STATISTICAL ANALYSIS OF LARGE HEALTH WARNINGS ON CIGARETTE PACKS IN MALAYSIA AND THAILAND

Report prepared for

JTI (Japan Tobacco International)

27 July 2018

© Case Associates. All rights reserved 27 July 2018

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ABSTRACT

Objective:

To determine whether the introduction or enhancement of large health warnings (LHWs) on cigarette packs has reduced the sales of cigarettes in Malaysia and Thailand.

Methods:

Using Nielsen monthly retail point of sales data for the period January 2010 to September 2017 for Malaysia and quarterly point of sale data for the period 2009 to 2017 for Thailand, various fixed effects panel and time series regressions have been estimated. The econometric results are supplemented by a general analysis of data trends.

Results:

The enhancement of LHWs during the periods concerned was not associated with a statistically

significant reduction in the volume of cigarette sales.

Conclusion:

There is no reliable statistical evidence using Nielsen panel data that LHWs have reduced cigarette sales in Malaysia and Thailand.

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TABLE OF CONTENTS I. INTRODUCTION ........................................................................................................................... 4

II. LARGE HEALTH WARNINGS ........................................................................................................ 5

Malaysia ............................................................................................................................. 6

Thailand ............................................................................................................................. 7

III. SHORT REVIEW OF EMPIRICAL RESEARCH ................................................................................ 8

IV. EMPIRICAL APPROACH .............................................................................................................. 9

Estimating techniques ........................................................................................................ 9

Caveats ............................................................................................................................. 10

V. MALAYSIA ................................................................................................................................. 14

Data .................................................................................................................................. 14

Empirical findings ............................................................................................................. 18

VI. THAILAND ............................................................................................................................... 19

Data .................................................................................................................................. 19

Empirical findings ............................................................................................................. 23

REFERENCES ................................................................................................................................. 24

ANNEX A: STATISTICAL APPROACHES ........................................................................................... 27

Basic estimating equation ................................................................................................ 27

Panel regressions ............................................................................................................. 27

Time series regressions .................................................................................................... 34

ANNEX B: MALAYSIA REGRESSION RESULTS ................................................................................ 35

Data .................................................................................................................................. 30

Panel regression ............................................................................................................... 31

Efficient dynamic panel estimators .................................................................................. 33

Time series analysis ......................................................................................................... 34

Main findings ................................................................................................................... 35

ANNEX C: THAILAND REGRESSION RESULTS ................................................................................ 35

Data .................................................................................................................................. 36

Panel regression ............................................................................................................... 37

Efficient dynamic danel estimators .................................................................................. 39

Time series analysis ......................................................................................................... 40

Main findings ................................................................................................................... 41

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I. INTRODUCTION

This report has been commissioned by JTI (Japan Tobacco International) to examine the impact, if

any, on cigarette sales of large health warnings (LHWs) placed on cigarette packaging in Malaysia

and Thailand.

The study uses a variety of multivariate panel and times series statistical techniques to analyse data

on cigarette sales to determine whether LHWs reduced cigarette sales.

The selection of Malaysia and Thailand from among South Eastern Asian countries was based solely

on the availability of suitable data to carry out a rigorous statistical study.

This report is an independent analysis, and the analysis and conclusions are those of its authors

alone.

Main Findings

Based on econometric analysis of Nielsen point of sale (POS) retail data no statistically reliable

evidence was found that LHWs reduced cigarette sales in either Malaysia or Thailand.

There was no statistically significant reduction in cigarette sales associated with the

introduction of enhanced LHWs in 2014 in Malaysia.

There was no statistically significant reduction in cigarette sales in Thailand associated

with the introduction of enhanced LHW in 2014.

Organisation of Report

The report is organised as follows:

A brief description of LHWs in Malaysia and Thailand (Section II)

Short survey of the empirical research on the impact of LHWs (Section III)

An overview of the statistical approaches used to assess the impact of LHWs on cigarette

sales (Section IV). These are explained in greater technical detail in the Annex A.

A description and summary results of the analysis of data for Malaysia (Section V)

A description and summary results of the analysis of data for Thailand (Section VI)

In order to make the study accessible to the general reader the technical aspects of the statistical

analysis have been placed in three annexes which provide more details on the statistical techniques

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used (Annex A), and the statistical results of applying these techniques to data for Malaysia (Annex

B) and Thailand (Annex C).

II. LARGE HEALTH WARNINGS

LHWs are text messages together with pictorial or graphic images which aim to warn of the health

risks associated with smoking, and which cover half or more of the exterior panels of tobacco

product packaging.

The World Health Organisation (WHO) Framework Convention on Tobacco Control (FCTC) suggests

that health warning labels cover at least 30% but ideally 50% or more of the main surface areas of

tobacco packaging. The FCTC goes further stating that ‘nothing shall prevent a country from

imposing stricter requirements’ than provided by the Treaty itself. The coverage of LHWs for SE Asia

countries mainly exceeded 50% in 2017 with Thailand having the largest coverage in excess of 80%

of a cigarette pack (Figure 1).

Figure 1 Coverage of LHW in SE Asia, 2017

Source: Sancelmec (2017)

LHWs were first introduced by the Canadian government in 2001. Since then they have become

widespread. The rationale for LHWs is given by the WHO (2013):

Large graphic health warnings on tobacco packaging are the most effective means of communicating the risks of tobacco use to users. There is conclusive scientific evidence indicating that large graphic health warnings contribute to quitting tobacco and avoiding starting its use among young people. Further, the larger the warning, the more effective it is in protecting health. Research indicates that both adult and youth smokers report graphic warnings to be a credible source of information. Graphic health warnings reach all tobacco users, help people understand the physical impact of tobacco use, cost governments little to implement and are understood even by those who are illiterate. Pack-a-

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day smokers are potentially exposed to the message of the health warnings over 7000 times per year. Today 1 billion people live in countries that have graphic warning labels 50% or larger.

The central policy issue is whether this approach has had a discernible medium to long term effect

in reducing cigarette consumptions and sales.

Malaysia1

LHWs were first introduced in Malaysia in January 2009. This required that 40% on the front panel

and 60% on the back panel of a cigarette pack display one of six authorized health warnings, with a

graphic picture and accompanying text. The text was in Malay on the front panel and English on the

back panel. Health warnings are not required on packaging for tobacco products other than

cigarettes.

In January 2014 the LHWs were enhanced. The coverage on the front panel was increased to 50%,

and six new pictorials were issued which had to appear on a similar number of packs. A new text

message describing the contents and emissions was required on one side-panel, and on the front or

back panel for cartons.

Table 1. Development of LHWs for Malaysia, 2009-2017

Regulation Date Requirements

Control of Tobacco Product (Amendment) Regulations 2008

Jan 2009 Pictorial health warnings required to cover 40% of the front and 60% of the back of all cigarette packages

Control of Tobacco Product (Amendment) Regulations 2013

Jan 2014

A second set of six pictorial health warnings adopted with size increased to 50% of the front and 60% of back panels. Warning text must be in Malay (front) and English (back). Text on constituents and emission are required on the side panel. Each of the six authorized health warnings to appear as far as possible on an equal number of packs of each brand and type of cigarette. Misleading packaging and labelling, including terms such as “light” and “low tar” and other signs, is prohibited. Misleading descriptors such as ‘light’, ‘ultra light,’ ‘mild’, ‘cool’, ‘extra’, ‘low tar’, ‘special’, ‘full flavour’, ‘premium’, ‘rich’, ‘famous’, ‘slim’, ‘Grade A’ or similar terms are prohibited from appearing on packages.

Source: Euromonitor (2017)

The Malaysian government has imposed many other controls on tobacco packaging, advertising and

products including a ban on selling cigarettes at discounted prices, a ban on the sale of single sticks

1 Based on information collated on https://www.tobaccocontrollaws.org/legislation/country/malaysia/summary; https://www.tobaccocontrollaws.org/legislation/country/malaysia/pl-health-warnings

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and permitting sales in packs of 20 sticks only (2013), and the extension of the smoking in public

places (SIPP) ban too outdoor areas such as rest areas and parks (2014 and 2017).

Thailand2

LHWs were introduced in Thailand in March 2005 and enhanced and modified periodically in

subsequent years (Table 2).

The current LHWs were implemented in September 2014. The law requires that pictorial health

warnings cover 85 per cent of the front and back panels of cigarette packs. This was a significant

increase I coverage from the 55% required under the 2005 regulation. Thailand has the largest

LHWs of any country in Southeast Asia.

Table 2. Development of LHWs in Thailand, 2004-2017

Provision Date Requirements

Pictorial Warning Labels Law, 2004. Ministry of Public Health’s Announcement (No.8) B.E. 2547 On Rules, Procedures and Conditions of Cigarette Labelling and label Content According to the Tobacco Products Control Act B.E.2535.

March 2005

Overall 55% of the package space be devoted to pictorial health warnings with a set of six pictorials to be rotated on cigarette packages.

2006 New set of 9 health warnings.

Notification of the Ministry of Public Health No. 13 of 2007.

2007

For cigars, picture and text warnings must occupy 50% of both main surfaces PDAs (30% if the package is not rectangular). The warnings shall be printed in four colours.

2009 New set of 10 health warnings

Ministry of Public Health Notification No. 17 of 2012.

2012

LHWs on packages of shredded tobacco and blended shredded tobacco. LHWs must occupy 55% of both main surfaces PDAs (30% if the package is not rectangular) and be printed in four colours. Four LHWs are

2 Based on information collated on https://www.tobaccocontrollaws.org/legislation/country/thailand/summary; https://www.tobaccocontrollaws.org/legislation/country/thailand/pl-health-warnings

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prescribed and rotation must be at the rate of 500 packs/cartons per image. .

Ministry of Public Health Notice of Rules, Procedures, and Conditions for the Display of Images, Warning Statements, and Contact Channels for Smoking Cessation on Cigarette Labels, 2013.

June 2014 (given to

Sept 2014 to comply)

New LHWs required to cover 85% of both front and back panels printed in four colours. Images and text must be placed at the top edge of both main surfaces of the pack or carton. Rotation of images at the rate of 5,000 packs/cartons per image together with text quit-smoking hotline number. Also pictorial on each main surfaces, each side panel of the unit (e.g., pack) and outside (e.g. Carton) packaging must display a text warning statement. Text must include details about toxins, carcinogens, or other substances in tobacco products and printed on both sides and must occupy no less than 60% of each side. If the unit or outside packaging is not rectangular, the packaging must bear the statement on two sides, each side with an area of at least 10% of the total surface area of the pack or container.

Ministry of Public Health Notification No. 18, 2015.

2015 New text warnings on side panels of cigarette packaging.

Source: www.tobaccocontrollaws.org

Pictorial health warnings are also required on cigars (2007) and loose tobacco typically used for

hand-rolled cigarettes (2012).

The Thai government has imposed other controls under the Tobacco Products Control Act 19923

such as bans on media advertising and SIPPs in 1997; prohibitions on misleading packaging and

labelling in 2007; and the extension of SIPP ban to workplaces, public transport and other areas in

2010 and 2012.

III. SHORT REVIEW OF EMPIRICAL RESEARCH

Research on health warnings on cigarette packaging is considerable. A number of different

approaches have been used to measure the impact, if any, on cigarette sales, consumption and

smoking prevalence ranging from surveys to econometric analysis.

The published studies vary considerably in quality and do not generate consistent or reliable

evidence, positive or otherwise, of the impact of LHWs on smoking prevalence and/or cigarette

sales.

3 The Tobacco Products Control Act 2017 (effective July 4, 2017) replaces the 1992 Act. The 2017 Act gives the government expanded powers to regulate all tobacco products’ packaging (their size, colour, symbol, labels, trademark, pictures and messages) including the possible introduction of plain packaging.

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Rather than review the research on LHWs we have drawn on the independent and scholarly survey

by Monárrez-Espino et al (2014) published in the American Journal of Public Health. This critically

reviewed 21 of the more credible of the nearly 2,500 studies published between 1993 and 2013

which attempt to measure smokers’ responses to LHWs or what they term pictorial warnings on

cigarette packaging (PWCPs).

Monárrez-Espino et al found that the quality of the published research was “generally low”:

We found very large heterogeneity across studies, poor or very poor methodological

quality, and generally null or conflicting findings for any explored outcome.

… we considered 57% of the reviewed studies (n=12) to be of poor or very poor quality, and

only 1 could be classified as being of good quality. (p. e15)

Monárrez-Espino et al also state that had they used stricter criteria to assess the quality of published

studies even fewer studies would have been considered for review because they adopted

unacceptable methodologies and lacked sufficient scientific rigour. Notwithstanding these

methodological limitations and weaknesses Monárrez-Espino et al concluded that:

The evidence for or against the use of PWCP is insufficient, suggesting that any effect of

PWCP on behaviour would be modest.4

Of the studies reviewed, three are for Malaysia and/or Thailand - Silpasuwan et al. (2008) for

Thailand; Fathelrahman et al. (2010) for Malaysia; Yong et al. (2013) for Malaysia and Thailand -

which were assessed as having an “overall quality” rated as “poor” and “poor methodological

quality”, “poor” and “fair” respectively.

In the light of these concerns we have undertaken an exhaustive and clearly explained analysis of

the Nielsen data.

IV. EMPIRICAL APPROACH

Estimating Techniques

In this study panel and time series regression analyses has been used to investigate whether LHWs

have had a statistically significant effect in reducing cigarette sales. These techniques are described

in Annex A.

To test whether LHWs affect cigarette sales the volume of cigarette sales on the price of cigarettes

has been regressed on variables representing LHWs, other regulations on tobacco consumption

such as SIPPs; and a number of control variables expected to influence the demand for cigarettes

4 Wakefield et al (2014) in a critical assessment of earlier research (Kaul & Wolf, 2014) found that “graphic health warnings in Australia had no impact on smoking prevalence”.

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such as GDP per capita. The choice of variables used in the statistical analysis has been determined

by the availability of data over a suitably long period.

The data for each country has been collated by Nielsen. It is monthly point of sale (POS) for each

brand and type of cigarette sold in Malaysia and Thailand. For each month Nielsen has collated data

on cigarette sales and prices by brand, region and type of retail outlet. This enables the statistical

analysis to control for product heterogeneity, location and other sales outlet over time. This pooled

timeseries and cross-sectional data is called ‘panel data’, on retail cigarette sales.

The Nielsen data has been statistically investigated using different types of panel econometric

techniques and also by a time series technique. These are described in Annex A. To our knowledge

this is the first time Nielsen aggregate panel data has been used to assess the impact of LHWs and

tobacco controls.

Panel data has a number of attractive features suited to the empirical analysis of the effects of

regulation. First, panel data greatly expands the sample size thereby increasing the statistical

efficiency of the estimated coefficients and their standard errors. This leads to more credible

statistical analysis. Secondly, by providing repeated observations over time for the same group of

products it is possible to control for missing or unobserved variables which may affect the cross-

sectional data. This minimises so-called “heterogeneity bias” arising from omitted variables when

cross-sectional data are correlated with the dependent variable.5 In the context of cigarette

sales/smoking studies these unobserved variables can be factors such as tastes, preferences, age

and so on; or the different characteristics of cigarettes which may result in a different relationship

between consumption and demand over time. Thirdly, panel data allows product heterogeneity

and regional differences to be taken into account.

As a further check on the statistical robustness of the panel regressions a timeseries technique has

been used. This is described in Annex A.

Caveats

Any statistical analysis of tobacco packaging, health and price regulation must contend with a

number of challenges, and the results treated with some caution. The more important of these, and

their implications and the way they have been handled in this study, are briefly addressed in this

section.

The first issue is how the qualitative changes brought about by packaging regulations are to be

incorporated into a quantitative empirical analysis. Specifically, how can changes to the exterior

appearance of a cigarette pack which can be described but cannot be reduced to a single metric like

the price or volume of cigarettes sales be represented in a regression analysis? This is routinely done

in statistical research on cigarette consumption by time-related dichotomous or dummy variables.

5 Baltagi and Levin (1986) were the first to use (regional) panel data to study cigarette sales.

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These are binary variables which take the value of zero (0) when the regulation under investigation

is not present; and unity (1) for the months or quarters following its introduction.6 To illustrate the

use of these dummy variables, one can think of the relationship between cigarette sales and other

factors as a statistically measured straight line. When a dummy variable is included it shifts this line

up, down, or not at all for the months when the packaging control is in place indicating that the

control leads to higher, lower or negligible effects respectively on cigarette sales compared to the

situation in the absence of the LHW.7

There are limitations to the dummy variable approach. For example, it does not easily allow for any

delayed or diffused reaction by smokers. It is possible to investigate some delay in the reaction of

smokers by lagging the dummy variable by several time periods (which has been explored in the

statistical analysis below). Dummy variables also assume that the impact of a regulation is similar

throughout the period of the implementation of a regulation. This may not be a valid assumption –

it can disguise and confound the initial impact which can be assumed to be greater; with the

subsequent diffusion effects when smokers have adjusted to the regulation (which may clawback

some of the reduction in monthly sales or consumption of cigarettes). Dummy variables also do not

take account of changes in the level and intensity of public enforcement and/or compliance.

Secondly, often a large number of different tobacco controls and other measures are implemented

at around the same time or operate over the same period which may affect cigarette sales. LHWs

are one aspect of a multipronged approach to deter smoking which includes advertising and point

of sale display bans, SIPP bans, minimum pack size, content regulation and so on.

Table 3. Tobacco control regulations

Public information campaigns

Health warnings

Advertising and promotion bans

Point of sale display bans

Bans on Smoke in Public Places (SIPPs)

Minimum age

Content Controls

Ban on cigarette types e.g. menthol; coloured; flavoured

Specification of cigarette contents

Tar caps

6 This is called a “shift dummy”. Other dummies can be used – ‘slope dummies’ to test whether the health warnings alter the slope of the line; and ‘interaction dummies’ to see if the coefficient for selected variables alter.

7 E.g. Norashidah, Mustapha & Mastura (2013) time series (yearly 1980 -2009) use a dummy to represent the Malaysian Government’s ‘TakNak’ or ‘Don’t Want’ national antismoking campaign launched on 9 Feb 2004 for five years with a budget of RM100m. It considers no other regulatory intervention.

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Minimum pack size

Fiscal & Price Controls

Taxes

Minimum Price Laws (MPLs) (Malaysia)

Discounting & price promotion bans

For example, in Malaysia (see Figure 2) during the period being considered in this study, there were

changes to LHWs, SIPP bans were extended, the tar ceiling was reduced, and cigarettes could only

be sold in packs of 20 sticks. It may therefore be difficult to disentangle and isolate the individual

effects of these different measures (and the statistical problem known as multicollinearity).

Figure 2 Tobacco controls and tax changes in Malaysia, 2013-2017

Most government tax cigarettes heavily to, at least in part, discourage smoking. These taxes are

usually high and constitute a significant percentage of the retail price of a pack of cigarette. Other

price controls have been used - such as minimum prices and bans on price discounts. For example,

In Malaysia during the period 2013 to 2017 excise and other taxes on cigarettes were increased four

times, minimum prices increased twice (August 2015 and 2016)8 and the discounting of cigarette

prices banned. The statistical analysis reported below adjusts for the effects of cigarette excise and

other taxes (see Annex A).

8 Liber et al (2015) examine the impact of minimum cigarette prices and find that it did not reduce cigarette consumption.

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Thirdly, sufficiently detailed, reliable and consistent data extending over a sufficiently long period is

generally difficult to collect. Nielsen, the source of the data used in this study, does not keep a

central database of historical data, and the data we have used has been especially collated for this

study at our request. Despite the considerable effort by Nielsen to retrieve their historical data it

has only been possible to collate a consistent and reliable series going back to 2009/10. However,

the introduction of LWHs took place before 2009 in both Thailand and Malaysia. It has not,

therefore, been possible to test whether the introduction of LWHs in each country affected cigarette

sales; only the effect, if any, of the enhancement of LHWs which took place in 2014 in both

countries.

This is not as a significant drawback as it might first appear. The analysis below addresses the

important question of whether the enhancement and refreshing of LHWs by increasing their

coverage and introducing new pictorials do or do not lead to behavioural responses from smokers

which reduce cigarette sales. After all the premise behind Governments’ and health authorities’

policies to increase the size LHWs, and introduce new and rotating pictorials, is that the impact of

earlier LHWs on smokers may have waned with familiarity and/or to boost the deterrence effect of

LHWs by these enhancements.

Fourthly, in common with other data sources, the Nielsen data is only for sales of licit cigarettes. It

does not include data on the sales of roll your own (RYO) tobacco, pipe tobacco, cigars and illicit

cigarette sales. The last are particularly significant in Malaysia where Euromonitor (2016, p. 26)

estimates that one in two cigarettes sold were illicit. Given the clandestine and illegal nature of illicit

cigarette sales, reliable data on their volumes and prices are difficult to collect and unreliable.

Estimates exist, such as Nielsen (2016), Euromonitor (2017) and Oxford Economics (2017), but they

are incomplete and lack the detail, frequency and consistency that allow them to be matched to the

Nielsen data. However, proxies can be used. In previous studies the lower prices of cigarettes in

contiguous countries/regions have been used to represent the incentive to bring in illicit cigarettes

e.g. Baltagi and Levin (1986); Gruber & Sen (2003); or measures of the level of corruption and law

enforcement in the country using indicators such as published by the World Bank. In this study an

attempt has been made to take account of smuggled cigarettes by using the price differences

between contiguous countries as a proxy.

The omission of other tobacco products and illicit cigarette sales from the Nielsen data are unlikely

to bias the statistical results against finding that LHWs reduced cigarette sales. If as a result of LHWs

smokers substituted to illicit cigarettes, then our results would capture this as a decrease in licit

cigarette sales. This reaction would therefore bias the empirical work to finding that LHWs

decreased cigarette sales. On the other hand, the presence of a large volume of cheaper illicit

cigarettes would tend to bias upwards the estimated price elasticity as sales would appear more

responsive than was the case if all cigarettes sales were taken into account and increase the

perceived impact of tax increases in reducing cigarette sales and smoking prevalence.

The final area is the high and increasing tax rates imposed on licit cigarette sales. Most of the large

price increases for cigarettes discussed in the next sections were due to increases the taxes imposed

on cigarettes. These taxes have been levied to make licit cigarettes more expensive, and hence

reduce their sales and raise government revenues (Laffer, 2014). Given the significance of these

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taxes will have had a major impact on licit (and possibly illicit) cigarette sales and because they are

exogenous (that is imposed by governments rather than generated by the market) they may swamp

the effects of other measures. Moreover, a failure to control for tax changes will tend to bias the

statistical results which are designed to show the impact of LHWs and other tobacco control

measures on sales. This has been handled in the statistical analysis by using an instrumental variable

approach as discussed in Annex A.

V. MALAYSIA

This section describes the data, provides an overview of trends and relationship discernible for the

raw Nielsen data for Malaysia to guide the statistical analyses, and summarises the results of the

statistical analysis set out in detail in Annex B.

Data

Monthly Nielsen data is available for Malaysia for the period January 2010 to September 2017. It

reports sales and prices of cigarettes by brand/type categorised by:

Region - Central, East Coast, Northern, Southern North, Southern, East Malaysia; and

Sales channel - CVS/Petrol/Mini, Entertainment, Hawker Stall, Other retail, Refreshment,

Sundry Prov, Supermarkets.

The Nielsen data is available by region and sales channel separately (not both together) for brand

owner, cigarette brand and price segment9 (Base, Value, Premium and Unknown10).

Trends and Relationships in Nielsen Data

Figure 3 plots monthly cigarette sales, and nominal and real average selling prices (ASPs)11, the last

deflated by the Consumer Price Index (CPI). As can be seen cigarette prices increased significantly

over the period with large ‘jumps’ attributable to tax increases accompanied by a slow decline in

sales’ volumes until November 2015. In November 2015 there was a significant increase in prices

and a consequent steep decline in cigarette sales (see below). Cigarette sales have nearly halved

from period from around 1 billion sticks/month in 2010 600 million sticks/month in 2017.

9 By region only. Not by sales channel.

10 Nielsen identifies six brand segments - Base, Value, Sub-premium, Premium, Prestige, Unknown. These have been reduced to four by combining Value and Sub-premium into the Value segment; and Premium and Prestige into the Premium segment.

11 The Nielsen data reports sales revenues. From this and sales volumes the average sales price or ASP has been calculated for each type of cigarette for each month.

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Figure 3. Monthly sales and average cigarette prices Malaysia

In Figure 3 a vertical line has been drawn on the date (January 2014) the new enhanced LWHs were

implemented in Malaysia. As can be seen there was no discernible reduction in cigarette sales

around that date. The statistical analysis reported below (Annex B) seeks to see whether adjusting

for other factors influence cigarette sales this conclusion is also true.

As has been noted above increases in cigarette taxes affect cigarette prices and sales. This assumes

that the taxes are mostly passed through in increased retail prices; which research and general price

analysis shows to be the case (Laffer, 2014).

Table 4. Development of taxes/duties in Malaysia, 2011-2016 Tax 2011 2012 2013 2014 2015 2016

Import duty (MYR/kg) 200 200 200 200 200 200

Specific taxes (MYR/1,000 sticks)

220 220 250 280 400 400

Ad valorem excise (%) 20 20 20 20 0 0

VAT/sales tax (%) 5 5 5 5 6 6

Source: Euromonitor, Passport - Cigarettes in Malaysia, July 2017, Table 1.

In Malaysia over the period of this study there were significant increases in the taxes imposed on

cigarettes (Table 4). Taxes were increased three times in 2013, 2014, and 2015. 12 If the price

increases in Figure 3 are compared to the tax increases it is clear that they have been primarily

12 Narashidah et al (2013) and Hum et al (2016).

.4

.5

.6

.7

.8

MYR

/sti

ck

400

600

800

1,000

1,200

Mill

ion

sti

cks

LHW enhancement

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Volume (LHS) Price (RHS)

Price adjusted by CPI (RHS)

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responsible for the large price ‘jumps’ in 2013 and especially 2015. The 2015 tax increase from 280

MYR/1,000 sticks to 400MYR/1,000 sticks appears to have greatly reduced cigarette sales.

The cigarette industry classifies cigarette brands into Base, Value, Mid-price and Premium brand

segments based on their retail prices. These segments cater to smokers with different tastes and/or

income levels, who may react differently to price changes and tobacco controls, and specifically

‘downtrade’ from a higher priced segment to a lower priced segment when the relative prices of

cigarettes change.

The sales of Premium cigarettes, which are the largest segment by volume of sales in Malaysia,

declined significantly over the period. This was probably triggered by a large increase in their prices

from an average of 0.5 MYR/stick in 2010 to over 0.8 MYR/stick in 2017. However, there is no

evidence from the raw Nielsen data that smokers downtraded to cheaper brand segments since the

sales of the latter did not increase disproportionately. In any case, the sales pattern of Premium

cigarettes is significantly different from that of Value and Base brand segments.

Figure 4. Monthly volumes and average prices by price perception

Another possible explanation for the decline in the sales of Premium brands may have been an

increase in illicit cigarette sales. During the period studied cigarette prices in North Malaysia were

some 160% to 220% higher than those in the contiguous South region of Thailand. This is a large

inducement to smuggle cigarettes into Malaysia from Thailand (Figure 4).

0

200

400

600

800

1,000

Mill

ion

sti

cks

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Unknown Premium

Value Base

.2

.4

.6

.8

MYR

/sti

ck

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Unknown Premium

Value Base

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Figure 4. Malaysia North vs Thailand South average monthly prices

a) All cigarettes

b) By brand segment13

The data does not reveal any significant differences in regional cigarette price and sales patterns

with one exception. Regional cigarette sales and prices displayed similar patterns as for Malaysia as

a whole as shown for the Central region (Figure 5) with the exception of East Malaysia where

volumes increased to the end of 2013, then stagnated and then plummeted in response to the large

13 Comparable only to the extent that the same or similar cigarette brands are categorized under the different price segments in both countries.

.05

.1

.15

.2

USD

/sti

ck

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Malaysia Thailand

.05

.1

.15

.2

.25

USD

/sti

ck

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Malaysia Base Malaysia Value Malaysia Premium

Thailand Value Thailand Mid-price Thailand Premium

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price increase in November 2015. This suggests that different factors were at play in the East

Malaysia (Borneo) region (which represents only 5% of cigarette sales).

Figure 5. Monthly sales and average prices for Central and East Malaysia regions

Central East Malaysia (Borneo)

The analysis of the Nielsen data above suggests that any statistical analysis should:

Take account of regional differences.

Exclude Malaysia (Borneo) because of its different sales patterns from the other mainland

regions because it would otherwise introduce a bias in the statistical analysis.

Use the price differences between Thailand and Malaysia as a proxy for smuggled cigarette

sales/consumption from Thailand to Malaysia.

Where possible adjust for changes in cigarette taxes.

Empirical Findings

Using the panel and time series regression techniques discussed in Annex A, and the results reported

in Annex B for Malaysia the main finding are that:

The 2014 enhancement of LHWs did not reduce cigarette sales. This is supported by both

panel and time series regressions.

The estimated price elasticities were negative and statistically significant ranging from

between -0.4 and -1.7 depending on the statistical approach. This range is in line with price

elasticity estimates for other similar countries.

The explanatory power of the variables included in the model does not vary much across

different specifications indicating that the statistical results can be considered reliable.

.4

.5

.6

.7

.8

MYR

/sti

ck

150

200

250

300

350

Mill

ion

sti

cks

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Volume (LHS) Price (RHS)

.4

.5

.6

.7

.8

MY

R/s

tick

25

30

35

40

Mill

ion

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2010m

1

2012m

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2014m

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2016m

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2018m

1

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Volume (LHS) Price (RHS)

19 | P a g e

All regressions fail to find a statistically significant reduction in cigarette sales associated with the

enhancement of LHWs in 2014.

VI. THAILAND

The section describes the data, provides an overview of trends and relationships discernible in the

Nielsen data for Thailand which can guide the statistical analyses, and reports the main results of

the statistical analysis which is set out in greater detail in Annex B.

Data

Nielsen data for Thailand has been collected for the period January 2009 to September 2017. The

data includes information on cigarette sales volumes and revenues by brands, price segments

(Value, Mid-price, Premium), region (Central, North, South, Greater Bangkok, Northeast) and sales

channels (Convenience, Super/hypermarket, Traditional trade).

Data Trends and relationships

Figure 6 shows monthly and quarterly nominal and real monthly cigarette and sales; the quarterly

data providing a better picture of trends given the large variability in the monthly data.

Both cigarette sales and prices increased over the period, the former despite the very large price

increases in May 2009 and August/September 2012. This pattern changed after the large price

increase in February/March 2016 when cigarette sales declined significantly. Similar trends were

evident in all the administrative regions of Thailand.

Figure 6 show the date enhanced LHWs regulations were implemented as a vertical line. Again, this

does not appear to have led to a decline in cigarette sales.

20 | P a g e

Figure 6. Total volumes and nominal and real average prices

A. Monthly

B. Quarterly

Cigarette taxes were (and are) very high in Thailand. Between 2007 and 2017 the excise tax rate

increased from 80% to 90% (Table 5). Again, the price ‘jumps’ correspond more or less to the dates

when these tax increases were implemented.14

14 White & Ross (2015), WHO (2011), Tittabut (2014), Chonvihorpan & Lewis (20xx)

2

2.5

3

3.5

THB

/sti

ck

2,200

2,400

2,600

2,800

3,000

Mill

ion

sti

cks

LHW enhancement

2008m1

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Volume (LHS) Price (RHS)

Price adjusted by CPI (RHS)

2

2.5

3

3.5

THB

/sti

ck

7,000

7,500

8,000

8,500

9,000

Mill

ion

sti

cks

LHW enhancement

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Volume (LHS) Price (RHS)

Price adjusted by CPI (RHS)

21 | P a g e

Table 5. Development of excise taxes in Thailand, 2007-2017

Excise tax based on ex-factory price

(%)

Effective from

80 29 Aug 2007

85 13 May 2009

87 22 Aug 2012

90 9 Feb 2016

2-tier system based on RSP Effective from

24 THB + 40% if RSP > 60 THB/pack

16 Sep 2017 24 THB + 20% if RSP <= 60

THB/pack

Figure 7 shows cigarette sales and average prices by brand segment. It provides some evidence that

Thai smokers have downtraded from Medium-price to Value segment (sales of Premium cigarettes

were insignificant). This is most likely attributable to the price increase of Mid-price segment rather

than to a change in the relative price of Mid-price and Value segments15 (see explanation below).

The impact of price increases on the Value segment was, if anything, masked by downtrading from

Medium to Value segments, except for the price shock in 2016 after which Value segment sales

were stable.

Figure 7. Monthly and quarterly volumes and price by brand segment.

15 Value segment cigarettes became less expensive over time compared to Mid-price segment brands, but

this was not a gradual change. First, between 2009 and 2011 the price premium of Mid-price cigarettes increased from 40% to 55%. Then (between 2011 and 2016) Mid-price cigarettes became relatively cheap compared to the Value segment (from 55% to 45% price premium). This changed again after 2016: by 2017 the price premium on Mid-price cigarettes increased again to 55%. Despite these changes in the relative price difference between Mid-price and Value segment, neither the decrease in the Mid-price segment volumes nor the increase in the Value segment volumes seem to have been affected.

2

3

4

5

6

Pri

ce p

er

stic

k (T

HB

), d

ash

ed

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e

0

500

1,000

1,500

2,000

Mill

ion

sti

cks,

so

lid li

ne

2008m1

2010m1

2012m1

2014m1

2016m1

2018m1

Month

Premium Mid-price Value

Premium Mid-price Value

2

3

4

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6

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ce p

er

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k (T

HB

), d

ash

ed

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2,000

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Mill

ion

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cks,

so

lid li

ne

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Premium Mid-price Value

Premium Mid-price Value

22 | P a g e

The Nielsen data does not show significant differences in price patterns across the regions (Figure

8). However, sales patterns were different – sales did not decline in the Central Thailand and Greater

Bangkok regions when there were significant price increases; whereas in the other regions they did.

This indicates different factors at play across Thailand.

Figure 8. Total volumes and prices by region (quarterly)

a) Northeast b) Greater Bangkok c) Central

d) South e) North

The decline in cigarette sales after the 2016 price increase may be due (at least partially) to increases

in illicit cigarette sales. The three border regions – the Northeast region borders Laos; the North

region borders Burma and Laos; the South region borders Malaysia - all experienced declining or

stagnant sales of Value segment cigarettes possibly due to increased cross-border smuggling.16

The data suggests that the statistical analysis should:

Use quarterly rather monthly data to avoid masking potential statistical relationships

due to variability in monthly data.

Control for regional differences.

16 Smuggling from Malaysia to Thailand is unlikely as relative prices in Malaysia were significantly higher throughout the period.

2

2.5

3

3.5

THB

/sti

ck

1,600

1,800

2,000

2,200

2,400

Mill

ion

sti

cks

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Volume (LHS) Price (RHS)

2

2.5

3

3.5

THB

/sti

ck

1,400

1,600

1,800

2,000

2,200M

illio

n s

tick

s

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Volume (LHS) Price (RHS)

2

2.5

3

3.5

THB

/sti

ck

1,400

1,600

1,800

2,000

2,200

Mill

ion

sti

cks

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Volume (LHS) Price (RHS)

2

2.5

3

3.5

THB

/sti

ck

900

1,000

1,100

1,200

1,300

Mill

ion

sti

cks

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Volume (LHS) Price (RHS)

2

2.5

3

3.5

THB

/sti

ck

800

900

1,000

1,100

1,200

Mill

ion

sti

cks

2009q3

2011q3

2013q3

2015q3

2017q3

quarter

Volume (LHS) Price (RHS)

23 | P a g e

Seek to adjust analysis for smuggling activity.

Distinguish short and long-term impacts as they may reveal important market

characteristics.

Empirical Findings

The panel and time series statistical analysis for Thailand are reported in Annex C. In summary these

show that:

The enhancement of LHWs did not reduce cigarette sales.

The indoor SIPP ban had no effect on cigarette sales.

The estimated price elasticity was, as expected, negative and statistically significant with

ranges between -0.5 and -0.8.

The explanatory power of the variables used do not vary much across different

specifications of the statistical analysis, indicating that the results provide reliable

estimates.

The analysis for Thailand does not find a statistically significant reduction in cigarette sales

associated with the enhancement of LHWs introduced in 2014.

24 | P a g e

References Arellano, M and S R Bond (1991) ’Some Specification Tests for Panel Data: Monte Carlo evidence and an application to employment equations’ Review of Economic Studies, Vol. 58, pp. 277–298. Baltagi, B H (2013) Econometric Analysis of Panel Data, 5th edn, Wiley.

Baltagi, B H and J Griffin (2001) ‘The Econometrics of Rational Addiction: The case of cigarettes’ Journal of Business Economics & Statistics, Vol. 19, pp. 449-454.

Baltagi, B H, J M Griffin & W Xiong (2000) ‘To Pool or Not to Pool: Homogeneous versus heterogeneous estimators applied to cigarette demand’ Review of Economics and Statistics, Vol. 82, pp. 117-126

Baltagi, B H & D Levin (1986) ‘Estimating Dynamic Demand for Cigarettes Using Panel Data: The effects of bootlegging, taxation and advertising reconsidered’ Review of Economics and Statistics, Vol. 48, pp. 148-155.

Becker, G S & K V Murphy (1988) “A Rational Model of Addiction" Journal of Political Economy, Vol. 96, pp. 675 -700. Becker, G S, M Grossman & K V Murphy (1991) ‘Rational Addiction and the Effect of Price on Consumption’ American Economic Review, Vol. 81, Papers & Proceedings pp. 237-241. Blundell, R & S Bond (1998) ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’ Journal of Econometrics, Vol. 87, pp. 115–144. Cebula, R J (2013) ‘An Exploratory Panel Data Analysis of Impacts of Cigarette Excise Taxes and State-wide Bans on Cigarette Consumption’ International Journal of Applied Economics, Vol. 10, pp. 22-28

Chonviharnpan, B & P Lewis (2015) ‘The Effects of Tax Changes on Tobacco Consumption in Thailand’ Singapore Economic Review, Vol. 60, pp. 1-18.

Czubek, M & S Johal (2010) Econometric Analysis of Cigarette Consumption in the UK HMRC Working Paper Number 9. Deloitte (2011) Tobacco Packaging Regulation – An International assessment of the intended and unintended impacts Report prepared for British American Tobacco, May. Euromonitor (2017a) Passport - Tobacco in Malaysia, Euromonitor International.

Euromonitor (2017b) Passport Tobacco in Thailand, Euromonitor International.

Fathelrahman AI, M Omar, R Awang et al. (2010) ‘Impact of the New Malaysian Cigarette Pack Warnings on Smokers’ Awareness of Health Risks and Interest in Quitting Smoking’ International Journal of Environ Res Public Health, Vol. 7, pp. 4089-4099. Gallet, C A & J A List (2003) ‘Cigarette Demand: A meta-analysis of elasticities’ Health Economics, Vol. 12, pp. 821–835.

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Gruber, J, A Sen & M Stabile (2003) “Estimating Price Elasticities when there is Smuggling: The sensitivity of smoking to price in Canada” Journal of Health Economics, Vol. 22, pp. 821–842. Laffer, A B (2014) The Handbook of Tobacco Taxation – Theory and practice, The Laffer Center at the Pacific Research Institute. Liber, A C, H Ross, M Omar & F J Chaloupka (2015) ‘The Impact of the Malaysian Minimum Cigarette Price Law: Findings from the ITC Malaysia survey’ Tobacco Control, Vol. 24, pp. ii83-iii87. Minsoo, J (2016) ‘Implications of Graphic Cigarette Warning Labels on Smoking Behavior: An international perspective’ Journal of Cancer Prevention, Vol. 21, pp. 21–25. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4819662/ Monárrez-Espino, J, B Liu, F Greiner, S Bremberg & R Galanti (2014) ‘Systematic Review of the Effect of Pictorial Warnings on Cigarette Packages in Smoking Behavior’ American Journal of Public Health, Vol 104(10), pp. e11-e30. Nielsen (2016) Illicit Cigarettes Study in Malaysia (ICS) Report for CMTM Member Companies, Annual Report 2016 (Annualised).

Norashidah M N, R A N Mustapha & Y Mastura (2013) ‘Cigarettes Demand and Tax Strategy in Malaysia’ Malaysia Journal of Social Science & Humanities Vol 21(S), pp. 99-114.

Oxford Economics (2017) Asia Illicit Tobacco Indicator 2016: Malaysia, prepared for Philip Morris International Management SA.

Ross, H, A Nabilla & M Al-Sadat (2007) ‘Demand Analysis of Tobacco Consumption in Malaysia’ Nicotine & Tobacco Research, Vol. 9, pp. 1163–1169. Sancelmec, N (2017) ‘The Era of Plain Packaging is Coming in Thailand’ Vidon, https://www.vidon.com/en/news/249-the-era-of-plain-packaging-is-coming-in-thailand.html SEATCA (2015) SEATCA Tobacco Tax Index - Implementation of WHO Framework Convention on Tobacco Control Article 6 in ASEAN Countries 2015, Southeast Asia Tobacco Control Alliance.

Silpasuwan P, N Yaowaluk, C Viwatwongkasem et al. (2008) ‘Potential Effectiveness of Health Warning Labels Among Employees in Thailand’ Journal Medical Association of Thailand, Vol. 91, pp. 551-558. Tittabut J. (2014) ‘Does Cigarette Tax Affect Smokers? The Thailand controversy’ Journal of Health Research, Vol. 28(2), pp. 71-6.

WHO (2011) Tax Policies on Tobacco Products in Thailand: The way forward.

WHO (2013) ‘Press Release on Current Tobacco Packaging Situation in Thailand’ 30 September 2013 Http://Www.Searo.Who.Int/Thailand/News/Tobacco_Packaging/En/

Wakefield, M A, K Coomber, S J Durkin, M Scollo, M Bayly, M J Spittal, J A Simpson & D Hill (2014) ‘Time Series Analysis of the Impact of Tobacco Control Policies on Smoking Prevalence Among Australian Adults, 2001–2011’ Bulletin of World Health Organisation, Vol. 92(6), pp. 413–422.

White J S & H Ross (2015) ‘Smokers’ Strategic Responses to Sin Taxes: Evidence from panel data in Thailand’. Health Economics Vol. 24(2), pp. 127–141.

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Wooldridge, J M (2014) Introduction to Econometrics, Europe, Middle East & Africa edition, Cenage Learning. World Bank (2001) Economics of Tobacco Toolkit: Tool 3 - Demand Analysis: Economic Analysis of Tobacco Demand by N Wilkins, A Yurekli and T-w Hu. Yong HH, G T Fong, P Driezen, et al. (2013) ‘Adult Smokers’ Reactions to Pictorial Health Warning Labels on Cigarette Packs in Thailand and Moderating Effects of Type of Cigarette Smoked: Findings from the international tobacco control Southeast Asia survey’ Nicotine Tobacco Research, Vol 15, pp. 1339-1347. https://europepmc.org/articles/PMC3715385

27 | P a g e

ANNEX A: STATISTISCAL APPROACHES

BASIC ESTIMATING EQUATION

The panel regression equation used for both Malaysia and Thailand takes a double-log specification:

(1) ln Salesit = 𝑎𝑖 + β1 LHWt + β2 SIPPt + ln 𝑃𝑟𝑖𝑐�̂�it + β2 lnXt + eit

where variables i are collected for each time period t for each of

ln Salesit - the natural log of the sales of number of cigarette sticks.

ln 𝑃𝑟𝑖𝑐�̂�it - the natural log of the average sales price (ASP) of a cigarette stick (and for the IV

approach the instrumented price – see below)

LHWt - a dummy variable representing LHWs

𝑆𝐼𝑃𝑃 - dummies for SIPP ban or bans

𝑙𝑛 𝑋𝑡 is a vector of control variables such as GDP per capita

This specification is routinely in econometric analysis of the determinants of cigarette sales and

consumption e.g. World Bank (2001).

Two statistical approaches have been used - panel and time-series regressions.

PANEL REGRESSIONS

A fixed effects (FE) panel regression has been adopted to deal with the cross-section observations

(Wooldridge, 2014, Chap 14). This technique exploits the richness of the Nielsen panel data to take

account of the unobserved heterogeneity in the cross-sectional observations.

Three different specifications for have been estimated to take greater account of the structural

features of the cigarette market.

(1) Ordinary Least Squares (OLS) with regional dummies for fixed-effects (FE)

This is a static reduced-form equation of the relationship between cigarette sales and prices

and other variables which takes account of regional differences.

(2) Fixed-effects with instrumental variables (FE-IV)

Since market demand and supply are simultaneously determined, cigarette prices in

Equation (1) will be endogenously determined. Pooled OLS or fixed effects regression

estimates will therefore be biased and inconsistent. To adjust for this an instrumental

28 | P a g e

variable (IV) approach has been used. This is a two-staged regression procedure. In the first-

stage prices are regressed on a set of instrumental variables that are expected to explain

changes in supply but not in demand conditions (‘price regression’):

(2) ln Priceit =β1i+ β2Excise𝑡 + β3ln PPIt+ eit

Based on the available data the following instruments have been used - cigarette excise

taxes (Excise𝑡) which are assumed to be exogenously set by the government and which

have a major impact on cigarette prices; the producer price index (PPI) as a proxy for the

costs of production; and for Thailand only the market share of the state-owned Thailand

Tobacco Monopoly (TTM) as a proxy for any potential market power it may have exerted

over cigarette prices.

The adjusted (or instrumented) cigarette prices estimated from Equation (2) (ln 𝑃𝑟𝑖𝑐�̂�it ) are

then used in Equation (1) above (‘sale regression’).

(3) Dynamic fixed effects with instrumental variables (Dynamic FE-IV)

To take account of the adjustment process the preceding panel FE-IV regression has been

re-estimated with cigarette sales lagged by one period to take account of a delayed

response of sales due to smokers’ habits.17 This partial adjustment model focuses on

estimating short run elasticities and speed of adjustment e.g. Baltagi & Levin, (1986 & 1992),

Baltagi et al (2000).18

Dynamic panel regression models can result in estimators being correlated with the error

term giving rise to biased and inconsistent estimates. As a rule of thumb where the number

of time periods exceed 50 the bias is small but still present. The Malaysian data consists of

103 time-periods (months) and the Thai data 38 time-periods (quarters). Many methods

are available to potentially correct for this problem. Below the two most common are used

- the Arellano-Bond (1991) GMM procedure and Blundell-Bond (1998) estimators. These

confirm the estimates derived from specification (3).19

17 The dynamic panel regressions are routinely used in cigarette demand studies e.g. World Bank (2001), Gallet & List (2003), Baltagi & Levin (1986).

18 Another dynamic approach frequently used is the ‘rational addiction model’ developed by Becker & Murphy (1988). This model assumes that current consumption is based on past and future. Since the model is deduced from long term disaggregated individual behaviour it is unsuited to aggregate panel data used in this study e.g. Chaloupka (1991), Becker et al (1991)..

19 See generally Baltagi (2013) Chap 8.

29 | P a g e

TIME SERIES REGRESSIONS

As a check on the validity of the panel regression results the data has been analysed using a dynamic

time-series statistical technique known as the Engle-Granger two-step cointegration analysis.

The Engle-Granger approach has been used in previous cigarette demand studies such as by the UK

tax authorities to examine the impact of VAT on cigarette consumption (Czubek & Johal, 2010).

In time series data many variables exhibit trends which can give rise to ‘spurious regressions’ as they

are correlated to one another due to the trend while, in reality, there is no economic relationship

between them. This is a potential problem with cigarette data where prices and sales often exhibit

trends. However, if there is a linear combination of the variables that is stationary, the econometric

analysis will identify the true relationship between them by allowing standard econometric theory

to apply.

The presence of cointegration is tested by running an OLS regression on the level of the variables,

and if the residual (error term) is stationary then the time series are cointegrated. This is the first

step of the Engle-Granger cointegration test and also estimates the long-term relationship between

the time-series in the model (Wooldridge (2014) Chap 18).

If there is evidence of cointegration (first step), a dynamic error-correction procedure is carried out

in the second step, which estimate whether there are short-term effects that push the time series

back to its long-term equilibrium.

Although the delineation of long- and short- term effects is appealing, the Engle-Granger procedure

only allows for one observation per month/quarter. This greatly reduces the number of

observations to almost one-fifth of the number of observations in the panel regressions, and

adversely affects the explanatory power of the approach.

30 | P a g e

ANNEX B: MALAYSIA REGRESSION RESULTS

DATA

The variables used in the regression analysis for Malaysia together with the source of the data if

other than from Neilsen are listed in Table B.1.

To examine for the impact of tobacco controls, a dummy variable has been for the LWH introduced

in January 2014; and separate dummy variables for the SIPP bans introduced in 2013, 2014 and 2017

respectively.

The regressions also include controls for regional factors by including separate regional dummy

variables for East Coast, Northern, Southern, Southern North; an Index of the percentage difference

between the average cigarette prices in Malaysia and the average cigarette price in Southern

Thailand (Malay-Thai Price diff) as a proxy for the smuggling cigarettes into Malaysia; and as a proxy

for demand GDP per capita.

Table B.1. Definition of variables used in Malaysia regression equations

Variable name Description* Interpretation

Cigarette variables

lnSales

Natural log of monthly sales volumes as the dependent variable

LnPrice

Natural log of weighted average monthly cigarette prices adjusted by CPI Source of CPI: IECONOMICS.

1% change in real prices leads to x% change in sales

Malay-Thai Price diff

Index of % difference between the average cigarette prices between Malaysia and Southern Thailand Source: Nielsen and FED

Proxy for smuggling.

East Coast Northern Southern Southern North

Regional dummy variables in pooled OLS regressions20

Sales, on average, are lower/higher in the region indicated, in comparison to the comparator Central region.

lnSales lag Cigarette sales volume lagged by one-month

Sales volumes in previous month explain x% of current volumes.

20 Regressions by sales channels and by region cannot be run due to absence of data.

31 | P a g e

Large health warning dummies

LHW_2014

Binary variable which equals one in Jan 2014 after second set of LHWs introduced Source: Control of Tobacco Product (Amendment) Regulations.

Effect of LHW on sales from Jan 2010

Other tobacco controls

SIPP_ban 2013 SIPP_ban 2014 SIPP_ban 2017

Dummy variables for SIPP bans in 2013, 2014 and 2017 Source: Thai Government websites

Effect of SIPP bans on sales in comparison to previous sales

lnGDP/capita Natural log of real GDP per capita21 Source: World Bank.

1% change in GDP/capita leads to x% change in sales

Instrumental variables

Tax increase_2013 Tax increase_2014 Tax increase_2015

Dummy variables for specific tax increases Source: Euromonitor.

The effect of specific tax increases on prices, in comparison to sales before the tax change.

PPI Producer price index Source: IECONOMICS.

1 percentage point increase in PPI leads to 100% change in cigarette prices

* Unless otherwise indicated, the source of the data is Nielsen.

PANEL REGRESSIONS

Results Using OLS and FE Estimators

Three panel regressions have been estimated – OLS using regional dummies (1); fixed effects (2) and

dynamic fixed effects using regional panels (3).

Equations (2) and (3) also use an instrumental variable (IV) approach to take account of the

endogeneity of cigarette prices, with a first-stage price regression using separate dummies for the

2013, 2014 and 2015 tax increases, and the Producer Price Index (PPI) as a proxy for production

costs.

The Dynamic panel IV regression (3) uses the dependent variable (cigarette sales) lagged by one

period to represent a possible adjustment process. The dynamic panel regression (3) has also been

re-estimated using Arellano-Bond and Blundell-Bond approaches to generate more efficient

estimators (see next section).

The three sales panel regressions each explained over 90% of the variation in cigarette sales (Table

B.2 below).

Most of the non-tobacco control variables are statistically significant and have coefficients with the

expected sign i.e. have the expected impact on cigarette sales.

21 Only annual data. To avoid unrealistically sudden changes in the data, annual figures were converted to monthly figures by calculating 12-month moving averages. 2017 data are linear predictions.

32 | P a g e

Table B.2. Cigarettes by sales regressions for Malaysia, 2010 -2017

OLS Regional dummies

(1)

Panel Regressions (Fixed-effects)

Panel IV

Dynamic Panel IV

(2) (3)

lnSales regression LHW_2014

0.005 0.010 0.020

(0.802) (0.595) (0.117)

lnPrice -1.635*** -1.730*** -0.430***

(0.000) (0.000) (0.000)

lnSales lag 0.787***

(0.000)

East Coast -0.585***

[Regional Panels]

[Regional Panels]

(0.000)

Northern -0.713***

(0.000)

Southern -0.242***

(0.000)

Southern North -1.244***

(0.000)

SIPP_ban 2013 0.019 0.022 -0.002

(0.323) (0.141) (0.862)

SIPP_ban 2014 0.038* 0.055*** 0.007

(0.061) (0.002) (0.537)

SIPP_ban 2017 -0.129*** -0.121*** -0.016

(0.000) (0.000) (0.143)

GDP_capita -0.240 -0.084 0.386

(0.572) (0.842) (0.148)

Malay-Thai Price Diff 0.294*** 0.298*** 0.013

(0.000) (0.000) (0.619)

lnPrice regression

Tax increase_2013 0.107*** 0.105***

(0.000) (0.000)

Tax increase_2014 [omitted] [omitted]

Tax increase_2015 0.181*** 0.177***

(0.000) (0.000)

PPI 0.000 0.000

(0.208) (0.259)

No. observations 465 465 460

R2 lnSales regression 0.971 0.900 0.967

Prob > F 0.000 0.000 0.000

R2 lnPrice regression 0.944 0.890

Prob > F 0.000 0.000

Note: The 2014 tax and SIPP ban 2014 dummies are perfectly correlated and therefore the 2014 tax dummy has been dropped from the regression due to multicollinearity.

33 | P a g e

The IV price regressions for Equations (2) and (3) explain 90% to 98% of the variation in cigarette

prices. All the tax rate dummies had positive coefficients indicating that tax changes were passed

on in higher cigarette prices. The PPI had a negligible and statistically insignificant effect on cigarette

prices. Since the PPI is for all industry this is not surprising.

All three panel regressions show that the 2014 enhancement of LHWs did not reduce cigarette sales.

Indeed, the coefficients are positive in all regressions, implausibly suggesting that the LWH

‘increased’ cigarette sales although the estimates were not statistically significant.

The SIPP bans did not reduce cigarette sales.

EFFICIENT DYNAMIC PANEL ESTIMATORS

The dynamic panel regression has been re-estimated using the Arellano-Bond and Blundell-Bond approaches (Table B.3 and B.4 respectively).

The SIPP dummies have been excluded from the regression because the t-statistic cannot be calculated due to collinearity.

The Arellano-Bond estimates are in line with those reported for Equation (3) above confirming that the 2014 LWH enhancement did not have a statistically significant effect in reducing cigarette sales.

Table B.3. Arellano-Bond dynamic panel-data estimation

Group variable: region_num Number of groups = 5 Time variable: month Obs per group: min = 91; avg = 91; Max = 91 Number of instruments = 454 Number of obs = 455 Wald chi2(4) = 40.56 One-step results (SE adj for clustering on region_num Prob > chi2 = 0.0000

Variable Coef. Robust Std. Err.

z P>|z| [95% Conf. Interval]

LHW 2014 0.013 0.009 1.54 0.123 -0.004 0.030

LnSales lag(1) 0.827 0.022 36.99 0.000 0.784 0.871

lnPrice -0.347 0.068 -5.08 0.000 -0.480 -0.213

LnGDP capita 0.382 0.138 2.76 0.006 0.111 0.653

Malay-Thai Price Diff 0.006 0.011 0.57 0.566 -0.015 0.027

Constant -0.732 0.981 -0.75 0.456 -2.654 1.191

The Blundell-Bond estimators also show that the LWH dummy did not lead to a reduction in

cigarette sales (the dummy is significant but has the wrong sign suggesting that it increased cigarette

sales, which is implausible). The lagged cigarette sales coefficient is larger; hence the price elasticity

is smaller.

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Table B.4. Blundell-Bond dynamic panel-data estimation

Number of obs = 460 Group variable: region_num Number of groups = 5 Time variable: month Obs per group: Min = 92; Avg = 92; Max = 92 Number of instruments = 636 Wald chi2(4) = 80334.85 One-step results Prob > chi2 = 0.0000

Variable Coef. Robust Std.

Err. z P>|z| [95% Conf. Interval]

LHW 2014 0.019 0.008 2.55 0.011 0.004 0.034

LnSales lag(1) 0.970 0.016 61.84 0.000 0.939 1.000

LnPrice -0.081 0.031 -2.62 0.009 -0.141 -0.020

SIPP ban 2013 -0.008 0.002 -3.42 0.001 -0.013 -0.003

SIPP ban 2014 -0.011 0.004 -2.47 0.013 -0.019 -0.002

SIPP ban 2017 0.006 0.004 1.56 0.120 -0.002 0.014

LnGDP capita 0.387 0.147 2.63 0.009 0.098 0.675

Malay-Thai Price Diff -0.057 0.023 -2.44 0.015 -0.102 -0.011

Constant -2.170 0.886 -2.45 0.014 -3.905 -0.434

TIME SERIES ANALYSIS

The time series results indicate that there is no short-term effect for any of the variables included

(which were all statistically insignificant in the second-step table).

The first-step table indicates that price has a long-term strong negative impact on cigarette sales

with the LHW dummy having a small significant positive (unexpected) long-term effect on cigarette

sales.

The results are different from the panel regression estimates (and produce more unexpected

coefficients) which indicate the importance of panel variations vis-à-vis variations over time (see

comment on the reliability of time series models for short periods in Annex A).

Table B.5. Engle-Granger estimation, Malaysia- All variables; quadratic trend

Engle-Granger first-step regression (dependent variable: volume)

Coef. Std. Err. t P>t

[95% Conf. Interval]

LHW 2014 0.054 0.022 2.42 0.018 0.010 0.099

lnPrice -0.956 0.142 -6.73 0.000 -1.239 -0.674

SIPP ban 2013 0.030 0.019 1.6 0.114 -0.007 0.068

SIPP ban 2014 0.065 0.022 3.01 0.003 0.022 0.108

SIPP ban 2017 -0.001 0.032 -0.05 0.964 -0.065 0.062

Dif price Mal Tha 0.104 0.061 1.69 0.095 -0.018 0.226

lnGDP capita 0.105 0.929 0.11 0.910 -1.742 1.952

Trend 0.004 0.002 2.11 0.038 0.000 0.007

Trend2 0.000 0.000 -4.31 0.000 0.000 0.000

Constant 12.146 6.216 1.95 0.054 -0.218 24.510

35 | P a g e

Engle-Granger second-step ECM (dependent variable: D.volume)

Coef. Std. Err. t

P>t [95% Conf. Interval]

L1.egresid -0.638 0.115 -5.56 0.000 -0.867 -0.410

LD.LHW 2014 -0.019 0.037 -0.51 0.609 -0.092 0.054

LD.lnPrice -0.208 0.241 -0.87 0.389 -0.687 0.270

LD.SIPP ban 2013 -0.018 0.037 -0.5 0.617 -0.092 0.055

LD.SIPP ban 2014 0.003 0.039 0.08 0.940 -0.075 0.081

LD.SIPP ban 2017 0.006 0.040 0.15 0.885 -0.074 0.086

LD.Dif price Mal Tha -0.032 0.089 -0.36

0.716 -0.209 0.144

LD.lnGDP capita 1.940 1.193 1.63 0.108 -0.433 4.313

Constant -0.007 0.004 -1.82 0.072 -0.015 0.001

Note: LD = lagged differences of the respective variable.

MAIN FINDINGS

The main findings of the panel and time series regressions are that:

There is no statistical evidence that the 2014 LWH enhancement led to reduction in licit

cigarette sales.

The SIPP bans also did not reduce cigarette sales.

The estimated price elasticities range fall between -0.4 and -1.7.

The tax rate change dummies in all the IV price regressions had a statistically positive effect

on cigarette prices as expected.

The smuggling proxy had a positive coefficient and was statistically significant in Equations

(1) and (2) but not in the dynamic panel regression (Equation (3)). This unexpected result

suggests that the price differential variable is not a good proxy for smuggling.

The coefficients of LHWs, SIPP bans and cigarette prices do not vary much across the three

panel regression specifications which indicate that the models are statistically robust.

36 | P a g e

ANNEX C: THAILAND REGRESSION RESULTS

DATA

The variables used in the regressions for Thailand are listed in Table C.1.

Quarterly data has been used to adjust for the large variation in monthly data.

The LHW implemented in September 2014 is represented by the dummy variable LHW_2014.

In addition, a dummy has been included to capture the extension of the SIPP ban to indoor smoking

in September 2014 (SIPP Indoor).

Table C.1. Definition of variables used in Thailand regression equations

Variable name Description* Interpretation

Cigarette variables

lnSales Natural log of cigarette sales (quarterly22) – dependent variable

lnPrice

Natural log of weighted average (quarterly) price adjusted for regional CPI Source of CPI: Thailand Bureau of Trade and Economic Indices.

1% change in prices leads to x% change in sales

Greater Bangkok North Northeast South

Regional dummies23 in pooled OLS

regressions

Sales, on average, are lower/higher in the region indicated, in comparison to the comparator Central region.

lnSales lag Cigarette sales lagged by one quarter Sales volumes in previous month explain x% of current volumes.

Large health warning dummies

LHW 2014

Dummy equals 1 for each quarter after the

introduction of the Sep 2014 LHW which

increased minimum size to cover 85% of the

total packaging

Effect of LHW on sales from Q4 2014

22 2017 Q3 was removed from the data because new government regulations and taxes were introduced (see section II above).

23 Separate regressions for sales channels could be carried out but sales channel data are not split by region.

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Control variables

SIPP Indoor

Dummy variable equalling 1 for each quarter

after 2010q1,24 for ban on smoking indoors.

Source: Ministry of Public Health.

Effect of prohibition of smoking in indoor places from Q1 2010.

Ln GDP/capita Natural log of real GDP per capita25 Source: World Bank.

1% change in GDP/capita leads to x% change in sales

Instrumental variables

Tax Increase_2009 Tax Increase_2012 Tax Increase_2016

Dummies for excise increases Source: Euromonitor.

The effect of tax increases on prices

PPI Producer price index Source: IECONOMICS.

1 percentage point increase in PPI leads to 100x% change in cigarette prices

TTM Volume-based market shares of the Thailand Tobacco Monopoly (TTM)

1 percentage point decrease in TTM market share leads to 100x% change in cigarette prices

* Unless otherwise indicated, the source of the data is Nielsen.

Differences in regional effects are captured by dummy variables for Greater Bangkok, North,

Northeast, South.

As a proxy for smuggling an index of the per centage difference between the average price in

Thailand and the average yearly cigarette price in Laos has been used but in initial regressions this

generated and was dropped.

PANEL REGRESSIONS

Three panel regressions have been estimated – OLS using regional dummies (1); fixed effects (2) and

dynamic fixed effects (3); the latter two using instrumental variables (IV) to capture the endogeneity

of prices.

For Equations (2) and (3) three instrumental variables have been used in the price regression: (i) a

dummy variable for each of the tax increases in 2009, 2012 and 2016; (2) the PPI to take account of

changes in production costs; and (3) the market share of the state-owned Thailand Tobacco

Monopoly (TTM) to allow for the possibility that this state “monopoly” producer of cigarettes, albeit

one with a dwindling market share, had market power over the period that might have increased

cigarette prices.

The Dynamic panel IV regression (3) uses the dependent variable (cigarette sales) lagged by one

period to represent a possible adjustment process to changes in sales. The dynamic panel regression

has also been re-estimated using the Arellano-Bond and Blundell-Bond approaches.

Main results are reported in Table C.2.

24 It is assumed that indoor prohibition was implemented in January 2010.

25 Only annual data available. This has been smoothed in a 12-month moving average. Data for 2017 was estimated using a linear projection.

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Table C.2. Cigarettes sales regressions for Thailand, 2009-2017

OLS

Regional dummies

(1)

Panel Regressions (Fixed Effects)

IV Dynamic IV

(2) (3)

lnSales regression LHW 2014 0.038 0.035* 0.008

(0.117) (0.079) (0.578)

lnPrice -0.762*** -0.819*** -0.483***

(0.000) (0.000) (0.000)

lnSales lag(1) 0.616***

(0.000)

Greater Bangkok 0.085***

[Regional Panels]

[Regional Panels]

(0.000)

North -0.632***

(0.000)

Northeast 0.068***

(0.000)

South -0.493***

(0.000)

SIPP Indoor 0.073*** 0.071*** 0.030

(0.000) (0.000) (0.097)

lnGDP_capita 1.483*** 1.671*** 0.902**

(0.005) (0.001) (0.022)

lnPrice regression: Tax Increase_2009a 0.275*** 0.095***

(0.000) (0.000)

Tax Increase_2012 0.086*** 0.086***

(0.000) (0.000)

Tax Increase_2016 0.185*** 0.183***

(0.000) (0.000)

PPI -0.011*** -0.011***

(0.000) (0.001)

TTM 0.006*** 0.006***

(0.002) (0.003)

No. observations 170 170 165 R2 lnSales regressions 0.956 0.449 0.713

Prob > F 0.000 0.000 0.000

R2 lnPrice regression 0.894 0.831

Prob > F 0.000 0.000

Note: a: The tax effect in (3) is partial because the dynamic specification used omits the first quarter of the tax rise.

The sales panel regressions differ in explanatory power: with Equation (1) explaining over 90% of

the variation in cigarette sales; the static IV regression (2) only 45%; and the dynamic IV specification

around 71%.

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The coefficients for the price, GDP per capita, PPI, TTM, and the regional and tax dummy variables

all have the expected signs and are statistically significant at the 1 per cent level. They indicate that

GDP per capita had a large positive effect in cigarette sales.

The IV price regressions explain over 80 to 90 per cent of the variation in cigarette prices. Increases

in the PPI, excise tax rates and a higher market share of the domestic TTM cigarette producer all

increased cigarette prices and were statistically significant at the 1 per cent level.

The coefficients on (the instrumented) cigarette prices indicate price elasticities of between -0.48

to -0.82.

The LHW and SIPP dummies have, with one exception, positive coefficients which implausibly

indicate that they increased cigarette sales. The LHW was not statistically significant except in

Equation (2) but had the wrong sign.

EFFICIENT DYNAMIC PANEL ESTIMATORS

The Arellano-Bond and Blundell-Bond dynamic estimations show similar coefficients to the dynamic

panel specification. However, because of the singularity of the covariance matrix, standard errors

could not be estimated and therefore the results are not meaningful. They are reported in Table C.3

for completeness.

Table C.3. Arellano-Bond dynamic panel estimation

Arellano-Bond dynamic panel-data estimation Number of obs = 160 Group variable: region_num Number of groups = 5 Time variable: quarter Obs per group: min = 32; avg = 32; max = 32 Number of instruments = 159 Wald chi2(4) = . Prob > chi2 = . One-step results - (Std. Err. adjusted for clustering on region_num)

Blundell-Bond dynamic panel coefficients are in line with the above but again due to singularity in

the variance matrix, standard errors cannot be estimated. They are reported in Table C.4 for

completeness.

Variable Coef.

LHW 2014 0.012

Sales lag(1) 0.634

LnPrice -0.395

SIPP Indoor 0.036

LnGDP capita 0.618

Constant -0.404

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Table C.4. Blundell-Bond dynamic panel estimation

System dynamic panel-data estimation Number of obs = 165 Group variable: region_num Number of groups = 5 Time variable: quarter Obs per group: min = 33; avg = 33; Max = 33 Number of instruments = 223 Wald chi2(4) = . One-step results Prob > chi2 =

TIME SERIES ANALYSIS

The time series regressions (Table C.5) indicate that there is no short-term effect for any of the

variables included (insignificant coefficients in the second-step table).

The first-step results indicate that price has a strongly negative long-term impact on cigarette sales.

The LHW dummy is insignificant at 5% confidence level (but with the wrong sign). The other control

variables are insignificant. The results are in line with the dynamic panel regression results above.

Table C.5. Engle-Granger second-step estimation - first-step linear trend

Engle-Granger first-step regression (dependent variable: volume)

Coef. Std. Err. t P>t

[95% Conf. Interval]

LHW 2014 0.034 0.020 1.71 0.099 -0.007 0.074

lnPrice -0.568 0.089 -6.39 0.000 -0.750 -0.386

SIPP Indoor 0.023 0.018 1.28 0.212 -0.014 0.061

LnGDP capita -0.153 0.582 -0.26 0.795 -1.346 1.040

Trend 0.005 0.001 3.61 0.001 0.002 0.007

Constant 17.753 5.512 3.22 0.003 6.463 29.043

Engle-Granger second-step ECM (dependent variable: D.volume)

Coef. Std. Err. t P>t

[95% Conf. Interval]

L1. egresid -1.135 0.239 -4.75 0.000 -1.626 -0.643

LD.LHW 2014 -0.017 0.028 -0.6 0.552 -0.073 0.040

LD.lnPrice -0.183 0.126 -1.45 0.158 -0.443 0.076

LD.ISIPP Indoor 0.004 0.027 0.14 0.893 -0.052 0.059

LD.lnGDP capita 0.788 0.892 0.88 0.385 -1.045 2.621

Constant 0.004 0.005 0.71 0.482 -0.007 0.014

Note: LD = lagged differences of the respective variable.

Variable Coeff.

LHW 2014 0.008

LnSales lag(1) 0.891

LnPrice -0.205

SIPP Indoor 0.026

LnGDP capita 0.051

Constant 1.227

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To sense-check the results a separate regression has been run using volumes and prices only (Table

C.6). The Engle-Granger results confirm what the charts in Section VI show - there may be both

short-term and long-term effects of price changes on cigarette sales.

Table C.6. Engle-Granger 2-step estimation – sales & prices only; first-step linear trend

Engle-Granger first-step regression (dependent variable: volume)

Coef. Std. Err.

T P>t [95% Conf. Interval]

lnPrice -0.576 0.060 -9.66 0.000 -0.697 -0.454

Trend 0.006 0.000 12.66 0.000 0.005 0.007

Constant 16.307 0.050 325.68 0.000 16.205 16.409

Engle-Granger second-step ECM (dependent variable: D.volume)

Coef. Std. Err. T P>t [95% Conf. Interval]

L1. Egresid -1.017 0.208 -4.89 0.000 -1.443 -0.592

LD.lnPrice -0.211 0.120 -1.75 0.090 -0.457 0.035

Constant 0.006 0.005 1.21 0.235 -0.004 0.015

Note: LD = lagged differences of the respective variable.

MAIN FINDINGS

The main findings of the panel and timeseries regressions are that:

The LHW dummy is not associated with any reduction in cigarette sales.

The indoor SIPP ban had a positive statistically significant effect on cigarette sales. This is

implausible and is most likely to be due to spurious correlation.

The price elasticity estimates range between -0.5 (Dynamic FE-IV) and -0.8 (FE-IV) and are

all significant at the 1 per cent level.

The growth in GDP per capita increased cigarette sales and is statistically significant.

The tax rate changes increased cigarette prices in all regression.

The state TTM cigarette producer’s market share had a small but statistically significant

positive effect on cigarette prices indicating that the higher its market share the higher were

cigarette prices. (Or looked at the other way, greater competition from international

cigarette producers lowered cigarette prices all things equal)

The results do not vary much across the three panel regression specifications which indicate

that the panel models are robust.