For many years the annual European Commission reports (e.g. European Commission, 2018) have shown a relatively very high level of car ownership in Poland. According to the 2016 data, Poland was the sixth most motorised EU state (571 passenger cars per 1,000 inhabitants). It was by far the highest value among the post-socialist countries which joined the European Union in 2004. Poland’s car ownership rate amounted—in comparison to other socio-economic development indicators, and also in relation to other countries—to an above-average level, which necessitated specific recommendations in terms of transport policy. Likewise, international research on the drivers of change in car ownership clearly showed a non-standard course of this process in the country. In one of the most often quoted automotive articles (Dargay et al. 2007), where the authors compared car ownership changes and per capita national income in 45 countries in the years 1960–2002, the situation in Poland is shown to diverge considerably from the relationship between those two variables in the remaining states. It was a single case in the entire set (and the study concerned also a large group of developing countries). The analysis of car ownership changes and the income level in Poland in this period revealed that the income flexibility of car demand was more than twice as high as in the majority of European countries. This is certainly related to the development path Poland chose after 1989. However, what is equally important in shaping such a picture of the process (if not more important) is the quality of the Polish motorisation data used for research and international comparisons, which overestimate the car ownership rates.
For many years the studies on the car market have shown (e.g. Kublik 2005, 2013a, 2013b) that official data on the number of passenger cars do not reflect the actual state of this phenomenon. Professional specialists and car experts are quite well acquainted with this problem (it was also analysed by the Institute of Automotive Market Research SAMAR), but it has not been introduced to scientific discussion on motorisation so far, except in rare cases (e.g. Komornicki 2008, 2011; Menes 2018). This study attempts to fill the gap.
The article aims to identify the shortcomings of the statistics describing the size and structure of passenger car parc in Poland, and to formulate the implications of these limitations for geographical research. What made this objective possible was the creation of a new, increasingly reliable source of information, i.e. the Central Vehicle Register [PL:
The analysis presented in the later sections of the article was carried out on the basis of the data obtained from CEP on selected features of passenger cars (the year of the first registration in Poland, the year of production, permissible load capacity, the number of seats and the condition of a vehicle) and their owners (the district District (
The article is composed of four sections. The first section shows the most significant shortcomings of motorisation statistics in Poland based on the review of related literature. Then, in the second section we describe the way of collecting data and keeping records of motorisation statistics in Poland. The in-depth analysis of the CEP data, which makes it possible to estimate the size and structure of selected limitations of motorisation statistics, is dealt with in the third section. Finally, we attempt to determine the implications of these shortcomings for geographical research.
To date, the motorisation data have been used in the investigations of a series of socio-economic phenomena and processes. The significant proportion of information on the number of passenger cars in Poland comes from the Statistics Poland [PL:
Province (
The impact of the discussed shortcomings and their causes on the number of passenger cars registered in a given territorial unit (e.g. in a commune) is presented in Fig. 1.
All the six shortcomings identified in the motorisation statistics significantly influence the research conducted in passenger car parc in Poland and the type of knowledge that is developing. Unfortunately, even CEP cannot fully recognise their entire scale and structure. This article will analyse the first three shortcomings in detail (i.e. ‘dead souls’, ‘cars with a grid’ and company cars). The remaining three shortcomings are far more difficult to be analysed in depth here, as they require different research techniques.
As was mentioned earlier, Polish car ownership research has been primarily based so far on the data published by GUS. This information on vehicles (including passenger cars) is made available as at 31 December each year. These data were obtained from various sources (Table 1). Until 2001 they had been kept by provincial agencies, called the Provincial Vehicle Register [PL:
Authorities responsible for collecting and sharing motorisation statistics in Poland.
Period | Place of vehicle registration | Vehicle register | Source of GUS data |
---|---|---|---|
Until 1990 | Commune office | Province (n = 49) | WEP |
1990–1998 | Province (n = 49) | WEP | |
1999–2001 | District office | Province (n = 16) | WEP |
2002–2003 | District office | Province (n = 16) | District databases |
2004–2008 | District office | Central (version 1.0) | District databases |
2008–2017 | District office | Central (version 1.0) | CEP ver. 1.0 |
Since November 2017 | District office | Central (version 2.0) | CEP ver. 2.0 |
CEP – Central Vehicle Register; GUS – Statistics Poland; WEP – Provincial Vehicle Register.
– a subregional administrative unit.
Source: own elaboration.
The way of gathering and sharing car data was to change considerably after the establishment of the Central Vehicle Register. It was initially assumed that it would take place in the middle of 1999, pursuant to the Act of 1997 on the Traffic Code, introducing the provision for termination of WEP as of 30 June 1999. This fact caused widespread consternation because until that day the contractor for the new central base had not even been appointed (Centralna Ewidencja…, 2003). The Act of 1997 stated that from July 1, 1999, the Central Vehicle Register would start to operate (and thus the day before WEP would cease to function). However, it turned out quite impossible. Therefore, a few months later the provisions were amended (the Act of 31 March 2000) and WEP was reinstated alongside the need to update the regulations by heads of districts and cities’ mayors. Nevertheless, districts’ authorities were not obliged to enter the data on registered vehicles into WEP for almost a year (some of them publicly informed that they did not do it because there was no legal basis –
In 2003, the Ministry of the Interior and Administration [PL:
The complete functionality of CEP was originally planned for the end of 2006. This date was postponed several times due to the need to build the related infrastructure (a bunker for storing copies of data from the system and a communication network allowing data to be read and updated in a real time). The integration of the CEP system operated by MSWiA with the ‘
This state of affairs was to change finally after the launch of so-called CEP 2.0. In 2013, the Ministry of the Interior and Administration signed an agreement with the National IT Centre on implementing second-generation CEP, which was to be completed in 2016. This deadline was not met again and CEP 2.0 was launched as late as in November 2017. Since then, much more information on vehicles has been collected in this database, although its quality varies considerably. Today, the Central Vehicle Register is regulated by the amended Act on the Traffic Code (Journal of Laws 2020) and the Ordinance of the Ministry of Digitisation of 25 May 2018 on the catalogue of data gathered in the Central Vehicle Register (Ministry of Digital Affairs 2018). In the light of those two documents, the registry collects information on 60 different features describing technical data of each vehicle. Among them are the following: a vehicle brand, a model, a type, the year of production, engine capacity, permissible load capacity and the type of fuel. Some data, however, have poor information quality due to gaps and erroneous names. The weaknesses of CEP data limit their research use; however, it must be emphasised that despite those difficulties, this registry is a very detailed and precious source of data, making it possible to conduct many car ownership studies which are more reliable than before.
The development of CEP 2.0 and the integration of different databases connected with vehicles made it possible to verify the number of passenger cars in Poland, which in all likelihood is actually driving on Polish roads. Owing to this change, the so-called vehicle status, or its feature, which takes one of two categories coded as: ZAR-A (a registered end-of-life vehicle) or ZAR (a registered active vehicle)—has been determined since November 2017 for each vehicle registered in Poland. As was mentioned earlier, the ZAR-A code denotes an officially registered car, other than historic, for which >10 years elapsed from the date of its first registration in Poland, and for six years since the current date there has been no updates from a registration body, UFG, a vehicle inspection station or police. Vehicles that fail to meet those criteria acquire registered active (ZAR) status. The end-of-life status can be changed by a district’s head, a vehicle inspection station, UFG and police during standard procedures performed by those entities related to car registration.
According to the data of 31 December 2018, over six million passenger cars (i.e. about 26% of all registered) had end-of-life status. This is a relatively high value which increases a car ownership rate in Poland to a considerable degree. It turns out that after deducting end-of-life vehicles the car ownership rate should be about 450 cars per 1,000 inhabitants, which would place Poland somewhere around the 20th place among the EU states. This is quite a significant change, considering the multitude and importance of decisions taken on the basis of this rate value (it is a separate question whether the same phenomenon occurs on a similar scale in other post-socialist countries).
The age analysis of end-of-life vehicles (Fig. 2) confirms that these are mainly very old cars (
The spatial distribution of end-of-life vehicles in relation to the total number of passenger cars (Fig. 3) shows that this phenomenon is concentrated only in particular areas. However, as assumed by Komornicki (2006, 2011), they seem to have little connection with the location of state farms (
The second element affecting the quality of motorisation statistics in Poland is the underestimation of the number of passenger cars related to the phenomenon of ‘cars with a grid’, that is those approved as LGVs. These are officially trucks (and registered as such), but in practice they perform the function of passenger cars, or passenger and goods vehicles simultaneously. Statistics showing car ownership in Poland do not include them (although they should), thus reducing its rate. This underestimation varies in particular parts of Poland.
According to CEP data, on 31 December 2018 there were over 3.3 million trucks registered in Poland, of which just over 1 million (30%) were so-called ‘dead souls’. This means that truck parc in Poland is made up of about 2.3 million vehicles in real terms, and some of them are
With regard to the type of vehicle, trucks with permissible load capacity not exceeding 1.5 tonnes can be divided into three groups using popular segmentation of vehicles (e.g. Baltas, Saridakis 2013; Lansley 2016):
medium-sized vans (e.g. Volkswagen Transporter, Fiat Ducato, Ford Transit, Mercedes Sprinter, Renault Master and Peugeot Boxer); leisure activity vehicles (LAVs, e.g. Citroen Berlingo, Peugeot Partner, Renault Kangoo, Volkswagen Caddy, Fiat Doblo and Opel Combo); other cars representing various basic segments of passenger cars We identify the term ‘basic segments of passenger cars’ with the car segmentation used by the European Commission embracing segments from A (so-called mini cars, e.g. Fiat 500) to F (relatively large luxury cars) (Thiel et al. 2014). This is a classification that includes vehicles used exclusively for transporting people, both leisure activity vehicles and vans.
Of all the 1.88 million trucks with a permissible load capacity of <1.5 tonnes, the vast majority of vehicles can be found in the first (about 64%) and second (about 18%) group, because they are difficult to be automatically included in the group of passenger cars. Some of them are practically used only for transporting cargo. In that case, the number of passenger seats can be applied as an additional criterion for distinguishing passenger cars from trucks. With regard to medium-sized vans, the lower limit of their passenger use can be determined at the level of three passengers (in general, cars in this segment have three seats in the first row, including one for a driver) and for LAVs—at the level of two. Therefore, when including the entire third group mentioned above and also medium-sized vans having at least four seats, as well as LAVs with a minimum of three seats (including the driver’s seat), we obtain a figure of about 750,000 vehicles, which may represent the maximum limit of the number of LGV-approved passenger cars.
The spatial distribution of LGVs with a permissible load capacity up to 1.5 tonnes, which are divided into the distinguished groups, are shown in Fig. 4. The highest ratio of cars from basic segments and LAVs are observed in large cities and suburban zones (noticeable particularly in the case of basic segment cars). This is linked to the location of leasing companies and the market of so-called ex-lease cars that are bought mostly by residents of those territorial units. In contrast, the above-average representation of the medium-sized cars is visible in so-called orchard districts (Grójec, Białobrzegi, Rawa Mazowiecka, Sandomierz, Opole).
These conclusions are confirmed by a simple correlation analysis between the number of LGVs per 1,000 inhabitants in particular segments and four selected indicators (Table 2). A variable describing the share of orchards in the total area proves a relatively strong and statistically significant relationship with the number of cars in the segment of medium-sized vans. Furthermore, PIT (Personal Income Tax) revenues per capita (the highest in large cities and their suburban zones) show a relatively strong relationship with the number of cars in the basic and LAV segments.
Correlation coefficients between the number of LGVs (permissible load capacity <1.5 tonnes) per 1,000 inhabitants by their segments and selected socio-economic features.
Segment | Population density | Revenues from vehicle tax per inhabitant | PIT revenues per inhabitant | Share of orchards in total area |
---|---|---|---|---|
Basic segments | 0.312** | 0.328** | 0.603** | 0.088 |
LAVs | 0.271** | 0.456** | 0.564** | 0.075 |
Medium-sized vans | −0.128* | 0.383** | 0.063 | 0.502** |
Statistically significant correlation at the level of α < 0.05.
Statistically significant correlation at the level of α < 0.01.
LAVs – leisure activity vehicles; LGV – large goods vehicles.
Source: authors’ own elaboration based on CEP data.
‘Dead souls’ and ‘cars with a grid’ are vehicles which significantly affect the general number of passenger cars in Poland, used to compare car ownership in international research. The third shortcoming analysed, i.e. that related to company cars, does not change this overall value of this indicator, but has an impact on its spatial distribution, increasing the number of cars in a relatively small group of communes and districts. In the motorisation literature, company cars are often analysed separately and constitute in a sense an ‘autonomous’ variable influencing car ownership (de Jong et al. 2004; Whelan 2007).
Company cars are used mainly in medium-sized and large enterprises as a kind of ‘additional remuneration’ for middle- and high-level staff or sales representatives. This is typical not only of Poland, but also of many other countries, e.g. the United Kingdom (Whelan 2007). Company cars are registered according to the location of car owners’ business offices, i.e. a leasing company.
As at the end of 2018, there were nearly 1.5 million company passenger cars registered in Poland (about 10% of all the registered passenger cars except for end-of-life cars). The size of the company car fleet in 15 communes recording their greatest numbers is given in Table 3. It is clearly visible that about 30% of all company cars are registered in Warsaw and another 30% in five large Polish cities: Poznań, Wrocław, Krakow, Gdańsk and Łódź. Out of 15 communes with the greatest number of company cars there is only one small commune—Kampinos, located in the vicinity of Warsaw. The high car ownership rate in this unit results from the tax policy adopted by the local government that attracted leasing companies as to increase revenues from vehicle tax which is a local tax (although passenger cars are not subject to it, but leasing companies usually offer both passenger cars and trucks). A similar phenomenon can be observed in the suburban communes of: Cedry Wielkie near Gdańsk, Nadarzyn near Warsaw and Suchy Las near Poznań. The second factor determining the high number of company cars is communes’ greater economic activity and presence of many large enterprises (this can be illustrated again by big cities or heavily invested suburban communes, such as Tarnowo Podgórne near Poznań or Kobierzyce near Wrocław).
The 15 cities and communes in Poland with the largest number of company cars.
No. | City/commune | Number of passenger cars registered by | Number of passenger cars per 1,000 inhabitants | |||
---|---|---|---|---|---|---|
natural persons | business entities | natural persons | business entities | Total | ||
1 | Warsaw | 591,643 | 424,988 | 337 | 242 | 578 |
2 | Poznań | 192,088 | 96,411 | 356 | 179 | 535 |
3 | Wrocław | 223,571 | 89,406 | 350 | 140 | 490 |
4 | Krakow | 269,860 | 88,201 | 352 | 115 | 467 |
5 | Katowice | 101,949 | 59,623 | 343 | 201 | 544 |
6 | Gdańsk | 148,380 | 46,056 | 320 | 99 | 419 |
7 | Łódź | 241,237 | 43,812 | 348 | 63 | 411 |
8 | Szczecin | 134,205 | 27,073 | 332 | 67 | 399 |
9 | Rzeszów | 67,972 | 24,474 | 359 | 129 | 489 |
10 | Lublin | 122,279 | 24,424 | 359 | 72 | 431 |
11 | Gdynia | 83,067 | 24,194 | 337 | 98 | 435 |
12 | Kampinos | 2,261 | 21,456 | 527 | 4,998 | 5,525 |
13 | Bydgoszcz | 114,726 | 21,438 | 325 | 61 | 385 |
14 | Bielsko-Biała | 67,901 | 20,150 | 395 | 117 | 512 |
15 | Opole | 48,270 | 16,671 | 377 | 130 | 507 |
Source: authors’ own elaboration based on CEP data.
Owing to the clearly uneven spatial distribution, company cars should be excluded from car ownership analysis especially in communes or districts. This is so because it is known that a significant proportion of those vehicles are used in a completely different location than the city/commune of registration.
In the light of a rapid growth of statistical data, their increasingly easy acquirement and processing, one may observe a relatively low interest in their quality. This relates also to the application of over- or underestimated values of statistical indicators in geographical research, which may lead to a distorted spatial picture of the occurrence and dynamics of a given phenomenon or process, and as a result, to wrong interpretations and inappropriate practical measures. In this article, we have presented evidences that motorisation statistics do not fully reflect the real geography of car ownership in Poland.
The implications of the distortion of car ownership rates are twofold: direct and indirect. The direct consequences mean that the correction of the indicator’s value often drastically change the spatial distribution of the level (or dynamics) of car ownership and the position of a given territorial unit (country, province, district and commune) against other units. This discrepancy can be illustrated well by the comparison of Polish car ownership maps made using both GUS and CEP data, corrected by the authors of this article (Fig. 5). This correction consists in deducting end-of-life and company cars from the total number of registered cars and adding LGV-approved passenger cars (by criteria defined in section
The scale and spatial distribution of the discrepancy in the assessment of car ownership are shown in Fig. 6. It demonstrates the shifts of particular districts in the decile system after the authors’ correction of the car ownership rate, that is a change in the position of a given district against all such units in Poland. In the significant proportion of districts (256, i.e. about 66%), those changes have not occurred or are very small, and fall within the range of <−1; +1>. The most substantial shifts
The indirect implications are linked to the fact that geographical research results concerning the level and dynamics of car ownership are used frequently in other broader studies related, e.g. to regional and local development factors, delimitation of peripheral areas, spatial accessibility as well as in planning and decision-making activities (e.g. construction projects of new transport infrastructure in areas with the greatest or lowest concentration of car ownership). The adoption of inadequate car ownership rates may lead to misguided transport investments. This is so because there are government studies (e.g. Strategia rozwoju…, 2013: 36, 53), which refer to misleading statistics of individual car ownership in Poland (but also to the overestimated number of LGVs and buses), and on that basis inaccurate diagnoses were provided. This has far-reaching implications. In recent years the opinion-forming media has produced texts that are completely divorced from reality, e.g.
The need to eliminate these shortcomings of motorisation statistics (especially when these shortcomings are relatively serious as in Poland) is also driven by the needs of an ever more rapidly developing market geography, including the geography of car market and car ownership. For instance, car dealers should be accurately informed in which regions the passenger car market is saturated and where there are particularly big ‘relative shortages’ of car supply. This knowledge in turn entails further decisions, concerning, e.g. location of car dealerships and other services related to their operation. The studies of geographers, making data about car ownership credible in spatial terms, their correct interpretation and recommendations for planning and decision-making, may contribute significantly to enhancing the prestige of our discipline and increasing its social importance.