Research Briefing
Emerging markets
November 28, 2012
Authors
Jan Strasky
+49 69 910-31894
[email protected]
Tamara Pashinova
Editor
Maria Laura Lanzeni
Deutsche Bank AG
DB Research
Frankfurt am Main
Germany
E-mail: [email protected]
Fax: +49 69 910-31877
www.dbresearch.com
DB Research Management
Ralf Hoffmann | Bernhard Speyer
What drives FDI to
Russian regions?
In Russia, as a middle-income emerging market, foreign direct investment can
play an important role for the transfer of technology and human capital. This
positive impact may be strengthened by the long period of underinvestment in
Soviet times. Moreover, Russia’s strategic priorities of economic diversification
and modernisation of the economy reinforce the need for FDI.
In this note, we review trends and determinants of FDI inflows to Russia with a
focus on the Russian regions. Moscow, Saint Petersburg and their surrounding
regions attract most FDI. However, new destinations, such as the Kaluga
Region, are beginning to emerge.
Building upon a previous DBR study on the subject, we have updated a top-10
ranking of regions with above-average investment climate and below-average
investment risk, based on investment ratings derived by a Russian rating
agency. Again, Moscow, the Moscow Region and Saint Petersburg stand apart
in terms of their investment potential and overall investment rating. Saint
Petersburg is also the region with the lowest average investment risk over the
past decade. Furthermore, there seems to be spillover effects from Moscow to
the Moscow Region and a relative catch-up of the Moscow Region with Moscow
in terms of investment potential.
Next, we seek to assess the usefulness of those ratings as a gauge of
attractiveness for FDI into the regions in a more formal way. Using a panel
model estimated on annual data from 1995 to 2011, we investigate the
significance of different factors which, from an economic point of view, could be
important for FDI inflows into the Russian regions. Contrary to expectations, we
do not find that the investment ratings are a significant factor from a statistical
point of view. The most important drivers according to our model are past FDI
inflows (i.e. “FDI begets FDI”), regional competitiveness and common factors
such as the global real interest rate. These results seem to indicate that while
ratings of investment potential and risks may be useful to derive general trends,
they need to be utilised carefully as predictors of future FDI.
What drives FDI to Russian regions?
Russia's FDI stock: leading the BRICs
1
FDI stock, USD per capita (2010)
6,000
5,076
5,000
4,000
2,960
3,000
2,000
1,276
386
1,000
432
162
Ukraine
Kazakhstan
China
India
Brazil
Russia
0
Sources: UNCTAD, DB Research calculations
Foreign investment stock by industry
2
14.5
15.5
13.5
7.4
Mining and quarrying
Manufacturing
Construction
Wholesale, retail, motor vehicles
Financial intermediation
Real estate, renting, business activities
Other
Sources: Rosstat, DB Research calculations
Foreign investment inflows by industry
FDI flows, % of total (2009-2011)
12.8
In the case of Russia, as a medium-income emerging market, the central role of
FDI for the transfer of technology and human capital is strengthened by the long
period of underinvestment in Soviet times. Moreover, Russia’s strategic priorities
of economic diversification and modernisation of the economy reinforce the
need for FDI as key instrument, which is currently, outside of extraction
industries, relatively low compared to many emerging country peers. We thus
assume the beneficial role of FDI for Russian economy and focus on describing
existing trends and determinants of FDI inflows.
There are two principal sources of FDI data in Russia, Rosstat (the statistical
office) and the Central Bank of Russia (CBR). Their statistical methods differ
4
considerably and so do their figures. Generally, FDI statistics by Rosstat are
5
lower than the statistics given by the CBR. Since our focus is on medium-term
trends in regional FDI flows, in what follows we rely on the annual FDI flows
data compiled by Rosstat.
32.1
8.7
Cross-country studies of aggregate FDI flows, such as Borensztein et al. (1998)
and Alfaro et al. (2004) often find evidence that inward FDI contributes positively
to economic growth of the host country. On the other hand, there is a claim by
Dani Rodrik that “one dollar of FDI is worth no more and no less than a dollar of
1
any other investment” , supported by findings of Bloningen and Wang (2005)
and Contessi et al. (2008) who emphasise the need to distinguish between
2
various levels of country development.
Chart 1 shows FDI stock per capita in Russia, other BRIC countries, Ukraine
3
and Kazakhstan using the UNCTAD World Investment Report data for 2010 . By
this measure, Russia is well ahead of other BRIC countries, less impressively
ahead of Ukraine and lagging behind Kazakhstan, with considerable FDI
attracted by natural resources and a relatively small population.
FDI stock, % of total (2011)
8.3
FDI flows to Russian regions
3
Despite the often-mentioned concentration of FDI flows into natural resources
extraction (included in mining and quarrying), the most important destination of
inward FDI both in terms of stocks and flows is manufacturing with 32% and
28% of total, respectively (see charts 2 and 3). This suggests some degree of
FDI concentrated on re-exports (export-platform FDI) as well as FDI that serves
principally the domestic market (market-seeking FDI). FDI stock in mining and
quarrying is similar to that in real estate, renting and other business activities, as
well as financial intermediation, underlying the importance of services as a
destination of inward FDI.
19.9
18.5
28.3
5.2
1
15.3
5.5
2
Mining and quarrying
3
Manufacturing
Construction
4
Wholesale, retail, motor vehicles
Financial intermediaries
Real estate, renting and business activities
Other
Sources: Rosstat, DB Research calculations
2
| November 28, 2012
5
D. Rodrik, Appel Inaugural Lecture at Columbia University, March 2003, as quoted by Moran
(2005).
See Contessi and Weinberger (2009) for a somewhat sceptical recent review of literature on the
nexus between FDI and economic growth.
We use UNCTAD data in order to be able to compare across countries, although they differ from
Rosstat data on Russian FDI stocks.
Rosstat data are survey based and without adjustment for rouble appreciation, but provide
regional and sectoral breakdowns since 1995 and 2005, respectively. CBR data are on a
balance-of-payments basis, differentiating between residents and non-residents and adjusting for
exchange rate movements; see Vinhas de Souza (2008), p.62. The CBR methodology complies
with internationally accepted standards, but the regional and sectoral breakdowns are only
available since 2011 and 2010, respectively.
As for the regional FDI data that is our main concern here, the Rosstat and CBR data for 2011
(the only overlapping data point at annual frequency) indeed show substantial differences, but still
a relatively high coefficient of correlation of 0.77.
Research Briefing
What drives FDI to Russian regions?
FDI inflows by region
4
% of total, period averages
Moscow
Moscow R.
Sakhalin R.
Saint Petersburg
Kaluga R.
Leningrad R.
Arkhangelsk R.
Tumen R.
Chelyabinsk R.
Nizhny Novgorod R.
Rep. Tatarstan
Amur R.
Krasnodar T.
0
1995-2000
10
20
2001-2006
30
Moscow and the Sakhalin Region attracted more than 50% of FDI inflows
between 1995 and 2012 and Moscow remains the prime location of the FDI
flows into Russia. Between 2001 and 2006, the Sakhalin Region attracted
almost a third of all incoming FDI. This one-off inflow directed at development of
its vast oil and gas resources leveled off in the 2007-2012 period, when
Sakhalin’s share of FDI inflows dropped back towards 10%. There is, however,
considerable annual variation of inflows that, combined with sparse population,
results in huge per capita variations as in 2011 (see chart 5).
40
2007-2012
Sources: Rosstat, DB Research calculations
Leading regions in per capita FDI
FDI remains heavily concentrated in only a few of the 83 regions (chart 4).
Moscow, St. Petersburg and the surrounding Moscow and Leningrad Regions,
respectively. They attract FDI due to their high concentration of business
activities and the size of local markets. FDI inflows into the Sakhalin and
Arkhangelsk Regions have been directed into the oil and gas sector.
5
The Kaluga Region is an interesting case. It features among the top-3
destinations for FDI in per-capita terms (chart 5), despite the fact that it lacks
natural resources and has shortcomings in energy infrastructure and availability
of labour. Nevertheless, it is profiting from a proactive cluster strategy in
6
attracting foreign investors .
The lack of an R&D base in Russian regions seems of secondary importance for
FDI flows as many foreign firms bring their own technologies. The shortage of
qualified labour and steady migration flows towards Moscow and away from
poor regions are more significant deterrents of FDI inflows.
FDI inflows per capita, USD
Sakhalin R.
Chukotka R.
Kaluga R.
Amur R.
Arkhangelsk R.
Top regions by investment climate
Rep. Kharkassia
Moscow
Leningrad R.
Moscow R.
St. Petersburg
0
1,000 2,000 3,000 4,000 5,000
2011
2010
Sources: Rosstat, DB Research calculations
Investment climate rating scale
6
Maximum potential - minimal risk
1A
High potential - moderate risk
1B
High potential - high risk
1C
Medium potential - minimal risk
2A
Medium potential - moderate risk
2B
Medium potential - high risk
2C
Low potential - minimal risk
3A
Reduced potential - moderate risk
3B1
Reduced potential - high risk
3C1
Minor potential - moderate risk
3B2
Minor potential - high risk
3C2
Low potential - extreme risk
3D
Source: Expert RA
In this section we update the regional analysis in Kompalla and Nestmann
(2009) with data up to 2011, drawing on the ratings of Russian regions
periodically compiled by Expert RA, a leading Russian rating agency. The rating
focuses on the investment climate and comprises measures of investment risk
and investment potential. There are 12 investment climate categories ranging
from “maximum potential with minimum risk” (1A) to “low potential with extreme
risk” (3D); see table 6.
The investment risk indicator focuses on risk of a loss on investment related to
finance, economics, ecology, crime, administration and management, and social
7
framework.
The investment potential indicator is measured by sub-indices related to the
labour market, consumption, industrial production, finance, innovation,
infrastructure, institutions, natural resources and tourism.
Since the numerical values of the investment risk indicator and the investment
potential indicator are published by Expert RA, we derive our ranking of the best
8
regions using these two components. We compile a “top-ten” ranking by
selecting Russian regions that in all the years between 2001 and 2011 achieved
above-average scores on the investment potential indicator as well as belowaverage scores on the investment risk indicator (see table 7). In addition to the
average value we also show the latest (2011) values. Moscow, the Moscow
Region and Saint Petersburg stand apart in terms of their investment potential
and the overall rating (1B). Saint Petersburg is also the region with the lowest
average investment risk between 2001 and 2011. Furthermore, there seems to
6
7
8
3
| November 28, 2012
See KPMG (2010).
Prior to 2011, regions were ranked relative to the average Russian risk level (Russia=1). The
methodology changed in 2011 and the regions were rated relative to a new average Russian risk
level (Russia=0.252).
The complete “overall rating” is based on some 200 qualitative and quantitative indicators, but
detailed information on all components is not available.
Research Briefing
What drives FDI to Russian regions?
be spillover effects from Moscow to the Moscow Region and relative catching-up
of the Moscow Region with Moscow in terms of investment potential.
Interpreting the trends in investment risk is more complicated, due to the
methodology change in 2011.
Top 10 investment destinations
7
Regions with above-average investment potential and below-average risk (2001-2011)
Investment potential**
2001-2011 latest (2011)
Investment risk***
Investment climate
2001-2011
latest (2011)
2001-2011
latest (2011)
Moscow
16.86
14.71
0.81
0.22
1B
1B
Moscow R.
4.91
6.19
0.83
0.18
1B
1B
Saint Petersburg
5.85
5.23
0.76
0.19
1B
1B
Krasnodar T.
2.37
2.76
0.78
0.16
1C
2B
Rep. Tatarstan
2.10
2.39
0.80
0.22
2B
2B
Samara R.
2.09
2.01
0.91
0.25
2B
2B
Rep. Bashkortastan
1.85
1.98
0.83
0.23
2B
2B
Rostov R.
1.95
1.94
0.79
0.19
2A
2A
Nizhny Novgorod R.*
2.11
1.91
0.84
0.32
2B
2B
Khanty Mansiysky AR*
2.51
1.72
1.00
0.26
2B
2B
*Borderline cases: one period with an above-average risk score
**Investment potential defined as a share in investment potential of all regions
***Investment risk relative to average Russian risk
Sources: Expert RA, DB Research calculations
Determinants of regional FDI flows: An econometric analysis
In this section we take a closer look at the drivers of FDI flows into Russian
regions and particularly at the usefulness of the regional rating as a gauge of
9
attractiveness for foreign investment. Existing literature on the determinants of
FDI inflows into Russia can be broadly divided into two groups. First, there are
studies, including Campos and Kinoshita (2003), Zhuravskaya and Guriev
(2010) and Arbatli (2011), that compare Russia’s FDI inflows with those of other
transition and resource-rich countries. They generally find that the level of FDI in
Russia is comparatively low when adjusted for population and size, often due to
Russia’s institutional weakness. Second, studies such as Broadman and
Recanatini (2001), Iwasaki and Suganuma (2005), and Ledayeva and Linden
(2006) use regional data on FDI flows and other macroeconomic variables to
assess the determinants of FDI flows on Russia’s regional level. In addition,
spatial dependencies of FDI flows into Russian regions are found important by
Ledayeva (2007). Market size, infrastructure and natural resources are found to
be important drivers of FDI inflows. Some of the papers include the investment
rating by Expert RA or its subcomponents among regressors and conclude that
they are significant in some specifications. All papers in this group, however,
examine shorter data samples that do not include the global financial crisis of
2008-2009 and its aftermath.
Our approach is similar to empirical studies of FDI flow determinants by
Kinoshita (2011) and Arbatli (2011).The two studies look at determinants of FDI
flows across Eastern European and emerging market countries, respectively,
and hence aim at explaining what on average drives FDI into the two regions. In
contrast, we consider determinants of FDI flows across a group of 79 Russian
9
4
| November 28, 2012
Our regressor is compiled by mapping the ratings onto a numerical scale from 1 to 12. Lower
number indicates better rating, so we would expect this variable to have a negative coefficient in
our regression model.
Research Briefing
What drives FDI to Russian regions?
regions and our results are thus indicative of FDI drivers relevant for the
Russian Federation.
The dependent variable in our model is the log of FDI inflows to Russian regions
in US dollars. We include a lagged dependent variable to allow for partial
adjustment in actual FDI flows to the underlying desired level of FDI.
Furthermore, we include a set of regional determinants such as regional GDP (a
proxy for the market size, important for market-seeking FDI), household per
capita income and wages (as proxies for labour costs and competitiveness), the
share of trade in regional GDP (as a proxy for trade integration) and the share of
population enrolled in tertiary education (as a proxy for human capital). Based
on economic theory, we would expect a positive coefficient on the lagged
dependent variable, regional GDP, the share of trade and tertiary education
enrollment. On the other hand, we would expect a negative coefficient on the
two competitiveness proxies.
Following Arbatli (2011), we also include two global “push” factors that are likely
to influence FDI flows: real GDP growth and the real interest rate in G7
countries (both are weighted by GDP at PPP). We would expect a positive effect
of GDP growth on FDI inflows and a negative effect of real interest rates on FDI
flows. In addition, we include our updated version of the regional rating compiled
by Expert RA. In its construction, a lower rating implies higher investment
potential and lower risk. We would thus expect to obtain a negative regression
coefficient.
While FDI flows are reported in US dollars by Rosstat, regional gross value
added, household income per capita and wages are reported in rouble terms.
Thus, all nominal values in local currency were converted into US dollars using
annual average USD/RUR exchange rates.
Table 8 summarises the estimation results. Since the lagged dependent variable
and any endogenous regressors are correlated with the unobserved regional
fixed effects, a standard fixed-effects method will yield biased estimates,
especially when the time dimension of the panel is small as in our case. Hence
we prefer the system-GMM estimator of Blundell and Bond (1998) as our
benchmark. We use the lagged dependent variable, tertiary enrollment and the
regional rating as endogenous instruments. Exogenous instrumental variables
10
include regional inflation, FDI stock in the region and regional real GDP
11
growth. For robustness purposes, we report results of random and fixedeffects models as well as the system-GMM results (two-step estimation with
Windmeijer-corrected cluster-robust standard errors). All specifications include
(unreported) intercept and yearly dummies, the latter to remove universal timerelated shocks from the errors. We see from columns (1) to (4) that the lagged
dependent variable, household per capita income, and the G7 real interest rate
are significant and have the expected signs. In some specifications, US dollar
wages and the regional rating also turn out to be marginally significant. Trade
openness is only found to be significant in the random and fixed-effects
estimates, although the coefficient has the expected positive sign. We conclude
that the regional rating is not a very robust nor reliable determinant of regional
FDI flows.
10
11
5
| November 28, 2012
The regional stock of FDI is calculated simply as a cumulative sum of past FDI inflows.
We also use both one lag of endogenous regressors and three lags with the collapse option. In
both cases, the number of instruments never exceeds the number of groups (usually the 74
regions for which most of the data are available) – a reasonable rule of thumb for panel data
models specification. The estimation is conducted using the xtabond2 package in Stata 12.
Research Briefing
What drives FDI to Russian regions?
Inward FDI flows equation: Russian regions, annual data 1995-2011
Dependent variable: log of FDI
Log of FDI (lag)
Log of USD gross value added (lag)
Log of USD household income per capita
Log of USD wage
Exports and imports as share of GVA
Fraction of pop. in tertiary education
Regional rating
G7 real GDP growth
G7 long-term real interest rate
8
RE
FE
Sys-GMM
Sys-GMM
Sys-GMM
Sys-GMM
Sys-GMM
Sys-GMM
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.583***
0.352***
0.388***
0.398***
0.535***
0.388***
0.720***
0.576**
(0.047)
(0.061)
(0.109)
(0.136)
(0.096)
(0.146)
(0.082)
(0.226)
0.318**
-0.194
0.793
1.197
0.645
1.792
1.898**
2.428
(0.126)
(0.662)
(0.497)
(0.759)
(0.494)
(1.088)
(0.828)
(2.637)
0.096
-0.420
-3.045***
-4.769***
-2.761**
-4.113**
-1.697*
0.504
(0.472)
(0.770)
(0.876)
(1.708)
(1.148)
(1.788)
(0.949)
(3.046)
0.171
0.984
1.988**
2.687
1.740*
1.936
-0.322
-3.116
(0.405)
(1.119)
(0.947)
(1.656)
(1.039)
(1.646)
(1.239)
(3.807)
0.845***
0.801**
0.494
0.796
0.512
0.826
1.765**
2.273
(0.195)
(0.358)
(0.481)
(0.536)
(0.372)
(0.682)
(0.816)
(1.730)
-3.383
-7.667
-1.129
49.66
1.365
1.614
35.50**
-5.284
(4.269)
(13.58)
(22.12)
(34.35)
(20.99)
(32.02)
(15.04)
(85.28)
-0.034
-0.041
-0.412*
-0.252
-0.394*
-0.218
-0.291
0.271
(-0.041)
(0.049)
(0.239)
(0.320)
(0.235)
(0.320)
(0.212)
(0.845)
-0.092
-0.074
-0.062
-0.012
-0.052
0.003
0.001
0.052
(0.066)
(0.242)
-0.058
(0.069)
(0.041)
(0.065)
(0.066)
(0.132)
-0.448**
-0.293
-0.451***
-0.488***
-0.438***
-0.472***
-0.804***
-0.577
(0.190)
(0.367)
(0.138)
(0.141)
(0.112)
(0.108)
(0.189)
(0.372)
0.115
0.165
-0.191**
-0.244
(0.147)
(0.20)
(0.099)
0.362
-0.461
-0.909
--
--
(0.336)
(0.662)
--
--
-0.088
-0.239
0.0817
(0.181)
3.094**
2.406
(1.432)
(3.591)
-1.499**
-0.230
(0.732)
(2.574)
Log of distance to Moscow
Dummy for oil and mining
Log of privatised companies
Log of public administration (per 100 inhab.)
Log of regional tax revenues
Lags used
--
--
One
Three
One
Three
One
Three
Collapse option for instruments
--
--
No
Yes
No
Yes
No
Yes
Arellano-Bond test for AR(2)
0.639
0.565
0.092*
0.134
0.024**
0.052**
Hansen test of overid. restrictions
0.541
0.740
0.491
0.476
0.436
0.091*
Diagnostic tests (p-values):
Note: ***, **, and * denote significance at the 1%, 5%, and 10% significance level, respectively.
Source: DB Research
We supplement our analysis with two more specifications adding regressors that
attempt to capture infrastructure and institutional quality. We consider the
distance of regional capitals to Moscow, the dummy for the presence of oil and
mining in the region, the (log of) number of privatised companies, the (log of)
number of public administrators weighted by population and the (log of) regional
tax revenues. None of these proves to be robust: they are often insignificant and
incorrectly signed, or they eventually become insignificant with the inclusion of a
new variable or with a change of the instruments set; see column (8). Other
variables that were tested and found insignificant include the dummy for regions
6
| November 28, 2012
Research Briefing
What drives FDI to Russian regions?
with special economic zones, dummy for regions with the status of republic,
12
density of the roads network and net migration flows.
Our results are to some extent consistent with the finding by KPMG (2010) that
so-called “soft” factors, such as regional commitment to FDI and successful
expectations management, can be decisive in determining FDI flows outside
industries where so-called “hard” factors, such as the presence of natural
resources and geographical location, are decisive. Soft factors are more difficult
to quantify and, hence, unlikely to be fully captured by institutional variables in
our dataset. The study, based on interviews of foreign investors as well as
regional administrators, also finds that tax incentives are considered
unimportant by investors due to complexities in administration, additional
reporting and excessive disclosure requirements.
Jan Strasky (+49 69 910-31894, [email protected])
Tamara Pashinova
12
7
| November 28, 2012
Table 8 also reports two diagnostic tests. The Arellano-Bond test for residual autocorrelation has
the null hypothesis of no autocorrelation and is applied to the differenced residuals. The results
suggest that the most expansive specifications in columns (7) and (8) suffer from residual
autocorrelation at the 5% significance level. The Hansen test of over-identifying restrictions has
the null hypothesis of instruments exogeneity. The results suggest rejection of the null hypothesis
at the 10% significance level, and thus potential endogeneity problems, in the case of model (8).
Research Briefing
What drives FDI to Russian regions?
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Research Briefing
What drives FDI to Russian regions?
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