZ8rAAvQtgAL3IEUv1JzxzPRIRacUf7g9b6oCw6o364/xUKVhqACE7GXcel3lcM/CrCgSj9QtQ1uKJfE82Pb6qZW8P7L7b3caxksKPoij4duvyc9zhDAwhQ7IhezHM0oAoFdH+3vzFnmydAO0soSedoCLGxtsBBFK5DPmC3tqvlMli09+Gu1n2b759sDLEzIvinwIIIGyWAhDQhEICEAC66dddsCLExOjuP8ZXtf7UHTMK4YOVaEiMBEPxCIoIP/tzUJNKS1BViYBFRavNVbqYGWz+quCpEGBCLo4P8tsLdWW4AFqHBgwb+gmrt4uH1SGHzoUCscU6VDqvjSHUVIRHAireZBmKqRoEHqJCSBBf8LfRyGJDnz4e/uOXpghtov637VV63EoQY4cYdejEwYpN+THmcIYGEqHRHv2rEuZtZLCGsxkGslu7XGCz06bXtfuOJDsLJEnrYAC1sbLESrOsRteaxt7w1Xh3BtyfcVFI/7BQcOAAsACwALm6N4akT2qhAAC9Mpv3iiuApI6Y9eVEFG1ALAAgSNECyITmqsjkBfuD/ZyHIyQwdYARYipzr+tV/lvIppF6N3HNKd5b76EIo+hM44rZwVIzh0FEYPsH7Q6mkZMOQBC7r9nvQ4QwAL0yY/D56+lqtoo2L1iDDtIfhbnrYAC1scLPCijWwliE7nRtW+3L+9zf/m2PRhds+NgwaAha0AFnRSIdLAQl8qRApYQCrEVoML/XUT8oAFORUiDSwgFWJz7MyW+jQN47K3Cs+O2XfmF50vDAoW5L+lgQWkQkAAC4oLkuXby1+rdRzbUYIF3QiJUYIFFrnhhykHBRV5nQFFH8IUECk1RBvihMUboxQUuW6FLljQ6fekxxkCWJg2eXnwmmk/abAgilDw6zTkaQuwML1gIYpm8FeK8NrSvS+J7QAWJg8Lk6ISQme/VH+70enelL19N3X7tKgEnWMBLEzomveq9puXs8BCWqRBCBZ2ePb8fFRjIQUsBG017A6wMCIFY6/l5KdFGoSwYMfsO7ONzhejGgtZbbtfzHF/B1iAph8sMOc2qeBg+FU9AARpwGAcYEFMGRjEcc8LFuQiiGJbldPt1VWoVl/SGZM0iBOLMAj2kwcs6PZ70uMMASxMk+Qvy5oOy0UVLIhqKgRgIUdbgIUtDhY4PFCAhaj+gg8W2JwSrs0E6S9XCbCQX2krNfAlIAddFSLcnlX7z1gVQm4LsLCJzqYHecx/zQILadENfAlJorEqhNwWYGGy8lMjKheywELaqhB8CUmy5+DPslaFkNsCLECFAgvM2Uz6Qhd+yeZgISxamF7MT1XcsM+J7Sv0mJ77r44uUK9DPwhYSHKu02os8KUf0/qY6+VHAC66YCFPvyc9zhDAwtRAhXbt0CBzPGn1iBAWCCAhT1uAha0LFpiari0NYl2UgUAIFhTQQW6DiIXJadmmx1hefXmh+w3ROUz6Xbm9odi+3v+76re03wEWNuGaZ44ftd/QiR5Yru98yLXbJ+UD3W+KzqPq9zxtARYmeP2z8adzb2alQgRtH5wxyMe79698S7Jh3+952gIsQIUCC0lf21U1FaIif64DLC1NKUY/JK1AwKGDarUClVMbOsjs78JSiOzljIX+6y7vmAUWIhiS7aD74a1RscY0kJL0Ylm36o/LYy6upKELFvL0e9LjDAEsTAtUYJBQHjuWXlQ17WfSnH1eH0F2Snnqg/h7nrYAC1sbLCRtw9Mk0vYFsDB58XSGGSkywX2fec4glYtJ9RWU2wvRBq0K+alhsO2jtAeeDjFTzm4LsLA5alfMJ2uswKqG08fTITx7CtEG7Qr5CbOnmPaQpy3AwmTEoxBqDOjp2/uDmT0HXxSjDVRRD3naAixAxQILQQFCERSEq0RI0CGqKyCtGkDJebGtuFQhd2RV0Qp+PYf0L/6JKxYMWBNAdND9VAT7AWfNuVnuL6vYXqX0ND8W246NkWqpyKgYY/JKD7EbXTAOvC0bb15A0ft7lbwqj4m4EkXaOCf1e9LjDAEsTANUSAtV5w6i51hQ8r5XqFWKOGB1GQih67XmcjO8lhMiEMS23n5T2gIsTGgO8HQFy/6lygaB7d9jthcjFNJsmRStALCwiXAhSEWwmkdbLGLAj1agV8vNx+8XIwgCe/+GLVMnAodw+4Wj7XB7w91+YbUvAiFs23DbXnPb1pPbAiyMz6H0YA4tv7+/s/JlvtRru2o+eSe7ZiUnkzmJFZP82lue0OnSWJX/IJXBOtD5NtvOj0Awr5QPPNYXgZCnLcDC6BTWRyiVz93VOHr/K+5Ye/aumD/YaTVPEPtHn1PY21+OUl7FI0hl2H3gyGKj0bjOj0AwL+8+8Nj+BHtrtQVYgAoBFniUQVT8j79U0w15uUnxhq3TlheBjAoU+oUFVU6xmG8q5/n31SGQ9jOIwuKU7sti6NyL0ITS87W2c0iMRmD/Fp1vHk0grvAg/y3pgcciFhynWY7G0R9DEXCIjn3/MaJxyu73Xx/erHGGABamFSoQ3/GrCM6GEiz4zmntAX/ZSH87y24+nAQK8rQFWBijo8mLMCbApCywMKgtARY2T8vt2kJoc1o+11jq3t4XqZQAFlTbuw7rHUn3XK+todcWYGFM93hvVQByxeDXdvmeZ/d3VpV2SAMLffsqMXt2v5xkz6DtZZ22AAujA0lz1s6/MwwjXGayVKq8UV9Yuu8VhYOfBhb89wOrYRrGH7wVJUrl384fXvlK0nKVedoCLEDbHixAEASwAE2dvQEWiml3gIVi2RtgoZh2B1gopt0BFiCABQiCABYggAUIYAECWIAAFiCABQhgAYKg6X4gASwUy94AC8W0O8BCsewNsFBMuwMsFNPuAAsQwAIEQQALEMACBLAAASxAAAsQwAK07eZmUjEyCIIgCIIgCIIgaAsKYAECWIAgCIIgCIIgCIIAFqBtAhYwCBBUOJqIVIhi2RupEMW0O1IhimVvpEIU0+5IhSim3ZEKAQEsQBAEsAABLEAACxDAAgSwAAEsQAALEARN9wMJYKFY9gZYKKbdARaKZW+AhWLaHWChmHYHWIAAFiAIAliAABYggAUIYAECWIAAFiCABQiCpvuBBLBQLHsDLBTT7gALxbI3wEIx7Q6wUEy7AyxAAAsQBAEsQAALEMACBLAAASxAAAsQwAIEQdP9QAJYKJa9ARaKaXeAhWLZG2ChmHYHWCim3QEWIIAFCIIAFiCABQhgAQJYgAAWIIAFCGABgqDpfiABLBTL3gALxbQ7wEKx7A2wUEy7AywU0+4ACxDAAgRBAAsQwAIEsAABLEAACxDAAgSwAEHQdD+QABaKZW+AhWLaHWChWPYGWCim3QEWiml3gAUIYAGCIIAFCGABAliAABYggAUIYAECWJionLa9r2a3jw2+fe0QJWTDuwBp5azt9Mo6fxuH1hzn5rZdO8aOabXX5nW3O368c723HSXnaX15RWeblkVOEUI35HPqOc1dc5Z5khJ6Xv5b0jaDivXbtqyn7LazDxcbwAIEsAABLEAACxDAAgSwABUYLHgOYomcCSaxIOvSYq93y7g617LoCVKyzzSd3q5htu8cP359z7HLPkTwHee0v43jXHq9xVtsSl/mY5cHLPgOv7/dMGAhOk+2r/5zHTVY4CCjQslZ3X5DAAtbWWw+Vyl9LbwH0srZxtLqbXnvBRYhF+P3Uroxu7Rake+7bYs+7V6z67wNrbR+2Ol2bwBY2Azb22WLkve53WV7Zdj7Qv/zM1DJfrvR6dyYcKzP3GOd0z0WwMJon9mu3T4y5Ou0061m3TvZteu+YzwVv3abf9vodG/sO87K4q1V0/xng9+Xafnc/s7KHXnvzwALI7K7aw/brq2apvEBrR99lDQaQ10/y/WdDxmGeeWuwys1cu1aaNO1tc7N7vz60Iiex3HtqL/jzpfPAyyMT6uurS3T/EfDMPxnZKn82/nDK19x/3OgdyP/I6DluHPn3wzDteuO2XdmG50vpsyNB2cM8/Jdi86d5JVXZnJe7wAL0PSChfBG2Lbmx+2ARxccXaFW6wRz/Ad9CWR9FR1a1n8GQ9p/bf+HpL8NCjHynFdesBD1Tx8sZEMifRt6L7l158iwxwRcAFjYBo7GRSVg7Xa/pLsfx6YPE/llkkFOARh41wwlb7HfObhgUIP/lgcuACwMr7VF6142jrXmcpNdN067tsBsWG4+fn/WdcS3TQQLlUMviC+yvL3VXG4Jx/pU51gACyN8Vtv0mCHbrVR/23X2bsp83lHyhlGac6/d3u3MZsxZnaPkTe83YXt2T6n48EJyLsu/azjdEsDChK/zdm0fpfS/MyDEgBK9++h3hwELETwobfSBBfdYrvP5ceJ9Yc+hF4ltf07D7gALg9vmA9W1N99xduZ9P+o5839lGuT3zNbl2YVHF53ejrR9RMd35wbAAlRUsBC8WF/iX/rHd3P3nfxhoiHSHPFROelFAQsskmHY4/K5k/e8IYCFrSIWPVBrO4eiF4nmLg4arGbvPt3rby+lL2V9gfavd7outwuA6bru8QAWRgaULsgAoGmR5w1iXWx0uzt05o18vflzgbzJbMn/Fj9W9KLp3oOf0zkWwMJo5MMB+gv3+rNyOxiL1r0GoVflyIa1FXuPSciV8kL3G3wesaiGOz1Y5f+bAYggSuIzsR3AwuQBA3P6hwUL7Qr5ibsf9/7bDxbaFfPJO5tLTdmhDMDU6zVmfw1nE2BhUNuYP7izebTVCOy76l97HmjYfeDR/XkcfTZfTIP8wdgx+44X8XAt+7p17+nPzhjsHl36I8ACBLAwRrDAjzGsI8sdeNV+0v4GsDC+sfL2NWYoBQEsTPYF1LsuP9N19L32VutU1tj7UQ0KsBBETbjX4yO69gNYGE48wkSOGOCRBWm2YPZqNtX3WR8SWe+LsGCYYwEsjPA9xB3vGav5vAh3tJ9zLNLB6AcLPDqBzh59NM2J8MEE+QRgYbrBgudsVus/nqPGWwYprYtggX2tbjbX9qjsu7pk7zGNyq/nnS7VudYBFkZu9z/nAQtRpELlkm6kAzvOvwvmxoxRugqwAAEsaDqHYtFB5kizFymWb+85qlbrRPKLerLTG+Q4nxZzHsUw/XgdgXi4ctux9yX9jUdHhH0WahHIKRlyMcWwCKQ0LvK+2H6SwELWeYlggRW05ONIKD0vF0dMK9CYBBbkbYIvNi/3jRX92r98jZJ/if3mOkpyP1U1OLhtELUAsLBtHJDAOdRJhQjTG4Ic/Xpz+d6k+ygHFqTSeiHmYPrRXBfzpF4ALAwuHlUQ1MCw+m1PrsqRDLmA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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:Impacts of tax system scores on per-capita cigarettes sales\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" valign=\"bottom\" style=\"width: 450px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" valign=\"bottom\" style=\"width: 453px;\"\u003e\n \u003cp\u003eFirst lag (2 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eScore_overall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.048**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.088 - -0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.086 - -0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eScore_price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.063***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.066***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.097 - -0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.101 - -0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eScore_tax share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.069 - 0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.055 - 0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eScore_affordability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.022 - 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.023 - 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eScore_tax structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.037**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(-0.071 - -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-0.050 - 0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(GDP per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.507**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.507***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.535**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.542**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.540***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.577**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.597***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.611**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.593**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.624***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.101 - 0.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.125 - 0.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(0.121 - 0.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(0.129 - 0.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.135 - 0.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.129 - 1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.173 - 1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(0.137 - 1.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(0.130 - 1.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(0.165 - 1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(-2.592 - 5.525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(-2.301 - 5.283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-3.017 - 5.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-3.081 - 5.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(-2.948 - 5.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(-3.806 - 5.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(-3.709 - 4.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-4.503 - 5.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-4.207 - 5.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(-4.467 - 4.764)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003eObs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.088 - -0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003eN. of id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Confidence intervals in parentheses, clustered at the country level. *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. All regressions include country and year fixed effects\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Impacts of tax system scores on per-capita cigarettes sales, by income group (LMIC vs HIC)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"5\" valign=\"bottom\" style=\"width: 434px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_overall X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.070***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.119 - -0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_overall X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.047**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.084 - -0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_pricel X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.078***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.084 - -0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_pricel X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.052***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.084 - -0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_tax share X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.091 - 0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_tax share X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.074 - 0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_affordability X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.028 - 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_affordability X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.029 - 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_tax structure X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.076 - 0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eScore_tax structure X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.046**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.093 - -0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eLn(GDP per capita) X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.629***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.638***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.608**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.632***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.620***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.186 - 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.208 - 1.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.146 - 1.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.166 - 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.177 - 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eLn(GDP per capita) X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.229 - 0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.178 - 0.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.161 - 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.16 - 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.158 - 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e1.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-2.84 - 5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-2.70 - 5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-3.35 - 5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-3.43 - 5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e(-3.31 - 5.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNumber of id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Notes: Confidence intervals in parentheses, clustered at the country level. *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. All regressions include country and year fixed effects.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Impacts of prices on per-capita cigarettes sale\u0026mdash;direct, and moderated by overall tax system score\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" valign=\"bottom\" style=\"width: 416px;\"\u003e\n \u003cp\u003ePOLS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAll countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 227px;\"\u003e\n \u003cp\u003eCountries with specific excise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCountries with specific excise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(real price)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.219***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.168***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.322***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.237***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.563***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.494***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(-0.327 - -0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.271 - -0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.468 - -0.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.378 - -0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.864 - -0.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.783 - -0.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(real price) \u0026nbsp;X CTS index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.501***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.410**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.323*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.812 - -0.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.732 - -0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-0.691 - 0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn (GDP per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.591***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.564***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.749***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.705***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.713***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.678***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.224 - 0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.232 - 0.897)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.235 - 1.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.232 - 1.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.445 - 0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.411 - 0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(-2.778 - 4.553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-1.808 - 4.837)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-5.532 - 4.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-4.402 - 5.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-2.406 - 3.262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(-1.894 - 3.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNumber of id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: *** p \u0026lt; 0.01, ** p \u0026lt; 0.05, * p \u0026lt; 0.1. All models are estimated with standard errors clustered at the country level. 95% confidence intervals are presented in parenthesis. All regressions include country and year fixed effects (not reported).\u003c/p\u003e\n\u003cp\u003eTWFE = Two-way fixed effects.\u003cbr\u003e\u0026nbsp;2SLS = Two-stage least squares using the specific excise tax as instrument for price variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifications in columns (3)-(6) \u0026nbsp;are limited to \u0026nbsp;countries \u0026nbsp; with excise tax systems that includes a \u0026nbsp; specific excise (mixed or not). The \u0026ldquo;tax share index\u0026rdquo; is defined as a dummy equal to one if a country has an CTS tax share sub-score above the cross-country mean in all years. Prices are converted in international PPP dollars, obtained from the World Health Organization\u0026rsquo;s Global Health Observatory. GDP per capita is obtained from the World Bank \u003cem\u003eWorld Development Indicators\u003c/em\u003e database (Base year = 2021).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Impact of cigarette prices on per-capita cigarette sales--moderated by overall tax system score, by income group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" valign=\"bottom\" style=\"width: 451px;\"\u003e\n \u003cp\u003ePOLS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 225px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003eCountries with specific excise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(real price) X \u0026nbsp;LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.220***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.171***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.291***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.205***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.557***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.540***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e(-0.339 - -0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.280 - -0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.438 - -0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.335 - -0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.898 - -0.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.878 - -0.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(real price) X \u0026nbsp;HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.259***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.174**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.481***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.477***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.636***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.423*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e(-0.438 - -0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.306 - -0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.781 - -0.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.757 - -0.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-1.032 - -0.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.853 - 0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(real price) X \u0026nbsp;CTS index X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.770**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.724**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.778*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.398 - -0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.345 - -0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-1.620 - 0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(real price) X \u0026nbsp;CTS index X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.330**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.658 - -0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.388 - 0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.860 - 0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(GDP per capita) X LMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.661***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.617***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.715**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.667***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.774***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.746***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e(0.250 - 1.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.247 - 0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.170 - 1.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.178 - 1.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.454 - 1.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.427 - 1.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLn(GDP per capita) X HIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.455*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.744**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.753**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.586***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.552***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e(-0.136 - 0.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-0.061 - 0.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.068 - 1.420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.086 - 1.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.185 - 0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(0.149 - 0.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e(-2.353 - 5.420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-1.873 - 5.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-5.476 - 5.374)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-4.997 - 5.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-2.154 - 3.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(-1.712 - 4.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNumber of id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: *** p \u0026lt; 0.01, ** p \u0026lt; 0.05, * p \u0026lt; 0.1. All models are estimated with standard errors clustered at the country level. 95% confidence intervals are presented in parenthesis. All regressions include country and year fixed effects (not reported).\u003c/p\u003e\n\u003cp\u003eTWFE = Two-way fixed effects.\u003cbr\u003e\u0026nbsp;2SLS = Two-stage least squares using the specific excise tax as instrument for price variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifications in columns (3)-(6) \u0026nbsp;are limited to \u0026nbsp;countries \u0026nbsp; with excise tax systems that includes a \u0026nbsp; specific excise (mixed or not). The \u0026ldquo;tax share index\u0026rdquo; is defined as a dummy equal to one if a country has an CTS tax share sub-score above the cross-country mean in all years. Prices are converted in international PPP dollars, obtained from the World Health Organization\u0026rsquo;s Global Health Observatory. GDP per capita is obtained from the World Bank \u003cem\u003eWorld Development Indicators\u003c/em\u003e database (Base year = 2021).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-tax-and-public-finance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itax","sideBox":"Learn more about [International Tax and Public Finance](http://link.springer.com/journal/10797)","snPcode":"10797","submissionUrl":"https://submission.nature.com/new-submission/10797/3","title":"International Tax and Public Finance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cigarette taxation, Tobacco demand, Price elasticity, Cigarette Tax Scorecard, Excise taxes, Cross-country panel data","lastPublishedDoi":"10.21203/rs.3.rs-9200426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9200426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines whether the design and consistency of cigarette tax systems influence the effectiveness of tobacco taxation in reducing cigarette consumption. Using cross-country panel data for countries surveyed by the World Health Organization between 2014 and 2024, we use \u0026nbsp;the Cigarette Tax Scorecard (CTS) and its components to evaluate how different features of tax systems affect cigarette consumption and influence price responsiveness. In a first step, we estimate the association between CTS scores and per capita cigarette consumption using both contemporaneous and lagged CTS measures to capture short- and longer-term effects while mitigating potential endogeneity arising from the inclusion of price-related components in the score. Higher overall CTS scores are associated with significant reductions in consumption, with a one-unit increase in the CTS reducing consumption by approximately 5–5.5 percent. In a second step, we examine whether countries that consistently maintain strong tax systems experience greater demand responses to price increases. Using two-way fixed effects models and two-stage least squares estimates that instrument cigarette prices with specific excise taxes, we find that countries with consistently high CTS scores exhibit substantially larger price elasticities of demand than countries with weaker tax systems. This effect is particularly pronounced in low- and middle-income countries. Overall, the results suggest that maintaining comprehensive and consistent tax systems over time substantially enhances the effectiveness of tobacco taxation policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL codes:\u003c/strong\u003eI12, H23, H25, C23\u003c/p\u003e","manuscriptTitle":"Do Stronger Tax Systems Increase the Effectiveness of Cigarette Taxes? Evidence from Cross-Country Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 06:55:48","doi":"10.21203/rs.3.rs-9200426/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T03:14:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T07:42:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T09:41:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Tax and Public Finance","date":"2026-03-23T12:09:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-tax-and-public-finance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itax","sideBox":"Learn more about [International Tax and Public Finance](http://link.springer.com/journal/10797)","snPcode":"10797","submissionUrl":"https://submission.nature.com/new-submission/10797/3","title":"International Tax and Public Finance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ec1c8e09-de6f-4408-b6cb-6a977e6b12b8","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"2","date":"2026-05-06T03:14:25+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 06:55:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9200426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9200426","identity":"rs-9200426","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}