THE PRICE OF VANITY: EVIDENCE FROM LICENSE PLATE PRICES Word count: 16.403

Ilse Meerschaert Student number : 01305357 Supervisor: Prof. Dr. Koen Schoors Master’s Dissertation submitted to obtain the degree of: Master in Business Engineering: Finance Academic year: 2018-2019

THE PRICE OF VANITY: EVIDENCE FROM LICENSE PLATE PRICES Word count: 16.403

Ilse Meerschaert Student number : 01305357 Supervisor: Prof. Dr. Koen Schoors Master’s Dissertation submitted to obtain the degree of: Master in Business Engineering: Finance Academic year: 2018-2019

CONFIDENTIALITY AGREEMENT

PERMISSION I declare that the content of this Master’s Dissertation may be consulted and/or reproduced, provided that the source is referenced.

Ilse Meerschaert

FOREWORD

Before you lies the Master’s Dissertation “The price of vanity: evidence from license plate

prices”. It has been written to fulfil the graduation requirements of the Master degree in Business

Engineering (Finance) at Ghent University (UGent). I was engaged in researching and writing

this dissertation from October 2018 to June 2019.

My research question was formulated together with my supervisor, Prof. Dr. Koen Schoors. The

research was difficult since most of the literature was in Russian, but conducting extensive

investigation has allowed me to find an answer to my research question. Fortunately, both Prof.

Dr. Koen Schoors and Tom Eeckhout were always available and willing to answer my questions

and assist me where I needed help. Therefore, I would like to thank them both for their excellent

advice, guidance and support throughout the research process.

I also benefitted a lot from debating issues with my friends and family. If I ever lost motivation,

you kept me motivated. My parents deserve a special note of thanks: your kind words and wise

council have, as always, served me well. Thank you all for your unwavering support.

I hope you enjoy your reading.

Ilse Meerschaert

Ghent, June 4, 2019

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SUMMARY

After many years of living in a communist regime, many Russians needed a way to break with

their restricted social and economic past. They desired to be noticed by others by owning unique,

exceptional and expensive products. Consequently, many different status symbols were used to

distinguish themselves from others. One of the status goods that became really popular among

Russians is vanity plates. These are a special type of vehicle registration plates for which the

owner pays extra money. They follow the standard structure, but have a particular letter and/or

number combination that make them look more exclusive than regular license plates. Vanity

plates are used to show one’s place in society and display social power and prestige, whether it is

real or imaginary. Therefore, these plates are often sold for millions of Russian rubles.

This research attempts to estimate the willingness to pay for vanity plates and to find out the

specific characteristics that influence its price. Therefore, a theoretical price model is constructed

based on the hedonic price method. This method uses several characteristics of the license plate,

such as the letter and number combinations and the region where the license plate was issued, to

explain the overall price of the plate. Data from Nomera.net, an online platform for buying and

selling license plates, allows us to perform a regression and estimate the impact of these

characteristics on the price.

With this model, we are able to proof differences between several categories of letter and

number combinations. Combinations with 3 times the same letter or number seem to have a

significant positive impact on the overall price of the license plate. The prise is also positively

influenced by the repetition of the region code in the number part. Testing for the different

regions in Russia shows that the federal city of Moscow is among the most expensive regions to

acquire a vanity plate. Finally, we also test if the separate numbers and letters on the plate have

an impact on the price and show that the number 0 gives a license plate the most value.

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TABLE OF CONTENTS

1 INTRODUCTION.............................................................................................................................................1

2 STATUSSYMBOLS.........................................................................................................................................32.1 CONSPICUOUSCONSUMPTION.....................................................................................................................................32.2 FULFILMENTOFNEEDS................................................................................................................................................42.3 RUSSIANSANDSTATUSSYMBOLS...............................................................................................................................5

3. LICENSEPLATESASSTATUSSYMBOL...................................................................................................7

4 RUSSIANLICENCEPLATEFORMATANDVANITYPLATES..............................................................94.1 RUSSIANLICENSEPLATEFORMAT..............................................................................................................................94.2 VANITYPLATES...........................................................................................................................................................104.2.1 Definition................................................................................................................................................................104.2.2 Vanityplateallocation.....................................................................................................................................11

5 DATAANDMODEL.....................................................................................................................................125.1 DATADESCRIPTION....................................................................................................................................................125.1.1 Licenseplate.........................................................................................................................................................125.1.2 Price..........................................................................................................................................................................135.1.3 Views........................................................................................................................................................................145.1.4 Location..................................................................................................................................................................165.1.5 Numbercategory................................................................................................................................................175.1.6 Unusedvariables.................................................................................................................................................17

5.2 MODEL..........................................................................................................................................................................185.2.1 Hedonicpricingmethod..................................................................................................................................185.2.2 Existingresearchonlicenseplatesusingthehedonigmethod......................................................195.2.3 Finalmodel............................................................................................................................................................19

5.3 CONSTRUCTINGTHEKEYVARIABLES......................................................................................................................205.3.1 Regiondummies..................................................................................................................................................205.3.2 Valueofthenumberandletterpattern....................................................................................................21

5.3.2.1 Averagevalueperpattern........................................................................................................................................................215.3.2.2 Firstestimationofcontributionofnumberandletterpattern................................................................................255.3.2.3 Secondestimationofcontributionofnumberandletterpattern..........................................................................265.3.2.4 Iteration...........................................................................................................................................................................................275.3.2.5 Estimatingthefinalvalueperpattern................................................................................................................................31

5.3.3 Lettercategorydummies................................................................................................................................345.3.3.1 Appealinglookinglettercombinations..............................................................................................................................34

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5.3.3.2 Statelicenseplates......................................................................................................................................................................345.3.3.3 Threateninglettercombinations..........................................................................................................................................37

5.3.4 Numbercategorydummies............................................................................................................................385.3.4.1 Appealinglookingnumbercombination...........................................................................................................................385.3.4.2 Lettercombinationisthesame/similarasregioncode..............................................................................................38

5.3.5 Numberandlettercountdummies.............................................................................................................39

6 EMPIRICALRESULTS................................................................................................................................406.1 INTERPRETATIONOFCOEFFICIENTS......................................................................................................................456.1.1 Log-linearinterpretation................................................................................................................................456.1.2 Log-loginterpretation.....................................................................................................................................45

6.2 CONSTANTVALUE.......................................................................................................................................................466.3 VALUEOFLETTERANDNUMBERPATTERN...........................................................................................................466.4 REGION.........................................................................................................................................................................476.5 AMOUNTOFVIEWS.....................................................................................................................................................496.6 LETTERCATEGORY.....................................................................................................................................................506.7 NUMBERCATEGORY...................................................................................................................................................526.8 NUMBERCOUNT..........................................................................................................................................................536.9 LETTERCOUNT............................................................................................................................................................54

7 CONCLUSION...............................................................................................................................................55

REFERENCELIST...............................................................................................................................................57

A REGIONCODES..............................................................................................................................................II

B STATELICENSEPLATES...........................................................................................................................VI

LIST OF TABLES

Table 1: Different plate formats in Russia .................................................................................... 10

Table 2: Missing values per position in license plate .................................................................... 13

Table 3: Summary statistics: price ................................................................................................ 13

Table 4: Summary statistics: ln_price ........................................................................................... 14

Table 5: Summary statistics: views ............................................................................................... 15

Table 6: Summary statistics: ln_views .......................................................................................... 15

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Table 7: Frequency per type of number_category ........................................................................ 17

Table 8: 30 most expensive number patterns ................................................................................ 22

Table 9: 30 most expensive letter patterns .................................................................................... 23

Table 10: Summary statistics: dif_iter_1_num until dif_iter_9_num ........................................... 29

Table 11: Summary statistics: dif_iter_1_let until dif_iter_9_let ................................................. 30

Table 12: 30 most expensive number patterns .............................................................................. 32

Table 13: 30 most expensive letter patterns .................................................................................. 33

Table 14: Overview of state license plate series per region .......................................................... 36

Table 15: Overview of series with a threatening meaning ............................................................ 37

Table 16: Overview of series with an appealing looking number combination ............................ 38

Table 17: Statistical tests of regression model .............................................................................. 40

Table 18: Regression of ln_price over explanatory variables ....................................................... 44

Table 19: The 5 most expensive regions to buy a vanity plate ..................................................... 48

Table 20: The 5 least expensive regions to buy a vanity plate ...................................................... 48

Table 21: Region codes for different regions of Russia. ................................................................ V

Table 22: State license plate series for Republic of Bashkortostan .............................................. VI

Table 23: State license plate series for Altai Republic .................................................................. VI

Table 24: State license plate series for the Republic of Kalmykia ................................................ VI

Table 25: State license plate series for the Republic of Karelia .................................................. VII

Table 26: State license plate series for the Republic of Komi .................................................... VII

Table 27: State license plate series for the Republic of Sakha .................................................... VII

Table 28: State license plate series for the Republic of Tatarsta ................................................ VII

Table 29: State license plate series for Udmurt Republic .......................................................... VIII

Table 30: State license plate series for Krasnodar Krai ............................................................. VIII

Table 31: State license plate series for Krasnoyarsk Krai ............................................................. IX

Table 32: State license plate series for Primorsky Krai ................................................................ IX

Table 33: State license plate series for Arkhangelsk Oblast ......................................................... IX

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Table 34: State license plate series for Vladimir Oblast ................................................................ X

Table 35: State license plate series for Volgograd Oblast ............................................................. X

Table 36: State license plate series for Leningrad Oblast .............................................................. X

Table 37: State license plate series for Moscow Oblast ................................................................. X

Table 38: State license plate series for federal city Moscow ........................................................ XI

Table 39: State license plate series for federal city St. Petersburg ............................................... XI

LIST OF FIGURES

Figure 1: Original Pyramid of Needs .............................................................................................. 5

Figure 2: Histogram of price ......................................................................................................... 13

Figure 3: Histogram of ln_price .................................................................................................... 14

Figure 4: Histogram of views ........................................................................................................ 15

Figure 5: Histogram of ln_views ................................................................................................... 16

Figure 6: Histogram of mean values for dif_iter_1_num until dif_iter_9_num ........................... 29

Figure 7: Histogram of mean values of dif_iter_1_let until dif_iter_9_let ................................... 30

Figure 8: Size of each region’s impact on the overall price of the license plate. .......................... 48

Figure 9: The impact of state license plate series per region ........................................................ 51

1

1 INTRODUCTION

As long as cars exist, they have been used as status symbols to demonstrate someone’s worth.

Among other things, having the fastest, coolest or most expensive car on the market used to be

enough to fit in with higher social circles. But for some persons this is no longer the case.

Nowadays, some people feel the need to add that little extra to their car and choose for a

personalized license plate, or a vanity plate as they are sometimes called. In Russia, it’s not

possible to completely personalize your license plate by having, for example, your nickname on

it. It has however recently become possible to buy license plates on online platforms. These have

the exact same structure as regular license plates but are in some way or another more special

because of the specific letters and/or numbers that are on it. Getting your hands on the most

exclusive plate can cost a fortune. Despite that, Russians seem to be fond of this practice and use

it as a way of showing of their wealth and power towards others.

The structure of a vanity plate is the same as regular license plates, which makes it hard to

distinguish them. Moreover, what exactly is seen as a vanity plate can depend for a great deal on

personal taste. However, several plate characteristics seem to be appreciated by most people.

This research investigates which characteristics add the most value to a license plate by

constructing a theoretical price model based on the hedonic method.

Since vanity plates are used as status symbols, it behooves to begin this discussion with a deeper

look into how and why status symbols are used. This will be done in chapter 2, while chapter 3

examines the link between status goods and license plates. We proceed by taking a closer look at

the Russian license plate format and the allocation of vanity plates in chapter 4.

In chapter 5, we’ll examine our dataset in more detail and have a look at the variables that were

put at our disposal. We’ll also construct our theoretical price model based on the hedonic pricing

method and construct the necessary variables to execute this model. Since the main difference

between a valuable and non-valuable license plate is the specific numbers and/or letters that are

on it, we propose a method to estimate the value of the different letter and number patters. We

also analyze different letter and number categories that might have a significant impact on the

overall price of the plate. Special attention is provided to some series of letter combinations that

are reserved for government agencies. Apart from these series, there is also made a distinction

between several appealing looking patterns. Having preferable characteristics in both the letter

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part as well as in the number part can either turn out to have a complementary or supplementary

influence on the price, which our model will demonstrate.

Finally, the constructed price model is performed and analyzed in detail in chapter 6. We’ll have

a look at the letter and number categories that have the biggest impact on the license plate price,

as well as differences caused by the region where the license plate was issued.

This thesis adds to the literature about status goods and the willingness to pay for them. We

propose a pricing model and find interesting impacts on the price caused by several

characteristics of license plates.

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2 STATUS SYMBOLS

In some past cultures of East Asia, jade and pearls used to be reserved exclusively for royalty

and were therefore major status symbols. Today, a wide variety of status symbols are still

associated with luxury goods. It goes from very exclusive jewellery to haute couture fashionable

clothing, an impressive mansion in the countryside or a penthouse apartment in a vibrant city.

Apart from luxury goods, non-valuable things can give someone status as well. Scars, for

instance, represent honour and courage in countries where warriors are highly respected. And

while all of this may impress some people, others won’t care at all. In academic circles for

instance, people will be more impressed by a long list of publications and a securely tenured

position at a prestigious university than by having a different Rolex for every time zone. To top

this off, in business circles, rich businessmen sometimes go as far as showing of with a trophy

wife!

Status symbols clearly come in all different shapes and sizes. What exactly acts as a status

symbol has changed over time and has always been different per culture. However, if someone

that lived 100 years ago would visit us today, he would certainly be confused by the way some

people nowadays take selfies behind the wheel in their brand new car, but he would understand

the need to show it off. Over the years, not much has changed about the purpose of status

symbols. They all still have 1 thing in common: they serve as a way to associate yourself with

your own or a higher social group, or dissociate yourself from the social group below you. What

exactly is used as status symbol to achieve this has always been associated with the fundamental

differences between the upper and lower classes within a society. In capitalist societies for

instance, most status symbols are tied to monetary wealth to express that they can afford these

extremely high prices that are out of reach for lower economic classes.

2.1 CONSPICUOUS CONSUMPTION

But why the need to show of your wealth, when you already have more money than the social

class below you? The American economist and sociologist Thorstein Veblen argued in The

Theory of the Leisure Class (1899) that the accumulation of wealth alone isn’t what confers

status. It’s the evidence of wealth that is used as a way to gain and confirm status and prestige.

The evidence of wealth helps you look good in society and can be obtained by what he calls

conspicuous consumption. This can be defined as the situation in which people spend a lot of

4

money intentionally so that other people notice and admire them for their wealth (Cambridge,

2019).

This intentional spending of money seems to impress people. Belk, Bahn, and Mayer (1982)

show that people indeed draw conclusions about others based on their possessions. Richins

(1994) comes to the conclusion that the objects that symbolize success tend to have a high price

or are at least expensive relative to the average cost of products or services in the same category.

Veblen goods named after Thorstein Veblen indicate goods for which the demand increases as

the price increases. In contrast to normal goods, which have a downward sloping demand curve,

the demand curve of Veblen goods is upward sloping, meaning that the demand increase with the

price. Examples are designer handbags, jewellery, certain wines and luxury cars. These are also

often used as status goods since they signal that one has style, class, money or good taste.

Of course not all people use conspicuous consumption in the same way. Han, Nunes and Drézes

(2010) propose a taxonomy that assigns consumers to one of four groups based on wealth and

their need to show their status. They show how each group’s preference for conspicuously or

inconspicuously branded luxury goods correspond with their desire to either associate or

dissociate with members of their own and/or other groups. Some people for example avoid too

obvious wealth signals that separate them from a lower class and use rather ‘quiet’ signals that

only others of their group will recognize. Others on the other hand, use very conspicuously

branded luxury goods to be noticed by everyone.

2.2 FULFILMENT OF NEEDS

Another way to look at status goods is as a way to fulfil someone’s need to belong to a group or

to fulfil their need for esteem in order to move to a higher level of needs. The American

sociologist Abraham Maslow suggested in his paper A Theory of human Motivation (1943) his

world famous ‘Pyramid of needs’. This model originally classifies and ranks people’s needs in 5

stages, with at the bottom of the pyramid the most fundamental needs, which people will try to

meet first. Figure 1 gives an overview of the 5 original stages. 2 assumptions are made in this

model: 1) when someone suffers from a shortage in one stage, they will not try to fulfil a higher

need and 2) as soon as 1 stage is secured, people will seek to satisfy the next stage.

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Figure 1: Original Pyramid of Needs

After fulfilling the critical needs like food, water and oxygen (stage 1) and having a physically

safe feeling (stage 2), people start to feel the need to belong to a group. There are a wide variety

of status goods that can be used to make clear that you fit in a certain group or class of people.

You don’t even have to think beyond the school playground to remember a time when you

wished you had a better deck of Pokémon cards, just to fit in with the other kids at school.

When the need to belong to a certain group is fulfilled, status goods can even be used to fulfil the

next stage, which is the need for self-esteem. Esteem needs can be described as ego needs or

status needs. Using status goods to demonstrate our worth by what we own, can in some cases be

a way to get recognition, status, importance and respect from others.

2.3 RUSSIANS AND STATUS SYMBOLS

Since this research handles Russian license plates, it’s interesting to have a look at how Russians

feel about status symbols. Even though how one feels about status symbols and how they use

them is mostly personal, there are some trends. Many Russians have the desire to be noticed by

others by owning unique, exceptional and expensive products (Lecamp, 2013). For instance,

many of them have a preference for clothing and accessories that catch the eye and make

someone stand out from the crowd (Kulikova and Godart, 2014). This is not surprising, as they

have a strong desire to break with their extremely restricted social and economic past. This also

Self-

actualization

Self-esteem

Socialbelonging

Safetyneeds

Physiologicalneeds

6

made them less focused on the future. They tend to prefer spending money today, rather than to

save it and invest it later (Kulikova and Godart, 2014). You could say they have a “living for

today” mentality and are a huge fan of using status goods to show their wealth and power.

After the Soviet Communist regime, income dropped drastically for the majority of Russian

people between 1990 and 1998. However, there was one particular group of privileged and

politically connected Russians that benefited from this situation: The New Russians. They were a

newly rich business class that arose during the chaotic transition from a planned economy to an

open market economy in the 90s. Some people believe that they achieved their rapid wealth by

using criminal methods during this transition. They are often seen as gaudy, conspicuous

consumers and arrogant nouveau riche (or nouveau Russe as some would say) as they

prominently display their status symbols. In particular their red crimson jacket, their expensive

jewellery and of course their luxury cars. Back then many Russian consumers felt the need to

emulate the New Russians, which caused them to allocate more income towards luxury goods

instead of necessities. Now, the New Russians have become a stereotypical caricature that often

appears in various anecdotes, movies, plays and broadcasts, where they are pictured as true

heroes of that time (Gessen, 1995).

The importance of expensive luxury cars as a status symbol was nothing new. In the 50s, every

Soviet citizen used to dream about the Volga. The iconic car, which was often called the Russian

Mercedes, used to be extremely popular in Russia and was a favourite of communist party

officials and many businessmen (Bigg, 2005). Now that the Volga’s glory days are over,

Russians tend to use electric cars as status symbols. A survey done by the Avtonet National

Technology Initiative shows that more than one third of actual and potential owners in Russia

admit that they (would) buy an electric car for prestige reasons, rather than to cut their car

expenses or contribute to saving the environment (“The power of prestige”, 2018). There is also

a strong desire among many Russians to own a ‘supercar’. These high performance cars are

mostly designed for speed and visual appeal but have little practical use. The highest speeds

cannot even be attained legally nor safely. Since the full capabilities of these cars can rarely be

experienced (not even by the owner), they merely own these cars as an expression of

conspicuous consumption.

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3. LICENSE PLATES AS STATUS SYMBOL

While a great deal of research has been done on status symbols and status goods, little work of

which we are aware has examined the link between (personalized) license plates and status

goods yet. One exception might be the work of Ng, Chong and Du (2009) who estimate the

value of superstitions by studying the auction prizes of vehicle license plates in Hong Kong.

Despite the fact that they put a value on the importance of several characteristics of the license

plate, there is still little or no real link with status goods.

However, using license plates as status goods isn’t something new. The first time license plates

were used as a status symbol traces back to the beginning of the 20th century (Seurkamp, 2018).

In the United States, Delaware was one of the first states to require owners to register their car in

1905. At that time, buying a car was still really expensive and only a few people that were

wealthy enough could afford to own one. Due to the small amount of owners, a 1 to 3-digit

number was sufficient and car owners could assign it themselves and register with the state.

When Ford introduced its famous T-model in 1908, also the less fortunate could now afford to

buy a car, which caused the need for a 4-digit number. From that moment on, driving a low-digit

license plate became a sign that you were wealthy enough to buy a car before the introduction of

less expensive models like Ford’s T-model.

More than a century later, the license plate on the car became almost as much as a status symbol

as the car itself. It’s even said that in Delaware it’s now more important to drive a low-digit

license plate, than driving in a Rolls Royce. Having a unique license plate became a trend in

many countries. And people are willing to pay a lot to follow this trend. In 2008 for example, a

rich businessman called Saeed Abdul Ghafour Khouri paid $14,2 million for a Dubai license

plate with the number 1 (Rothaar, 2010). Some people in Dubai take their license plate very

serious and even say that, if your license plate costs less than your car, you’re not doing anything

for your reputation (Jones, 2013). In Rhode Island, USA, 2 brothers even went to court over their

late father’s 3-digit plate back in 1983 (Wallack, 2005). In England, the license plate with “F1”

on it was sold for £ 375.000 and is now installed on a Bugatti Veyron SuperSport (Felebchuk,

2019).

There are probably a lot of people that find it ridiculous to spend so much money on ‘just’ a

license plate while it doesn’t give you any more advantages. Vanity plates only allow you to use

8

your car on the road, just like regular license plates do. However, there are some reasonable

motives for longing one. These special combinations not only allow you to easily recognize your

car from hundreds of others, it also tells a lot about the car and its owner since it emphasizes the

value assigned to it and symbolizes the success or wealth of the owner (“Красивые номера на

авто, мотоциклы и прицепы”, n.d.). It also shows a great political network, because you

typically need some connections to get your hands on the most special license plates. When these

are installed on an expensive car, some traffic police officers may even find the driver a person

with great connections and money and thus won’t stop him (“Как получить красивый номер в

ГАИ официально? И возможно ли это?”, n.d.). The drivers themselves often even believe that

along with their license late, they were given special privileges on the road and start violating all

sorts of traffic rules without a twinge of conscience. Funny enough, the police tracking system in

Russia is automated by almost 90%, so there won’t be made any distinction between regular

license plates and license plates that look special, but have no meaning in reality (Панфилова,

2018).

Furthermore, not only the driver’s behavior changes because their vanity plate. They show

priority over other cars on the road and consequently change the behavior of other drivers as

well. People will react differently when seeing a car with a “thug” number and won’t go into

conflict with them. Some people go even further, and combine their unique license plate with a

flashing siren, to make it look like they are really important.

Finally, some people even believe in the “magical power of numbers” and assume that certain

combinations of numbers and letters can bring good luck on the road and thus consider it as a

way to take their luck with them on all their trips. In a similar study as this one performed in

Hong Kong, Ng, Chong and Du (2009) showed that the number “8” carries a significant

premium on a license plate since it rhymes with the Chinese word for “prosperity”. The number

“4” on the other hand rhymes with the word for “die” and “death”, and therefore, carries a

significant discount.

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4 RUSSIAN LICENCE PLATE FORMAT AND VANITY PLATES

4.1 RUSSIAN LICENSE PLATE FORMAT

As in most countries, Russian vehicle registration plates are mandatory and display the unique

registration mark of a vehicle. It serves no other purpose than legalizing a car to use the road,

and the plate number itself does not change this legal function. For Russian passenger cars, every

license plate has the same format: first comes a letter, followed by 3 digits and then again 2

letters. In short, this format can be indicated as LDDDLL, where L and D stand for letter and

digit, respectively. Every number from 0 to 9 can be used to fill in the 3 digits. While there are

no restrictions in the use of the different digits, there is only a small amount of letters that are

being used on Russian license plates. To be conform with the Vienna International Agreement

adopted in 1968, only a subset of Cyrillic characters that looks like Latin characters can be used

(“Vienna Convention on Road Signs and Signals”, 2019). This measure improves legibility,

which is better for automatic plate-number recognition. It also makes it possible for people

outside of Russia to read the license plate correctly. The 12 Latin letters that are currently used

are А, В, C, Е, Н, K, М, О, Р, Т, X and Y.

Besides the registration mark of the vehicle, every license plate also has a region number (or

region code), consisting of 1,2 or 3 digits. The standard license plate format can now be written

as LDDDLL|Reg, where Reg stands for the region number of the plate. Keeping in mind the

limitations on the use of the letters, only 1.726.272 combination can be issued of the standard

plate format LNNNLL|Reg within one administration unit. Since certain regions eventually

needed to issue an amount of plates exceeding this number, they ran out of combinations with

one region code and therefore needed multiple region codes per region. The federal city of

Moscow for example, currently has the following 8 region codes: 77, 97, 99, 177, 197, 199, 777

and 799. St. Petersburg on the other hand, has 4 different region numbers: 78, 98, 178 and 198.

The drawback of multiple region numbers per region is that it becomes harder to identify where

one’s from and that, eventually, Russia will run out of numbers again. Finally, every passenger

car also has the national flag of Russia on its license plate.

Even though the format described above is the most common license plate format one would see

when in Russia, other formats are in use as well. Vehicles used by certain organizations carry

special plates e.g. vehicles for police force, public transport and military forces. Table 1 shows

10

their specific format. Also license plates for motorcycles, mopeds and scooters are different.

Most of these other license plates are different in color and measurements as well ("Vehicle

registration plates of Russia", 2016).

Despite all of these different formats being very interesting, this research will focus on the

standard Russian vehicle registration plates for passenger cars, being the ones of the format

LDDDLL|Reg.

Group Plate format

Passenger cars

Police forces

LDDDLL|Reg

LDDDD|Reg

Diplomatic DDDLLD|Reg

Military forces DDDDLL|Reg

Exported form Russia LLDDDL|Reg

Public transport vehicles LLDDD|Reg

Trailers LLDDDD|Reg

Temporary and transit plates LLDDDL|Reg

Table 1: Different plate formats in Russia

4.2 VANITY PLATES

4.2.1 DEFINITION

Vanity plates, defined as number plates on a vehicle that have particular digits or letters on them

that the vehicle’s owner has specially chosen and paid to have (Cambridge, s.d.), are not

officially available when registering a car in Russia. The Russian traffic police issue the

registration marks randomly and only if you’re lucky, you receive a ‘cool’ looking plate. Even

though what exactly is considered as a cool looking plate depends on personal taste, some

particular categories seem to be preferred by a lot of people. Plates with the same 3 letters such

as the series A***AA|Reg and O***OO|Reg or same 3 digits such as *111**|Reg and

*777**|Reg are of particular interest to car owners. So are plates that have 5 identical digits e.g.

a series *777**|Reg combined with region code 77 (federal city Moscow) gives *777**|77,

which is considered very exclusive (Захаров, 2016). Of course, this exclusivity comes with a

price. On online platforms, prices for these license plates can range from a couple of thousand

11

rubles for the most affordable ones, to even a few million rubles for the rarest and most unusual

plates (“Как и где купить красивые номера на автомобиль”, s.d.).

4.2.2 VANITY PLATE ALLOCATION

As mentioned before, nowadays, Russian vanity plates are often sold on online platforms since

there is no official way of getting a vanity plate when registering a car in Russia. Although,

Eeckhout (2017) reveals a strong evidence for bribery among police officers that are responsible

for the registration of cars in order to receive vanity plates. It should be noted that this method,

of course, is illegal. Since the law prohibits the sale of license plates by one person to another,

the only legal way to obtain a personalized plate used to be by being lucky enough to receive one

when registering a car (Cергеев, 2019). Notwithstanding, there have been multiple attempts to

legalize the sale of vanity plates in the last decade (“Можно ли официально получить

красивый номер в гибдд?”, n.d.); (Захаров, 2016). In 2010, a project was developed aimed at

legalizing the issuing of vanity plate sales through auctions, but was interrupted later. In 2013

however, license plates officially became separable from the car, meaning the car owner could

keep the license plate when selling his car. This loophole opened up many (legal) opportunities.

People interested in a vanity plate could now acquire a car (and its vanity plate) from its owner,

and then sell the car back to the owner (without the plate), and thus keeping the plate only.

Today there are many companies acting as intermediary organizations to provide assistance in

the selection of the best and most unique vanity plates.

It’s expected that it will soon be possible to officially buy a vanity plate at the traffic police. In

2018 the Russian government introduced the idea to trade vanity plates directly when buying a

car at the salon or during check-in with the inspectorate. This would lead to a large income

stream for the government. Unfortunately, there is no agreement yet, so until then, there still

remains need for intermediary commercial organizations to provide help in the vanity plate

market.

12

5 DATA AND MODEL

5.1 DATA DESCRIPTION

The data used in this work belongs to the website Nomera.net, a bulletin board for selling

Russian licence plates and telephone numbers. All of the license plates that are placed on the

website belong to their real owners, so Nomera.net only acts as an intermediary by providing the

service of placing information and contacting the owner. Both parties still need to carry out the

buying and selling themselves (Nomera, s.d.).

The data tracks asking prices of Russian license plates from Nomera.net for a time period 19

October 2017 – 24 October 2017. Originally, the dataset contains 32.866 observations and has 7

variables, which will be explained one by one below.

5.1.1 LICENSE PLATE

The first and probably most important variable of this research is the variable with the licence

plate for every observation. All observations are of the standard plate format LDDDLL for

passenger cars. However, for some observations not every letter or digit was filled in. Luckily,

this didn’t cause any problems since every letter or digit of the license plate still could be

assigned to the correct position on the plate format. Table 2 contains the amount of missing

values per position in the license plate. For example, *DDDLL implies that 5,915 observations

had no value for the first position of the license plate. These categories are not mutually

exclusive, meaning that it’s possible that one observation appears in more than one of these

categories when there are multiple missing letters and/or digits e.g. an observation of the form

*D**LL would appear in category *DDDLL, LD*DLL and LDD*LL.

If we would choose to only work with license plates without missing letters and/or digits, and

consequently delete all of these incomplete observations, our database would be narrowed down

to only 17.091 observations. Nevertheless, we chose to work with the complete database, which

does include several observations with missing letters and/or digits. A motive for this decision is

that license plates with some missing letters or digits for certain positions could be seen as an

important source of information about the part of the plate that is known, since the price of the

plate will only be influenced by the known part of the plate. Therefore, they were kept in our

dataset.

13

Missing value position Observations

*DDDLL 5.915 L*DDLL 1.836 LD*DLL 2.886 LDD*LL 3.301 LDDD*L 7.291 LDDDL* 8.431

Table 2: Missing values per position in license plate

5.1.2 PRICE

A second important variable of the database is the variable containing the asking price

(expressed in Russian ruble). We assume that all prices should be strictly positive. Yet 1.181

observations seem to have a price of 0, which might imply that the price can still be negotiated.

This would disrupt our results since they do not show the actual value of the plate. Therefore,

they were deleted from the original dataset. The remaining 31.685 observations have a strictly

positive price. Table 3 shows a summary of the statistics of the variable price. The histogram in

Figure 2 illustrates the frequency of the different values for this variable.

Variable Obs. Mean Std. Dev. Median Min. Max.

price 31.685 151.975,2 304.305,6 80.000 1 9999999

Table 3: Summary statistics: price

Figure 2: Histogram of price

14

Clearly the variable price is highly skewed to the right, as the tail on the right hand side is much

longer than the one on the left hand side. Therefore we perform a logarithmic transformation on

the price. For this research, the natural logarithm of the price (ln_price) will be used. Table 4

gives a summary of the statistics for this variable, while Figure 3 shows the frequency of its

different values. The variable ln_price has a more normal distribution compared to the variable

price.

Variable Obs. Mean Std. Dev. Median Min. Max.

ln_price 31.685 11,15818 1,608533 11,28978 0 16,1181

Table 4: Summary statistics: ln_price

Figure 3: Histogram of ln_price

To get a better idea about the value of the prices in Russian ruble, some of these prices will be

converted to euro further in this research. For this, the exchange rate of 17 October 2017 will be

used (1 RUB = 0,0148 EUR), which is the first day of the 5-day period the data was tracked.

5.1.3 VIEWS

The variable views indicates how many times a particular license plate has been seen on the

website before it was sold. There could be a link with the price of the plate in either direction.

On the one hand, vanity plates could attract more views than regular ones. However, on the other

hand, since there is a higher demand for vanity plates, these could be sold much faster, which

results in a lower amount of views. Either way, the amount of views will serve as a control

15

variable in the model we’ll construct later on. Table 5 summarizes the statistics of the variable

views. The histogram in Figure 4 shows the frequency of its different values.

Variable Obs. Mean Std. Dev. Min. Max.

views 31.685 287,5876 512,8434 20 23454

Table 5: Summary statistics: views

Figure 4: Histogram of views

As Figure 4 shows, the variable views is highly right skewed with a much longer tail on the right

side than on the left side. Therefore, a logarithmic transformation is done for this variable as

well. The summary statistics for the variable ln_views can be found in Table 6. The histogram in

Figure 5 shows the frequency for different values of ln_views. Comparing the histogram in

Figure 4 with the one in Figure 5, clearly shows that ln_views leans more towards a normality

distribution than the variable views.

Variable Obs. Mean Std. Dev. Min. Max.

ln_views 31.685 287,5876 512,8434 20 23454

Table 6: Summary statistics: ln_views

16

Figure 5: Histogram of ln_views

5.1.4 LOCATION

By being the biggest country on earth, Russia has a lot of diversity when it comes to their

culture, ethnics, climate and so on. Since regional differences could be an important cause of

price differences for vanity plates, it’s important to have a clear understanding of the different

regions in Russia. As of 18 March 2014, the Russian Federation consists of 85 federal subjects,

all having equal federal rights and equal representation in the Federal Council. Yet, they differ in

their degree of autonomy (“Subdivisions of Russia”, 2019).

All of these 85 federal subjects have their own region number for their license plates. As

previously stated, some even have multiple ones. All of these 85 federal subjects can be

subdivided in 6 types. There are 22 republic, 9 krais, 46 oblasts, 1 autonomous oblast, 4

autonomous okrugs and 3 federal cities. However, the republic of Crimea, and the federal city

Sevastopol are internationally recognized as part of Ukraine (“Federal cities of Russia”, 2019).

In our dataset, the variable location tells us in which republic, krai, (autonomous) oblast,

autonomous okrug or federal city the license plate was issued for the first time. Since our dataset

has no information on the exact region number, the variable location only reveals this number

for federal subjects with only 1 region code. For example, all plates issued in the Republic of

Adygea have region code 1, since this is Adygea’s only region code. Unfortunately, when a

federal subject has multiple region codes, no distinction can be made between them in this

research. For instance, all observations having the federal city of Moscow as their location can

either have 77, 97, 99, 177, 197, 199, 777 or 799 as their region code.

17

A closer look at our dataset reveals that there are no observations for the Republic of Crimea and

the federal city of Sevastopol, plausibly since they are both part of Ukraine. Furthermore, also

the oblasts Kemerovo and Sakhalin have no observations in this database. Another 1.222

observations had no assigned location.

Attachment A gives an overview of every republic, krai, (autonomous) oblast, autonomous

okrug and federal city accompanied by its region code(s) and the amount of observations with

this location in our database.

5.1.5 NUMBER CATEGORY

A fifth variable contains information regarding the type of pattern the digit part of the license

plate has. 4 categories occurred. A first category is the ‘triples’, which include all plates with 3

times the same digit in the number part. All plates with 000, 111, 222 until 999 belong to this

category. Secondly, the category ‘top ten’ contains all plates with 001, 002 until 009 in the digit

part. The number part of the category ‘hundred’ has the inverse form compared to the previous

category, including all plates with 100, 200 until 900. Finally the category ‘mirrors’ includes all

plates with a mirrored number part e.g. 121, 484, 919, etc. All plates of which the number part

doesn’t belong in one of these categories are incorporated in the category ‘other’. Table 7 gives

an overview of the frequency of each number category in this database.

Number category Observations

Triple 9.754

Top ten 5.598

Hundred 2.430

Mirrors 4.333

Other 9.570

Table 7: Frequency per type of number_category

5.1.6 UNUSED VARIABLES

2 variables in this database couldn’t be used. The first one is a variable that contains the date of

sale. Unfortunately, this variable has a date for only 134 observations, and is therefore kind of

meaningless for further research. The second one is another category variable, which has the

same value for every single observation, namely 777. Likewise, this variable won’t be useful for

further research.

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5.2 MODEL

5.2.1 HEDONIC PRICING METHOD

The model used to estimate the willingness to pay for vanity plates is based on the hedonic price

method and its accompanying regression analysis. This method studies how the price of goods or

services is related to its different attributes. It was Andrew Court and other early practitioners

who introduced the first hedonic regression analysis and its methodology in 1939. However, the

popularity of hedonic pricing came with a work of Griliches (1961). Later on, Lancaster (1966)

and Rosen (1974) further developed the theoretical foundations of the method, followed by a

number of subsequent authors who each contributed to the theoretical and practical development

of the technique.

The main idea behind the hedonic method is that many goods and services are not homogeneous,

but consist of bundles of atomistic characteristics that make them different form others. For

example, cars differ by characteristics such as safety, comfort and fuel economy; while

computers differ by characteristics such as speed, display resolution and memory capacity. These

different characteristics result in different prices of the overall good. In most cases we only

observe this overall price instead of the prices of the different components, which makes it

difficult to put a price on the components separately. Assuming that the overall price is

determined by internal and/or external characteristics of the good, hedonic pricing seeks to

identify price factors for these characteristics. This is achieved statistically by regressing the

overall price of the good or service onto the various atomistic characteristics.

The hedonic pricing method has been predominantly used in the housing market to value

ecosystem or environmental services such as noise or air quality, which are otherwise difficult to

put a price on. According to this method, the price of a property is determined by characteristics

of the property (e.g. amount of bedrooms, living space etc.), the location (e.g. the

neighbourhood, distance to city centre etc.) and the environment (e.g. noise, air quality etc.).

When running a regression on house prices, the extent to which each factor affects the overall

price of the home can be calculated and analysed, which makes it possible to estimate the

quantitative values of all characteristics that have a direct impact on market prices of homes.

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5.2.2 EXISTING RESEARCH ON LICENSE PLATES USING THE HEDONIG METHOD

In the past, similar studies that use the hedonic model to analyse license plate prices have been

performed in Hong Kong and have already acquired many interesting insights. Woo and Kwok

(1994) used the hedonic approach for Hong Kong license plates auctioned during 1989-1991.

They found substantial and significant effects of vanity and superstition. Further research by

Woo et al. (2008) estimates a hedonic price model of willingness to pay for superstition. Chong

& Du (2008) attempt to link Chinese numerology to vehicle registration mark prices in Hong

Kong by analysing the value of different patterns of numbers and letters. They find a strong

preference for license plates with the number 8 as well as plates that are visually appealing in

general. Ng, Chong & Du (2009) estimate the value of superstitions by analysing auctions of

vehicle license plates across different policy regimes and different macroeconomic

environments.

5.2.3 FINAL MODEL

This research attempts to analyse the impact of different characteristics of a Russian license plate

on its overall price. There are many possible characteristics that could affect the price such as the

specific number or letter pattern, the region where the plate was first issued, the amount of views

on the web page ect. With the hedonic price method, we will measure the relative importance of

each of the explanatory variables on the licence plate prices through use of regression analysis. A

function for the price of a license plate can be written as:

ln(price) = f (price of number pattern, price of letter pattern, region, views, letter category,

number category, amount of specific letters and numbers)

More precisely, this theoretical model can be written as:

ln(price) = α

+ β1 ln(value of number pattern)

+ β2 ln(value of letter pattern)

+ β3 ln(price of number pattern)*ln(price of letter pattern)

+ β4 (region dummies)

+ β5 ln(views)

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+ β6 (number category dummies)

+ β7 (letter category dummies)

+ β7 (number counts of “0” to “9”)

+ β8 (letter counts of “A” to “Y”)

+ ε

In this model, α is a constant value and the notations β1 until β8 represent vectors. The dependent

variable, being the natural logarithm of the price of a license plate (ln_price) is a function of

several explanatory variables. First of all, the price of the number pattern as well as the price of

the letter pattern influences ln_price. Both of these prices are predicted based on our data. We

also interact these 2 characteristics to see the effect of having both on the price. Furthermore, the

region where the plate was first issued is also expected to have an effect on the price. Therefore,

dummy variables will be assigned to each possible region, based on the variable location in the

original data. We also expect the price to be influenced by ln_views. To analyse if certain

number or letter categories tend to have a higher or lower price, some number and letter

categories are included in the model as well. Finally, our model also tests if having one or

multiple specific numbers or letters in the license plate has an impact on the overall price. This

impact will be measured by using number count and letter count variables.

5.3 CONSTRUCTING THE KEY VARIABLES

To be able to perform the regression constructed in the previous part, a series of new variables

were constructed by making use of the original data. All of these new variables will be discussed

below.

5.3.1 REGION DUMMIES

As mentioned previously, the dataset contains the variable location. Attachment A provides all

possible values for this variable. To make this important information usable in our regression,

this variable was transformed into a series of 81 dummy variables, having value 1 if the name of

the dummy variable corresponds to the location of the plate, and 0 otherwise. To perform the

regressions in this research, the federal city Moscow was chosen as reference category, so only

80 dummy variables were included in the regressions.

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5.3.2 VALUE OF THE NUMBER AND LETTER PATTERN

The next 2 variables that need to be constructed are the ones that contain the value of the number

pattern and the value of the letter pattern of the license plate. It’s assumed that having some

number or letter patterns on your license plate is worth more than having others, and

consequently, the specific number and letter pattern on the license plate are expected to have an

impact on the overall price. To analyse the influence of the number and letter pattern on the

overall price, a value needs to be assigned to every single pattern. The method used to estimate

the value of each pattern consists of several steps, which will be described one by one below.

5.3.2.1 Average value per pattern

As a first approach to estimate the value of each number and letter pattern, the average value of

all license plates with the same pattern is calculated. For example, the dataset contains 953

license plates with 111 as its number pattern. The average value for the number pattern 111 can

then be calculated by the taking the average overall price of these 953 license plates. The value

for the other number patterns, as well as all of the letter patterns is calculated in the same way.

As noted earlier, the license plate is not complete for all observations in the dataset, since some

numbers or letters are unknown. Consequently, this results in several incomplete number

patterns like *19 or 2**, as well as several incomplete letter patterns like A** and M*M, where

* stands for a missing number or letter. It’s important to value all incomplete patterns as unique

number and letter patterns, since they contain a lot of information about the value of the part that

is known. Therefore, 1.229 unique number patterns and 2.162 unique letter patterns can be

found, instead of 1.000 (complete) number patterns and 1.728 (complete) letter patterns.

Since ln_price is used as the dependent variable in the regression of the price model, the value of

the number and letter patterns are also estimated based on ln_price.

Table 8 shows the 30 most expensive number patterns based on this way of estimation. Table 9

shows the 30 most expensive letter patterns according to this method. Both the estimations

based on the real price in Russian rubles (price) as well as on its natural logarithm (ln_price) are

given in these tables. Despite the fact that the estimations based on ln_price will be used later on

in the model, the estimation based on the price in Russian rubles still gives a better idea about

how much some patterns are really worth.

22

Number

pattern

Estimation based

on price (in RUB)

Estimation based

on ln_price

203 2.000.000 14,5087

671 2.000.000 14,5087

0*1 1.333.366 14,1032

*03 1.033.333 13,8483

1*7 1.000.000 13,8155

20* 969.047 13,7840

9*9 891.326 13,7004

*80 875.000 13,6820

0*9 868.000 13,6739

2*7 866.666 13,6724

326 839.500 13,6405

493 802.000 13,5948

0*7 766.000 13,5489

0** 765.242 13,5479

07* 746.078 13,5226

10* 734.444 13,5068

52* 732.500 13,5042

164 731.666 13,5037

00* 726.416 13,4958

**1 678.750 13,4280

*00 653.292 13,3897

0*4 630.000 13,3534

693 609.900 13,3210

563 600.000 13,3046

381 566.666 13,2475

769 550.000 13,2176

478 525.444 13,1719

139 525.000 13,1711

32* 518.333 13,1583

05* 505.509 13,1333

Table 8: 30 most expensive number patterns

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Number

pattern

Estimation based

on price (in RUB)

Estimation based

on ln_price

TEO 1.555.000 14,2570

*HT 1.200.000 13,9978

TAK 957.142 13,7717

BPK 878.571 13,6860

OEK 865.000 13,6705

YB* 753.125 13,5320

MMP 710.989 13,4744

AMP 687.290 13,4405

YME 650.000 13,3847

TMP 592.857 13,2927

MHE 562.142 13,2395

**X 544.523 13,2076

BKC 541.111 13,2013

H*A 537.500 13,1946

ETC 520.040 13,1616

MMA 504.538 13,1313

MPA 497.500 13,1173

CEE 493.333 13,1089

OTP 486.000 13,0939

OP* 452.466 13,0224

A*H 450.000 13,0170

YK* 432.500 12,9773

PTO 427.504 12,9657

*MM 424.324 12,9582

CK* 424.082 12,9576

*MP 413.958 12,9335

PMP 413.818 12,9331

OX* 412.142 12,9291

HAH 410.908 12,9261

**A 404.125 12,9094

Table 9: 30 most expensive letter patterns

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It’s important to note that the dataset does not include the entire population of all license plates

that exist in Russia. On websites like Nomera.net, only valuable license plates are sold. When a

non-valuable number combination appears in the data, it is probably almost always accompanied

by a valuable letter combination. The average value of a non-valuable number combination is

then boosted by all those expensive letter patterns with which it occurs. The reverse applies to

valuable number combinations, which often have letter combinations that are not that valuable,

and contribute little or nothing to the price of the license plate. The estimated value of valuable

combinations is consequently lower than their real value. Therefore, this estimation method is

probably biased due the lack of rather worthless license plates in the dataset.

Keeping this bias in mind, it’s still interesting to have a look at the most valuable number and

letter patterns. Table 8 shows that the number patterns 203 and 671 both have the highest value

of 2.000.000 RUB (≈ 29.600 EUR). These number combinations both occur only 1 time in the

dataset, namely on the license plates A203MP and O671OO, which both have an asking price of

2.000.000 RUB. Since there is only 1 observation of each number pattern, the average value of

these 2 patterns equals the price of the only license plate they appear on. Furthermore, the letter

combinations AMP and OOO have a special, political meaning behind them, which will be

explained later on. According to Table 9, AMP even appears to be the 8th most expensive letter

patterns. OOO on the other hand is also visually appealing because of the triple O’s. As

explained above, the values for the number combinations 203 and 671 are probably biased

because of their valuable letter pattern.

Something else that catches the eye is that lots of the most expensive number patterns consist out

of a zero, another number and an unknown number. This is not surprising, as patterns of the form

X00, 0X0 and X00 are known to be very popular.

When looking at the top 30 of most valuable letter patterns in Table 9, TEO seems to be the most

expensive with a worth of 1.555.000 RUB (≈23.000 EUR). However, at first sight, there is

nothing special about this combination that could explain this high value. When looking at the

dataset in more detail, the letter pattern TEO occurs only 2 times, on the plates T888EO and

T999EO. Since these are both rather valuable number patterns because they consist of 3 times

the same number, the high estimated value for the pattern TEO can be explained by the number

patterns that occur with it.

The most valuable number patterns 203 and 671, and the most valuable letter pattern TEO are a

perfect example that this estimation method only accounts for one part of the license plate,

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without taking into account the other part. However, despite the fact that this method should be

used with some caution, it certainly serves as a good first estimation for some patterns.

5.3.2.2 First estimation of contribution of number and letter pattern

In the next step, the values of the number and letter patterns that were calculated in the previous

section are used to make predictions about the contribution of the other part of the license plate.

Since the previous estimations only account for the value of the number pattern or the value of

the letter pattern, some adjustments need to be done to these price estimations. These

adjustments are supposed to reduce the bias that is caused by estimating the letter pattern without

taking into account the accompanying number pattern on the license plate, and vice versa. In the

first step to adjust these values, the following pair of regressions is used:

ln_price = α1 + β1 ln_value_letter_pattern + γ1 ln_views + δ1 region dummies + ε1

ln_price = α2 + β2 ln_value_number_pattern + γ2 ln_views + δ2 region dummies + ε2

The numbers 1 and 2 in subscript of each parameter refer to regression 1 and regression 2

respectively. α refers to the constant value of each regression. The β’s capture the impact of the

variables ln_value_letter_pattern in (regression 1) and ln_value_number_pattern (in regression

2) on ln_price. These are the values for each pattern that were estimated in the previous section.

The γ’s capture the impact of the amount of views the license plate had on Nomera.net on the

price. A series of region dummies are also included in both regressions, and their impact on the

price is given by δ1 and δ2, which are both vectors. Moscow was used as a reference category for

the regions in both regressions. Finally, the ε’s are the error terms of each regression. It’s clear

that ln_value_letter_pattern and ln_value_number_pattern are the most important variables for

each regression respectively, while ln_views and the region dummies serve as control variables.

In the first regression of the pair above, the value of the letter pattern is assumed to be known,

while the value of the number pattern is unknown. Regression 1 estimates, among other things,

the impact of the value of the letter pattern on the price of the license plate. Since the value of

the number pattern is not known in this regression, it’s assumed that ε1 captures a lot of

information about what the numbers on the license plate still contribute to the overall license

plate price. Therefore, the error term ε1 is used to make a new prediction about the relative

contribution of the numbers to the price of the license plate compared to the average price of a

26

license plate sold in Moscow on Nomera.net. The average value of all residuals with the same

number pattern is captured by a new variable: contribution_of_numbers_2.

contribution_of_numbers_2 = average of ε1 per number pattern

The number 2 in the name of this variable denotes that this variable will be used as input in the

second iteration of this pair of regressions (see 5.3.2.3).

The same applies to the second regression of this pair, where only the value of the number

pattern is supposed to be known. Here, the error term ε2 contains a lot of information about the

value of the letters, as the variable ln_value_letter_pattern is not included in this regression. The

residuals ε2 can thus be used to make a prediction about the contribution of the letter pattern to

the price of the license plate. This contribution is captured in the variable

contribution_of_letters_2 and is calculated as follow:

contribution_of_letters_2 = average of ε2 per letter pattern

5.3.2.3 Second estimation of contribution of number and letter pattern

Now that the contributions of the number and the letter patterns are estimated by the variables

contribution_of_numbers_2 and contribution_of_letters_2 respectively, a second pair of

regressions can be performed:

ln(price) = α1 + β1 contribution_of_letters_2 + γ1 ln_views + δ1 region dummies + ε1

ln(price) = α2 + β2 contribution_of_numbers_2 + γ2 ln_views + δ2 region dummies + ε2

This new pair of regressions is mostly the same as the previous pair, with each regression only

differing in 1 variable. Instead of using ln_value_letter_pattern and ln_value_number_pattern as

the independent variables, the variables contribution_of_letters_2 and

contribution_of_numbers_2 are used. These were estimated in section 5.3.2.2. The parameters in

this pair of regression are of course unique and different from the parameters in the previous pair

of regressions, but have the same notation and interpretation.

After performing the first regression of this pair, the residuals will again contain information

about the contribution of the numbers to the price of the license plate. Therefore, the variable

contribution_of_numbers_3 can be created in a similar way as the variable

contribution_of_numbers_2 was created. The same applies for the second regression. Here the

27

residuals can be used to update the values of the contribution of the letters to

contribution_of_letters_3 as follow:

contribution_of_numbers_3 = average of ε1 per number pattern

contribution_of_letters_3 = average of ε2 per letter pattern

5.3.2.4 Iteration

In this section, the previous step is repeated a certain amount of times. Each time, the variables

contribution_of_letters_n and contribution_of_numbers_n that were created with the n-1th pair of

regressions, are used to perform the nth pair of regressions and are afterwards updated to the

variables contribution_of_letters_n+1 and contribution_of_numbers_n+1 to use in the following

pair of regressions, which is the n+1th pair. In general, this is the nth pair of regression:

ln(price) = α1 + β1 contribution_of_letters_n + γ1 ln_views + δ1 region dummies + ε1

ln(price) = α2 + β2 contribution_of_numbers_n + γ2 ln_views + δ2 region dummies + ε2

with:

contribution_of_numbers_n+1 = average of ε1 per number pattern

contribution_of_letters_n+1 = average of ε2 per letter pattern

It’s expected that the residuals of each regressions will converge to one value after a certain

amount of iterations. This would mean that the differences between the residuals of 2

consecutive iterations becomes smaller and smaller the more iterations that are performed.

To test if this is the case after the first iteration, the variables contribution_of_letters_2 and

contribution_of_numbers_2 needs to be compared with to 2 similar variables that existed before

this iteration. Since the first pair of regressions uses the variables ln_value_letter_pattern and

ln_value_number_pattern, these variables needs to be adjusted in order to make it possible to

compare them with contribution_of_letters_2 and contribution_of_numbers_2 that are created

out of the first pair of regressions. To do this, the mean of the variable ln_price is subtracted

from ln_value_letter_pattern and ln_value_number_pattern respectively, which results in the

contribution of the letter pattern and number pattern to the average of ln_price.

contribution_of_numbers_1 = ln_value_number_pattern - mean(ln_price)

28

contribution_of_letters_1 = ln_value_letter_pattern - mean(ln_price)

As Table 3 shows, the mean of ln_price is 11,1582 so that the 2 equations above can be written

as:

contribution_of_numbers_1 = ln_value_number_pattern - 11,1582

contribution_of_letters_1 = ln_value_letter_pattern - 11,1582

These two contributions can now be compared with the contributions calculated in the first pair

of regressions, which are contribution_of_numbers_2 and contribution_of_letters_2, in order to

see the difference between them. The variables dif_iter_1_num and dif_iter_1_let contain the

difference between the second and first contributions for the numbers and letters respectively.

Since it’s possible that the residuals reach their final value by following an oscillating path,

we’re only interested in the absolute value of the differences between them. Therefore, the

differences between the residuals of the second and first pair of regressions can be written by the

following 2 equations:

dif_iter_1_num = | contribution_of_numbers_2 - contribution_of_numbers_1 |

dif_iter_1_let = | contribution_of_letters_2 - contribution_of_letters_1 |

It’s important to note that the variables dif_iter_1_num and dif_iter_1_let now contain the

difference of the average value of the residuals per number and letter pattern respectively

because of the way the variables contribution_of_numbers_1, contribution_of_numbers_2,

contribution_of_letters_1 and contribution_of_letters_2 were constructed.

In a similar way, the differences between the third and second pair of regressions can be

calculated after the second iteration as follows:

dif_iter_2_num = | contribution_of_numbers_3 - contribution_of_numbers_2 |

dif_iter_2_let = | contribution_of_letters_3 - contribution_of_letters_2 |

Or in general, the differences between the n+1th and nth pair of regressions can be calculated after

performing the nth iteration in a similar way:

dif_iter_n_num = | contribution_of_numbers_n+1 - contribution_of_numbers_n |

dif_iter_n_let = | contribution_of_letters_n+1 - contribution_of_letters_n |

29

Now that these differences are known, it’s time to compare them with one another. Table 10

gives a summary of the differences in contribution of the number patterns for the first 9

iterations. Figure 6 shows the mean of these differences for the contribution of the numbers. As

can be seen, most of change is caused by the first 3 iterations, but after that the average change is

very close to 0. Also the standard deviation becomes smaller and smaller with each iteration.

Variable Mean Standard deviation

dif_iter_1_num 0,09683 0,07398

dif_iter_2_num 0,07981 0,08057

dif_iter_3_num 0,08071 0,06949

dif_iter_4_num 0,01915 0,02971

dif_iter_5_num 0,02498 0,02765

dif_iter_6_num 0,00573 0,01310

dif_iter_7_num 0,00752 0,01219

dif_iter_8_num 0,00189 0,00665

dif_iter_9_num 0,00235 0,00613

Table 10: Summary statistics: dif_iter_1_num until dif_iter_9_num

Figure 6: Histogram of mean values for dif_iter_1_num until dif_iter_9_num

30

Table 11 shows a summary of the statistics for variables dif_iter_1_let until dif_iter_9_let. The

histogram in Figure 7 shows the mean of these differences for the letters. It’s clear that the

biggest changes take place after the first and second iteration. From then on, the changes are all

very close to 0. Also the standard deviations are becoming smaller and smaller by each extra

iteration.

Variable Mean Standard deviation

dif_iter_1_let 0,58557 0,58466

dif_iter_2_let 0,15462 0,12264

dif_iter_3_let 0,03573 0,04797

dif_iter_4_let 0,05089 0,04602

dif_iter_5_let 0,01109 0,01945

dif_iter_6_let 0,01493 0,01834

dif_iter_7_let 0,00354 0,00918

dif_iter_8_let 0,00452 0,00853

dif_iter_9_let 0,00119 0,00491

Table 11: Summary statistics: dif_iter_1_let until dif_iter_9_let

Figure 7: Histogram of mean values of dif_iter_1_let until dif_iter_9_let

31

5.3.2.5 Estimating the final value per pattern

The purpose of all this was to find a more accurate estimation for the values of each letter and

number pattern. So far, only the contribution of the letter and number patterns to the price has

been calculated, not their total value. However, it’s the value of the number and letter pattern

that will be used in the pricing model later on. Therefore the contribution has to be added to the

constant of the regressions so that the final value of the letter and number patterns can be

estimated. This is done as follow:

For example, the 9th pair of regressions can be written like this:

ln(price) = α1 + β1 contribution_of_letters_9 + γ1 ln_views + δ1 region dummies + ε1

ln(price) = α2 + β2 contribution_of_numbers_9 + γ2 ln_views + δ2 region dummies + ε2

Where the variables contribution_of_letters_9 and contribution_of_numbers_9 were estimate by

the pervious pair of regressions. The values of the letter and number patterns can be estimated

and updated as follow:

ln_value_of_letter_pattern = α1 + contribution_of_letters_9

ln_value_of_number_pattern = α2 + contribution_of_numbers_9

Since α1 equals 12,56998 and α2 equals 12,53904, these equations become:

ln_value_of_letter_pattern = 12,56998 + contribution_of_letters_9

ln_value_of_number_pattern = 12,53904 + contribution_of_numbers_9

The variables ln_value_of_letter_pattern and ln_value_of_number_pattern are now ready to be

used as 2 characteristics in the Hedonic pricing model that was introduced earlier. Table 12 and

Table 13 give an overview of the 30 most expensive number and letter patterns respectively.

Comparing these with Table 8 and Table 9, which was the first approach, there are some

differences. For example, the combinations MMM, AMO, and COO entered the top 30 most

expensive letter patterns. All 3 have a political meaning that is highly valued by many people.

The number combination 777 entered the top 30 most expensive number combinations. Lastly, it

should be noted that these are only the top 30 most expensive patterns out of 1.229 unique

number patterns and 2.162 unique letter patterns. It would be a mistake to assume that

combinations that are not included in these tables immediately have a low value.

32

Number pattern Ln value

671 15,6467

024 14,4063

203 14,3637

*98 14,3147

635 14,2843

0*7 13,9803

*09 13,8667

03* 13,8593

*06 13,8507

693 13,7939

698 13,7651

742 13,7464

10* 13,7410

9*6 13,7339

563 13,7169

*80 13,7084

945 13,6954

405 13,6825

2*7 13,6698

769 13,6540

*03 13,6229

164 13,6145

777 13,5902

*61 13,5383

1*7 13,5344

551 13,5005

048 13,4857

326 13,4854

0*4 13,4832

19* 13,4606

Table 12: 30 most expensive number patterns

33

Letter pattern Ln value

CHM 14,6079

*HT 13,9775

TEO 13,9562

MMP 13,9168

AMP 13,8871

OEK 13,8623

EKE 13,8027

BAA 13,7818

*AE 13,7688

MXP 13,7540

OKM 13,6886

YB 13,6837

YME 13,6770

HAX 13,6417

AKP 13,5977

AAA 13,5977

MHE 13,5956

*MP 13,5813

TMP 13,5469

HT* 13,5123

BOA 13,5089

CCX 13,5086

PMP 13,5049

EAO 13,5037

MMM 13,4986

BPK 13,4881

*KC 13,4800

AMO 13,4776

YXO 13,4704

COO 13,4416

Table 13: 30 most expensive letter patterns

34

5.3.3 LETTER CATEGORY DUMMIES

Several dummy variables were used to detect the effect from different letter categories on the

price. The letter pattern on the license plate is often called the series of the license plate. There

can be made a distinction between 3 types of letter categories: the ones that have an appealing

look, and the ones that have a specific meaning, where we can distinguish state license plates and

series with a threatening meaning.

5.3.3.1 Appealing looking letter combinations

First there are some categories that are appealing. The category triples has AAA as letter pattern.

Observations with letter patterns such as BBB, CCC etc. belong to this group and are indicated

by the dummy variable l_triple. An other kind of letter combination that is considered appealing

are the ones that have the same letter twice, for example BAA, ABA and AAB. To make a

distinction between these 3 types, dummy variables l_double1, l_double2 and l_double3 were

created, where the number 1, 2 and 3 in the name of the variable refers to the position of the

‘other’ letter. To avoid confusion with the number categories, which will be discussed later on,

the letter “l” in front of the variable names indicates that these are categories for the letters.

5.3.3.2 State license plates

As mentioned before, license plates can tell a lot about the identity of the owner of the car. In

Russia, it’s a thoroughly established practice where the letter part of the plate can have a specific

political meaning. Several letter series are traditionally associated with various government

agencies. For instance, the series AMP is reserved for the Administration of Russian Militia. The

website Nomera, where our dataset originates form, points out special meanings behind various

letter combinations per region (“Спецсерии автомобильных номеров”, s.d.). Table 14 gives an

overview of the series per region that are used in this research to indicate state license plates.

Attachment B gives an overview of the specific government institution each of these series are

linked with. These institutions can be city hall, FSB, FSO and other departments. Most of these

series thus show that a car belongs to someone with a high political status. In this case, the status

is prescribed, meaning that the particular letter combination on the license plate was obtained

when registering the vehicle based on the right of the owner to drive this series of registration

plates (Панфилова, 2018). Driving around in cars with these letters has some advantages of

preferential treatment. The traffic police officers for example are recommended to assist them,

35

they won’t stop them and won’t inspect their cars because of their working position. The license

plate numbers of these high-ranking officials are even entered on a “white list” that insulates

drivers from speeding tickets.

However, it should be borne in mind that not all of these political series are exclusively reserved

for these important people. Several series are only partly reserved for a governmental institution.

The remaining license plates of these series can be obtained when registering a car at the traffic

police. Of course, you need a great amount of luck to receive one of these special series, but it’s

possible. However, it’s no secret that with a good friend who works at the traffic police, it

becomes easier to get a state license plate (“государственные номера автомобилей – какие

буквы и что означают”, 2018). Even if you have no friends at the traffic police, you can always

bribe them to give you a more special one (Eeckhout, 2017). The status given by these license

plates are in these cases acquired instead of prescribed. It’s these license plates that circulate on

secondary markets like Nomera.net and that are part of the database at hand for this research.

To take these special series into account in our model, a dummy variable was constructed for

each of the regions in Table 14, having a value of 1 for series that have a political meaning for

this particular region, and 0 otherwise. These dummy variables were named

special_name_region.

No reference category was chosen for these dummy variables, since there are a lot of license

plates in the dataset that don’t have a special political meaning, and thus don’t belong to one of

these categories. Also, Altai Republic and Krasnoyarsk Krai had no state license plates in this

dataset, so the dummy variables for these regions were excluded in the regression we’ll construct

later on.

Region State license plate series

Republic of Bashkortostan P***KC|02, K***KC|02, O***OO|02, A***AA|02

Altai Republic C***CC|04, H***HH|04, O***OO|04, M***PA|04,

P***PP|04, P***PA|04, X***XX|04, M***YK|04,

M***OB|04, M***OP|04, M***OC|04, A***CK|04

Republic of Kalmykia O***OO|08, A***AA|08, M***MM|08, C***CC|08

Republic of Karelia T***TT|10, H***HH|10, M***MM|10, E***MP|10

Republic of Komi B***AT|11, P***PP|11, Y***YY|11, T***TT|11,

O***OO|11, M***MM|11

36

Republic of Sakha (Yakutia) M***MM|14, P***PP|14, A***AA|14

Republic of Tatarstan M**MM|16, B***MM|16, B***KM|16, B***TM|16,

O***AA|16, P***AO|16

Udmurt Republic A***AA|18, O***OO|18

Krasnodar Krai P***PP|23, K***KK|93, H***HH|23, O***OO|23,

A***AA|23, O***OO|93 A***AA|93, M***KK|93,

M***KK|93, M***KK|93, A***CK|23, A***AB|23,

A***AC|23, T***TT|23, C***KC|23, A***HY|93,

E***KX|93, C***CM|23, M***MM|23, K***AA|23,

Y***YY|23, Y***YY|93, C***C*|93

Krasnoyarsk Krai K***PK|24, A***OO|24, M***KK|24, M***CK|24,

K***CM|24, B***CP|24

Primorsky Krai H***HH|25, T***TT|25, AAA|125, MMM|25, CCC|25,

M***OO|25, M***OO|125, B***OO|25, BOO|125,

H***OO|25, H***OO|125, Y***OO|25, Y***OO|125,

C***OO|25, C***OO|125

Arkhangelsk Oblast T***TT|29, P***PP|29, M***AO|29, E***PE|29

Vladimir Oblast A***BO|33, A***AA|33, O***OO|33

Volgograd Oblast A***AA|34, Y***YY|34, P***AA|34, A***AM|34,

O***OO|34, M***MM|34

Leningrad Oblast O***OA|47, O***AO|47, O***OM|47

Moscow Oblast A***MM|50, A***MM|90, A***MO|50, A***MO|90,

A***MO|150, A***MP|50, A***MP|90,

M***MM|50,M***MM|90, O***OO|50

Moscow A***MP|77, A***MP|97, E***KX|77, E***KX|97,

E***KX|99, E***KX|177, A***OO|77, B***OO|77,

C***OO|777, M***OO|777, A***MO|77, C***CC|77,

C***CC|99, E***PE|177, M***MP|177, B***MP|77,

P***MP|77

St. Petersburg A***AA|98, O***OO|78, O***KO|78, O***KO|98,

O***KO|98, O***OA|78, O***OA|98, O***AO|78,

O***OC|78, O***OM|78, O***TT|78

Table 14: Overview of state license plate series per region

37

5.3.3.3 Threatening letter combinations

A last category of letter combinations has an underlying meaning that is seen as rather negative

by being threatening towards other drivers. Table 15 shows an overview of the combinations that

were used in this research with their underlying meaning. Since what exactly is seen as

threatening is often personally, it should be noted that some combinations are probably not

included in this list. However, the most common ones are included. The dummy variable

l_threat indicates all observations that are of this type, with the letter “l” indicating that this is a

letter category dummy variable.

Letter combination Meaning

BOP Thief

AYE Anonymous criminal unity

AKM Modernized automatic Kalashnikov

XAM Boor

OPK Orc

CPY I defecate

YXY Woohoo

PBY Rip-off

PPP The sound of an engine revving

Table 15: Overview of series with a threatening meaning

Many people support the idea of banning license plates that contain negative, swearing or

obscene words or abbreviations on them. It’s often said that if it’s not allowed to use this kind of

‘language’ in the media, it should definitely not appear on the road either. Others argue that all

license plates should have the right to exist. Even if the letters turn out to be a word with

negative nuance, every person still interpret it in it’s own way. However, there is a long queue

for the license plates of series B***OP in many different regions and it’s doubtful that this is

because of its aesthetic appeal… (“Автомобильные номера "ВОР" могут запретить”,2015).

38

5.3.4 NUMBER CATEGORY DUMMIES

The number categories can be divided in 2 main groups. The ones with an appealing looking

number combination, and the ones with a number combination that is the same or similar as the

region code.

5.3.4.1 Appealing looking number combination

5 types of appealing number combinations can be distinguished. Table 16 gives an overview of

these 5 categories. The dummy variables n_triple, n_top_ten, n_ten, n_hundred and n_mirror

were created to indicate these different types. The letter ‘n’ in each variable name indicates that

these are categories for number combinations.

Triples Top ten Ten Hundred Mirror

111 001 010 100 101

222 002 020 200 383

333 003 030 300 747

444 004 040 400 687

555 005 050 500 292

666 006 060 600 848

777 007 070 700 959

888 008 080 800 030

999 009 090 900 …

Table 16: Overview of series with an appealing looking number combination

5.3.4.2 Letter combination is the same/similar as region code

License plates of which the region code appears in the number part of the plate are also preferred

by some people. For instance, one of the region codes of St. Petersburg is 198. When this is

combined with the same numbers in the number part of the license plate, it results in

*198**|198, which is very unique and therefore is often preferred by some people.

39

Since the lack of data about the specific region code of each license plate, the location variable

was used as an indication about the region code. This is correct for regions with only 1 region

code. For regions with multiple region codes, it’s impossible to tell which one belongs to the

license plate. Therefore, a distinction is made between regions with only one region code and

regions that have multiple. For regions with only one region code, the dummy variable n_region

equals 1 if the region code appears in the number part of the license plate, and 0 otherwise. For

example, if the region code is 47 (Leningrad Oblast) then n_region will be 1 for license plates

with 047, 470, 847, 479 etc. as their number part. n_region will have a value of 0 for if 47 does

not appear on the number part of the license plate e.g. number combinations 407, 874, 298 etc.

The same applies for regions with multiple region codes, which is indicated by the dummy

variable n_region_multiple. Important to note is that this is less accurate because multiple region

codes are assigned to 1 region, and even if one of these appears in the number combination of

the license plate, it’s perfectly possible that it’s another one in reality. For instance, Moscow

Oblast has region codes 50, 90, 150, 190 and 750. The variable n_region_multiple will have a

value of 1 for the license plate A150AA|750, because 150 is one of the region codes for Moscow

Oblast, while in reality the region code of this license plate is 750 and thus different from the

number combination on the plate. Since we have no idea about the specific region number on the

license plate, this mistake is unavoidable. Attachment A gives an overview of all regions of

Russia with the accompanying region code(s) that were used to assign to each region.

5.3.5 NUMBER AND LETTER COUNT DUMMIES

Apart form estimating different number and letter combinations jointly, the value of every letter

and number will be estimated separately as well. This avoids ambiguous interpretation of the

coefficients of some patterns as well as it allows estimating the discount or premium of a letter

or number. As showed by Ng, Chong and Du (2009), the number 8 is associated with

significantly higher prices for Hong Kong license plates, while the number 4 is associated with

plates with significantly lower prices.

For the different numbers, the variables number_0 until number_9 were constructed to count the

amount of times a particular number appears on the license plate. Each of these variables can

thus have the value 0, 1, 2 or 3, since there are only 3 digits on the license plate, apart from the

region code. For the letters, the variables letter_A until letter_Y were constructed in a similar

way for all 12 possible letters.

40

6 EMPIRICAL RESULTS

Now that all variables of our model are discussed, it’s time to regress ln_price on the

explanatory variables of our model. Table 17 shows the statistical tests for the overall regression

analysis. The coefficients, t-tests and p-values of each variable in this regression are given in

Table 18.

With an F-statistic of 154,87 and a p-value of 0,000, the null hypothesis that our model explains

none of the variation in ln_price can be rejected. Further, the coefficient of determination R2 has

a value of 0,3939 meaning that nearly 40% of the variation in ln_price is determined by our

model, while the other 60% is due to error or other influences that are not explained by our

theoretical model. These other influences could be personal preferences for a particular license

plate, because for example the letter combination has the initials of the owner in it or the number

combination is related to their birthday. Such personal preferences are impossible to account for

with this dataset, since no information about the buyer of the plate is available.

Statistical test Coefficient

F value (132, 31123) 154,87

Prob (F) 0,0000

R-squared 0,3964

Adjusted R-squared 0,3939

Table 17: Statistical tests of regression model

Variable Coefficient t P>|t|

constant -31,9403 -15,60 0,000

ln_value_of_letter_pattern 2,5358 15,73 0,000

ln_value_of_number_pattern 2,5322 15,55 0,000

ln_value_of_letter_pattern

*ln_value_of_number_pattern

-0,1223 -9,56 0,000

ln_views -0,2439 -28,81 0,000

41

Altai_Krai -0,5686 -7,70 0,000

Amur_Oblast -0,2210 -1,04 0,300

Arkhangelsk_Oblast -0,5958 -2,99 0,003

Astrakhan_Oblast -0,3534 -2,59 0,009

Belgorod_Oblast -0,7243 -10,79 0,000

Bryansk_Oblast -0,8723 -11,81 0,000

Vladimir_Oblast -0,5037 -8,73 0,000

Volgograd_Oblast -0,6792 -9,24 0,000

Vologodskaya_Oblast -0,5444 -4,42 0,000

Voronezh_Oblast -1,5843 -18,88 0,000

Jewish_Autonomous_Oblast -0,5353 -2,21 0,027

Zabaykalsky_Krai -0,2700 -1,50 0,134

Ivanovo_Oblast -0,6376 -5,37 0,000

Irkutsk_Oblast -0,5350 -5,91 0,000

Kabardino_Balkaria_Republic -0,5397 -3,15 0,002

Kaliningrad_Oblast -0,9953 -7,84 0,000

Kaluga_Oblast -0,9842 -11,92 0,000

Kamchatka_Krai -0,3683 -3,39 0,001

Kemerovo_Oblast -0,7513 -10,90 0,000

Kirov_Oblast -0,9840 -10,41 0,000

Kostroma_Oblast -0,5762 -6,58 0,000

Krasnodar_Krai -0,6490 -15,58 0,000

Krasnoyarsk_Krai -0,5176 -7,67 0,000

Kurgan_Oblast -0,9954 -6,95 0,000

Kursk_Oblast -1,4011 -14,20 0,000

Leningrad_Oblast -0,5925 -6,82 0,000

Lipetsk_Oblast -1,0845 -9,05 0,000

Magadan_Oblast -1,1729 -7,24 0,000

Moscow_Oblast -0,4818 -15,97 0,000

Murmansk_Oblast -0,2625 -1,79 0,074

Omsk_Oblast -0,3674 -5,69 0,000

Orenburg_Oblast -0,6948 -8,86 0,000

Oryol_Oblast -1,7303 -12,30 0,000

42

Penza_Oblast -0,5339 -4,40 0,000

Perm_Oblast -0,6778 -10,26 0,000

Pskov_Oblast -0,2095 -0,80 0,425

Adygea_Republic -0,1031 -0,98 0,326

Altai_Republic -0,5523 -2,90 0,004

Bashkortostan_Republic -0,6981 -11,38 0,000

Buryatia_Republic 0,0290 0,19 0,849

Dagestan_Republic -1,1227 -9,87 0,000

Ingushetia_Republic -0,9577 -3,48 0,000

Kalmykia_Republic -0,9360 -6,04 0,000

Karachay_Cherkessia_Republic -0,2850 -1,58 0,114

Karelia_Republic -0,6069 -4,44 0,000

Komi_Republic -0,4852 -3,87 0,000

Mari_El_Republic -1,0355 -6,40 0,000

Mordovia_Republic -0,5855 -3,79 0,000

Sakha_Republic -0,1248 -0,31 0,754

North_Ossetia_Republic -0,4355 -1,89 0,058

Tatarstan_Republic -0,6634 -14,48 0,000

Tyva_Republic -0,8611 -2,47 0,014

Khakassia_Republic -0,8411 -4,96 0,000

Rostov_Oblast -0,5689 -12,53 0,000

Ryazan_Oblast -0,6230 -6,97 0,000

Samara_Oblast -0,5387 -11,00 0,000

Saratov_Oblast -1,2143 -18,47 0,000

Sakhalin_Oblast -0,4062 -1,07 0,285

Sverdlovsk_Oblast -0,3753 -6,64 0,000

Smolensk_Oblast -0,5565 -4,45 0,000

Stavropol_Oblast -0,2912 -6,35 0,000

Tambov_Oblast -0,4382 -4,92 0,000

Tver_Oblast -0,7676 -9,37 0,000

Tomsk_Oblast -0,6814 -11,35 0,000

Tumen_Oblast -0,6468 -6,65 0,000

Udmurtia_Republic -0,7029 -5,47 0,000

43

Ulyanovsk_Oblast -0,7995 -6,03 0,000

Khabarovsk_Krai -0,3507 -2,66 0,000

Khanty_Mansiysk_Autonomous_Okrug -0,3677 -5,07 0,000

Chelyabinsk_Oblast -0,7704 -14,67 0,000

Chechen_Republic -0,3052 -1,59 0,113

Chuvash_Republic -0,6666 -5,91 0,000

Chukotka_Autonomous_Okrug 0,8276 1,32 0,188

Yaroslavskaya_oblast -0,7642 -10,05 0,000

St_Petersburg -0,4824 -13,00 0,000

Nizhny_Novgorod_Oblast -0,6167 -10,27 0,000

Novgorod_Oblast -0,5208 -2,95 0,003

Novosibirsk_Oblast -0,8532 -9,50 0,000

Primorsky_Krai 0,0167 0,06 0,953

l_triple 0,1660 6,44 0,000

l_double1 0,0411 1,19 0,235

l_double2 0,0468 1,37 0,171

l_double3 0,0379 1,05 0,296

l_threat 0,1234 2,74 0,006

special_Bashkortostan_Republic 0,2103 0,84 0,401

special_Karelia_Republic 0,5935 1,12 0,264

special_Tatarstan_Republic 0,9222 2,19 0,029

special_Vladimir_Oblast -0,2754 -1,61 0,108

special_Moscow_Oblast 0,2451 4,18 0,000

special_Komi_Republic 0,2617 0,57 0,571

special_Udmurtia_Republic 0,3663 0,57 0,568

special_Volgograd_region -0,4445 -2,30 0,021

special_Moscow 0,4919 8,83 0,000

special_Kalmykia_Republic -0,4089 -0,70 0,483

special_Krasnodar_Krai -0,1491 -1,34 0,182

special_Arkhangelsk_Oblast 0,3206 0,35 0,725

special_Leningrad_Oblast 0,6274 1,74 0,082

special_St_Petersburg -0,1076 -1,23 0,219

special_Primorsky_Krai -0,2349 -0,25 0,801

44

n_triple 0,2268 8,87 0,000

n_top_ten 0,1277 2,88 0,004

n_hundred 0,0916 1,89 0,059

n_mirror 0,1320 4,93 0,000

n_ten -0,0550 -0,87 0,385

n_region 0,4150 7,78 0,000

n_region_multiple 0,2926 10,30 0,000

number_1 0,0689 4,11 0,000

number_2 0,0460 2,59 0,010

number_3 0,0491 2,79 0,005

number_4 0,0547 3,03 0,002

number_5 0,0578 3,42 0,001

number_6 0,0497 2,96 0,003

number_7 0,0403 2,50 0,013

number_8 0,0577 3,41 0,001

number_9 0,0374 2,24 0,025

number_0 0,1180 4,30 0,000

letter_A 0,0411 3,18 0,001

letter_B 0,0195 1,23 0,217

letter_C 0,0005 0,03 0,975

letter_E 0,0199 1,25 0,212

letter_H 0,0124 0,71 0,479

letter_K 0,0323 2,07 0,039

letter_M 0,0350 2,55 0,011

letter_O 0,0238 1,76 0,078

letter_P 0,0195 1,15 0,249

letter_T 0,0180 1,05 0,295

letter_X 0,0247 1,59 0,111

letter_Y 0,0094 0,52 0,605

Table 18: Regression of ln_price over explanatory variables

45

6.1 INTERPRETATION OF COEFFICIENTS

Before analyzing the regression in more detail, it’s important to note that the dependent variable

in this regression is ln_price, which is a logarithmic variable. The independent variables on the

other hand are either linear or logarithmic. Therefore, there needs to be made a distinction

between the interpretations of the coefficients of each kind of variable.

6.1.1 LOG-LINEAR INTERPRETATION

Independent variables that are not logarithmic need the interpretation of a log-linear model. A

log-linear model is of the form:

log Y = α + β1X1 + β2X2 + … + ε

In a regression model of this form, it’s important to understand the interpretation of the betas, as

this is a little more challenging compared to a linear regression model. In general,β1 needs to be

seen as the expected increase in log Y caused by a one-unit increase in the variable X1, while all

other dependent variables remain the same. In terms of the variable Y itself, this means that the

expected value of Y must be multiplied by 𝑒!!.To know the effect of a c-unit change (instead of

a one-unit change) in the variable X1, the expected value of Y must be multiplied by 𝑒!!!..

Further, for rather small values of β, the following rule of thumb can be used for a quick

interpretation:

eβ ≈ 1+β

This means that the expected percentage change in Y for a unit increase in X can be

approximated by 100 ⋅ β. For instance, if β=0,06 then eβ = 1,06183 ≈ 1,06 so that a one-unit

increase in X corresponds to an expected increase of 6% in Y.

6.1.2 LOG-LOG INTERPRETATION

When both the dependent and independent variable are in logarithmic form, the interpretation of

a log-log model needs to be followed to analyze the coefficient of the independent variable. A

log-log model has the following form:

log Y = α + β1 logX1 + β2 logX2 + … + ε

To get the proportional change in Y associated with a p percent increase in X1 (with all other

variables remaining the same), the value for α first needs to be calculated:

46

α = log (100+ p)100

With this α, we can now calculate the change in Y, caused by a p percent increase in X1:

𝑒!!!

where β1 is the coefficient of X1.

6.2 CONSTANT VALUE

The value of the constant of this regression can be interpreted by using the log-linear

interpretation. Having a value of -31,9403, this means that the impact of the constant on Y is

close to 0, since e-31,94 ≈ 0. This is not surprising because the constant accounts for a license plate

without any of the characteristics that are included in this regression.

6.3 VALUE OF LETTER AND NUMBER PATTERN

As the coefficients of ln_value_of_number_pattern and ln_value_of_letter_pattern show, the

value of the letter pattern as well as the number pattern both have a positive impact on the price

of the license plate. The coefficient of the letter pattern (+2,5358) is slightly higher than the

coefficient of the number pattern (+2,5322). Both are also statistically significant.

It’s important to note is that both ln_value_of_number_pattern and ln_value_of_letter_pattern

are estimated based on ln_price, and thus are both logarithmic variables. Therefore, the log-log

interpretation of section 6.1.2 is used to understand the impact of each variable.

For a 1% change in the value of the number pattern (or the value of the letter pattern),

α becomes:

α = log (100+ 1)100

α = 0,0043

The resulting estimated change in the price of a license plate, caused by a 1% increase in the

value of the number pattern can then be calculated as follow:

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𝑒!" = 𝑒!,!!"#⋅(!!,!"##) = 1,0109

Similar, for a 1% increase in the value of the letter pattern, the increase in the license plate price

is calculated as follow:

𝑒!" = 𝑒!,!!"#⋅(!!,!"!#) = 1,0109

A 1% increase in the value of the letter pattern or the number pattern both results in a 1,09%

increase of the estimated value of the license plate.

Furthermore, the number pattern as well as the letter pattern can signal wealth. Since the

coefficient of the interaction between the variables ln_value_of_number_pattern and

ln_value_of_letter_pattern (-0,1223) is negative, which means that these are supplementary

variables. This indicates that once you have an expensive letter combination, people are willing

to pay less for yet another signal of wealth by an expensive number combination, and vice-versa.

6.4 REGION

Luxury items like vanity plates are driven by history, culture… and for a great extend also by

geography. Despite its communist history, Russia was already the eleventh largest luxury market

in the world in 2014 (Biryukov, 2015). Besides a different willingness to pay for luxuries among

different countries, there are also differences within counties. This is definitely the case for

countries as big as Russia.

Figure 8 gives an overview of the magnitude of each region’s impact on the overall price of the

license plate. The precise coefficients for each region can be found in the regression in Table 18.

Table 19 gives an overview of the 5 most expensive regions to buy a license plate, while Table

20 gives the 5 least expensive regions. When analyzing the impact of the various regions, it’s

important to keep in mind that the federal city of Moscow is the reference category for every

region in this regression. The impact of the other regions must thus be interpreted relative to the

impact that one of Moscow’s region codes has on the price of a vanity plate.

48

Figure 8: Size of each region’s impact on the overall price of the license plate.

Region Coefficient Impact on price

Chukota Autonomous Okrug + 0,8276 + 128%

Buryatia Republic + 0,0290 + 3%

Primorsky Krai + 0,0167 + 1,5%

Moscow 0 + 0%

Adygea Republic - 0,1031 - 10%

Table 19: The 5 most expensive regions to buy a vanity plate

Region Coefficient Impact on price

Oryol Oblast - 1,7303 - 82%

Voronezh Oblast - 1,5843 - 80%

Kursk Oblast - 1,4011 - 75%

Saratov Oblast - 1,2143 - 70%

Magadan Oblast - 1,1729 - 69%

Table 20: The 5 least expensive regions to buy a vanity plate

49

It’s clear that most regions have a negative impact on ln_price compared to the federal city of

Moscow. Out of the 80 other regions that are included in this research, only 3 regions have a

strictly positive impact on the price: Chukota Autonomous Okrug, Buryatia Republic and

Primorsky Krai. This implies that Moscow is among the most expensive regions to acquire a

license plate, which is not completely surprising. The trend of having a cool license plate has

occurred all over the country for many years, however drivers in Moscow always showed a

particular desire for them (“Что означают номера АМР в Москве”, 2019). This might be due

to the status of the capital, as Moscow is the political heart of Russia. The headquarters of the

Government of Russia as well as many other state administration bodies are located in Moscow.

License plates reserved for these institutions are recognized by almost everyone, even people

living outside the capital city.

Another reason for the high prices for license plates in Moscow comes from Kulikova and

Godart (2014). They argue that the Russian luxury market is highly concentrated in Russia’s 2

key cities Moscow and St. Petersburg. While Moscow covers 60 to 70 percent of this market, St.

Petersburg only consists of 10 to 15 percent of the market (Kulikova & Godart, 2014). Despite

the fact that their research primarily focuses on exclusive brands in the fashion industry, it

certainly gives a good idea of where in Russia most luxury items are bought. Since there is only

a limited amount of vanity plates in Moscow, the high prices for them is just a consequence of

the law of supply and demand.

Or model shows that the cheapest license plates can be found in Oryol Oblast. License plates

sold in this region are over 80% cheaper compared to license plates form Moscow.

6.5 AMOUNT OF VIEWS

As the regression in Table 18 shows, ln_views seems to have a negative and significant impact

on ln_price, with the coefficient of ln_views being -0,2439. It’s important to note that since the

variables ln_price and ln_views are the natural logarithmic values of the variables price and

views respectively, the interpretation of a log-log model must be used here as well.

For a 1% change in the amount of views, α becomes:

α = log (!""!!)!""

= 0,0043

50

The resulting estimated change in the price of a license plate, caused by a 1% increase in the

amount of views can then be calculated as follow:

𝑒!" = 𝑒!,!!"#⋅(!!,!"#$) = 0,9990

This means that if the amount of views increases with 1%, the price of the license plate decreases

with approximately 0,10%. This negative relation between both price and the amount of views

could be because the most special license plates are sold much faster than regular ones, as they

are really low in supply, and vice versa. Since they are sold quickly, it’ harder for special license

plates to have a high amount of views on their page on Nomera.net.

It should be noted that the amount of views serves as a control variable in this regression, and

thus isn’t the most important explanatory variable.

6.6 LETTER CATEGORY

It seems that all appealing letter categories have a positive impact on ln_price. The variable

l_triples (+0,1660) has the biggest impact, followed by l_double2 (+0,0468), l_double1

(+0,0411) and l_double3 (+0,0379). This means that if a license plate’s letter combination has 3

times the same letter, its price is approximately 18% higher than if the letter combination is of a

type that is not described in this section. When the letter combination contains 2 times the same

letter, the license plate price increase with roughly 4% in every case.

The fact that the category of triple letter combinations has the biggest positive impact on the

price is not surprising. It’s obvious that there are fewer combinations with 3 times the same

letters than with only 2 times the same letter. For instance, for the letter A, there is only 1 series

of ‘AAA’, but there are 12 series of ‘A*A’, since * can represent the letters B, C, E, F, H, K, M,

O, P, T, X and Y. Besides, there are also 12 series of ‘*AA’ and 12 series of ‘AA*’, which also

have 2 times the letter A in them. The higher estimated price of license plates with 3 times the

same letter is thus due to its exclusivity and is a consequence of the law of supply and demand.

Apart from the restricted amount of combinations with 3 times the same letters, there are also

several of these triple combinations e.g. AAA, CCC, MMM, OOO… that are linked to

governmental institutions in various regions. Therefore, all of them might unconsciously be

associated with the wealth and power of the driver, even if they have no political meaning at all.

51

In general, license plates of triple series are considered to be very privileged and are favored by

many people. (“Что означают буквы на номерах машин России”, n.d.).

For the letter patterns of the double types, the category l_double2 has a slightly more positive

impact on ln_price compared to the other double types. For clarity, the number 2 in the variable

name refers to the location of the ‘other’ letter, meaning that letter series in this category are

‘mirrors’ e.g. ABA, MPM, etc. The slightly higher value of mirrors could be due to the more

aesthetic feel a mirrored pattern has compared to a series of the type AAB or BAA. However, all

of this is completely personal.

Threatening letter combinations seem to have a positive impact on ln_price, with an estimated

coefficient of +0,1234 in this regression. This means that if the letter combination on the license

plate is threatening towards others, the price is approximately 13% higher compared to non-

threatening combinations.

A last important type of letter series that needs to be analyzed is the state license plates. Since

these particular series are reserved for specific government agencies, driving around with a

license plate of this kind on your car must mean that you are important. However, they are also

sold to private individuals. These are the ones that are included in this research. Figure 9 gives

an overview of the impact on ln_price of this type of license plates for the regions that were

analyzed in this research.

Figure 9: The impact of state license plate series per region

52

When having a look at the dummy variables that categorize the plates with a political meaning in

the letter pattern of the plate, the Republic of Tatarstan (+0,9222) and Leningrad Oblast

(+0,6274) seem to have the highest positive and significant impact on ln_price, while Volgograd

Oblast seems to have the most negative significant impact (-0,4445).

For the Republic of Tatarstan, state license plates increase the price with more than 150%. This

is impressive as the region variable of Tatarstan has a coefficient of -0,663 in this regression,

meaning that license plates from this republic have an approximately 49% lower value compared

to license plates of the federal city of Moscow.

6.7 NUMBER CATEGORY

Most of the number categories seem to have a quite large impact on the overall price of the

license plate. The category of triple number patterns, which has 3 times the same number, has

the largest positive significant influence on ln_price, with an estimated coefficient of +0,2268.

This means that having a number pattern of this type on your license plate increases its price

with about 25%, compared to having a number pattern that is not included in this section.

License plates of this type are usually very sought after and mostly sign someone’s wealth.

The effect of a ‘mirror’ number pattern seems to be quite high as well. The coefficient of

+0,1327 in our regression means that the expected overall price increases with 14% when the

number pattern is of the type ‘X*X’. Compared to mirrored letter patters, where there was only

an increase of 4%, this is really high.

Number categories with 2 zero’s in it increase the estimated price as well. The magnitude of the

increase depends on the place of the zero’s. The category of patterns of the type 001, 002, 003

etc. have the biggest estimated impact (+0,1277) on ln_price. There is an estimated increase in

the overall price of almost 14% when the number pattern is of the type ‘00*’, where * can be any

number (except 0). Also the number type ‘*00’ has quite an impact. Having 100, 200, 300 etc. as

a number pattern increases the estimated price of the license plate with approximately 10%. The

last number pattern with 2 zero’s is of the type ‘0*0’ and has, at first sight, a negative impact (-

0,0551) on ln_price. However, we also included a category that measures the impact of

‘mirrors’, being number patterns of the type ‘X*X’, where X can be any number, including 0.

Number patterns like 010, 020, 030 etc. thus appear in both categories. Consequently, the effect

must also be measured based on the impacts of both categories. As mentioned before, mirrors

53

have a positive impact on ln_price. They increase the expected value of the license plate with

14%, while the combination of the type ‘0*0’ decreases this effect to only 8%.

When the number combination on the license plate includes the region number, it’s expected that

its price will increase. As previously stated, the region number is not included in this database,

but the region where the plate was issued is. Since some regions have more than one region

number, a distinction was made between regions with only one region number and regions with

multiple ones. It’s important to note there are 2 different variables to measure this effect simply

because of a difference in accuracy, not because we expect a different effect based on the

amount of region codes a region has.

That said, this effect can be analyzed based on the regression in Table 18. Regions with only 1

region number have an estimated impact of +0,4150 on ln_price, which is an increase of more

than 50% compared to the situation where the region number does not appear in the number

combination. The effect is smaller for regions with multiple region codes (+0,2927). When their

region number appears on the license plate, its price increases with roughly 34%. This difference

is completely in line with our expectations as this last variable is not as accurate as the one that

measures the effect for regions with only 1 code. Despite the difference in accuracy, there is still

a serious effect in both cases. The repetition of the region number in the number pattern of the

license plate is thus a characteristic that is highly valued.

6.8 NUMBER COUNT

As explained before, the series of number count variables count the amount of times a specific

number appears on the plate, apart form the region number. It can thus have the values 0, 1, 2 or

3. When having a look at the results of the regression in Table 18, it’s clear that the impact of the

separate numbers is positive for each number.

What immediately strikes is that having an extra 0 on your license plate seems to have the

biggest impact on the overall price (+0,118). This is equal to an increase of 12,5%. It even

almost has a twice as big impact compared to the number 1, which is the number with the second

biggest impact (+0,0689) on ln_price. The number 1 thus increases the price with 7%. Also the

numbers 5 and 8 have a significant positive impact on the price with +0,0579 and +0,0577 as

coefficient respectively. This means that they both increase the price with roughly 6%. Having

54

an extra number 9 on your license plate seems to have the smallest impact on the price

(+0,0374).

Because of the popularity of number combinations like 001, 002 and so on, it’s not surprising

that having the number 0 in your license plate adds a lot of value to the overall price. Several

things can cause the huge difference in impact between the number 0 and the other numbers. A

first explanation could be that the number 0 is often used to make it look like you have a low

digit license plate. As mentioned before, in other countries where different license plate

structures are possible, some structures can either have 1, 2, 3 or even more digits on it. There,

license plates with only 1 or 2 digits are preferred and often associated with exclusivity, wealth

and power. However, Russian license plates have a fixed structure where the number pattern will

always have exactly 3 digits. The only way to achieve a license plate that looks like a low digit

license plate in Russia is by having zero’s in the license plate. In that way, the pattern “001” can

be associated with “1”, the pattern “002” with “2” and so on. This theory is also in line with the

impact of the number 9, which has the lowest impact of all numbers. It’s harder to achieve a low

digit looking license plate with this number since this is the highest number.

Further, replacing 1 or 2 numbers by zero’s can make your license plate look more appealing.

Most people will be indifferent between the number patterns “214” and “839” and won’t

systematically value one higher than the other, but will expect the patterns “014” and “800” to be

more expensive than “214” and “839” because of the extra zero(s) that makes the license plates

more appealing.

Besides that, some people believe that being surrounded by certain numbers can have a

psychological impact. The shape of the number 0 is often linked to eternity, and can therefore be

associated with a long life, due to fewer accidents. Zero can thus be used as a way to bring good

luck to the road.

6.9 LETTER COUNT

Similarly as in the previous section, the series of letter count variables count the amount of times

a specific letter appears on the license plate. Each variable can have the values 0, 1, 2 or 3. With

a coefficient of +0,0412 in our regression, the letter A has the biggest expected impact on

ln_price compared to other letters. One appearance of the letter A approximately adds 4% to the

expected overall value of a license plate. An extra letter C or Y hardly has any impact on the

55

price, with a coefficient of +0,0005 and +0,0094 respectively, which is not even an increase of

1%.

The higher impact of the letter A is probably caused by our subconsciousness. The letter A is in

the first place of both the Cyrillic and the Latin alphabet. Therefore it’s often associated with

being the first, the best and the fastest. It has a similar power as the number 1.

7 CONCLUSION

In Russia, vanity plates are often used as a way to show that someone is wealthy and/or has

political power. Since a few years, these license plates can be bought on online platforms like

Nomera.net. Using a dataset from this website made it possible to test the theoretical pricing

model that we constructed in this research. In this model, many license plate characteristics were

included such as several letter and number categories and the region where the license plate was

first issued. We also provide a method to estimate the values of all letter and number patterns.

Our pricing model shows that all number and letter categories have a different impact on the

overall price. For instance, triple number or letter combinations have a significant positive effect.

License plates of which the region code happens to appear in the number pattern of the plate also

seem to cause a significant price increase. The effect of state license plates depends on the

specific region where they were issued. For some regions, series that are linked with

government agencies lead to a huge prise increase, whereas in other regions, state series have a

negative effect on the price. When testing for the different regions in Russia in general, the

federal city of Moscow is among the most expensive regions to acquire a vanity plate. We also

test if the separate numbers and letters on the plate have an impact on the price and show that the

number 0 gives a license plate the most value. Finally, we found that the number and letter

pattern on the license plate are supplementary, meaning that once 1 part of a license plate signals

wealth, people are less willing to pay extra in order to have special numbers or letters in the

other part as well.

There are clearly several license plate characteristics that significantly increase the overall price.

Since having these on a license plate doesn’t change a thing about one’s permissions on the road,

they are simply used as status symbols to show off wealth, power and prestige.

56

57

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II

A REGION CODES

Region code The region of Russian Federation Obs.

1 Republic of Adygea 151

02, 102 Republic of Bashkortostan 472

3 Republic of Buryatia 69

4 Altai Republic 44

5 Republic of Dagestan 126

6 Republic of Ingushetia 21

7 Kabardino-Balkar Republic 55

8 Republic of Kalmykia 76

9 Karachay-Cherkess Republic 49

10 Republic of Karelia 96

11 Komi Republic 119

12 Mari El Republic 61

13, 113 Republic of Mordovia 67

14 Sakha Republic 10

15 Republic of North Ossetia–Alania 30

16, 116, 716 Republic of Tatarstan 849

17 Tuva Republic 13

18 Udmurt Republic 101

19 Republic of Khakassia 56

(20), 95 Chechen Republic 43

21, 121 Chuvash Republic 127

22 Altai Krai 313

23, 93, 123 Krasnodar Krai 1210

III

24, 84*, 88*, 124 Krasnoyarsk Krai 366

25, 125 Primorsky Krai 24

26, 126 Stavropol Krai 835

27 Khabarovsk Krai 94

28 Amur Oblast 35

29 Arkhangelsk Oblast 42

30 Astrakhan Oblast 87

31 Belgorod Oblast 370

32 Bryansk Oblast 309

33 Vladimir Oblast 584

34, 134 Volgograd Oblast 357

35 Vologda Oblast 106

36, 136 Voronezh Oblast 232

37 Ivanovo Oblast 114

38, 85*, 138 Irkutsk Oblast 199

39, 91 Kaliningrad Oblast 105

40 Kaluga Oblast 244

41, 82* Kamchatka Krai 137

42, 142 Kemerovo Oblast 0

43 Kirov Oblast 181

44 Kostroma Oblast 223

45 Kurgan Oblast 80

46 Kursk Oblast 170

47 Leningrad Oblast 229

48 Lipetsk Oblast 112

49 Magadan Oblast 72

50, 90, 150, 190, 750 Moscow Oblast 2896

IV

51 Murmansk Oblast 74

52, 152 Nizhny Novgorod Oblast 467

53 Novgorod Oblast 51

54, 154 Novosibirsk Oblast 203

55 Omsk Oblast 413

56 Orenburg Oblast 270

57 Oryol Oblast 81

58 Penza Oblast 130

59, 81*, 159 Perm Krai 380

60 Pskov Oblast 23

61, 161, 761 Rostov Oblast 889

62 Ryazan Oblast 204

63, 163, 763 Samara Oblast 736

64, 164 Saratov Oblast 436

65 Sakhalin Oblast 0

66, 96, 196 Sverdlovsk Oblast 537

67 Smolensk Oblast 103

68 Tambov Oblast 208

69 Tver Oblast 245

70 Tomsk Oblast 470

71 Tula Oblast 382

72 Tyumen Oblast 172

73, 173 Ulyanovsk Oblast 91

74, 174 Chelyabinsk Oblast 628

75, 80* Zabaykalsky Krai 49

76 Yaroslavl Oblast 289

77, 97, 99, 177, 197, 199, 777, 799 Moscow 8540

V

78, 98, 178, 198 St. Petersburg 1654

79 Jewish Autonomous Oblast 27

83 Nenets Autonomous Okrug 6

86, 186 Khanty-Mansi Autonomous Okrug 320

87 Chukotka Autonomous Okrug 4

92 Sevastopol 0

Note. Those regions with an asterisk (*) beside them were involved in mergers with

other regions and have their codes listed with an asterisk with the region they are now

a part of.

Table 21: Region codes for different regions of Russia.

VI

B STATE LICENSE PLATES

Series State agency

P***KC|02, K***KC|02 State Assembly - Kurultai

O***OO|02 Managers of large enterprises and ministries of the republic

A***AA|02 Government of the Republic

Table 22: State license plate series for Republic of Bashkortostan

Series State structure

C***CC|04 Judicial structure

H***HH|04 Tax Administration and Departments

O***OO|04 Altai Republic leadership

M***PA|04 Ministry of Internal Affairs of the Republic

P***PP|04, P***PA|04 Republic Prosecutor's Office

X***XX|04 Traffic police department

M***YK|04 Municipal Department of Internal Affairs

M***OB|04 Department of Internal Affairs of Barnaul, district police

departments of Barnaul

M***OP|04, M***OC|04 Regional and rural police departments of the Altai Territory

A***CK|04 Investigation Department of the IC at the Prosecutor's Office

Table 23: State license plate series for Altai Republic

Series State agency

O***OO|08 Members of the Parliament of the Republic

A***AA|08 Administration of the Republic

M***MM|08 Ministry of Internal Affairs of the Republic

C***CC|08 Prosecutors and judges

Table 24: State license plate series for the Republic of Kalmykia

VII

Series State agency

T***TT|10 Government of the Republic and the FSB

H***HH|10 Administration of the Republic, heads of districts

M***MM|10, E***MP|10 Ministry of Internal Affairs of the Republic

Table 25: State license plate series for the Republic of Karelia

Series State agency

B***AT|11 Government of the Republic

P***PP|11 Prosecutor's office

Y***YY|11 Investigation Committee under the Prosecutor's Office

T***TT|11 Government of the Republic

O***OO|11 Managers of large industrial companies

M***MM|11 Ministry of Internal Affairs of the Republic

Table 26: State license plate series for the Republic of Komi

Series State agency

M***MM|14 Ministry of Internal Affairs of the Republic

P***PP|14 Republic Prosecutor's Office

A***AA|14 President of the republic, government and parliament and

mangers of state-owned enterprises

Table 27: State license plate series for the Republic of Sakha

Series State agency

M**MM|16, B***MM|16,

B***KM|16

Ministry of Internal Affairs of the Republic

B***TM|16 State officials, Ministry of Internal Affairs, prosecutors, heads

of large enterprises

O***AA|16 Government of the Republic of Tatarstan, administration of

cities and districts of Tatarstan

P***AO|16 City administration

Table 28: State license plate series for the Republic of Tatarsta

VIII

Series State agency

A***AA|18 Administration of the Republic

O***OO|18 Managers of enterprises and factories of the republic

Table 29: State license plate series for Udmurt Republic

Series State agency

P***PP|23, K***KK|93 Administration of the region

H***HH|23 Territory Tax Service

O***OO|23, A***AA|23 Mainly owned by deputies and district administrations, also

seen on the cars of entrepreneurs

O***OO|93 A***AA|93 Mostly, influential private owners, according to the letter and

decision of the traffic police of the region, also have the

administrations of the districts

M***KK|93 Ministry of Internal Affairs of the region

A***CK|23 Judicial structure

A***AB|23 Prosecutor's office

A***AC|23 Legislative Assembly, deputies of the regional assembly

T***TT|23 Customs

C***KC|23 FSO Sochi Krasnodar region and cities

A***HY|93 With numbers 001-009 - high-ranking employees of traffic

police

E***KX|93 FSO Regional Krasnodar Territory and Cities

C***CM|23 Initial numbers only - Administration of Sochi

M***MM|23 Ministry of Internal Affairs of the region

K***AA|23 City administration

Y***YY|23 The cars of the Ministry of Internal Affairs are also often found

on the personal cars of the department employees.

Y***YY|93 Some numbers of this series are fixed on the cars of OJSC

"Gazprom"

C***C*|93 Investigative Committee

Table 30: State license plate series for Krasnodar Krai

IX

Series State agency

K***PK|24 Administration of the region

A***OO|24 City administration

M***KK|24 Ministry of Internal Affairs of the region

M***CK|24 Ministry of Internal Affairs of the region

K***CM|24 Operational units of the Ministry of Internal Affairs

B***CP|24 Judicial structure

Table 31: State license plate series for Krasnoyarsk Krai

Series State agency

H***HH|25, T***TT|25 Administration of Vladivostok

AAA|125 The prosecutor's office edge

MMM|25 Legislative Assembly of the region

CCC|25 Administration of the region

M***OO|25,

M***OO|125, B***OO|25,

BOO|125, H***OO|25,

H***OO|125, Y***OO|25,

Y***OO|125, C***OO|25,

C***OO|125

Ministry of Internal Affairs, Ministry of Emergency Situations

Table 32: State license plate series for Primorsky Krai

Series State agency

T***TT|29, P***PP|29 Administration of the region

M***AO|29 Ministry of Internal Affairs of the region

E***PE|29 The leadership of the party "United Russia"

Table 33: State license plate series for Arkhangelsk Oblast

X

Series State agency

A***BO|33, A***AA|33 Administration of the region

O***OO|33 Businessmen and law enforcement officers

Table 34: State license plate series for Vladimir Oblast

Series State agency

A***AA|34 Prosecutor's office of the region

Y***YY|34 FSB (Federal Security Service)

P***AA|34 Cars of regional and city administration

A***AM|34 Previously, the series belonged to the regional administration

garage

O***OO|34 Commercial Series

M***MM|34 Previously belonged to the Ministry of the Interior

Table 35: State license plate series for Volgograd Oblast

Series State agency

O***OA|47 Administration of the Leningrad Oblast

O***AO|47 FSB, Customs, GUIN

O***OM|47 GUIN, FSB, Federal Drug Control Service and the Prosecutor's

Office

Table 36: State license plate series for Leningrad Oblast

Series State agency

A***MM|50, A***MM|90 Administration of the Moscow Region

A***MO|50, A***MO|90,

A***MO|150, A***MP|50,

A***MP|90

Various state structures of the Moscow Region

M***MM|50,M***MM|90 Traffic police, Ministry of Internal Affairs or Ministry of

Emergency Situations

Table 37: State license plate series for Moscow Oblast

XI

Series Stage Agency

A***MP|77, A***MP|97 Administration of Russian Militia

E***KX|77, E***KX|97,

E***KX|99, E***KX|177

Federal Protective Service

A***OO|77, B***OO|77,

C***OO|777,

M***OO|777

Presidential Affairs Office

A***MO|77 Administration of Moscow

C***CC|77 Center for Special Communications, Courier Service, Ministry

of Communications and other thematic departments.

C***CC|99 Federal Tax Service, State Customs Committee,

GOKHRANA, GUINA, customs.

E***PE|177 United Russia party functionaries.

M***MP|177, B***MP|77 Law enforcement agencies

P***MP|77 Ministry of Justice

Table 38: State license plate series for federal city Moscow

Series State agency

A***AA|98 Administration of St. Petersburg

O***OO|78, O***KO|78 Administration of St. Petersburg, law enforcement agencies

O***KO|98 Prosecutor's office, bailiff service

O***OA|78, O***OA|98 Belonging to deputies of the Assembly, Federal Tax Service,

Federal Treasury Department

O***AO|78 FSB, Customs, GUIN

O***OC|78 SES, Ministry of Foreign Affairs, on auto closed State.

institutions GUIN, FSB, Federal Drug Control Service and the

Prosecutor's Office

O***OM|78 GUIN, FSB, Federal Drug Control Service and the Prosecutor's

Office

O***TT|78 Traffic police department

Table 39: State license plate series for federal city St. Petersburg