SHORT-RUN MACROECONOMIC FACTORS AFFECTING CAR SALES
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SHORT-RUN MACROECONOMIC FACTORS AFFECTING CAR SALES
Viktor Smusin
Natalia Makayeva
School of Business and Management of Technology
Belarusian State University, Belarus
Abstract
Purpose – The aim of this paper is to study the relationship between selected macroeconomic variables and national car sales based
on a sampling from 3 national markets: Belarus, Russia, and Ukraine.
Design/methodology/approach – Ten macroeconomic variables are discussed in relation to car sales; seven of them, including five
monthly variables and two yearly variables, are compared to car sales data statistically.
Findings – The two main findings are: a) that macroeconomic variables are a significant factor of car sales, if observed as a
composite linear regression model; b) that real estate prices proved to be the most strongly correlated variable with car sales.
Research limitations/implications – A promising direction of further research is examining the links between other macroeconomic
variables (stock market indexes etc.) and car sales.
Practical implications – Results of this work can be used by car makers and vendors to predict car market fluctuations depending on
changes in the macroeconomic fluctuations, and by policymakers and economy experts to evaluate the eventual impact of economic
fluctuations on car manufacturers and vendors, who build the backbone of some countries’ economies and are a very significant
sector of the economy in others.
Originality/value – This paper studies some very recent data from a perspective that gained little attention in earlier research,
contributing to the practical branch of economic studies.
Keywords Car sales, Macroeconomics, Economic fluctuations, Recession
Paper type Research paper
Introduction
The aim of this paper is to study the relationship between selected macroeconomic variables and national car
sales based on a sampling from 3 national markets: Belarus, Russia, and Ukraine. Results of the study can be
used:
• by car manufacturers to predict future car sales,
• by policymakers to forecast macroeconomic fluctuations on the basis of monitoring car sales,
and to justify the potential need for bailout for car producers,
• and by researchers as a foundation for further studies of the topic.
Research on the topic is rather fragmentary, although many macroeconomic variables have been
compared to car sales. For example, Barber et al. (1999) test the correlation between exchange rate of U.S.
dollar to Japan yen and U.S. sales of American and Japanese cars and find that the exchange rate explains
about 4 percent of the variance in monthly car sales, concluding that this is not a major factor. Research by
Dargay et al., (2007) is a comprehensive study of car sales over a very long period of time and across many
countries towards respective national economies, offering an interesting model utilizing Goempertz function.
This paper is faced slightly in another direction; rather than building elaborate mathematical models, we
instead examine the data using basic instruments like correlation and linear regression, and, even more
important, we discuss the data from a qualitative perspective, building theoretical hypotheses and observing
whether the hypotheses prove right or wrong on real-world data.
Methodology
To properly introduce the subject of our research, some definitions are in order. Vehicles are non-living
means of transport. Note that a vehicle as such doesn’t have to have a motor and/or wheels; in case it has a
motor and/or wheels, it is explicitly called a motor and/or wheeled vehicle. An automobile or motor car is a
wheeled motor vehicle for transporting passengers. So, “vehicle” is the most general term, while“automobile” and “car” are more specific and are synonyms. Buses and trucks (apart from pickup trucks) are
not considered cars.
An economy is described by a vast number of variables, many of which are interrelated. Car sales are
among them. So if we study, for example, the relationship between car sales and real estate prices, and then
find some correlation, it doesn’t necessarily follow that a causal relationship exists, though they might be a
function of another common variable. For example, a very strong correlation of oil prices and Russian real
estate prices was shown by some analysts (Lenta.Ru, 2009).
Belarus, Russia and Ukraine (the countries are listed in alphabetical order throughout the paper) are
chosen for the analysis for the following reasons:
• detailed and comparable data could be found for these countries;
• these countries, albeit geographically and culturally close to each other, have different patterns
of economical development;
• they’re different in terms of what concerns car production: Belarus doesn’t produce its own
cars in considerable amounts, whereas Russia and Ukraine each has its own car production
facilities.
After some brief notes on the national car markets of the 3 respective countries, the remainder of the
paper is laid out according to a repeating structure:
• factor (as name of the section);
• hypothesis (theoretical assumption) on how this factor is expected to correspond with car
sales;
• verification of the hypothesis enlisting real-world data;
• conclusion.
Ten factors have been selected for analysis. They’re discussed in no particular order.
In this paper, we aim to cover various macroeconomic aspects and factors of national car sales, and we
opted to conduct a more descriptive and numerical evaluation of the situation instead of building complicated
mathematical models. Recognizing that any one of these factors warrants deep research, we strongly believe
that this overview can yield valuable insights into the multiple and varying macroeconomic determinants of
car sales.
The three macroeconomic variables deemed most important by macroeconomic textbooks – gross
domestic product, inflation rate, and unemployment rate (Mankiw, 2002) – are also discussed among other
factors.
In some instances, simple averages of correlation coefficients are calculated; strictly speaking, a simple
average of several correlation coefficients doesn’t have a separate sense, but still gives an idea of the
combined result.
Where possible, monthly data were analyzed. For car sales, data from January 2006 to July 2009 were at
our disposal, i.e. 43 consecutive months.
We don’t pay much attention to used car markets in this paper, mostly due to restricted data availability.
An interesting remark to be made here is the impact of bans on used cars and auto parts on auto sales and on
the economy as a whole. Czaga and Fliess (2005) name 17 countries as of 2004 that ban import of used
motor vehicles. An earlier but more comprehensive study (Pelletiere, 2003) lists most of the world’s
countries according to their used automobiles import regulations, including 22 countries with complete
prohibition. It is conspicuous that those countries banning used car import are not among the world’s most
developed nations, except Canada.
General notes on the car markets of Belarus, Russia, and Ukraine
Belarusian car market is dominated by used cars, but new car sales have been rising rapidly during the last
few years, exceeding all expectations (it is sometimes said to be the quickest growth in new car sales in
world’s history). Still, the market is unsaturated as compared with other countries, with many world car
brands having only one official dealer representing the whole country, and some other brands having none.
New car sales had been growing almost constantly (except for some seasonal declines) until winter 2008,
when they started falling very significantly. During the month of January 2009, 23% more cars were soldthan in January 2008. February 2009 saw a 3% decline as compared to February 2008, and sales realized an even more severe decline of 26% in March 2009 as compared to March 2008 (see Figure 1). Figure 1. Car sales in Belarus, Russia, and Ukraine from January 2006 to July 2009 (logarithmic scale). The principal distinction between Belarusian and Russian car markets is that Russia has a long history of own car production, while Belarus fully relies on import. Actually, Iran Khodro Samand cars are assembled in Belarus, but only in small quantities and without any material impact on the domestic market. That’s why Russia had good reasons to raise import tariffs in order to protect its own car manufacturers. Ukraine is a hybrid; it has some of its own car production, though not as extensive as that of Russia, and many cars of foreign brands are assembled in the country. The 3 markets proved to be relatively tightly related; the pairwise correlation coefficients averaged to 80%. The data in all 3 regions showed the same seasonality patterns, with the clearest common pattern being a sales slowdown in January. Starting from late 2008, many car dealership networks in Belarus, Russia, and Ukraine have been experiencing serious troubles with sales, which even led to several dealership closures. 1. Real estate prices. Hypothesis On the one hand, rising real estate prices can force some people who were saving up for housing to discover that their savings aren’t sufficient for an apartment anymore, abandon their plans and buy a car instead (it might be especially relevant for the post-Soviet mentality, which considers car ownership an important sign of prosperity, probably the second to apartment ownership). In this sense, cars are like substitutes for housing, despite having totally different functions. On the other hand, real estate prices often rise while the economy is growing, and economic growth also results in increased car sales. So, the net effect of rising (or falling) real estate prices is not readily apparent from the theoretical point of view.
Verification
Real estate prices are analyzed in the capitals of the countries: Minsk, Moscow, and Kyiv. The reasons for
this are:
• income is far from being equally distributed in these countries, with the most wealthy part of the
population living in the capitals, so the capital cities account for a very significant portion of
national car sales, while real estate prices in the provinces could result in misleading
conclusions;
• data availability restricted the scope of research: the available data were mostly limited to
capitals.
Prior to comparing real estate prices to car sales, we calculated the interdependency between regions. It
proved to be high, averaging to 87%. And then the data were compared to car sales: in all 3 cases, the
correlation was quite strong, with the 3 correlation coefficients averaging to 79%.
End of 2006 and first half of 2007 saw a very rapid rise in housing prices in Belarus, especially in Minsk.
For example, in February and March 2007 monthly price growth was over 10 percent. Similarly, housing
prices grew in Kyiv by about 10 percent in October 2006. These real estate price fluctuations were in tune
with rapidly rising car sales in all 3 countries during the same months.
Later events reversed the prices in all 3 regions: in July 2009, prices have fallen by a third in Russia and
Ukraine and by a quarter in Belarus as compared to July 2008. Once again, this decline in prices coincided
with falling car sales.
Conclusion
Real estate prices are closely related to car sales, moving in the same direction, and, unfortunately, almost
simultaneously; unfortunately, because this means that none of them can serve as a leading indicator for the
remaining variable. Still, it is an important result, meaning that if a growth or decline in one of the variables
is predicted using some external data and/or facts, then this conclusion can also be transferred to the
remaining variable. Real estate markets tend to draw closer analysts’ attention than car markets, so if these
markets are proved to be interrelated, then the results of their analysis can be used for predicting the
dynamics of both markets.
2. Exchange rates.
Hypothesis
If a significant portion of all cars bought is comprised of imports, and if foreign currency becomes more
expensive in relation to domestic currency, then a decline in car sales is expected. If car prices are fixed in
foreign currency, and foreign currency becomes more expensive to buy for domestic currency, then the car
will also become more expensive. It might be especially the case for countries with little or no own car
production, like Belarus.
If, however, cars produced within the country account for a big part of car sales, then a rise in price of
foreign currency might just as well turn the customers to domestic cars. Still, a portion of customers will not
do that.
To sum it up, we predict that a rising price for foreign currency will lower car sales, and the magnitude of
the impact will directly depend on the portion of imported cars among all car sales, with no effect at all when
all cars bought are produced domestically.
Verification
In Belarus, a onetime devaluation of the national currency of about 20% was conducted in early January
2009. In Russia, the process was more gradual. In Ukraine, the quickest devaluation of the hryvna was seen
in late 2008; it was neither a onetime devaluation nor a gradual devaluation over a long period of time. The
hryvna was devaluated by about ⅓ over 2 months. The exchange rate has been fairly stable since then.Exchange rate is understood as the amount of units of Belarusian, Russian or Ukrainian currency per 1 U.S.
dollar or euro.
Correlation of monthly dollar exchange rates in all 3 countries with car sales was studied. In all three
cases, correlation was negative, thus supporting the hypothesis that car sales will decline if dollars become
more expensive. But the absolute value of the correlation coefficient wasn’t high, especially for Belarus,
where it was only -11%. It was higher in Ukraine (-51%), and the highest correlation was observed in
Russia: -64%.
Euro exchange rates of all 3 national currencies were also compared to car sales, and the results were
even weaker than the results of comparing car sales with U.S. dollar exchange rates, with a surprising
positive correlation in Belarus (27%), and negative but small correlations in the remaining two countries.
The positive correlation for Belarus seems hardly explicable, and it might be a sign that exchange rate
fluctuations accounted only for a small portion of car sales fluctuations – at least in the period that we
examine.
Conclusion
In general, for the U.S. dollar, the hypothesis is supported: expensive foreign currency leads to smaller car
sales. For some reason, the link was much less clear for the euro, which may be attributed to the fact that the
data were observed on a relatively restricted period of time where other more important factors came to the
fore. But anyway, even for the dollar, the link isn’t very tight, which is also in tune with our hypothesis that
negative relationship isn’t quite obvious and is influenced by numerous other factors.
3. Gross domestic product.
Hypothesis
Under dispute is whether GDP growth causes car sales growth or vice versa. It is even more probable that
there is no cause-effect relationship between these two variables, and that their fluctuations are together a
consequence of changes in many other variables. Nevertheless, 2 points are worth noting here:
1. Growth in GDP is highly likely to correlate with growth in car sales.
2. If GDP and car sales respond to a common third factor with different speed, then they can be very
useful in predicting fluctuations of each other.
The main hypothesis is thus that car sales are closely related to GDP fluctuations.
Verification
Monthly and quarterly GDP statistics are unreliable at best (and they’re, in fact, quite rarely published), so
we have to content ourselves with yearly data.
Despite being the most universally accepted indicator of well-being of an economy, GDP has the
drawback of reflecting only the amount of produced goods and services, independent of whether they were
sold or not (unsold inventory registers in the Investment section of GDP). This characteristic of GDP as an
indicator can be especially significant in some venues, like in Belarus, where production wasn’t largely
stopped or shortened, so GDP statistics looked quite optimistic, but inventories had risen rapidly in the 1st
half of 2009.
GDP rose every year from 2003 to 2008 in all 3 countries, with growth rate ranging from 102.1%
(Ukraine, 2008) to 112.1% (Ukraine, 2004). Correlation comparison of these data delivered mixed results:
for Belarus, the correlation was -50%, which means that car sales go down when GDP goes up (it obviously
contradicts what was expected); for Russia and Ukraine, the coefficients were positive in line with our
expectations, but still, they weren’t particularly high: 56% for Russia and 39% for Ukraine.
Analyzing correlation of car sales with later and earlier GDP would be very interesting, but data
constraints (5 years) makes time series analysis impossible; even for a 1 year’s shift, the analysis would be
restricted to comparing 4 numbers, which doesn’t allow interpreting the results with confidence. Anotherreason for unclear results of the analysis is that the years from 2004 to 2008 formed a period of stable
growth, and the variance of GDP growth data was thus very small. Such data makes the correlation
coefficient unreliable. It is to be regretted that the scope of yearly car sales data limits our ability to extend
the analysis to other periods.
Conclusion
Despite being probably the most promising macroeconomic indicator in our analysis, GDP didn’t prove very
efficient in terms of correlation with car sales – at least with the data on hand.
4. Interest rates.
Hypothesis
Many cars are bought on credit, though some countries (like the United States) have deeper traditions and
habits of buying on credit than others (like Belarus). If interest rates rise, then credits become less easily
available, leading to a decline in car sales.
Buying a car (just like any other good) on credit can increase its effective price significantly. Denote by P
the explicit price of the car, by r the credit interest rate, and by N the number of years for which credit is
issued. Accounting for credit financing, the effective price of the car can be estimated by the following
equation: P + rPN = P * ( 1 + rN ). For example, for Belarus the common values for r and N are r = 20%
and N = 5 years, so 1+rN = 1+20%*5 = 2. That means that the price of a car bought on credit is two times
higher than if it had not been bought on credit. If the portion of cars bought on credit is c (0≤c≤1), then the
actual average market price of cars is cP(1+rN)+(1-c)P = P(c+crN+1-c) = P(1+crN). In Belarus (see
below), about a half of cars were bought on credit until recently, so c = 0.5, and the average car market price
is by a half greater than the retail price. In these circumstances, rising r will lead to a rising market price, and
the sensitivity of the market price towards r depends on c.
Verification
In July 2009, credits in foreign currency were prohibited in Belarus (National Bank of the Republic of
Belarus, 2009). The prohibition was initially declared to be an interim measure, to be effective until January
2011, but later the National Bank communicated that this decision would most likely be permanent
(Telegraf.By, 2009). A similar (slightly less restrictive) measure was introduced in Ukraine in June 2009
(Національний банк України, 2009). Such changes are predicted to have a significant impact on car sales
because many cars (almost a half in Belarus) were bought on credit, but mostly in foreign currency; of 17
banks offering credits for new car purchases, only 5 offered credits in Belarusian rubles (Кожемякин, 2009).
In Russia, special-rate credits were introduced by the authorities in spring 2009 to recover the car market;
but as of the time of writing this paper, efficacy of such measures remains to be seen: after 5 months since
the program’s introduction, only about 23 thousand credits had been issued (Bfm.ru, 2009). It corresponds to
about 4,600 credits monthly, but compare: in May 2009 alone, more than 83,000 cars were sold in Russia,
and this is already the amount after the rapid decline in sales.
Interest rates on car loans are theoretically expected to be lower than those on credits for general purposes
because the former are more reliable than the latter; bad debts make up less than 2% of all auto credits. In
reality, the situation is sometimes reverse and can be explained by the fact that car credits are mostly offered
for long periods of time (like 5 years), whereas general-purpose credits can be obtained for shorter periods.
Still, interest rates for any types of credits are significantly higher in Belarus (but on the other hand, deposit
interest rates are also significantly higher) than in Western Europe.
As shown by the data, a rapid rise in credit interest rates in foreign currency started in October 2008 in
Belarus. This corresponds to a sharp decline in car sales in November 2008. In March 2009, interest rates
evidenced a declining trend (though not returning to mid-2008 level), and April 2009 saw a significant rise in
car sales. This 1-month lag can be explained by the following: firstly, the credit is negotiated with the bankand received, then a car is actually bought, and secondly, the statistics most likely represent monthly
registrations of new cars, and a car is often registered several days after it was bought. These fluctuations
affected only credits in foreign currency; interest rates on credits in Belarusian rubles stayed roughly on a
uniform level. Now that currency credits are prohibited, similar fluctuations are likely to be transferred to
credits in Belarusian rubles.
Correlation was quite weak (58% for Belarus and -9% for Russia; no credit interest rate data could be
found for Ukraine) and quite unreliable because of the small variance of credit interest rates.
Conclusion
Verifying the theoretical assumption is quite cumbersome because of the unclear statistics (the very term
“credit interest rate” can take on various meanings, depending on the methodology employed). But there are
strong indications that the hypothesis is right; car sales are quite sensitive to credit interest rate fluctuations.
Also notice that the central banks of the three countries under study have a substantial impact on the
interest rates by altering the Federal funds rate, which is quite often exercised: for example, the rate was
altered 5 times in 2008 in Belarus and once (as of September 2009) in 2009 (Национальный банк
Республики Беларусь, 2009). Every time it was a rate increase, accelerating in late 2008 (compare to the
dynamics of credit interest rates shown above).
5. Employment.
Hypothesis
The theoretical relationship is rather clear: higher employment leads to higher disposable income,
encouraging people to buy more cars.
By the way, the car industry is itself a major employment sector, employing about 50 million people
worldwide (i.e. slightly less than 1% of the world’s population, and just about 1% after subtracting those
unable to work) (OICA, 2007). Employment generation is also one reason why governments of many
countries that are historically heavily dependent on own car production (the U.S.A., Germany and others)
support their auto manufacturers.
Verification
Employment statistics about Belarus don’t mirror reality: many enterprises are state-owned, and layoffs are
avoided to prevent protests (the Belarusian economy is officially declared to be “socially oriented”) even if it
would be economically rational. A more radical example of what Belarus experiences can be found in North
Korea, where many enterprises are inactive, but people are still expected (and even forced) to be in
attendance (Ланьков, 2007). Some people who lose their jobs just don’t register themselves at labor
exchange authorities because the unemployment benefit is very low (about $17 monthly in Belarus (Afn.by,
2009)), and moreover, the unemployed can be forced to do public works. Conversely, some people are
officially unemployed (whether registered at labor exchanges or not) but have some sources of income,
which aren’t always fully transparent. There are even cases that fuel the irony of Russian mass media
resulting in headlines like “More Lexus cars are hijacked from unemployed Muscovites” (Avto.ru, 2008),
when the officially unemployed are discovered to be in possession of luxury cars.
Another trap in using employment data is that it is often calculated as the ratio of employed people to the
total quantity of jobs available. So if jobs become fewer, the unemployment statistics can still look good.
In any case, the unemployment rate in Belarus, Russia and Ukraine is most probably quite close to the
average European rate and not fluctuating much, so even if reliable unemployment data were available, it
wouldn’t prove to be strongly related to car sales. Even the classical correlation coefficient works well only
for fluctuating variables; if one of the variables has small variance, the correlation coefficient becomes less
stable, and eventually undefined for zero variance.Conclusion The aforementioned factors make unemployment statistics largely unusable, and thus, the investigation of the link between car sales and unemployment isn’t worth studying in full detail. 6. Industrial production. Hypothesis As with GDP, the link between car sales and industrial production is probably rather indirect. There isn't any evident relation between car sales and industrial production (at least as long as we talk about passenger cars), but still, rising industrial production is evidence of economic revival, which has, as one of its consequences, elevated consumption, of which car sales are part. Verification Late 2008 and first half of 2009 saw a decline in industrial production in Belarus, also some major investment projects were frozen, both by foreign investors and by Belarusian businessmen and authorities. For example, Ukrainian “Bogdan” company abandoned the plans of building a bus factory in Belarus (Naviny.By, 2009). There were also some big projects in oil industry in Venezuela that were abandoned in mid-2009 due to a decline in world oil prices (Хартыя'97, 2009). It was also reflected in the statistics: index of industrial production was very uneven during this period, wavering from growth to decline almost monthly. But correlation with car sales proved rather disappointing: for Belarus, it was only 25%, whereas for Russia it was 88%, and 74% for Ukraine. For Ukraine, monthly data for a much longer period were available (from January 2006, as opposed to January 2008 for the other two countries), so the result of a relatively high correlation appears to be quite reliable. Conclusion Results were mixed: fluctuation of car sales correlate with fluctuations of industrial production in Russia and Ukraine quite strongly, while much weaker correlation was seen in the case of Belarus. 7. Retail turnover. Hypothesis Car sales are part of retail turnover. So, provided that the structure of retail turnover is approximately constant, car sales will fluctuate along with total retail turnover. On the other hand, the existence of a constant retail structure is far from evident; some components (like food) are fairly stable, whereas others fluctuate significantly, depending on the income (i.e. the weights of components in retail turnover can be explained by income elasticity of demand). Verification The very assumption about a constant structure of retail turnover in at least some of the observed countries may not hold, because the retail market is still far from being established, and many retail facilities are opening each year. Only yearly retail turnover data for Belarus for 2003-2008 could be found, and even they give little food for reflection. Retail turnover growth was very stable these years, while car sales obviously weren’t. The correlation coefficient of car sales growth vis-à-vis retail turnover growth turned out to be only 19%, which means that there is no statistically significant link. The data for Ukraine showed a very surprising contrast: a 91% correlation. But even more surprising was the result for Russia; the correlation proved to be negative,
amounting to -65%. These discrepancies show that analyzing retail turnover data probably makes little sense,
and the obtained correlation coefficients are due to little retail turnover growth data variance.
Conclusion
The data on hand is evidence that retail turnover in general isn’t tightly related to car sales and cannot serve
as a means of predicting and/or explaining car sales dynamics.
8. Salaries and wages.
Hypothesis
The expected link is obvious: bigger salaries and wages probably lead to bigger car sales (unless the market
is saturated, which isn't the case in the overwhelming majority of the world's countries). Of course, real
wages are meant here, not nominal (see also the section about inflation).
Verification
Some salaries and wages statistics are published by the national statistical services of the discussed countries,
but this part of the analysis makes much less sense than others because there are serious reasons to believe
that a very significant portion of car buyers have shadow sources of income that aren't taken into account by
official statistics.
For example, in Belarus, the average monthly salary in the first half of 2009 amounted to about $250
(Літвінаў, 2009), which means that the price for an average new car starts from about 5 years’ worth of
average Belarusian salaries. Of course, these numbers don’t give a clear idea of the ability of Belarusians to
buy cars due to 3 reasons:
• shadow sources of income, as mentioned above;
• new cars aren’t part of what an average Belarusian family can afford, they’re mostly bought by
the wealthy;
• many used cars are bought instead of new cars.
Conclusion
Similarly as with employment, salary, though probably quite a significant factor for car sales in reality, isn’t
promising to shed a light on car sales using officially available data, and, moreover, misleading results can
be obtained (as it is often the case with unreliable data), so it is reasonable to omit salary from our analysis.
9. Inflation.
Hypothesis
The relatedness of inflation towards car sales is very multifold. On the one hand, rising prices in an economy
include car prices as well, and if cars become more expensive, the quantity of cars bought declines (by the
law of supply and demand). On the other hand, rising prices often coincide with rising salaries and wages,
and thus, the net effect of inflation is merely incidental.
Thus, inflation is probably the least clear factor from the theoretical point of view in our analysis vis-à-vis
car sales. Actually, we included inflation in our analysis to make the picture complete as inflation ranks as
one of the most important macroeconomic variables. There is little chance that this factor will prove
significant in relation to car sales. Or, at least, there is no evident hypothesis in this case.Verification
We account for inflation by employing its most widely used indicator; the consumer price index (CPI). CPI
is part of the Special Data Dissemination Standard issued by the International Monetary Fund, and Belarus,
Russia, and Ukraine all obey this standard. Accordingly, detailed (i.e. monthly) CPI data are readily
available on the web sites of the Ministries and Committees of Statistics of the respective countries.
Notice that the 3 countries use different base periods for calculating CPI (with Ukraine having changed
the base period since 2009 in addition, which was accounted for in our analysis – CPI for 2009 was
recalculated in order to have comparable data), but it is not relevant for the purpose of checking correlation
because changing a base period means solely multiplying all numbers by the same factor, which doesn’t alter
correlation.
The basket of goods that is used as a basis for calculating CPI in Belarus includes cars (Bsb.By, 2009).
Still, the correlation isn’t particularly high: only 54%. For Russia, it was even smaller; only 28%, which
means there is no correlation (the extensive and detailed amount of data enables us to draw quite confident
conclusions). For Ukraine, the correlation coefficient was the smallest among the 3 countries in question:
only 4%, i.e. no statistical relevance at all.
These facts are in line with our prior assumption, which assigned little importance to inflation in
explaining car sales.
Conclusion
In accordance with our hypothesis, inflation is weak in explaining car sales, and there is no evidence that the
general price level fluctuates with car sales, with the 3 correlation coefficients averaging to 28%.
This conclusion can be considered quite confident, as detailed data for 3 countries over more than 3 years
were examined, and these data, in addition, showed quite big variance (in contrast to retail turnover, see
above).
10. State bailout.
Hypothesis
The assumption is that state bailout helps car sales, whether it is in the form of preferable conditions for car
makers in comparison to other manufacturers, or changed import tariffs for auto parts (or export tariffs for
ready-to-use cars), or credits for car purchases with low interest rates, or encouraging selling old cars and
buying new ones.
Verification
This is going to be the less precise (in terms of numbers) part of our analysis, primarily because state bailout
is hard to quantify. Belarus doesn’t have a bailout program because the country’s economy is not reliant on
the passenger car sector, albeit truck and heavy machinery production occupies a very significant part of the
nation’s output. Russia and Ukraine have their own passenger car production, so their governments pay more
attention to supporting car sales and production. Russia and Ukraine didn’t establish incentives like the Car
Allowance Rebate System (also known as “Cash for Clunkers”) introduced in the U.S.A. in July 2009.
However, in Ukraine, import tariffs for auto parts that are used for assembling cars were lowered. Actually,
this law took effect several years before the downturn, but it was extended to the end of 2008
(Корреспондент.net, 2008), after the heavy consequences of the crises became palpable.
In Russia, an alternate approach was chosen; the government opted not to help car makers directly, but
rather encouraged car sales by offering credits on favorable conditions. The results were disappointing, so in
mid-2009, the amount of money spent on this program was curtailed (Bfm.ru, 2009).Conclusion
Evidently, state bailout, if used wisely, can help car makers. But this fact wasn’t proven by the data from
Belarus, Russia, and Ukraine: despite all the measures taken to increase car sales in Russia and Ukraine, the
relative decline in car sales was the smallest in Belarus, although the Belarusian government did nothing to
stimulate car sales. These incongruent outcomes can be also a consequence of the fact that Belarus was
shaken by the crisis to a smaller extent than the other two countries, and also evidence that state bailout alone
cannot save the situation. So, all in all, the hypothesis wasn’t confirmed by the data; nevertheless, it is quite
possible that data from other countries will show different results.
General Conclusion
Table I presents in a short form the summary of the above discussion.
Table I. Summary of the findings.
No. Factor Level of detail Period Correlation
1 Real estate prices Monthly August 2005 – July 2009 Strong
2 Exchange rates Monthly January 2005 – July 2009 Medium
3 Gross domestic product Yearly 2003 – 2008 Medium
4 Interest rates Monthly January 2006 – June 2009 Weak
5 Employment Discussed without analysis
January 2008 – July 2009 for Belarus and Russia
6 Industrial production Monthly Strong
January 2006 – July 2009 for Ukraine
7 Retail turnover Yearly 2003 – 2008 Weak
8 Salaries and wages Discussed without analysis
9 Inflation Monthly January 2006 – July 2009 Weak
10 State bailout Analyzed qualitatively Weak
The links between most macroeconomic variables and car sales aren’t very clear. Real estate prices
proved to be most strongly correlated variable with car sales.
We’ve also made an attempt to build a multiple regression model of monthly car sales as a function of
other factors (namely, CPI, interest rates, exchange rates, and real estate prices – these are the factors for
which monthly data are available). The absolute values of coefficients are of little significance, as they’re
linked to the units of measurement (and therefore, often incomparable between countries: for example, CPIs
use different base years, and in Belarus, current CPI exceeds 109, whereas Russian CPI is only slightly over
300). However, the R2 coefficient is of great interest, showing the portion of car sales fluctuations that can be
explained by macroeconomic fluctuations. R2 - values were calculated to be 83%, 84%, and 75% for Belarus,
Russia, and Ukraine respectively. For the Ukrainian model, interest rate had to be omitted because no
reliable monthly data for the relevant period were available, which might be the reason for a smaller R2 in
this country model.
Possible future research directions are:
• Used car market and macroeconomics. In Belarus, Russia, and Ukraine, as well as in many
other countries, used cars account for a big portion of car sales.
• Car repair services and macroeconomics. It is sometimes said (rather by assumption) that in
times of recession, people repair their old cars instead of buying new ones – this is to be
checked.
• Other countries and time spans. Analyzing a longer list of countries over a longer period of
time is beyond the scope of this study, but it is a very promising direction of research.
• Study of how different car segments (luxury cars, compact cars etc.) respond to
macroeconomic fluctuations.Data sources
Monthly car sales statistics in the 3 countries are published at http://atlant-m.ua/rus/analitics.php by analysts
of Atlant-M, an automobile holding company with subsidiaries in these 3 countries.
Real estate prices were taken from the following 3 web links:
• http://realt.by/statistics/dynamics/show/town/price_m2/all-time/month/usd/ for Minsk
• http://www.realprice.ru/ for Moscow
• http://www.domik.net/mod/web/stat?act=display for Kyiv
Exchange rate and interest rate data were taken from the websites of the national central banks. GDP,
industrial production, retail turnover and inflation data were taken from the websites of the national
statistical services. These institutions are cited in Table II.
Table II. Central banks and statistical services of Belarus, Russia, and Ukraine.
Central bank Statistical service
Country
Name Website Name Website
National Bank of the National Statistical Committee of the
Belarus www.nbrb.by belstat.gov.by
Republic of Belarus Republic of Belarus
Central Bank of the Russian
Russia www.cbr.ru Federal State Statistics Service www.gks.ru
Federation
Ukraine National Bank of Ukraine www.bank.gov.ua State Statistics Committee of Ukraine ukrstat.gov.ua
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Biographic notes:
Viktor Smusin was born in 1982. I graduated from Belarusian State University in 2004 with a bachelor’s degree in
computer science and later earned a master’s degree in physical and mathematical sciences in 2007 at the same
University (my main research topic was music recognition, and I presented the results at 3 scientific conferences and in
my thesis). He is currently a student at the MBA program of SBMT BSU. His current job position is an IT consultant in
Atlant-M Holding Company, a car dealership network with headquarters in Minsk and subsidiaries in Belarus, Russia,
and Ukraine.
Natalia Makayeva is a Master of Business Administration (MBA) and a Bachelor of Science in Physics. She is MBA
program director and chief teacher at School of Business and Management of Technology, Belarusian State University,
and a part-time consultant at “Pervaya Consaltingovaya” Company, Minsk, Belarus. Her past positions include
Financial and Administrative director at American Jewish Joint Distribution Committee (AJJDC), Representation office
in Belarus.You can also read
