The Impact of Distance on Interregional Migration in Russia: A Spatial
Econometric Approach
SASCHA SARDADVAR1 and ELENA VAKULENKO2
1
2
WU Vienna, Welthandelsplatz 1/D4.3.268, 1020 Vienna, Austria.
E-mail: sascha.sardadvar@wu.ac.at
National Research University Higher School of Economics, Moscow.
E-mail: evakulenko@hse.ru
KEYWORDS: Regional migration, spatial econometrics, Russia
JEL classification: J61, R23, C21
Abstract
Since the transition to a market economy, internal migration within Russia follows
individual targets instead of central planning. According to theory, labour migration is
motivated by employment and income opportunities (Greenwood, 1975). Empirical evidence
shows that the probability of migration is sensitive to the income differential between the
destination and the origin, employment growth in the origin as well as distance (Borjas,
2010). With no legal barriers, people should therefore follow respective opportunities.
Indeed, Russians appear to be very mobile. Official data on net migration shows that
from 1995 to 2010, some Russian regions’ population numbers grew, while others shrank by
two digit percentage numbers (Guriev and Vakulenko, 2013). One reason for these high
numbers was the abolishment of subsidies, price controls and external trade restrictions
virtually overnight in 1992. As a consequence, interregional relations were characterised by
high spatial disequilibria, creating high incentives for factor relocations. Another reason is the
phenomenon of low household assets within a middle-income country, which should increase
the propensity to move (Banerjee and Kanbur, 1981).
The aim of this paper is to explore the drivers of interregional migration in posttransition Russia using a spatial econometric approach. As noted by Greenwood (1997),
differential characteristics of sending and receiving regions provide potential incentives for
moving. Possible determinants are (i) general labour market conditions as well as employment
composition, which help determine the probability of gaining employment during a period of
job search, (ii) prevailing conditions in land and housing markets, (iii) state and local taxes
and the associated availability of public goods, (iv) topological, climatological, and
environmental amenities, and (v) the potential for natural and technological hazards
(Greenwood 1997). In addition, the concept of distance is widely accepted in the literature to
have an impact on migration, as distance increases (i) direct costs of moving as such, (ii)
opportunity costs, (iii) information costs, (iv) psychic costs and, furthermore, (v) migrants
often follow past migrants, who may have moved to near destinations (Greenwood 1997).
Considering the gigantic distances within Russia, it is likely that the interplay of inand out-migration is largely influenced by distances between regions. Sardadvar and RochaAkis (2013) show both theoretically and empirically how this interplay works within the
European Union, while Vakulenko et al. (2011) provide evidence that distances matter for
migration within Russia. Spatial econometric model specifications provide a tool to study the
acknowledged determinants of migration and the role of distance simultaneously, with each
variable in the regression being included both un-lagged as well as spatially lagged. The
econometric specification corresponds to a spatial lag of X model (SLXM) or, by inclusion of
a spatial autocorrelation term, a spatial Durbin error model (SDEM) and is applied to data on
realised net-migration in 78 Russian regions over the period 1995-2010. The available data set
allows to distinguish between internal in- and out-migration as well as external migration for
each Russian region.
Various variables which are named in the literature as influencing migration decisions
are used as explanatory variables. Of these, the most important variables are personal income,
GDP growth in connection with price levels, employment, unemployment, and urbanity.
Apart from unemployment, all of these variables are expected to have a positive impact on net
migration rates. In the case where only internal migration is considered, the paper
hypothesises that a factor which works in favour of one destination must work to the
disadvantage of at least one other region. Therefore, if distance plays a role, the variables’
coefficients are expected to display contrasting signs with respect to non-lagged and spatially
lagged values. Studies have found that migration decisions tend to occur after a time lag, but
this time lag may differ across economies. For instance, it takes considerably more time for an
economic shock to be accommodated by migration in Europe than in the US (Decressin,
Fatás, 1995). For this reason, the SLXM and the SDEM are both applied for cross-sectional as
well as panel specifications, with various time lags being considered.
The paper is structured as follows. In the first section, theory on interregional
migration is discussed in order to deduce the relevant variables. The second section compares
empirical findings with respect to the large single markets of the European Union and the
USA and discusses these in relation to studies on migration within Russia. The subsequent
two sections describe the data and the econometric methods. After that, results are presented
and discussed. The final section concludes.
References
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