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Illicit/cheap cigarettes in South Africa
Kirsten van der Zee
1
&Corné van Walbeek
1
&Sibahle Magadla
1
Published online: 22 November 2019
#The Author(s) 2019
Trends in Organized Crime (2020) 23:242–262
https://doi.org/10.1007/s12117-019-09372-9
*Kirsten van der Zee
kirsten.vanderzee@uct.ac.za
1
Research Unit on the Economics of Excisable Products (REEP), University of Cape Town,
Cape Town, South Africa
Abstract
Using wave 5 of the National Income Dynamics Study (conducted in 2017), this paper
investigates the market for very low-priced cigarettes in South Africa, which, in all
probability, are illicit. Since the sum of the excise tax and VAT in 2017 amounted to
R16.30 (1.22 USD) per pack, any cigarettes selling for R20 (1.50 USD) per packor less
are likely to be illicit, assuming reasonable production costs. By this definition,
approximately 30% of cigarettes consumed in South Africa in 2017 were illicit. Illicit
cigarettes are found across all nine provinces. At the margin, the purchase of illicit
cigarettes is associated with lower socio-economic characteristics, such as having lower
levels of income and education. As illicit cigarettes undermine both the fiscal and
health agendas of tobacco taxation policy, these results highlight the need for the South
African government to implement urgently effective measures in order to curb illicit
trade.
Keywords Cigarette prices .Tobacco control .Illicit trade .Tobacco industry.Tax
enforcement
Introduction
The illicit trade in tobacco products poses a serious threat to public health because it
increases access to tobacco by making cigarettes more affordable. People who other-
wise might have quit smoking continue to smoke, and people who might never have
started smoking initiate a habit that will cause them harm, and that they are likely to
regret in the future (Pechacek et al. 2018). The illicit tobacco trade often has the biggest
impact on individuals in low socio-economic groups, as these smokers are most
sensitive to prices (International Agency for Research on Cancer 2011). The illicit
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Trends in Organized Crime (2020) 23:242–262 243
trade also leads to a loss of government revenues, and often contributes to the funding
of international criminal activities (Joossens and Raw 2012; World Health Organization
2013). It is therefore important to implement strong tobacco control policies, and to
minimize the illicit trading of tobacco products.
For many years, South Africa was regarded as a model country in terms of tobacco
control policy. In 1994, the country announced a strategy to increase excise taxes
rapidly, with the explicit aim of reducing tobacco use, making it one of the first middle-
income countries to do so (van Walbeek 2005). Between 1993 and 2003, aggregate
cigarette consumption reduced by a third and adult smoking prevalence fell from
roughly 33% to 24% (van Walbeek 2005). However, since 2004, the decrease in
cigarette consumption and smoking prevalence has levelled off (Linegar and van
Walbeek 2018).
The South African Revenue Service (SARS) is responsible for collecting excise
taxes on locally produced and imported excisable products. For most of the post-2000
period, SARS was esteemed for its tax collection and enforcement capabilities, its use
of modern technology, and its establishment of dedicated investigation units to pursue
tax evaders (Judge Nugent 2018a,b). One of these investigation units was the High-
Risk Investigative Unit (HRIU), which, together with other specialized units within
SARS, collectively pursued tax evaders and those practising other forms of tax abuse in
the tobacco industry. Under the codename “Project Honey Badger”, the then-head of
the HRIU wrote to the two tobacco industry bodies (representing the majority of
cigarette manufacturers) and other independent manufacturers in 2013 and 2014,
warning them that SARS was aware of illicit activity in the industry, and that it would
be intensifying its formal investigations into tobacco-tax evasion and other forms of tax
abuse (Bailey 2013;Pauw2017). By 2014, SARS had launched proceedings or was
acting against at least 13 tobacco manufacturers for crimes including corruption,
bribery, attempted murder, money laundering, racketeering, tax evasion and fraud
(Pauw 2017).
In September 2014, then-president Jacob Zuma appointed a retired Commissioner of
Correctional Services, Tom Moyane, as the new Commissioner of SARS. Within a
month of becoming Commissioner, and following reports in the Sunday Times, South
Africa’s largest newspaper, about the existence of a “rogue unit”within SARS, Moyane
announced that he had no confidence in the SARS executive committee and disbanded
it (Pauw 2017). The HRIU was identified as the “rogue unit”and was disbanded,
together with a number of other specialized units in SARS. As a result, Project Honey
Badger came to an abrupt end (Pauw 2017). In the months following Moyane’s
appointment, many key executives and experienced officials, including the head of
the HRIU, were suspended (Judge Nugent 2018a,b).
The appointment of Tom Moyane, a close ally of Jacob Zuma, is generally perceived
as an integral part of “state capture”, which has cast a long shadow over Jacob Zuma’s
presidency. State capture entails systemic and high-level political corruption, whereby
the government’s decision-making processes and institutions are compromised in order
to advance the interests of a small number of well-connected politicians and their allies.
A judicial commission, under the chairmanship of Deputy Chief Justice Raymond
Zondo, was established in 2018 to investigate state capture in South Africa. The report
has not yet been published, but the evidence presented suggests that a large number of
government departments and law enforcement agencies were “captured”by the
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244 Trends in Organized Crime (2020) 23:242–262
president and members in his circle (https://www.sastatecapture.org.za/). In the process,
a number of private sector organisations have been accused of aiding and abetting the
process of “state capture”.
In April 2016, the Sunday Times retracted its series of explosive articles—published
between 2014 and 2015—about the SARS “rogue unit”(Pillay 2016;Siqoko2016;van
Loggerenberg 2016). It acknowledged that there were factual errors and omissions in its
coverage of the story. In 2018, the Sunday Times again retracted these stories and
acknowledged that they had allowed themselves to be manipulated by “a parallel
political project aimed at undermining our democratic values and destroying state
institutions and removing individuals who were seen as obstacles to this project”
(Rupiah 2018). However, the damage to SARS and to the people implicated in the
Sunday Times articles was irreversible.
The purging of SARS was so destructive to the organization that a dedicated
Commission of Inquiry into the state of SARS was launched in 2018. The Nugent
Commission, headed by Judge Robert Nugent, a retired judge of the Supreme Court of
Appeal, found that “The restructuring of the organization displaced some 200 mana-
gerial employees from their jobs, many of whom ended up in positions that had no
content or even job description, and in exasperation many skilled professionals have
left. Others remain in supernumerary posts with their skills going to waste. Measures to
counter criminality have been compromised and those who trade illicitly in commod-
ities like tobacco operate with little constraint”(Judge Nugent 2018a,b). The final
report by the commission also highlighted the growth in the illicit trade of cigarettes as
one of the consequences of the institutional meltdown at SARS (van Walbeek et al.
2019).
In March 2018, the incoming president, Cyril Ramaphosa, suspended Mr. Moyane
as the SARS Commissioner (Petersen 2018). He was officially removed from his
position in November 2018, on recommendation of the Nugent Commission (Brown
2018; Judge Nugent 2018a,b).
Developments in the tobacco industry in South Africa
While SARS was failing, there were also major developments in the tobacco industry in
South Africa. The industry has traditionally been highly concentrated, with British
American Tobacco (BAT) having a market share in excess of 90%, followed by other
multinationals (primarily Philip Morris and Japan Tobacco) (van Walbeek 2005).
Despite new tobacco control legislation and substantial increases in the excise tax after
1994, the multinationals were able to increase their net-of-tax turnover by raising retail
prices substantially (Linegar and van Walbeek 2018). Thus, even though the number of
cigarettes sold decreased by about a third between 1994 and 2009, the real (inflation-
adjusted) net-of-tax price per cigarette doubled, allowing the multinationals to maintain
their profitability (Linegar and van Walbeek 2018). The large profits earned by the
incumbent multinationals attracted the attention of competitors, which ultimately
caused substantial disruption in the tobacco industry.
The year 2010 marked a turning point for South Africa’s tobacco sector. The illicit
market, which was previously too small to affect industry profitability or government
revenue significantly, increased significantly in 2010, to about 10% of the total cigarette
market (van Walbeek 2014). After 2010, the multinationals’market power was
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Fig. 1 Real excise revenue and legal cigarette consumption in South Africa, 1980–2018. Source: Author’s
own calculations, derived from various issues of the National Treasury Budget Review (1980–2018). Real
excise revenue is displayed in millions of Rands, with 2016 as the base year. Consumption is in millions of 20-
packs (Republic of South Africa 1980–2018)
Trends in Organized Crime (2020) 23:242–262 245
threatened by small and medium-sized local cigarette producers. The new entrants’
strategy was to undermine the incumbents by offering cigarettes at substantially lower
prices. A large proportion of these cigarettes were sold at prices that were so low that it
was impossible for the full tax amount to have been paid (Liedeman and Mackay
2015). There was also a noticeable drop in government excise revenue in 2010 (Fig. 1).
Over the past 10 years, the South African tobacco market has become increasingly
fragmented as new entrants have entered the market and have taken market share away
from the multinationals. The Tobacco Institute of Southern Africa (TISA) represents the
multinationals and other established players, while the Fair-Trade Independent Tobacco
Association (FITA) represents the smaller, independent manufacturers. Most of the
FITA-aligned manufacturers are based in South Africa, but a number are based in
neighbouring countries. There is decided animosity between these two industry bodies.
Since its relaunch in 2006, TISA’s main argument has been that the illicit market is
substantial and growing (van Walbeek and Shai 2015). TISA made this claim despite
the fact that, prior to 2009, they could offer no evidence to support this position (van
Walbeek 2014; Vellios et al. 2019). Based on international precedents (Smith et al.
2013), it seems that the primary rationale for making this argument was to dissuade
National Treasury from increasing the excise tax on cigarettes.
In the second half of 2018, TISA launched a major public relations campaign, called
#TakeBackTheTax, in which members of the public were encouraged to sign a petition
to “implore the South African Revenue Service, the Parliament of the Republic of
South Africa and law enforcement agencies to act with urgency and take decisive steps
in combatting the trade of illicit cigarettes”(TISA 2018). This campaign was triggered
by an industry-funded survey which found that, in June 2018, 27% of cigarettes in
South Africa were sold at a price below the excise tax and VAT amount (Ipsos 2018a,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
246 Trends in Organized Crime (2020) 23:242–262
b). Gold Leaf Tobacco Company was publicly identified as the major producer of these
cigarettes. A subsequent round of data collection in September 2018 indicated that the
illicit market had grown to 33% of the total cigarette market (Ipsos 2018a,b).
Estimates of the size of the illicit market in South Africa
A number of independent studies have estimated the size of the illicit market over the
past decade (Blecher 2010;vanWalbeek2014; Liedeman and Mackay 2015;van
Walbeek and Shai 2015; van der Zee et al. Forthcoming; Vellios et al. 2019). The
techniques vary, but an overarching finding is that, until about 2015, the estimates of
the independent studies were substantially lower than those ofthe tobacco industry. The
consistently small illicit market between 2000 and 2014 coincides with a period when
SARS was strengthening its capacity, particularly to fight illicit trade, for example with
Project Honey Badger (Serrao 2014). Over this period there were also significant
increases in the excise tax on cigarettes. Recent academic estimates suggest that illicit
trade rose substantially after 2014 when SARS came under pressure, to as high as 35%
of the market in 2017 (Vellios et al. 2019). Between 2015 and 2018, real (inflation-
adjusted) government revenue fell by 23% (Fig. 1), the first substantial decrease in
more than 25 years. The estimates of the size of the illicit market that are produced by
the tobacco industry and recent estimates by independent researchers are converging.
Against the background of institutional failure and dramatic changes in the tobacco
industry, this paper aims to provide an estimate of the size of the market for very cheap,
probably illicit, cigarettes in South Africa. This is the first independent study to use a
nationally representative survey to estimate the illicit market in South Africa. Although
TISA claims that its Ipsos study is nationally representative, Ipsos has not released the
methodology or the raw data for public scrutiny, and therefore this claim cannot be
verified (Lopez Gonzalez et al. 2018).
We also investigate various covariates of illicit trade. Using both descriptive statis-
tics and regression analysis, we investigate which demographic, geographic and
product-specific characteristics are associated with illicit trade.
Data and methodology
Data
The National Income Dynamics Study (NIDS) is a nationally representative panel
survey of South Africans (Southern Africa Labour and Development Research Unit
2018). The first wave of the NIDS survey was conducted in 2008, with a sample of
roughly 28,000 individuals, in 7300 households, most of whom have been re-
interviewed approximately every 2 years since. Due to attrition amongst primarily
White, Indian/Asian and high-income respondents, a top-up of 2775 individuals was
added in wave 5 to maintain the representativeness of the sample.
In wave 5, NIDS also introduced questions for smokers about their most recent
purchase of cigarettes. This paper uses these questions to estimate the proportion of
cigarettes purchased at specific price points. The NIDS survey consists of several
questionnaires: household, adult (age 15+), proxy, and child. Our analysis uses
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responses from the adult questionnaire only, since the child questionnaire does not have
tobacco-related questions.
Methodology
The focus of this study is the price of cigarettes, which is used to estimate the size of the
illicit market. In the survey, smokers were asked to describe their most recent purchase
of cigarettes, specifically the packaging type (which could include single sticks), the
number of items/packs purchased, and the total amount that they paid for the cigarettes.
We use the responses to these questions to calculate per-stick and equivalent per-pack
prices. In South Africa, packs of 20 are the most popular packaging type, and therefore
we report all prices as their 20-pack equivalent price. Price is expressed in nominal
terms (data were collected between February and December 2017). To ensure that the
data represent total cigarette consumption in the country, we weight each price obser-
vation by the respondent’s smoking intensity (cigarettes per day), as well as by their
NIDS population weight. For example, an individual who reports consuming 10
cigarettes per day at R2 per cigarette, and who has a population weight of 3000 (i.e.
represents 3000 people in the population), accounts for 30,000 cigarettes consumed per
day (10 × 3000), at R2 each.
Defining cheap cigarettes
For the majority of the survey period (April to December 2017), the excise tax on a
pack of 20 cigarettes was R14.30 (approximately 1.07 USD at the time
1
). Combined
with the VAT rate of 14%, the full tax amount was R16.30 (1.22 USD). Any cigarettes
sold at a price below this could not have met the full tax amount. Anecdotal evidence
from personal communications with employees in the tobacco industry suggests that
cigarettes can be manufactured for as little as R2.50 a pack. When distribution costs and
retail margins are included, it is unlikely that fully tax-paid cigarettes would be sold for
less than R20 (1.50 USD) per pack-equivalent. For comparison, BAT’sPeterStuyve-
sant, the most popular brand in South Africa, sold for about R35 per pack in 2017
(ACP 2019).
To account for the uncertainty regarding the minimum retail price of legal cigarettes,
we define four price thresholds for an equivalent pack of 20 cheap cigarettes: less than
R16.30; less than R20; R20 or less; and less than R23 (1.73 USD). The four definitions
of cheap cigarettes allow us to estimate the illicit market with varying degrees of
strictness for the minimum price of legal cigarettes; the estimate at R16.30 will be the
most conservative, and that at R23 the least conservative, estimate. Although we
present the data for all four thresholds, the discussion focuses on packs that are sold
for R20 or less, because we believe that this is the most accurate estimate of illicit trade.
Since there is currently no legal minimum retail price for cigarettes in South Africa,
we cannot be certain that prices observed below the thresholds are in fact illegal, and
therefore refer to these cigarettes as “cheap”and “illicit”, using these terms
interchangeably.
1
The average exchange rate for 2017 was R13.32 to the US dollar (South African Reserve Bank 2017)
Trends in Organized Crime (2020) 23:242–262 247
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Reporting errors and data cleaning
There were a number of responses regarding ‘most recent cigarette purchase’that
yielded nonsensical prices. For example, an individual reports spending a total of
R0.50 (0.035 USD) for 5 single sticks (per stick price of R0.10 (0.007 USD)); this is
likely a data error, since there is no record of a single stick selling for less than R0.50 in
South Africa in recent years, whereas there is extensive evidence of cigarettes being
sold for R0.50 each by informal vendors (ACP 2019). Thus, it is reasonable to assume
that this individual incorrectly reported spending R0.50 in total, and instead spent
R0.50 per stick.
We used our knowledge of the South African cigarette market and the African
Cigarette Prices (ACP) dataset (ACP 2019) to develop informed rules to correct
obvious reporting errors. We assume that a single stick sells for between R0.50 and
R4, a 10-pack sells for between R5 and R35, a 20-pack sells for between R8 and R60, a
30-pack sells for between R12 and R90, and a carton of 200 cigettes sells for between
R50 and R600. These rules are described in detail in Appendix 1. To the extent that the
original data were, in fact, valid, we would have distorted the data. However, we
believe that this distortionary effect is likely to be very limited, given the fact that the
rules substantially reflect the reality of cigarette pricing in South Africa and are
supported by other surveys (Liedeman and Mackay 2015; ACP 2019).
Of the 25,075 adults successfully interviewed in NIDS wave 5, 4224 indicated that
they smoked cigarettes (Table 1), representing almost 6.7 million of the 34.6 million
South African adults. This implies a smoking prevalence of 19.3%, with 6.9% of
females and 34% of males smoking, which is in line with other national estimates
(SADHS 2016;MukongandTingum2018). The cleaned data gives a sample of 3507
smokers, representing approximately 5.6 million smokers (84% of smokers from the
uncleaned data). Of the 4224 smokers in NIDS, 3002 observations were left un-
changed, 717 were excluded due to missing data and/or complexities that we were
unable to resolve with the rules described in the Appendix, and 505 observations were
corrected using these rules.
Table 1 Summary of data cleaning
Action Detail Observations
Initial Data Collected 4224
Data Removed During Cleaning No Packaging Reported 191
No Price Information Reported 94
No Consumption Reported 91
Consumption >100 Cigarettes per day 3
Removed Due to Price Reporting Errors Price per Stick <R0.50 (could not be corrected) 45
Price per Stick >R4.00 293
Remaining Sample Including Corrections 3507
NIDS wave 5 (2017). We altered 505 prices using the rules described in Appendix 1
248 Trends in Organized Crime (2020) 23:242–262
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Model specification
We assess the socio-economic correlates of smoking cheap cigarettes using the follow-
ing specification:
SmokerCheapiP ¼β0þβ1Packagingiþβ2Ind þβ3HH þεið1Þ
Where SmokerCheapiP is an indicator variable for whether smoker ipurchases ciga-
rettes at price threshold P, where P< R16.30, P<R20,P≤R20, or P< R23. Packaging
is the packaging type purchased by the smoker (including single stick, 10-pack, 20-
pack, 30-pack and carton of 200 cigarettes). Ind isavectorofindividualcharacteristics
including gender, race, age, education, employment status, marital status, the impor-
tance of religion, and the number of cigarettes smoked per day. HH is a vector of
household characteristics, including the natural logarithm of household income per
capita, location type (urban or rural), and province. Since SmokerCheapiP is a dichot-
omous variable, eq. (1) is specified as a logit regression model, and we report the
marginal effects.
Results
Characteristics of cigarette prices and cheap cigarettes
The average price of cigarettes is almost R31 per 20-pack (Table 2). Packs with 10
cigarettes are the most expensive at R38.20 per 20-pack equivalent, followed by single
sticks at R37.17. These two packaging types also have the greatest variation in price.
For all packaging types, the median price is above the mean, suggesting left-skewed
distributions, with lower prices pulling down the average. Cartons are the cheapest at
R19.81 per 20-pack equivalent.
Overall, 19.6% of cigarettes were bought for less than R16.30, which was the tax
amount at the time of the survey (Table 3), and 30.7% of cigarettes were bought for
R20 per pack or less.
Table 2 Average cigarette prices, expressed in price per 20-pack equivalent
Mean (Confidence Interval) Median St. Dev N
Overall Price 30.73 (30.24; 31.22) 30 14.82 3507
Packaging Type
Single 37.17 (36.20; 38.14) 40 17.48 1253
10-Pack 38.20 (36.93; 39.48) 40 14.49 498
20-Pack 28.01 (27.40; 28.62) 29 12.16 1541
30-Pack 25.37 (22.79; 27.96) 26.67 11.44 78
Carton 19.81 (18.05; 21.57) 22.5 10.42 137
NIDS wave 5 (2017). 95% confidence intervals in brackets. Data are weighted using the NIDS wave 5
population weights. Prices are also weighted for consumption. All prices are normalized to a pack of 20
cigarettes
Trends in Organized Crime (2020) 23:242–262 249
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For all price thresholds, cartons and 30-packs are most likely to be cheap. For
example, 43% of cigarettes sold in cartons, and 29% of cigarettes sold in 30-cigarette
packs, were sold at less than R16.30 per 20-pack equivalent, compared to 23% of packs
of 20, 12% of single sticks and 3% of packs of 10 cigarettes. The finding that a greater
percentage of cigarettes sold in cartons and 30-cigarette packs are cheaper than other
packaging types holds for all price thresholds.
About 20% of all single sticks are sold at R1 per stick (i.e. R20 per pack), indicating
that this is a common price point. Surveys of cigarette prices in South African
townships indicate that cheap single sticks are sold mostly for R0.50 or R1.00
(Liedeman and Mackay 2015; ACP 2019). Few are sold for an amount between
R0.50 and R1.00. Therefore, it comes as no surprise that the estimates of the volumes
sold between <R16.30 and < R20 per pack are very similar (especially for single
sticks). There is a spike in the volume of cigarettes at the R20 or less threshold, since
this includes all R1 single sticks.
Characteristics of smokers who buy cheap cigarettes
Tab le 4describes some of the characteristics of smokers who buy cheap cigarettes at
the various price thresholds, as well as smokers overall. Although there is some
variation in the proportion of smokers buying cheap cigarettes, an important finding
is the widespread prevalence across all demographic and socio-economic groups.
A higher proportion of females purchase cheap cigarettes than males at all price
thresholds. The largest prevalence gradient is for education, where 40.4% of smokers
with little to no education purchase cigarettes priced at R20 or less per pack, compared to
16% of smokers with tertiary education. For the other demographic and socio-economic
characteristics, the difference in the prevalence of cheap cigarette use is smaller.
Table 3 Percentage of cheap cigarettes bought at various price cut-offs
<R16.3 <R20 ≤R20 <R23 N
Overall 19.6 20.9 30.7 32.8 3507
(18.3; 21.0) (19.5; 22.2) (29.2; 32.2) (31.3; 34.4)
Packaging Type
Single 11.8 12.1 32.0 32.3 1253
(10.0; 13.6) (10.3; 14.0) (29.4; 34.6) (29.7; 34.9)
10-Pack 3.4 3.4 13.0 13.4 498
(1.8; 5.0) (1.8; 5.0) (10.0; 16.0) (10.4; 16.4)
20-Pack 23.0 24.1 31.4 33.9 1541
(20.9; 25.1) (22.0; 26.3) (29.0; 33.7) (31.6; 36.3)
30-Pack 29.1 32.1 34.1 38.3 78
(18.8; 39.4) (21.6; 42.8) (23.3; 44.8) (27.3; 49.4)
Carton 42.8 48.7 49.3 55.7 137
(34.4; 51.2) (40.2; 57.2) (40.8; 57.8) (47.3; 64.1)
NIDS wave 5 (2017). 95% confidence intervals in brackets. Data are weighted using the NIDS wave 5
population weights. Prices are also weighted for consumption
250 Trends in Organized Crime (2020) 23:242–262
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Table 4 Descriptive statistics of smokers of cheap cigarettes, compared to smokers overall
Overall <16.30 <20 ≤20 <23
Observations 3507 601 658 1115 1182
Average Age 37.1 39.8 39.8 38.9 39.0
(36.7; 37.5) (38.7;
40.8)
(38.8;
40.8)
(38.1;
39.7)
(38.3;
39.8)
Average Household Income Per
Capita
4136 2924 3020 2695 2911
(3848; 4424) (2610;
3239)
(2713;
3328)
(2469;
2922)
(2677;
3144)
Average Consumption (Sticks per
day)
8.0 9.7 9.6 9.1 9.0
(7.7; 8.2) (9.2; 10.3) (9.1; 10.2) (8.7; 9.5) (8.7; 9.4)
Relative Share of
Sub-Group
Proportion of Smokers who Smoke Cheap
Cigarette, by Sub-Group
Male 80.2 15.0 16.1 25.6 27.4
(78.9; 81.5) (13.6;
16.3)
(14.7;
17.5)
(23.9;
27.3)
(25.7;
29.1)
Female 19.8 20.5 21.8 32.6 35.1
(18.5; 21.1) (17.9;
23.1)
(19.2;
24.5)
(29.6;
35.6)
(32.0;
38.1)
Race
African 66.4 13.5 14.6 24.3 25.8
(64.8; 67.9) (12.0;
15.0)
(13.0;
16.2)
(22.4;
26.2)
(23.8;
27.7)
Mixed Race 19.6 19.6 20.6 33.9 35.4
(18.3; 20.9) (17.3;
21.9)
(18.3;
22.9)
(31.2;
36.6)
(32.7;
38.1)
Indian/Asian 2.8 10.4 10.6 17.2 19.0
(2.3; 3.4) (3.1; 17.6) (3.3; 18.0) (8.2; 26.2) (9.7; 28.4)
White 11.2 26.7 28.7 33.5 38.6
(10.1; 12.2) (21.5;
31.8)
(23.4;
34.0)
(28.0;
39.0)
(32.9;
44.3)
Education
None to Primary School
Completed
16.5 22.4 24.0 40.4 41.7
(15.3; 17.7) (19.6;
25.2)
(21.1;
26.9)
(37.1;
43.8)
(38.3;
45.0)
Grades 8–11 (Incomplete Second-
ary School)
53.9 17.0 18.3 28.6 30.7
(52.2; 55.5) (15.3;
18.8)
(16.5;
20.1)
(26.5;
30.6)
(28.6;
32.8)
Secondary School Completed 16.9 11.3 11.8 17.7 19.4
(15.6; 18.1) (8.5; 14.0) (9.0; 14.6) (14.4;
21.0)
(16.0;
22.9)
Some or Completed Tertiary
Education
12.8 10.5 11.8 16.0 18.2
(11.6; 13.9) (7.2; 13.8) (8.4; 15.3) (12.0;
19.9)
(14.0;
22.3)
Employment Status
Employed 81.5 15.2 16.8 24.5 26.9
Trends in Organized Crime (2020) 23:242–262 251
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Table 4 (continued)
Overall <16.30 <20 ≤20 <23
(79.9; 83.1) (13.5;
16.8)
(15.1;
18.5)
(22.5;
26.5)
(24.8;
28.9)
Unemployed 18.5 16.5 16.8 28.9 29.4
(16.9; 20.1) (13.3;
19.7)
(13.6;
20.0)
(25.0;
32.8)
(25.5;
33.3)
Geographic Location
Urban 77.7 16.9 18.2 27.2 29.0
(76.4; 79.1) (15.4;
18.4)
(16.7;
19.7)
(25.4;
28.9)
(27.2;
30.7)
Rural 22.3 13.1 14.0 26.4 28.7
(20.9; 23.6) (11.0;
15.3)
(11.8;
16.2)
(23.6;
29.1)
(25.9;
31.5)
Province
Western Cape 19.5 14.4 14.9 24.7 26.0
(18.2; 20.9) (12.0;
16.9)
(12.4;
17.4)
(21.7;
27.7)
(22.9;
29.0)
Eastern Cape 8.5 15.7 16.0 24.9 26.8
(7.6; 9.4) (11.8;
19.6)
(12.1;
19.9)
(20.3;
29.5)
(22.1;
31.5)
Northern Cape 4.6 19.3 20.6 37.9 40.2
(3.9; 5.3) (15.9;
22.8)
(17.1;
24.1)
(33.7;
42.1)
(35.9;
44.4)
Free State 4.2 16.7 16.7 27.0 30.6
(3.5; 4.9) (11.4;
22.0)
(11.4;
22.1)
(20.7;
33.4)
(24.0;
37.2)
KwaZulu-Natal 12.1 8.9 10.8 22.8 24.3
(11.0; 13.2) (6.5; 11.3) (8.2; 13.3) (19.3;
26.3)
(20.7;
27.8)
North West Province 5.6 16.3 17.6 25.4 34.0
(4.8; 6.4) (11.3;
21.3)
(12.4;
22.7)
(19.5;
31.4)
(27.5;
40.4)
Gauteng 30.9 18.8 20.5 29.1 30.5
(29.4; 32.4) (15.5;
22.1)
(17.1;
24.0)
(25.2;
32.9)
(26.6;
34.4)
Mpumalanga 8.6 20.3 21.2 27.5 29.0
(7.6; 9.5) (14.9;
25.7)
(15.7;
26.7)
(21.5;
33.5)
(22.9;
35.1)
Limpopo 6.0 12.8 14.3 27.5 27.9
(5.2; 6.8) (7.4; 18.2) (8.7; 20.0) (20.3;
34.7)
(20.7;
35.1)
NIDS wave 5 (2017). 95% confidence intervals in brackets. Data are weighted using the NIDS wave 5
population weights. The “overall”column gives the average characteristics and proportions for smokers
overall, while the following four columns give the shares of smokers buying cheap cigarettes, within each sub-
group. Income is reported in March 2017 Rands
252 Trends in Organized Crime (2020) 23:242–262
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Cheap cigarettes are purchased in significant proportions in all nine provinces.
While there are some differences in the point estimates across the different price
thresholds and provinces, an analysis of the 95% confidence intervals shows that they
overlap in most cases, indicating that the prevalence of buyers of cheap cigarettes in the
different provinces is, mostly, not statistically different. For example, for cigarettes sold
at ≤R20, a 26% proportion lies in the 95% confidence interval for eight provinces; the
only exception is the Northern Cape, where the confidence interval lies above 26%.
The implication is that one cannot reject the null hypothesis that 26% (in fact, anything
between 25.2% and 26.3%) of smokers in each of the eight provinces, other than the
Northern Cape, buy cigarettes at a price of ≤R20.
For other price thresholds, a similar pattern holds. For prices <R16.30, one cannot
reject the null hypothesis that the proportion of smokers buying cigarettes at this price
or lower is between 15.9% and 16.9% for all provinces, other than KwaZulu-Natal,
where the prevalence is slightly lower. For prices <R20, one cannot reject the null
hypothesis that the relevant proportion of buyers is between 17.1% and 17.4% for all
provinces other than KwaZulu-Natal.
Regression analysis: Correlates of cheap cigarette smoking
Tab le 5presents the marginal effects at the average, taken from the logit regression, for
smokers purchasing cigarettes at the four price thresholds (<R16.30, <R20, ≤R20 and
<R23).
Some packaging types are more likely to be cheap than others. Compared to single
sticks, packs of 10 cigarettes are less likely, while cartons (200 cigarettes) are substan-
tially more likely, to be cheap for all price thresholds. Packs of 20 cigarettes are more
likely to be cheap than single cigarettes, but only for very low price thresholds (less
than R20 per pack).
With regard to individual-level characteristics, the likelihood of purchasing cheap
cigarettes varies substantially by race. White and Mixed Race smokers are more likely
to purchase cheap cigarettes than African smokers (who make up about 70% of the
smoking population) at all price thresholds. There is a strong and consistent age
gradient, with older smokers more likely to purchase cheap cigarettes. Smokers with
more education (especially those who have completed secondary school or have tertiary
education) are significantly less likely to purchase cheap cigarettes than smokers with
little or no education. Smokers who have never married are more likely to purchase
cheap cigarettes than married smokers, at all price thresholds. The number of cigarettes
smoked per day is insignificantly associated with the purchase of cigarettes for less than
R20 per pack, but is significantly positively associated with cigarettes for between R20
and R23 per pack.
Individual-level characteristics that have an insignificant association with the pur-
chase of cheap cigarettes, when all else is held constant, include gender, employment
status, and the importance of religion to the respondent.
For household characteristics, there is a consistent negative relationship between
household income per capita and the likelihood of purchasing cheap cigarettes. The
marginal effect of around −0.04 for all price thresholds indicates that a 10% increase in
per capita household income decreases the likelihood of purchasing cheap cigarettes (at
the chosen price threshold) by 0.4%. For the individual provinces and the urban/rural
Trends in Organized Crime (2020) 23:242–262 253
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 5 Characteristics of smokers of cheap cigarettes, marginal effects from logit regression
VARIABLES <R16.3 <R20 ≤R20 <R23
Packaging (Base = Single Sticks)
10-Pack −0.0740*** −0.0791*** −0.1527*** −0.1532***
(0.0209) (0.0212) (0.0319) (0.0324)
20-Pack 0.1083*** 0.1177*** 0.0131 0.0392
(0.0280) (0.0286) (0.0332) (0.0343)
30-Pack 0.1442 0.1545 0.0041 0.0383
(0.1447) (0.1369) (0.1388) (0.1354)
Carton of 200 0.3446*** 0.3968*** 0.2603*** 0.2806***
(0.0724) (0.0745) (0.0782) (0.0766)
Individual Level Characteristics
Gender (Base = Male)
Female −0.0021 0.0015 0.0013 0.0025
(0.0227) (0.0235) (0.0289) (0.0295)
Race (Base = African)
Mixed Race 0.0859*** 0.0837** 0.1495*** 0.1459***
(0.0325) (0.0339) (0.0433) (0.0436)
Asian/Indian 0.0520 0.0193 0.0256 0.0244
(0.0543) (0.0494) (0.0688) (0.0678)
White 0.1479** 0.1377** 0.1564** 0.1844***
(0.0696) (0.0681) (0.0707) (0.0685)
Age Category (Base= Age 15–29)
30–44 0.0613*** 0.0626** 0.0559* 0.0503
(0.0237) (0.0245) (0.0301) (0.0313)
45–59 0.1091*** 0.0936** 0.1135*** 0.1073**
(0.0375) (0.0375) (0.0433) (0.0440)
60 and older 0.1453*** 0.1465*** 0.1523*** 0.1498***
(0.0524) (0.0530) (0.0564) (0.0575)
Education (Base = None to Primary School Completed)
Grades 8–11 (Incomplete Secondary School) −0.0316 −0.0418 −0.0900*** −0.0776**
(0.0301) (0.0309) (0.0346) (0.0351)
Secondary School Completed −0.1125*** −0.1343*** −0.2084*** −0.2068***
(0.0368) (0.0381) (0.0454) (0.0461)
Some or Completed Tertiary Education −0.0992** −0.1142** −0.2070*** −0.2071***
(0.0472) (0.0482) (0.0534) (0.0530)
Employment (Base = Not Economically Active)
Unemployed 0.0218 0.0209 0.0199 0.0143
(0.0290) (0.0293) (0.0364) (0.0370)
Employed −0.0011 0.0099 −0.0058 −0.0001
(0.0230) (0.0236) (0.0297) (0.0306)
Marital Status (Base = Married)
Living with Partner 0.0713** 0.0475 0.0533 0.0427
(0.0304) (0.0325) (0.0378) (0.0399)
254 Trends in Organized Crime (2020) 23:242–262
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divide, the majority of the marginal effects are not statistically significant. In fact, of the
Table 5 (continued)
VARIABLES <R16.3 <R20 ≤R20 <R23
Widow/Wido wer −0.0216 −0.0376 −0.0425 −0.0232
(0.0293) (0.0325) (0.0424) (0.0464)
Divorced or Separated 0.0039 −0.0180 −0.0322 −0.0460
(0.0306) (0.0328) (0.0425) (0.0447)
Never Married 0.0816*** 0.0620* 0.0894** 0.0724*
(0.0296) (0.0326) (0.0371) (0.0391)
Importance of Religion (Base = Not Important)
Important −0.0046 −0.0076 0.0303 0.0346
(0.0338) (0.0339) (0.0342) (0.0347)
Intensity (Sticks/day) 0.0013 0.0010 0.0042** 0.0037*
(0.0014) (0.0015) (0.0019) (0.0019)
Household Characteristics
Log of Household Income Per Capita −0.0391*** −0.0388*** −0.0490*** −0.0469***
(0.0113) (0.0116) (0.0141) (0.0149)
Geographical Type (Base = Urban)
Rural −0.0023 −0.0137 0.0111 0.0176
(0.0235) (0.0236) (0.0282) (0.0295)
Province (Base = Eastern Cape)
Wes te rn C ap e −0.0396 −0.0374 −0.0444 −0.0543
(0.0274) (0.0274) (0.0391) (0.0409)
Northern Cape −0.0121 −0.0019 0.0473 0.0532
(0.0357) (0.0358) (0.0450) (0.0471)
Free State 0.0670 0.0594 0.0722 0.0978*
(0.0486) (0.0483) (0.0522) (0.0554)
KwaZulu-Natal −0.0699** −0.0462 −0.0194 −0.0241
(0.0280) (0.0293) (0.0397) (0.0416)
North West Province 0.0328 0.0484 0.0266 0.0967
(0.0445) (0.0459) (0.0503) (0.0620)
Gauteng 0.0381 0.0515 0.0748* 0.0655
(0.0358) (0.0359) (0.0427) (0.0445)
Mpumalanga 0.0630 0.0697 0.0550 0.0446
(0.0434) (0.0432) (0.0476) (0.0492)
Limpopo −0.0195 −0.0027 0.0405 0.0143
(0.0568) (0.0578) (0.0640) (0.0643)
Pseudo R Squared 0.1490 0.1488 0.1048 0.1036
Observations 3444 3444 3444 3444
NIDS wave 5 (2017). Data are weighted using the NIDS wave 5 population weights. The dependent variable
is equal to one if the individual smokes cigarettes below R16.30, R20, R20 or less, and R23, and zero if not.
Robust standard errors are given in parentheses, with significance stars defined as *** p< 0.01, ** p< 0.05, *
p<0.1
Trends in Organized Crime (2020) 23:242–262 255
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
32 provincial coefficients (nine provinces less the base province, multiplied by the four
price thresholds), only three are significant (two at the 10% level and one, KwaZulu-
Natal, for the <R16.30 threshold, at the 5% level), consistent with the hypothesis that
there is no significant spatial variation in the prevalence of illicit cigarettes. The
regression results support the descriptive statistics of Table 4which showed that there
is limited provincial variation in the prevalence of cheap cigarettes.
Discussion
The substantial increase in the illicit cigarette trade in South Africa since 2010 and
especially since 2015 is cause for concern. Illicit trade undermines the country’sfiscal
and public health agendas, and supports organized crime (Pauw 2017; Judge Nugent
2018a,b; Vellios et al. 2019). At least three academic studies, using different method-
ologies and survey techniques, have estimated the size of the illicit market since 2015
and find that the illicit market comprises between 30% and 40% of the total market
(Liedeman and Mackay 2015; van der Zee et al. Forthcoming; Vellios et al. 2019). The
best estimate of the size of the illicit market for the present study is 30.7%, which is in
line with the range of estimates in the other studies.
Although industry estimates of illicit trade should be treated with care, the most
recent TISA-funded study found that 33% of cigarettes are sold at a price that does not
cover the tax (Ipsos 2018a,b). Historically, academic estimates of the size of the illicit
market in South Africa have differed substantially from those of the tobacco industry
(Blecher 2010;vanWalbeek2014; van Walbeek and Shai 2015), but in the past few
years there has been a convergence in these estimates. Whereas past industry studies
have “talked up”the illicit trade problem (van Walbeek and Shai 2015), presumably to
alarm National Treasury and discourage them from raising the excise tax (Smith et al.
2013), the fact that industry estimates and independent researchers’estimates of the size
of the illicit market are converging indicates that the problem is real.
Other than being the first nationally representative independent study to estimate the
size of the illicit market, this paper quantifies and describes the characteristics of
smokers of illicit cigarettes. From the regression results we find that specific socio-
economic groups are more likely to purchase illicit cigarettes than others, specifically
smokers who are White or Mixed Race, smokers who are older, have low levels of
education, and have low household income per capita. An important finding is that the
prevalence of illicit cigarettes does not differ much between the nine provinces of South
Africa.
Although our data does not allow us to investigate the source of the illicit cigarettes,
we can make inferences from the provincial data. KwaZulu-Natal, the province with the
lowest prevalence of illicit cigarette purchases, has the country’s busiest seaport. This
indicates that it is unlikely that large quantities of illicit cigarettes are imported from
overseas. The prevalence of illicit cigarettes in Limpopo, the province neighbouring
Zimbabwe, is similar to that of most other provinces, which suggests that cross-border
trade with Zimbabwe is not driving the illicit trade. Of the nine provinces, Gauteng, the
economic heartland of South Africa and the province where most cigarettes are
manufactured, has the second-highest prevalence of illicit trade. This finding, read in
conjunction with the well-publicized information about the institutional breakdown at
256 Trends in Organized Crime (2020) 23:242–262
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
SARS, as well as data identifying a large proportion of local brands selling for below
the tax price (Liedeman and Mackay 2015; ACP 2019), suggests that the problem of
illicit trade in South Africa is not primarily one of smuggling, but rather of undeclared
local production.
This undeclared local production poses an enormous challenge for the country. The
illicit trade has not only caused much revenue loss to the government, but it has greatly
undermined the country’s public health agenda.
The new president, Cyril Ramaphosa, has made the rebuilding of SARS a priority. In
2018, SARS established an Illicit Economy Unit that focuses on tax enforcement
relating to industries where illicit trade is a problem, including tobacco (Khumalo
2018).
The World Health Organization’s Protocol to Eliminate Illicit Trade in Tobacco
Products (ITP), which became effective on 25 September 2018, includes recommended
best practices for curbing illicit trade. Although South Africa has not ratified the ITP,
the Minister of Finance in 2018 committed the government to “extend the use of ‘fiscal
markers’, which are required under the tracking and tracing obligations of the World
Health Organization’s Protocol to Eliminate Illicit Trade in Tobacco Products”
(National Treasury 2018). South Africa’s problem with illicit trade is real, and it
requires a coordinated and comprehensive response.
Caveats and data limitations
Data cleaning
The sample has been reduced as a result of data cleaning and the removal of price
reporting errors. Table 6below presents the packaging distribution for the original and
final samples. The last column indicates that a larger proportion of smokers who
purchased single cigarettes was excluded in the cleaning process, compared to broadly
similar proportions of smokers who purchased other packaging types. To the extent that
Table 6 Comparison of original and final samples, by packaging type
Original Sample Final Sample Ratio of Final to Original
Percentage
Number of
Smokers
Percentage Number of
Smokers
Percentage
Single 2,527,054 39.16 1,931,119 34.58 0.88
10-Pack 826,656 12.81 738,710 13.23 1.03
20-Pack 2,707,006 41.95 2,542,545 45.53 1.09
30-Pack 157,990 2.45 149,489 2.68 1.09
Carton of 200 234,929 3.64 222,193 3.98 1.09
Total 6,453,635 100 5,584,056 100 1.00
Pack Type not Reported 216,888 3.25 –––
NIDS wave 5 (2017). Data are weighted using the NIDS wave 5 population weights. The ratio represents the
ratio of the final sample percentage share to the original sample percentage share
Trends in Organized Crime (2020) 23:242–262 257
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
the excluded smokers of single cigarettes are systematically different from those
included in the sample, this could create some bias in the sample.
Measurement error
Individuals may not answer truthfully about whether they are smokers, or the number
of cigarettes they smoke, because there may be stigmas associated with smoking,
especially for specific demographic and cultural groups (Pérez-Stable et al. 1990;Roth
et al. 2009; Dietz et al. 2011).
The one instance in which an under-reporting of smoking or smoking-intensity
would have implications for our results is if smokers of cheap (illegal) cigarettes are
more likely to under-report than smokers of more expensive cigarettes, for fear of being
caught out for buying illegal cigarettes. If this is the case, then our estimates will
understate the size of the illicit market.
Conclusion
Other than the obvious fiscal impact, illicit cigarettes are more affordable and accessible
than taxed cigarettes, thus exposing more people to the harms of smoking, particularly
those who are most vulnerable. The tax-collecting authority plays a crucial role in
ensuring that tobacco companies pay the excise taxes that are due to the government.
The institutional failure at SARS since 2015 has helped the illicit cigarette market to
flourish. This study has shown that the illicit trade in cigarettes has become widespread
in South Africa, at 30% of the overall market. Smokers in low socio-economic
subgroups are most likely to purchase illicit cigarettes, and although there is some
spatial variation in prevalence, there is a sizable share of smokers buying illicit
cigarettes in all provinces.
Decisive action needs to be taken. The establishment of the Illicit Economy Unit at
SARS is a step in the right direction. South Africa also needs to ratify the World Health
Organization’s Protocol to Eliminate Illicit Trade in Tobacco Products and to imple-
ment an effective, independent track and trace system for cigarettes. The matter is
serious and urgent.
Other notes for publishers
Acknowledgements We would like to thank Elizabeth Baldwin for her assistance in editing this document.
We would also like to thank Nicole Vellios for reviewing this document, and for her helpful comments and
feedback. Any errors or omissions are the authors’own.
Funding African Capacity Building Foundation, award number 28741.
Data availability The data used in this paper are publicly available and can be accessed at https://www.
datafirst.uct.ac.za/dataportal/index.php/catalog/712.
258 Trends in Organized Crime (2020) 23:242–262
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
Research involving human participants and/or animals This article does not contain any studies with
human participants or animals performed by any of the authors.
Informed consent As no individual participants were involved in the study, no informed consent was
required.
Appendix 1
Price Correction Rules
We applied price correction rules where obvious reporting errors where identified. For
example, if an individual reports spending a total of R0.50 for 5 single sticks (resulting
in a per stick price of R0.10); this is likely a data error, since there is no record of a
single stick selling for less than R0.50 in South Africa, whereas there is evidence of
single cigarettes being sold for R0.50 by informal vendors. Thus, it is reasonable to
assume that this individual incorrectly reported spending R0.50 in total, and actually
spent R0.50 per stick.
The formal rules applied to the data are:
Reported purchasing singles:
Pr=Cig
i¼TotExpiif1≤TotExpi≤4TotExpi
Sticks j
<0:5ð1Þ
Reported purchasing 10-packs:
Pr=Cig
i¼Tot Expi
10 if 5≤Tot Expi≤35& Tot Expi
Sticksj
<0:5ð1Þ
Pr=Cig
i¼TotExpi
NumItemsi
;if0:5≤TotExpi
NumItemsi
≤4TotExpi≥5ð2Þ
Reported purchasing 20-packs:
Pr=Cig
i¼Tot Expi
20 if 8≤Tot Expi≤60& Tot Expi
Sticksj
<0:5ð1Þ
Pr=Cig
i¼TotExpi
NumItemsi
if0:5≤TotExpi
NumItemsi
≤4TotExpi≥8ð2Þ
Trends in Organized Crime (2020) 23:242–262 259
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260 Trends in Organized Crime (2020) 23:242–262
Reported purchasing 30-packs:
Pr=Cig
i¼Tot Expi
30 if 12≤Tot Expi≤90& Tot Expi
Sticksj
<0:5ð1Þ
Pr=Cig
i¼TotExpi
NumItemsi
if0:5≤TotExpi
NumItemsi
≤4TotExpi≥12 ð2Þ
Reported purchasing cartons (200 sticks):
Pr=Cig
i¼Tot Expi
200 if 50≤Tot Expi≤600& Tot Expi
Sticksj
<0:5ð1Þ
Where Pr/Cigiis the price per cigarette for smoker i,Tot E xpiis the reported total
expenditure for the most recent purchase, Sticksjis the number of sticks given the
reported packaging type j, where j = 1, 10, 20, 30, 200, and Num Itemsiis the number of
items (singles, packs or cartons) purchased.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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